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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 20 August, 2024 • 13 min read

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn't make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers and make your presentation as clear as the day? Let's check out these best ways to present data. 💎

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Data Presentation - What Is It?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways to cut a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza - we mean to present your data - that will make your company’s most important asset as clear as day. Let's dive into 10 ways to present data efficiently.

#1 - Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

#3 - Pie chart

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

#4 - Bar chart

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of data presentation. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

#5 - Histogram

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

#6 - Line graph

Recordings to ways of displaying data, we shouldn't overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

#7 - Pictogram graph

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

#8 - Radar chart

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

#9 - Heat map

A heat map represents data density in colours. The bigger the number, the more colour intensity that data will be represented.

Most US citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

#10 - Scatter plot

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 - assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quizzes and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 - Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 - Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 - Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 - Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should end your session with open-ended questions to see what your participants really think.

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none! Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors' behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

Frequently Asked Questions

What is a chart presentation.

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for the presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should you use charts for presentation?

You should use charts to ensure your contents and visuals look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro. We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

• Clarity : Data presentations make complex information clear and concise.
• Engagement : Visuals, such as charts and graphs, grab your audience's attention.
• Comprehension : Visual data is easier to understand than long, numerical reports.
• Decision-making : Well-presented data aids informed decision-making.
• Impact : It leaves a lasting impression on your audience.

Types of data presentation:

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

• Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
• Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
• Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
• Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation?

When creating your data presentation, remember these key components:

• Data points : Clearly state the data points you're presenting.
• Comparison : Highlight comparisons and trends in your data.
• Graphical methods : Choose the right chart or graph for your data.
• Infographics : Use visuals like infographics to make information more digestible.
• Numerical values : Include numerical values to support your visuals.
• Qualitative information : Explain the significance of the data.
• Source citation : Always cite your data sources.

How to structure an effective data presentation?

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

• Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
• High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
• Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
• Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

• Use visuals : Incorporate charts and graphs to enhance understanding.
• Keep it simple : Avoid clutter and complexity.
• Highlight key points : Emphasize crucial data.
• Engage the audience : Encourage questions and discussions.
• Practice : Rehearse your presentation.

Don'ts:

• Overload with data : Less is often more; don't overwhelm your audience.
• Fit Unrelated data : Stay on topic; don't include irrelevant information.
• Neglect the audience : Ensure your presentation suits your audience's level of expertise.
• Read word-for-word : Avoid reading directly from slides.
• Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

• Definition : Data presentation is the art of visualizing complex data for better understanding.
• Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
• Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
• Choosing methods : Select the right method based on data, audience, and purpose.
• Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
• Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
• Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

FAQ's on a data presentation

1. what is data presentation, and why is it important in 2024.

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

• Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
• On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
• Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
• Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
• Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive! Sign up for our free trial or book a demo ! ‍

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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9 Data Presentation Tools for Business Success

• By Judhajit Sen
• May 29, 2024

A data presentation is a slide deck that shares quantitative information with an audience using visuals and effective presentation techniques . The goal is to make complex data easily understandable and actionable using data presentation examples like graphs and charts, tables, dashboards, and clear text explanations. 

Data presentations help highlight trends, patterns, and insights, allowing the audience to grasp complicated concepts or trends quickly. This makes it easier for them to make informed decisions or conduct deeper analysis.

Data visualization in presentations is used in every field, from academia to business and industry. Raw data is often too complex to understand directly, so data analysis breaks it down into charts and graphs. These tools help turn raw data into useful information.

Once the information is extracted, it’s presented graphically. A good presentation can significantly enhance understanding and response.

Think of data presentation as storytelling in business presentations with charts. A common mistake is assuming the audience understands the data as well as the presenter. Always consider your audience’s knowledge level and what information they need when you present your data.

To present the data effectively:

1. Provide context to help the audience understand the numbers.

2. Compare data groups using visual aids.

3. Step back and view the data from the audience’s perspective.

Data presentations are crucial in nearly every industry, helping professionals share their findings clearly after analyzing data.

Key Takeaways

• Simplifying Complex Data: Data presentations turn complex data into easy-to-understand visuals and narratives, helping audiences quickly grasp trends and insights for informed decision-making.
• Versatile Tools: Various tools like bar charts, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables each serve unique purposes, enhancing the clarity and impact of the data.
• Audience Consideration: Tailor your presentation to the audience’s knowledge level, providing context and using simple visuals to make the information accessible and actionable.
• Effective Data Storytelling: Combining clear context, organized visuals, and thoughtful presentation ensures that the data’s story is conveyed effectively, supporting better business decisions and success.

Following are 9 data presentation tools for business success.

Bar charts are a simple yet powerful method of presentation of the data using rectangular bars to show quantities or frequencies. They make it easy to spot patterns or trends at a glance. Bar charts can be vertical (column charts) or horizontal, depending on how you want to display your data.

In a bar graph, categories are displayed on one axis, usually the x-axis for vertical charts and the y-axis for horizontal ones. The bars’ lengths represent the values or frequencies of these categories, with the scale marked on the opposite axis.

These charts are ideal for comparing data across different categories or showing trends over time. Each bar’s height (or length in a horizontal chart) is directly proportional to the value it represents. This visual representation helps illustrate differences or changes in data.

Bar charts are versatile tools in business reports, academic presentations, and more. To make your bar charts effective:

• Ensure they are concise and have easy-to-read labels.
• Avoid clutter by not including too many categories, making the chart hard to read.
• Keep it simple to maintain clarity and impact, whether your bars go up or sideways.

Line Graphs

Line graphs show how data changes over time or with continuous variables. They connect points of data with straight lines, making it easy to see trends and fluctuations. These graphs are handy when comparing multiple datasets over the same timeline.

Using line graphs, you can track things like stock prices, sales projections, or experimental results. The x-axis represents time or another continuous variable, while the y-axis shows the data values. This setup allows you to understand the ups and downs in the data quickly.

To make your graphs effective, keep them simple. Avoid overcrowding with too many lines, highlight significant changes, use labels, and give your graph a clear, catchy title. This will help your audience grasp the information quickly and easily.

A data dashboard is a data analysis presentation example for analyzing information. It combines different graphs, charts, and tables in one layout to show the information needed to meet one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs) by displaying visuals you’ve already made in worksheets.

It’s best to keep the number of visuals on a dashboard to three or four. Adding too many can make it hard to see the main points. Dashboards are helpful for business analytics, like analyzing sales, revenue, and marketing metrics. In manufacturing, they help users understand the production scenario and track critical KPIs for each production line.

Dashboards represent vital points of data or metrics in an easy-to-understand way. They are often an  interactive presentation idea , allowing users to drill down into the data or view different aspects of it.

Pie charts are circular graphs divided into parts to show numerical proportions. Each portion represents a part of the whole, making it easy to see each component’s contribution to the total.

The size of each slice is determined by its value relative to the total. A pie chart with more significant points of data will have larger slices, and the whole chart will be more important. However, you can make all pies the same size if proportional representation isn’t necessary.

Pie charts are helpful in business to show percentage distributions, compare category sizes, or present simple data sets where visualizing ratios is essential. They work best with fewer variables. For more variables, it’s better to use a pie chart calculator that helps to create pie charts easily for various data sets with different color slices. 

Each “slice” represents a fraction of the total, and the size of each slice shows its share of the whole. Pie charts are excellent for showing how a whole is divided into parts, such as survey results or demographic data.

While pie charts are great for simple distributions, they can get confusing with too many categories or slight differences in proportions. To keep things clear, label each slice with percentages or values and use a legend if there are many categories. If more detail is needed, consider using a donut chart with a blank center for extra information and a less cluttered look.

A histogram is a graphical presentation of data  to help in understanding the distribution of numerical values. Unlike bar charts that show each response separately, histograms group numeric responses into bins and display the frequency of reactions within each bin. The x-axis denotes the range of values, while the y-axis shows the frequency of those values.

Histograms are useful for understanding your data’s distribution, identifying shared values, and spotting outliers. They highlight the story your data tells, whether it’s exam scores, sales figures, or any other numerical data.

Histograms are great for visualizing the distribution and frequency of a single variable. They divide the data into bins, and the height of each bar indicates how many points of data fall into that bin. This makes it easy to see trends like peaks, gaps, or skewness in your data.

To make your histogram effective, choose bin sizes that capture meaningful patterns. Clear axis labels and titles also help in explaining the data distribution.

Scatter Plot

Using individual data points, a scatter plot chart is a presentation of data in visual form to show the relationship between two variables. Each variable is plotted along the x-axis and y-axis, respectively. Each point on the scatter plot represents a single observation.

Scatter plots help visualize patterns, trends, and correlations between the two variables. They can also help identify outliers and understand the overall distribution of data points. The way the points are spread out or clustered together can indicate whether there is a positive, negative, or no clear relationship between the variables.

Scatter plots can be used in practical applications, such as in business, to show how variables like marketing cost and sales revenue are related. They help understand data correlations, which aids in decision-making.

To make scatter plots more effective, consider adding trendlines or regression analysis to highlight patterns. Labeling key data points or tooltips can provide additional information and make the chart easier to interpret.

A pictogram is the simplest form of data presentation and analysis, often used in schools and universities to help students grasp concepts more effectively through pictures.

This type of diagram uses images to represent data. For example, you could draw five books to show the number of books sold in the first week of release, with each image representing 1,000 books. If consumers bought 5,000 books, you would display five book images.

Using simple icons or images makes the information visually intuitive. Instead of relying on numbers or complex graphs, pictograms use straightforward symbols to depict data points. For example, a thumbs-up emoji can illustrate customer satisfaction levels, with each emoji representing a different level of satisfaction.

Pictograms are excellent for visual data presentation. Choose symbols that are easy to interpret and relevant to the data to ensure clarity. Consistent scaling and a legend explaining the symbols’ meanings are essential for an effective presentation.

Textual Presentation

Textual presentation uses words to describe the relationships between pieces of information. This method helps share details that can’t be shown in a graph or table. For example, researchers often present findings in a study textually to provide extra context or explanation. A textual presentation can make the information more transparent.

This type of presentation is common in research and for introducing new ideas. Unlike charts or graphs, it relies solely on paragraphs and words.

Textual presentation also involves using written content, such as annotations or explanatory text, to explain or complement data. While it doesn’t use visual presentation aids like charts, it is a widely used method for presenting qualitative data. Think of it as the narrative that guides your audience through the data.

Adequate textual data may make complex information more accessible. Breaking down complex details into bullet points or short paragraphs helps your audience understand the significance of numbers and visuals. Headings can guide the reader’s attention and tell a coherent story.

Tabular Presentation

Tabular presentation uses tables to share information by organizing data in rows and columns. This method is useful for comparing data and visualizing information. Researchers often use tables to analyze data in various classifications:

Qualitative classification: This includes qualities like nationality, age, social status, appearance, and personality traits, helping to compare sociological and psychological information.

Quantitative classification: This covers items you can count or number.

Spatial classification: This deals with data based on location, such as information about a city, state, or region.

Temporal classification: This involves time-based data measured in seconds, hours, days, or weeks.

Tables simplify data, making it easily consumable, allow for side-by-side comparisons, and save space in your presentation by condensing information.

Using rows and columns, tabular presentation focuses on clarity and precision. It’s about displaying numerical data in a structured grid, clearly showing individual data points. Tables are invaluable for showcasing detailed data, facilitating comparisons, and presenting exact numerical information. They are commonly used in reports, spreadsheets, and academic papers.

Organize tables neatly with clear headers and appropriate column widths to ensure readability. Highlight important data points or patterns using shading or font formatting. Tables are simple and effective, especially when the audience needs to know precise figures.

Elevate Business Decisions with Effective Data Presentations

Data presentations are essential for transforming complex data into understandable and actionable insights. Data presentations simplify the process of interpreting quantitative information by utilizing data presentation examples like charts, graphs, tables, infographics, dashboards, and clear narratives. This method of storytelling with visuals highlights trends, patterns, and insights, enabling audiences to make informed decisions quickly.

In business, data analysis presentations are invaluable. Different types of presentation tools like bar charts help compare categories and track changes over time, while dashboards consolidate various metrics into a comprehensive view. Pie charts and histograms offer clear views of distributions and proportions, aiding in grasping the bigger picture. Scatter plots reveal relationships between variables, and pictograms make data visually intuitive. Textual presentations and tables provide detailed context and precise figures, which are essential for thorough analysis and comparison.

Consider the audience’s knowledge level to tailor the best way to present data in PowerPoint. Clear context, simple visuals, and thoughtful organization ensure the data’s story is easily understood and impactful. Mastering these nine data presentation types can significantly enhance business success by making data-driven decisions more accessible and practical.

Frequently Asked Questions (FAQs)

1. What is a data presentation?

A data presentation is a slide deck that uses visuals and narrative techniques to make complex data easy to understand and actionable. It includes charts, graphs, tables, infographics, dashboards, and clear text explanations.

2. Why are data presentations important in business?

Data presentations are crucial because they help highlight trends, patterns, and insights, making it easier for the audience to understand complicated concepts. This enables better decision-making and deeper analysis.

3. What types of data presentation tools are commonly used?

Common tools include bar charts, line graphs, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables. Each tool has a unique way of representing data to aid understanding.

4. How can I ensure my data presentation is effective?

To ensure effectiveness, provide context, compare data sets using visual aids, consider your audience’s knowledge level, and keep visuals simple. Organizing information thoughtfully and avoiding clutter enhances clarity and impact.

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Enhance your team’s skills with Zenith Learning, our interactive workshops that blend structured problem-solving with visual storytelling. Learn to present data effectively and make a lasting impact in your business communications.

Prezentium’s services are designed to help you make the most of your data, from bar charts to dashboards, ensuring your presentations are informative and visually engaging. Let us help you tell your data’s story in a way that resonates. Contact Prezentium today to elevate your business presentations.

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Corporate Communication Functions and its Importance

Barriers to effective communication: 14 common communication barriers, bad powerpoint: 6 poor powerpoint slide practices to avoid.

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How to Present Data Effectively

You’re sitting in front of your computer and ready to put together a presentation involving data.   The numbers stare at you from your screen, jumbled and raw.   How do you start?   Numbers on their own can be difficult to digest. Without any context, they’re just that—numbers.   But organize them well and they tell a story.   In this blog post, we’ll go into the importance of structuring data in a presentation and provide tips on how to do it well. These tips are practical and applicable for all sorts of presentations—from marketing plans and medical breakthroughs to project proposals and portfolios. 

What is data presentation?

3 essential tips on data presentation, use the right chart, keep it simple, use text wisely and sparingly.

In many ways, data presentation is like storytelling—only you do them with a series of graphs and charts.  One of the most common mistakes presenters make is being so submerged in the data that they fail to view it from an outsider’s point of view.   Always keep this in mind: What makes sense to you may not make sense to your audience. To portray figures and statistics in a way that’s comprehensible to your viewers, step back, put yourself in their shoes, and consider the following: 

• How much do they know about the topic?
• How much information will they need?
• What data will impress them?

Providing a context helps your audience visualize and understand the numbers. To help you achieve that, here are three tips on how to represent data effectively.  

Whether you’re using Google Slides or PowerPoint, both come equipped with a range of design tools that help you help your viewers make sense of your qualitative data.  The key here is to know how to use them and how to use them well. In these tips, we’ll cover the basics of data presentation that are often overlooked but also go beyond basics for more professional advice. 

The downside of having too many tools at your disposal is that it makes selecting an uphill task.   Pie and bar charts are by far the most commonly used methods as they are versatile and easy to understand. 

If you’re looking to kick things up a notch, think outside the box. When the numbers allow for it, opt for something different. For example, donut charts can sometimes be used to execute the same effect as pie charts. 

But these conventional graphs and charts aren’t applicable to all types of data. For example, if you’re comparing numerous variables and factors, a bar chart would do no good. A table, on the other hand, offers a much cleaner look.

Pro tip : If you want to go beyond basics, create your own shapes and use their sizes to reflect proportion, as seen in this next image.

Their sizes don’t have to be an exact reflection of their proportions. What’s important here is that they’re discernible and are of the same shape so that your viewers can grasp its concept at first glance.  Note that this should only be used for comparisons with large enough contrasts. For instance, it’d be difficult to use this to compare two market sizes of 25 percent and 26 percent. 

When it comes to making qualitative data digestible, simplicity does the trick.  Limit the number of elements on the slide as much as possible and provide only the bare essentials. 

See how simple this slide is? In one glance, your eye immediately goes to the percentages of the donut because there are no text boxes, illustrations, graphics, etc. to distract you.  Sometimes, more context is needed for your numbers to make sense. In the spirit of keeping your slides neat, you may be tempted to spread the data across two slides. But that makes it complicated, so putting it all on one slide is your only option.  In such cases, our mantra of “keep it simple” still applies. The trick lies in neat positioning and clever formatting.  

In the above slides, we’ve used boxes to highlight supporting figures while giving enough attention to the main chart. This separates them visually and helps the audience focus better.  With the slide already pretty full, it’s crucial to use a plain background or risk overwhelming your viewers.  

Last but certainly not least, our final tip involves the use of text.  Just because you’re telling a story with numbers doesn’t mean text cannot be used. In fact, the contrary proves true: Text plays a vital role in data presentation and should be used strategically.  To highlight a particular statistic, do not hesitate to go all out and have that be the focal point of your slide for emphasis. Keep text to a minimum and as a supporting element. 

Make sure your numbers are formatted clearly. Large figures should have thousands separated with commas. For example, 4,498,300,000 makes for a much easier read than “4498300000”. Any corresponding units should also be clear.  With data presentation, don’t forget that numbers are still your protagonist, so they must be highlighted with a larger or bolder font.  Where there are numbers and graphics, space is scarce so every single word must be chosen wisely.   The key here is to ensure your viewers understand what your data represents in one glance but to leave it sufficiently vague, like a teaser, so that they pay attention to your speech for more information.  → Slidesgo’s free presentation templates come included with specially designed and created charts and graphs that you can easily personalize according to your data. Give them a try now! 

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Data Presentation Techniques that Make an Impact

Create beautiful charts & infographics get started, 10.05.2016 by anete ezera.

Presenting data doesn’t need to be boring. In fact, it is a great way to spice up your presentations and share important facts and figures with your audience. Data has the power to be engaging, persuasive and memorable.

If you have a compelling story to tell with data, you should present it in a clear and powerful way. We will help you get started with a few effective data presentation techniques!

If you’d like more information about designing great presentations, download our new eBook ‘How to Design PowerPoint Presentations that Pack a Punch in 5 Easy Steps.’

What Presentations Benefit from Data?

Data doesn’t necessarily make all presentations better, but certain types of presentations are prime for the incorporation of data visualizations:

• Sales Reports
• PR and Marketing Research
• Marketing and Advertising Campaigns
• Executive and CEO Presentations
• Educational Reports
• Political Speeches
• Annual Reports
• Shareholder Presentations
• Financial Reports
• Product Launches, and more!

Why Use Charts in Presentations?

Visuals make information stick in our brains . A study from the Wharton School of Business found that 67% of the audience surveyed were persuaded by verbal presentations that had accompanying visuals. Charts are great visual aids for multiple reasons:

• Charts are easy to read
• Charts are visually appealing
• Charts simplify complex information
• Charts make it possible to quickly make comparisons and spot trends
• Charts are memorable and make an impact
• Charts give your presentation credibility

How to Add Data to Your Presentation

1) define your message.

Before you can even think about adding data to your presentation, you need to ask yourself, ‘what story am I trying to tell?’ Once you have a concrete idea of what your message is, you’ll have an easier time crafting the right visualization to share with your audience.

2) Clean and Organize Your Data

Now that you know what point you want to make with your data, it’s time to make sure your numbers are ready to be visualized.   Every good data visualization starts with good data. Make sure your spreadsheet is formatted and labeled exactly how you want it. Think about the message you want to share with your data and get rid of anything that doesn’t help you tell your story.

Data that is clean and organized is easier to display and analyze. Here are five awesome free data analysis tools to help you extract, clean, and share your data.

3) Pick the Right Chart Type

We can’t emphasize enough how important it is to make sure you pick the right chart type for the data you want to present. While your data might technically work with multiple chart types, you need to pick the one that ensures your message is clear, accurate, and concise.

4) Simplicity is Key

Charts and graphs turn complex ideas or data sets into easy-to-understand visual concepts. Remember that your data is the star of the show, so keep it simple. Avoid visual clutter, excessive text, poor color selection, and unnecessary animations. Make sure your legend and data labels are printed in a large, visible font. You don’t want your audience to get distracted. Less is more!

5) Create a Narrative

People understand stories better than they understand spreadsheets. Craft a compelling story around your data to make it memorable. Find a way to drive emotion from the numbers. Give your audience something they can relate to and resonate with. Data visualization speaker Bill Shander offers five tips to make you a better data storyteller. 

6) Visualize Data with Infogram

Before you add data to your presentation you need to visualize it. While many presentation tools allow you to create charts , they often leave much to be desired. Infogram makes it easy to create beautiful, engaging data visualizations your audience won’t forget.

You can embed interactive and responsive data visualizations into your presentations if you’re using Bunkr or any other HTML based presentation platform. Or, if you upgrade to one of our paid plans , you can download static versions of your charts and graphs to enhance your work. You can even make the background of PNG downloads transparent so they slip seamlessly into your presentation.

Would you like to experience the full power of  data visualization ? Try Infogram for Teams or Enterprise for free! With a Team or Enterprise account, you can create up to 10,000+ projects, collaborate with your team in real time, use our engagement analytics feature, and more. Request your free demo  here .

7) Make a Handout

Leave your audience with a physical or virtual copy of your charts. This makes it possible for them to look at the numbers more closely after your presentation. It’s also nice to include extra information, beyond what you covered, in case someone wants to delve deeper into the material.

Now that you know how to add data to your presentations, it’s time to learn how to design a PowerPoint that really gets people talking. Download our latest eBook ‘How to Design PowerPoint Presentations that Pack a Punch in 5 Easy Steps’ – for free!

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A Guide to Effective Data Presentation

Key objectives of data presentation, charts and graphs for great visuals, storytelling with data, visuals, and text, audiences and data presentation, the main idea in data presentation, storyboarding and data presentation, additional resources, data presentation.

Tools for effective data presentation

Financial analysts are required to present their findings in a neat, clear, and straightforward manner. They spend most of their time working with spreadsheets in MS Excel, building financial models , and crunching numbers. These models and calculations can be pretty extensive and complex and may only be understood by the analyst who created them. Effective data presentation skills are critical for being a world-class financial analyst .

It is the analyst’s job to effectively communicate the output to the target audience, such as the management team or a company’s external investors. This requires focusing on the main points, facts, insights, and recommendations that will prompt the necessary action from the audience.

One challenge is making intricate and elaborate work easy to comprehend through great visuals and dashboards. For example, tables, graphs, and charts are tools that an analyst can use to their advantage to give deeper meaning to a company’s financial information. These tools organize relevant numbers that are rather dull and give life and story to them.

Here are some key objectives to think about when presenting financial analysis:

• Visual communication
• Audience and context
• Charts, graphs, and images
• Focus on important points
• Design principles
• Storytelling
• Persuasiveness

For a breakdown of these objectives, check out Excel Dashboards & Data Visualization course to help you become a world-class financial analyst.

Charts and graphs make any financial analysis readable, easy to follow, and provide great data presentation. They are often included in the financial model’s output, which is essential for the key decision-makers in a company.

The decision-makers comprise executives and managers who usually won’t have enough time to synthesize and interpret data on their own to make sound business decisions. Therefore, it is the job of the analyst to enhance the decision-making process and help guide the executives and managers to create value for the company.

When an analyst uses charts, it is necessary to be aware of what good charts and bad charts look like and how to avoid the latter when telling a story with data.

Examples of Good Charts

As for great visuals, you can quickly see what’s going on with the data presentation, saving you time for deciphering their actual meaning. More importantly, great visuals facilitate business decision-making because their goal is to provide persuasive, clear, and unambiguous numeric communication.

For reference, take a look at the example below that shows a dashboard, which includes a gauge chart for growth rates, a bar chart for the number of orders, an area chart for company revenues, and a line chart for EBITDA margins.

To learn the step-by-step process of creating these essential tools in MS Excel, watch our video course titled “ Excel Dashboard & Data Visualization .”  Aside from what is given in the example below, our course will also teach how you can use other tables and charts to make your financial analysis stand out professionally.

Learn how to build the graph above in our Dashboards Course !

Example of Poorly Crafted Charts

A bad chart, as seen below, will give the reader a difficult time to find the main takeaway of a report or presentation, because it contains too many colors, labels, and legends, and thus, will often look too busy. It also doesn’t help much if a chart, such as a pie chart, is displayed in 3D, as it skews the size and perceived value of the underlying data. A bad chart will be hard to follow and understand.

Aside from understanding the meaning of the numbers, a financial analyst must learn to combine numbers and language to craft an effective story. Relying only on data for a presentation may leave your audience finding it difficult to read, interpret, and analyze your data. You must do the work for them, and a good story will be easier to follow. It will help you arrive at the main points faster, rather than just solely presenting your report or live presentation with numbers.

The data can be in the form of revenues, expenses, profits, and cash flow. Simply adding notes, comments, and opinions to each line item will add an extra layer of insight, angle, and a new perspective to the report.

Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation,  past events, and possible conclusions and recommendations that can be made for the future.

The simple diagram below shows the different categories of your audience.

  This chart is taken from our course on how to present data .

Internal Audience

An internal audience can either be the executives of the company or any employee who works in that company. For executives, the purpose of communicating a data-filled presentation is to give an update about a certain business activity such as a project or an initiative.

Another important purpose is to facilitate decision-making on managing the company’s operations, growing its core business, acquiring new markets and customers, investing in R&D, and other considerations. Knowing the relevant data and information beforehand will guide the decision-makers in making the right choices that will best position the company toward more success.

External Audience

An external audience can either be the company’s existing clients, where there are projects in progress, or new clients that the company wants to build a relationship with and win new business from. The other external audience is the general public, such as the company’s external shareholders and prospective investors of the company.

When it comes to winning new business, the analyst’s presentation will be more promotional and sales-oriented, whereas a project update will contain more specific information for the client, usually with lots of industry jargon.

Audiences for Live and Emailed Presentation

A live presentation contains more visuals and storytelling to connect more with the audience. It must be more precise and should get to the point faster and avoid long-winded speech or text because of limited time.

In contrast, an emailed presentation is expected to be read, so it will include more text. Just like a document or a book, it will include more detailed information, because its context will not be explained with a voice-over as in a live presentation.

When it comes to details, acronyms, and jargon in the presentation, these things depend on whether your audience are experts or not.

Every great presentation requires a clear “main idea”. It is the core purpose of the presentation and should be addressed clearly. Its significance should be highlighted and should cause the targeted audience to take some action on the matter.

An example of a serious and profound idea is given below.

To communicate this big idea, we have to come up with appropriate and effective visual displays to show both the good and bad things surrounding the idea. It should put emphasis and attention on the most important part, which is the critical cash balance and capital investment situation for next year. This is an important component of data presentation.

The storyboarding below is how an analyst would build the presentation based on the big idea. Once the issue or the main idea has been introduced, it will be followed by a demonstration of the positive aspects of the company’s performance, as well as the negative aspects, which are more important and will likely require more attention.

Various ideas will then be suggested to solve the negative issues. However, before choosing the best option, a comparison of the different outcomes of the suggested ideas will be performed. Finally, a recommendation will be made that centers around the optimal choice to address the imminent problem highlighted in the big idea.

This storyboard is taken from our course on how to present data .

To get to the final point (recommendation), a great deal of analysis has been performed, which includes the charts and graphs discussed earlier, to make the whole presentation easy to follow, convincing, and compelling for your audience.

CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant resources below:

• Investment Banking Pitch Books
• Excel Dashboards
• Financial Modeling Guide
• Startup Pitch Book
• See all business intelligence resources
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Mastering the Art of Presenting Data in PowerPoint

Presenting data in PowerPoint is easy. However, making it visually appealing and effective takes more time and effort. It’s not hard to bore your audience with the same old data presentation formats. So, there is one simple golden rule: Make it not boring.

When used correctly, data can add weight, authority, and punch to your message. It should support and highlight your ideas, making a concept come to life. But this begs the question: How to present data in PowerPoint?

After talking to our 200+ expert presentation designers, I compiled information about their best-kept secrets to presenting data in PowerPoint. 

Below, I’ll show our designers ' favorite ways to add data visualization for global customers and their expert tips for making your data shine. Read ahead and master the art of data visualization in PowerPoint!

Feel free to explore sections to find what's most useful!

How to present data in PowePoint: a step-by-step guide

Creative ways to present data in powerpoint.

• Tips for data visualization

Seeking to optimize your presentations? – 24Slides designers have got you covered!

How you present your data can make or break your presentation. It can make it stand out and stick with your audience, or make it fall flat from the go.

It’s not enough to just copy and paste your data into a presentation slide. Luckily, PowerPoint has many smart data visualization tools! You only need to put in your numbers, and PowerPoint will work it up for you.

Follow these steps, and I guarantee your presentations will level up!

1. Collect your data

First things first, and that is to have all your information ready. Especially for long business presentations, there can be a lot of information to consider when working on your slides. Having it all organized and ready to use will make the whole process much easier to go through.

Consider where your data comes from, whether from research, surveys, or databases. Make sure your data is accurate, up-to-date, and relevant to your presentation topic.

Your goal will be to create clear conclusions based on your data and highlight trends.

2. Know your audience

Knowing who your audience is and the one thing you want them to get from your data is vital. If you don’t have any idea where to start, you can begin with these key questions:

• What impact do you want your data to make on them?
• Is the subject of your presentation familiar to them?
• Are they fellow sales professionals?
• Are they interested in the relationships in the data you’re presenting?

By answering these, you'll be able to clearly understand the purpose of your data. As a storyteller, you want to capture your audience’s attention.

3. Choose a data visualization option

One key to data visualization in PowerPoint is being aware of your choices and picking the best one for your needs. This depends on the type of data you’re trying to showcase and your story.

When showcasing growth over time, you won’t use a spider chart but a line chart. If you show percentages, a circle graph will probably work better than a timeline. As you can see, knowing how to work with charts, graphs, and tables can level up your presentation.

Later, we’ll review some of the most common tools for data visualization in PowerPoint. This will include what these graphs and charts are best for and how to make the most of each. So read ahead for more information about how to present data in PowerPoint!

4. Be creative!

PowerPoint can assist with creating graphs and charts, but it's up to you to perfect them. Take into account that PowerPoint has many options. So, don't be afraid to think outside the box when presenting your data.

To enhance your presentation design, try out different color schemes, fonts, and layouts. Add images, icons, and visual elements to highlight your ideas.

If this sounds complicated to you, there's no need to worry. At the end of this article, you’ll find some easy tips for upgrading your data visualization design!

At this point, you might wonder: what is the best way to present data in PowerPoint? Well, let me tell you: it's all about charts. To accomplish a polished presentation, you must use charts instead of words. When visualizing quantitative data, a picture is worth a thousand words.

Based on +10 years of expertise, we've identified key chart types and creative ways to work with them. Let's delve into each one!

Line Charts

Line charts are a classic, which can make them boring. However, if done correctly, they can be striking and effective. But where does their popularity come from? Here's the answer: Line charts work great to show changes over time.

Another critical difference is that line charts are accumulative. For example, you can join them to a column chart to show different data at a glance. They allow data visualization effectively, making it easier to figure out.

To make the most of them, mastering how to work with line charts is essential. But there is good news: you will have a lot of freedom to customize them!

Download our Free Line Chart Template here .

Bar and column charts

Bar and column charts are another classic choice. Again, they are simple and great for comparing different categories. They organize them around two axes: one shows numbers, and the other shows what we want to compare.

But when should you use a bar chart or a column chart? A bar chart is better when comparing different categories and having long labels. A column chart, on the other hand, is better if you have a few categories and want to show changes over time.

You also have the waterfall option, which is perfect for highlighting the difference between gains and losses. It also adds a dynamic touch to your presentation!

Unsure how to implement these charts? Here's how to add a bar or a column chart in PowerPoint.

Download our Bar and Column Chart Template here .

Venn diagram

Venn diagrams are definitely something to consider when discussing data visualization—even if its focus is not quantitative data! Venn diagrams are best for showcasing similarities and differences between two (or more) categories or products. 

By using overlapping circles, you can quickly and easily see common features between separate ideas. The shared space of the circles shows what is the same between the groups. However, items in the outer parts of each circle show what isn’t a common trait.

They make complex relationships easy to understand. Now, you only need to know how to create a Venn diagram in PowerPoint —quite simple!

Download our Free Venn Diagram Template here .

Pie charts are a great way to show different percentages of a whole. They immediately identify the largest and smallest values. This means that they are great options for drawing attention to differences between one group and another.

However, many people misuse pie charts by overpacking them. As a rule, keep the chart to six or fewer sections. That way, the data is striking, not confusing. Then, make the pie chart your own with small, individual details and designs.

Once again, the powerful presentation of data is in simplicity.

Are you considering incorporating it into your presentation? Here’s how to easily add a pie chart in PowerPoint.

Download our Free Pie Chart Template here .

Bubble Charts

Bubble charts playfully present data in an incredibly visual way. But, what makes them so unique? It's easy: they show different values through varying circle sizes.

Squeezed together, the circles also show a holistic viewpoint. Bigger bubbles catch the eye, while small bubbles illustrate how the data breaks down into smaller values. ¿The result? A presentation of data in a visual form.

It can be one of the most graphic ways to represent the spending distribution. For example, you can instantly see your biggest costs or notice how important finances are getting lost in a sea of bubbles. This quick analysis can be incredibly handy.

Download our Free Bubble Chart Template here .

Maps are the go-to solution for presenting geographic information . They help put data in a real-world context. You usually take a blank map and use color for the important areas.

Blocks, circles, or shading represent value. Knowing where certain data is can be crucial. A consistent color scheme makes it easy to show how valuable each section is.

They also work great when paired with other forms of data visualization. For example, you can use pie charts to provide information about offices in different cities around the world or bar charts to compare revenue in different locations.

Download our Free World Map Template here .

If you want to display chronological data, you must use a timeline. It’s the most effective and space-efficient way to show time passage.

They make it easy for your audience to understand the sequence of events with clear and concise visuals.

You can use timelines to show your company’s history or significant events that impacted your business. Like maps, you can easily mix them with other types of data visuals. This characteristic allows you to create engaging presentations that tell a comprehensive story.

At this point, it's a matter of understanding how to add a timeline correctly in PowerPoint . Spoiler: it's incredibly easy.

Download our Free Timeline Chart Template here .

Flowcharts, like timelines, represent a succession of events. The main difference is that timelines have determined start and finish points and specific dates. Flowcharts, on the other hand, show the passing from one step to the next.

They are great for showing processes and info that need to be in a specific order. They can also help you communicate cause-and-effect information in a visually engaging way.

Their best feature is that (unlike timelines) they can also be circular, meaning this is a recurrent process. All you need now is to become familiar with creating a flowchart in PowerPoint .

Download our Free Flowchart Template here .

5 Tips for data visualization in PowerPoint

Knowing how to present data in PowerPoint presentations is not hard, but it takes time to master it. After all, practice makes perfect!

I've gathered insights from our 200+ expert designers , and here are the top five tips they suggest for enhancing your data presentations!

1. Keep it simple

Don’t overload your audience with information. Let the data speak for itself. If you write text below a chart, keep it minimalist and highlight the key figures. The important thing in a presentation is displaying data in a clear and digestible way.

Put all the heavy facts and figures in a report, but never on a PowerPoint slide.

You can even avoid charts altogether to keep it as simple as possible. And don't get me wrong. We've already covered that charts are the way to go for presenting data in PowerPoint, but there are a few exceptions.

This begs the question: when shouldn't you use charts in PowerPoint? The answer is quite short. If your data is simple or doesn't add much value to your presentation, you might want to skip using charts.

2. Be original

One of the best ways to make your data impactful is originality. Take time to think about how you could present information uniquely. Think of a whole new concept and play around with it. Even if it’s not yet perfect, people will appreciate the effort to be original.

Experiment with creative ways to present your data, adding storytelling techniques , unique design elements, or interactive features. This approach can make the data more appealing and captivating for your audience.

You can even mix up how to present data in PowerPoint. Instead of just one format, consider using two different types of data presentation on a single slide. For instance, try placing a bar chart on the left and a pie chart showcasing different data on the right.

3. Focus on your brand

Keeping your presentation on-brand can genuinely make you stand out from the crowd! Even if you just focus on your brand’s color scheme, it will make your presentation look more polished and professional. 

Have fun experimenting with data visualization tools to ensure they match your company’s products and services. What makes you different from others?

Add your brand's style into your visualization to ensure brand consistency and recognition. Use colors, fonts, and logos aligned with your company's image.

You can even make a presentation that more subtly reflects your brand. Think of what values you want to associate with your company and how you can display these in your presentation design.

4. Highlight key information

Not distracting your audience nicely brings us to our third point: Highlight key information. Being detailed and informative is important, but grabbing and keeping the audience's attention is crucial.

Presenting numbers in PowerPoint can be difficult, but it doesn’t must be. Make your audience listen to the bigger message of your words, not just the exact details. All the smaller particulars can be confirmed later.

Your listeners don’t want to know the facts and figures to the nearest decimal. They want the whole number, which is easy to spot and understand.

The meaning of the number is more important than its numerical value. Is it high or low? Positive or negative? Good or bad for business? These are the questions to which you want the answers to be clear.

Using colors is an excellent way to work with this. Colors are also a great visual tool to showcase contrast. For example, when you're working on a graph to display your revenue, you can showcase expenses in red and earnings in green. This kind of color-coding will make your data visualization clear from first sight!

5. Use Templates!

Presentation templates can be your best friend when you want to present data effectively in PowerPoint.

They offer pre-designed layouts and styles that can ensure consistency throughout your presentation. Templates allow you to adjust colors, fonts, and layouts to match your branding or personal preferences.

Microsoft Office has its own library of templates, but you can also find some pretty amazing ones online. Take some extra time to search and pick one that truly fits your needs and brand. 

¿The good news? Our Templates by 24Slides platform has hundreds of PowerPoint chart templates, all completely free for you to use . You can even download different templates and mix and match slides to make the perfect deck. All are entirely editable, so you can add your own data and forget about design.

If you liked the look of some examples in this article, you might be in luck! Most are part of these, and you can also find them on our Templates platform.

In this article, I've shown why knowing how to present data efficiently in PowerPoint is crucial. Data visualization tools are a must to ensure your message is clear and that it sticks with your audience.

However, achieving results that really stand out could be a huge challenge for beginners.  So, If you want to save time and effort on the learning curve of presenting data in PowerPoint, you can always trust professionals!

With 10+ years of experience and more than 200 designers worldwide, we are the world’s largest presentation design company across the globe.

24Slides' professional PowerPoint designers work with businesses worldwide, helping them transform their presentations from ‘okay’ to ‘spectacular.’ With each presentation, we're crafting a powerful tool to captivate audiences and convey messages effectively!

Looking to boost your PowerPoint game? Check out this content:

• PowerPoint 101: The Ultimate Guide for Beginners
• How to Create the Perfect B2B Sales Presentation
• The Ultimate Brand Identity Presentation Guide [FREE PPT Template]
• 7 Essential Storytelling Techniques for your Business Presentation
• The Cost of PowerPoint Presentations: Discover the hidden expenses you might overlook!

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10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation with AI nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Top 10 Data Presentation Templates with Samples and Examples

Data presentations play a crucial role in decision-making in today's data-rich environment, influencing sectors such as boardrooms, classrooms, marketing strategies, and scientific advancements. Yet, in a vast spectrum of data presentations, the question is, can data presentations communicate, engage, educate, and convince?

The answer is yes.  Data presentations go beyond mere numbers on a page, revealing insights, guiding decisions, and shaping action plans in every segment. Now, the challenge is, in a world full of information, how can we make sure that the data presentations you prepare captivate your audiences and motivate them to take action?

Since presenting data by accommodating a sheer volume of information can be stressful. But now you do not have to be stressed about it.   SlideTeam Data Presentation Templates perfectly solves all your presentation challenges by connecting raw data with visual narratives, offering intuitive design and user-friendly features for better comprehension of complex data. The complete editable features help you to deal with formatting issues in a simplified manner. Our Templates offer a seamless experience, enabling you to concentrate on delivering presentations that inform, engage, and inspire your audience.

Keep reading to learn more about our exceptional templates, which will transform the way you present your information.

Also, look at our blog on Database Diagram Templates to organize your data effectively.

Template 1: Data Governance PPT Set

Data governance has evolved as a critical component of modern enterprises seeking efficiency and compliance. This Slide details why companies struggle without adequate data governance and its repercussions. It provides a detailed comparison of human versus automated data governance approaches, outlining their merits for optimizing procedures and ensuring data integrity.  In addition, you will find efficient data governance architecture to manage assets effectively. This PPT is designed to engage the audience while outlining the roles and responsibilities required for effective data governance implementation. 

DOWNLOAD NOW 

Template 2: Data Analytics Powerpoint Presentation Slides

This PPT Slide provides social media platforms such as Google, Facebook, Twitter, YouTube, and Instagram, which serve as critical sources of data for analysis and insight development. Moreover, it demonstrates cloud computing's disruptive potential, real-time information, and on-demand insights using cloud infrastructure. It delves deeper into the complexities of data analytics , including web data, IoT devices, and databases. Moreover, it provides a thorough overview of data analytics technologies, ranging from warehouse appliances and big data sources to network monitoring technologies and in-line monitoring solutions. Download today.

Template 3: Data Migration Strategies Powerpoint Presentation Slides

This PPT Slide addresses every aspect of data migration strategies for your business and provides a road map for easily navigating its intricacies. It provides an overview for understanding and implementing data transfer plans. From defining our migration strategy to outlining step-by-step process details, it also offers simplified illustrations of data migration procedures accompanied by visually engaging images. Beyond data transfer solutions, it provides an in-depth look at our mission, corporate culture, and team competence. It also includes a four-step workflow architecture to help you plan and execute more efficiently. 

Template 4: Talent Acquisition Dashboard of Organization Showing Cost and Open Position Data

This PPT Slide provides a complete picture of critical metrics and data points required to optimize recruitment strategies. It presents a detailed analysis of application sources and sources. Monthly metrics in the slide assist in evaluating progress toward fulfilling recruiting goals and objectives, whereas decline reasons provide insight into areas for improvement, allowing you to optimize processes and improve candidate experiences. Moreover, efficiency indicators provide insight into the effectiveness of your recruitment activities, allowing for targeted adjustment. A recruiting funnel illustrates how candidates advance through each stage, which aids in identifying bottlenecks and streamlining hiring processes. Furthermore, it includes a pipeline that reveals talent that may be available for current or future openings. 

Template 5: Data Stewardship IT PowerPoint Presentation Slides

This PPT Slide introduces data stewardship, including its goals, life cycle overview, framework, and components. Our presentation is an excellent resource for people of all experience and expertise levels. Data Stewardship highlights its importance and benefits by describing its role in ensuring data quality, integrity, and compliance. Data stewardship is critical to driving organizational success across industries, from strengthening decision-making processes to limiting risks. It also investigates data stewards' credentials, abilities, roles, and responsibilities, delivering actionable insights for anyone considering this vital profession. Furthermore, several data stewardship models are included to develop realistic frameworks for successful deployment.

Check out our blog on Demographic Data Presentation Templates to learn more. 

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Template 6: Monthly and Annual Sales Data with MQL and SQL

This PPT Slide includes monthly and annual sales data with MQLs (Marketing Qualified Leads) and SQLs (Sales Qualified Leads). This presentation will provide information on crucial areas such as year, lead source, average deal size, sales cycle time, and opportunity close rates. Our presentations present a detailed analysis of sales data over time and across categories, allowing for informed decision-making and strategic planning. These slides address opportunities, lead sources, and sales KPIs that will be useful to a wide range of stakeholders, including sales managers, marketing professionals, business analysts, and executives. Whether it's analyzing monthly trends, comparing annual results, or evaluating the efficacy of various lead sources, it is a crucial resource for driving sales growth and optimizing marketing strategy.

Template 7: Proper Data Management in Healthcare Company to Reduce Cyber Threats Complete the Deck

This Data management slide can arm your healthcare organization with comprehensive cyber threat mitigation methods and sensitive data security. It covers a wide range of crucial issues necessary for improving cybersecurity posture and meeting regulatory compliance. This deck provides an in-depth analysis of the healthcare organization, including its mission, beliefs, and core strengths. Furthermore, you will find frightening statistics about healthcare cybersecurity breaches around the world, an in-depth assessment of healthcare records exposed globally, cybersecurity issues, and the effects of malware infestations on enterprise records. Quantifying the costs associated with cybersecurity vulnerabilities highlights the importance of proactive risk management. 

Template 8: Data Cleaning Powerpoint Ppt Template Bundles

This PPT Slide covers key issues and practices for effective data cleansing across multiple domains. This bundle includes slides that address essential aspects of HR analytics data cleaning, such as the 4-step process and framework methodology, as well as icons meant to demonstrate removal techniques graphically. Moreover, it includes applying data-cleaning strategies and the essential tools and technology required to streamline the process. Download now to revolutionize your data-cleaning efforts!

Template 9: Data architecture strategy PPT slides download

This PPT Slide includes key data strategy components such as statistics, pattern recognition, artificial intelligence, machine learning, data sources, databases, mathematical modeling, management science, and information systems. These slides will help business managers, database administrators, and system engineers show the deep elements of data strategy development. Our executive summary PPT template provides a clear overview of your data architecture plan, making it easy for immediate managers and internal team members to understand. Download now!

Template 10: Data integration big data example of ppt

This PPT Slide shows real-world examples of big data integration, including location data and email communication, web interactions and social media engagements, transactional and sensor data from IoT devices, and organizational and self-service data requests. By displaying all of these data sources together, it highlights their complexity and integration as a source of actionable insights. Furthermore, it also includes Pentaho Data Integration, Pentaho Analyzer, and Pentaho Reports products, providing a comprehensive overview of their capabilities and features. This slide is essential for conveying data integration strategy and best practices to internal and external stakeholders. Download now.

Begin your Path to Data-driven Success with us!

Creating captivating data presentations doesn't have to be a challenging task! SlideTeam offers high-quality data presentation templates with actual samples and examples to help you quickly create engaging visuals for your audience. Whether you're a novice or a seasoned professional, our templates provide the perfect balance of simplicity and professionalism to enhance your presentations! Whether you're a business professional, educator, or aspiring entrepreneur, our high-quality data presentation templates can help your ideas stand out! Don't hesitate - SlideTeam can be your ally in achieving presentation success! 

Explore more by checking our blog on Proposal Templates and take your proposals to a new level.

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Blog Beginner Guides 8 Types of Presentations You Should Know [+Examples & Tips]

8 Types of Presentations You Should Know [+Examples & Tips]

Written by: Krystle Wong Aug 11, 2023

From persuasive pitches that influence opinions to instructional demonstrations that teach skills, the different types of presentations serve a unique purpose, tailored to specific objectives and audiences.

Presentations that are tailored to its objectives and audiences are more engaging and memorable. They capture attention, maintain interest and leave a lasting impression. 

Don’t worry if you’re no designer —  Whether you need data-driven visuals, persuasive graphics or engaging design elements, Venngage can empower you to craft presentations that stand out and effectively convey your message.

Venngage’s intuitive drag-and-drop interface, extensive presentation template library and customizable design options make it a valuable tool for creating slides that align with your specific goals and target audience. 

Click to jump ahead:

8 Different types of presentations every presenter must know

How do i choose the right type of presentation for my topic or audience, types of presentation faq, 5 steps to create a presentation with venngage .

When it comes to presentations, versatility is the name of the game. Having a variety of presentation styles up your sleeve can make a world of difference in keeping your audience engaged. Here are 8 essential presentation types that every presenter should be well-acquainted with:

1. Informative presentation

Ever sat through a presentation that left you feeling enlightened? That’s the power of an informative presentation. 

This presentation style is all about sharing knowledge and shedding light on a particular topic. Whether you’re diving into the depths of quantum physics or explaining the intricacies of the latest social media trends, informative presentations aim to increase the audience’s understanding.

When delivering an informative presentation, simplify complex topics with clear visuals and relatable examples. Organize your content logically, starting with the basics and gradually delving deeper and always remember to keep jargon to a minimum and encourage questions for clarity.

Academic presentations and research presentations are great examples of informative presentations. An effective academic presentation involves having clear structure, credible evidence, engaging delivery and supporting visuals. Provide context to emphasize the topic’s significance, practice to perfect timing, and be ready to address anticipated questions. 

2. Persuasive presentation

If you’ve ever been swayed by a passionate speaker armed with compelling arguments, you’ve experienced a persuasive presentation . 

This type of presentation is like a verbal tug-of-war, aiming to convince the audience to see things from a specific perspective. Expect to encounter solid evidence, logical reasoning and a dash of emotional appeal.

With persuasive presentations, it’s important to know your audience inside out and tailor your message to their interests and concerns. Craft a compelling narrative with a strong opening, a solid argument and a memorable closing. Additionally, use visuals strategically to enhance your points.

Examples of persuasive presentations include presentations for environmental conservations, policy change, social issues and more. Here are some engaging presentation templates you can use to get started with: 

3. Demonstration or how-to presentation

A Demonstration or How-To Presentation is a type of presentation where the speaker showcases a process, technique, or procedure step by step, providing the audience with clear instructions on how to replicate the demonstrated action. 

A demonstrative presentation is particularly useful when teaching practical skills or showing how something is done in a hands-on manner.

These presentations are commonly used in various settings, including educational workshops, training sessions, cooking classes, DIY tutorials, technology demonstrations and more. Designing creative slides for your how-to presentations can heighten engagement and foster better information retention. 

Speakers can also consider breaking down the process into manageable steps, using visual aids, props and sometimes even live demonstrations to illustrate each step. The key is to provide clear and concise instructions, engage the audience with interactive elements and address any questions that may arise during the presentation.

4. Training or instructional presentation

Training presentations are geared towards imparting practical skills, procedures or concepts — think of this as the more focused cousin of the demonstration presentation. 

Whether you’re teaching a group of new employees the ins and outs of a software or enlightening budding chefs on the art of soufflé-making, training presentations are all about turning novices into experts.

To maximize the impact of your training or instructional presentation, break down complex concepts into digestible segments. Consider using real-life examples to illustrate each point and create a connection. 

You can also create an interactive presentation by incorporating elements like quizzes or group activities to reinforce understanding.

5. Sales presentation

Sales presentations are one of the many types of business presentations and the bread and butter of businesses looking to woo potential clients or customers. With a sprinkle of charm and a dash of persuasion, these presentations showcase products, services or ideas with one end goal in mind: sealing the deal.

A successful sales presentation often has key characteristics such as a clear value proposition, strong storytelling, confidence and a compelling call to action. Hence, when presenting to your clients or stakeholders, focus on benefits rather than just features. 

Anticipate and address potential objections before they arise and use storytelling to showcase how your offering solves a specific problem for your audience. Utilizing visual aids is also a great way to make your points stand out and stay memorable.

A sales presentation can be used to promote service offerings, product launches or even consultancy proposals that outline the expertise and industry experience of a business. Here are some template examples you can use for your next sales presentation:

6. Pitch presentation

Pitch presentations are your ticket to garnering the interest and support of potential investors, partners or stakeholders. Think of your pitch deck as your chance to paint a vivid picture of your business idea or proposal and secure the resources you need to bring it to life. 

Business presentations aside, individuals can also create a portfolio presentation to showcase their skills, experience and achievements to potential clients, employers or investors. 

Craft a concise and compelling narrative. Clearly define the problem your idea solves and how it stands out in the market. Anticipate questions and practice your answers. Project confidence and passion for your idea.

7. Motivational or inspirational presentation

Feeling the need for a morale boost? That’s where motivational presentations step in. These talks are designed to uplift and inspire, often featuring personal anecdotes, heartwarming stories and a generous serving of encouragement.

Form a connection with your audience by sharing personal stories that resonate with your message. Use a storytelling style with relatable anecdotes and powerful metaphors to create an emotional connection. Keep the energy high and wrap up your inspirational presentations with a clear call to action.

Inspirational talks and leadership presentations aside, a motivational or inspirational presentation can also be a simple presentation aimed at boosting confidence, a motivational speech focused on embracing change and more.

8. Status or progress report presentation

Projects and businesses are like living organisms, constantly evolving and changing. Status or progress report presentations keep everyone in the loop by providing updates on achievements, challenges and future plans. It’s like a GPS for your team, ensuring everyone stays on track.

Be transparent about achievements, challenges and future plans. Utilize infographics, charts and diagrams to present your data visually and simplify information. By visually representing data, it becomes easier to identify trends, make predictions and strategize based on evidence.

Now that you’ve learned about the different types of presentation methods and how to use them, you’re on the right track to creating a good presentation that can boost your confidence and enhance your presentation skills . 

Selecting the most suitable presentation style is akin to choosing the right outfit for an occasion – it greatly influences how your message is perceived. Here’s a more detailed guide to help you make that crucial decision:

1. Define your objectives

Begin by clarifying your presentation’s goals. Are you aiming to educate, persuade, motivate, train or perhaps sell a concept? Your objectives will guide you to the most suitable presentation type. 

For instance, if you’re aiming to inform, an informative presentation would be a natural fit. On the other hand, a persuasive presentation suits the goal of swaying opinions.

2. Know your audience

Regardless if you’re giving an in-person or a virtual presentation — delve into the characteristics of your audience. Consider factors like their expertise level, familiarity with the topic, interests and expectations. 

If your audience consists of professionals in your field, a more technical presentation might be suitable. However, if your audience is diverse and includes newcomers, an approachable and engaging style might work better.

3. Analyze your content

Reflect on the content you intend to present. Is it data-heavy, rich in personal stories or focused on practical skills? Different presentation styles serve different content types. 

For data-driven content, an informative or instructional presentation might work best. For emotional stories, a motivational presentation could be a compelling choice.

4. Consider time constraints

Evaluate the time you have at your disposal. If your presentation needs to be concise due to time limitations, opt for a presentation style that allows you to convey your key points effectively within the available timeframe. A pitch presentation, for example, often requires delivering impactful information within a short span.

5. Leverage visuals

Visual aids are powerful tools in presentations. Consider whether your content would benefit from visual representation. If your PowerPoint presentations involve step-by-step instructions or demonstrations, a how-to presentation with clear visuals would be advantageous. Conversely, if your content is more conceptual, a motivational presentation could rely more on spoken words.

6. Align with the setting

Take the presentation environment into account. Are you presenting in a formal business setting, a casual workshop or a conference? Your setting can influence the level of formality and interactivity in your presentation. For instance, a demonstration presentation might be ideal for a hands-on workshop, while a persuasive presentation is great for conferences.

7. Gauge audience interaction

Determine the level of audience engagement you want. Interactive presentations work well for training sessions, workshops and small group settings, while informative or persuasive presentations might be more one-sided.

8. Flexibility

Stay open to adjusting your presentation style on the fly. Sometimes, unexpected factors might require a change of presentation style. Be prepared to adjust on the spot if audience engagement or reactions indicate that a different approach would be more effective.

Remember that there is no one-size-fits-all approach, and the best type of presentation may vary depending on the specific situation and your unique communication goals. By carefully considering these factors, you can choose the most effective presentation type to successfully engage and communicate with your audience.

To save time, use a presentation software or check out these presentation design and presentation background guides to create a presentation that stands out.    

What are some effective ways to begin and end a presentation?

Capture your audience’s attention from the start of your presentation by using a surprising statistic, a compelling story or a thought-provoking question related to your topic. 

To conclude your presentation , summarize your main points, reinforce your key message and leave a lasting impression with a powerful call to action or a memorable quote that resonates with your presentation’s theme.

How can I make my presentation more engaging and interactive?

To create an engaging and interactive presentation for your audience, incorporate visual elements such as images, graphs and videos to illustrate your points visually. Share relatable anecdotes or real-life examples to create a connection with your audience. 

You can also integrate interactive elements like live polls, open-ended questions or small group discussions to encourage participation and keep your audience actively engaged throughout your presentation.

Which types of presentations require special markings

Some presentation types require special markings such as how sales presentations require persuasive techniques like emphasizing benefits, addressing objections and using compelling visuals to showcase products or services. 

Demonstrations and how-to presentations on the other hand require clear markings for each step, ensuring the audience can follow along seamlessly. 

That aside, pitch presentations require highlighting unique selling points, market potential and the competitive edge of your idea, making it stand out to potential investors or partners.

Need some inspiration on how to make a presentation that will captivate an audience? Here are 120+ presentation ideas to help you get started. 

Creating a stunning and impactful presentation with Venngage is a breeze. Whether you’re crafting a business pitch, a training presentation or any other type of presentation, follow these five steps to create a professional presentation that stands out:

• Sign up and log in to Venngage to access the editor.
• Choose a presentation template that matches your topic or style.
• Customize content, colors, fonts, and background to personalize your presentation.
• Add images, icons, and charts to enhancevisual style and clarity.
• Save, export, and share your presentation as PDF or PNG files, or use Venngage’s Presentation Mode for online showcasing.

In the realm of presentations, understanding the different types of presentation formats is like having a versatile set of tools that empower you to craft compelling narratives for every occasion.

Remember, the key to a successful presentation lies not only in the content you deliver but also in the way you connect with your audience. Whether you’re informing, persuading or entertaining, tailoring your approach to the specific type of presentation you’re delivering can make all the difference.

Presentations are a powerful tool, and with practice and dedication (and a little help from Venngage), you’ll find yourself becoming a presentation pro in no time. Now, let’s get started and customize your next presentation!

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• Presentation of Data

Data Presenting for Clearer Reference

Imagine the statistical data without a definite presentation, will be burdensome! Data presentation is one of the important aspects of Statistics. Presenting the data helps the users to study and explain the statistics thoroughly. We are going to discuss this presentation of data and know-how information is laid down methodically. 

In this context, we are going to present the topic - Presentation of Data which is to be referred to by the students and the same is to be studied in regard to the types of presentations of data. 

Presentation of Data and Information

Statistics is all about data. Presenting data effectively and efficiently is an art. You may have uncovered many truths that are complex and need long explanations while writing. This is where the importance of the presentation of data comes in. You have to present your findings in such a way that the readers can go through them quickly and understand each and every point that you wanted to showcase. As time progressed and new and complex research started happening, people realized the importance of the presentation of data to make sense of the findings.

Define Data Presentation

Data presentation is defined as the process of using various graphical formats to visually represent the relationship between two or more data sets so that an informed decision can be made based on them.

Types of Data Presentation

Broadly speaking, there are three methods of data presentation:

Diagrammatic

Textual Ways of Presenting Data

Out of the different methods of data presentation, this is the simplest one. You just write your findings in a coherent manner and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture. Yes, the introduction, summary, and conclusion can help condense the information.

Tabular Ways of Data Presentation and Analysis

To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year, sex, age, and other things like these). It is against these attributes that data is written within a cell.

Diagrammatic Presentation: Graphical Presentation of Data in Statistics

This kind of data presentation and analysis method says a lot with dramatically short amounts of time.

Diagrammatic Presentation has been divided into further categories:

Geometric Diagram

When a Diagrammatic presentation involves shapes like a bar or circle, we call that a Geometric Diagram. Examples of Geometric Diagram

Bar Diagram

Simple Bar Diagram

Simple Bar Diagram is composed of rectangular bars. All of these bars have the same width and are placed at an equal distance from each other. The bars are placed on the X-axis. The height or length of the bars is used as the means of measurement. So, on the Y-axis, you have the measurement relevant to the data. 

Suppose, you want to present the run scored by each batsman in a game in the form of a bar chart. Mark the runs on the Y-axis - in ascending order from the bottom. So, the lowest scorer will be represented in the form of the smallest bar and the highest scorer in the form of the longest bar.

Multiple Bar Diagram

(Image will be uploaded soon)

In many states of India, electric bills have bar diagrams showing the consumption in the last 5 months. Along with these bars, they also have bars that show the consumption that happened in the same months of the previous year. This kind of Bar Diagram is called Multiple Bar Diagrams.

Component Bar Diagram

(image will be uploaded soon)

Sometimes, a bar is divided into two or more parts. For example, if there is a Bar Diagram, the bars of which show the percentage of male voters who voted and who didn’t and the female voters who voted and who didn’t. Instead of creating separate bars for who did and who did not, you can divide one bar into who did and who did not.

A pie chart is a chart where you divide a pie (a circle) into different parts based on the data. Each of the data is first transformed into a percentage and then that percentage figure is multiplied by 3.6 degrees. The result that you get is the angular degree of that corresponding data to be drawn in the pie chart. So, for example, you get 30 degrees as the result, on the pie chart you draw that angle from the center.

Frequency Diagram

Suppose you want to present data that shows how many students have 1 to 2 pens, how many have 3 to 5 pens, how many have 6 to 10 pens (grouped frequency) you do that with the help of a Frequency Diagram. A Frequency Diagram can be of many kinds:

Where the grouped frequency of pens (from the above example) is written on the X-axis and the numbers of students are marked on the Y-axis. The data is presented in the form of bars.

Frequency Polygon

When you join the midpoints of the upper side of the rectangles in a histogram, you get a Frequency Polygon

Frequency Curve

When you draw a freehand line that passes through the points of the Frequency Polygon, you get a Frequency Curve.

Ogive 

Suppose 2 students got 0-20 marks in maths, 5 students got 20-30 marks and 4 students got 30-50 marks in Maths. So how many students got less than 50 marks? Yes, 5+2=7. And how many students got more than 20 marks? 5+4=9. This type of more than and less than data are represented in the form of the ogive. The meeting point of the less than and more than line will give you the Median.

Arithmetic Line Graph

If you want to see the trend of Corona infection vs the number of recoveries from January 2020 to December 2020, you can do that in the form of an Arithmetic Line Graph. The months should be marked on the X-axis and the number of infections and recoveries are marked on the Y-axis. You can compare if the recovery is greater than the infection and if the recovery and infection are going at the same rate or not with the help of this Diagram.

Did You Know?

Sir Ronald Aylmer Fisher is known as the father of modern statistics.

FAQs on Presentation of Data

1. What are the 4 types of Tabular Presentation?

The tabular presentation method can be further divided into 4 categories:

Qualitative

Quantitative

Qualitative classification is done when the attributes in the table are some kind of ‘quality’ or feature. Suppose you want to make a table where you would show how many batsmen made half-centuries and how many batsmen made centuries in IPL 2020. Notice that the data would have only numbers - no age, sex, height is needed. This type of tabulation is called quantitative tabulation.

If you want to make a table that would inform which year’s world cup, which team won. The classifying variable, here, is year or time. This kind of classification is called Temporal classification.

If you want to list the top 5 coldest places in the world. The classifying variable here would be a place in each case. This kind of classification is called Spatial Classification.

2. Are bar charts and histograms the Same?

No, they are not the same. With a histogram, you measure the frequency of quantitative data. With bar charts, you compare categorical data.

3. What is the definition of Data Presentation?

When research work is completed, the data gathered from it can be quite large and complex. Organizing the data in a coherent, easy-to-understand, quick to read and graphical way is called data presentation.

Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

• The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
• Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

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• Loss-of-function variants in JPH1 cause congenital myopathy with prominent facial and ocular involvement
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• http://orcid.org/0000-0002-3549-558X Mridul Johari 1 , 2 , 3 ,
• Ana Topf 4 ,
• Chiara Folland 1 ,
• Jennifer Duff 4 ,
• Lein Dofash 1 ,
• Pilar Marti 5 ,
• Thomas Robertson 6 , 7 ,
• Juan Vilchez 5 ,
• Anita Cairns 8 ,
• Elizabeth Harris 4 ,
• Chiara Marini-Bettolo 4 ,
• Khalid Hundallah 9 ,
• Amal M Alhashem 10 ,
• Mohammed Al-Owain 11 , 12 ,
• Reza Maroofian 13 ,
• http://orcid.org/0000-0003-3634-211X Gianina Ravenscroft 1 ,
• Volker Straub 4
• 1 Harry Perkins Institute of Medical Research , Centre for Medical Research, University of Western Australia, Nedlands , Perth , Western Australia , Australia
• 2 Folkhälsan Research Center , Helsinki , Finland
• 3 Department of Medical and Clinical Genetics , Medicum, University of Helsinki , Helsinki , Finland
• 4 The John Walton Muscular Dystrophy Research Centre , Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust , Newcastle upon Tyne , UK
• 5 Neuromuscular Research Group , IIS La Fe and CIBERER U763, Hospital Universitari i Politècnic La Fe , Valencia , Spain
• 6 Anatomical Pathology , Queensland Pathology , Brisbane , Queensland , Australia
• 7 School of Biomedical Sciences , University of Queensland , Brisbane , Queensland , Australia
• 8 Neurosciences Department , Queensland Children's Hospital , Brisbane , Queensland , Australia
• 9 Division of Pediatric Neurology, Department of Pediatric , Prince Sultan Military Medical City , Riyadh , Riyadh , Saudi Arabia
• 10 Division of clinical genetic and metabolic medicine, Department of Pediatric , Prince Sultan Military Medical City , Riyadh , Saudi Arabia
• 11 Department of Medical Genomics, Centre for Genomic Medicine , King Faisal Specialist Hospital and Research Centre , Riyadh , Saudi Arabia
• 12 College of Medicine , Alfaisal University , Riyadh , Saudi Arabia
• 13 Department of Neuromuscular Disorders, Queen Square Institute of Neurology , University College London , London , UK
• Correspondence to Dr Gianina Ravenscroft, UWA, Nedlands, Australian Capital Territory, Australia; gina.ravenscroft{at}perkins.uwa.edu.au

Background Weakness of facial, ocular and axial muscles is a common clinical presentation in congenital myopathies caused by pathogenic variants in genes encoding triad proteins. Abnormalities in triad structure and function resulting in disturbed excitation-contraction coupling and Ca 2+ homeostasis can contribute to disease pathology.

Methods We analysed exome and genome sequencing data from four unrelated individuals with congenital myopathy characterised by facial, ocular and bulbar involvement. We collected deep phenotypic data from the affected individuals. We analysed the RNA-sequencing (RNA-seq) data of F3-II.1 and performed gene expression outlier analysis in 129 samples.

Results The four probands had a remarkably similar clinical presentation with prominent facial, ocular and bulbar features. Disease onset was in the neonatal period with hypotonia, poor feeding, cleft palate and talipes. Muscle weakness was generalised but prominent in the lower limbs with facial weakness also present. All patients had myopathic facies, bilateral ptosis, ophthalmoplegia and fatigability. Muscle biopsy on light microscopy showed type 1 myofiber predominance and ultrastructural analysis revealed slightly reduced triads, and structurally abnormal sarcoplasmic reticulum.

DNA sequencing identified four unique homozygous loss-of-function variants in JPH1 , encoding junctophilin-1 in the four families; one stop-gain (c.354C>A;p.Tyr118*) and three frameshift (c.373delG;p.Asp125Thrfs*30, c.1738delC;p.Leu580Trpfs*16 and c.1510delG;p. Glu504Serfs*3) variants. Muscle RNA-seq showed strong downregulation of JPH1 in the F3 proband.

Conclusions Junctophilin-1 is critical for the formation of skeletal muscle triad junctions by connecting the sarcoplasmic reticulum and T-tubules. Our findings suggest that loss of JPH1 results in a congenital myopathy with prominent facial, bulbar and ocular involvement.

• congenital, hereditary, and neonatal diseases and abnormalities
• neuromuscular diseases
• exome sequencing

Data availability statement

Data may be obtained from a third party and are not publicly available. All data relevant to the study are included in the article or uploaded as supplementary information. ES and srGS data of probands and family members is available on seqr. All relevant clinical data are shared as part of this study. Identified variants in JPH1 have been submitted to ClinVar with accession numbers SCV004228294-SCV004228296 and SCV004697810. Code for generating plots is available at: https://github.com/RAVING-Informatics/jph1-cm .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/jmg-2024-109970

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Previous studies have shown that pathogenic variants in genes encoding triad proteins lead to various myopathic phenotypes, with clinical presentations often involving muscle weakness and myopathic facies.

The triad structure is essential for excitation-contraction coupling and Ca 2+ homeostasis and is a key element in muscle physiology.

WHAT THIS STUDY ADDS

This study identified novel homozygous loss-of-function variants in the JPH1 gene, linking them to a form of congenital myopathy characterised by severe facial and ocular symptoms.

Our research sheds light on the critical impact on junctophilin-1 function in skeletal muscle triad junction formation and the consequences of its disruption resulting in a myopathic phenotype.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study establishes that homozygous loss-of-function mutations in JPH1 cause a congenital myopathy predominantly affecting facial and ocular muscles.

This study also provides clinical insights that may aid the clinicians in diagnosing similar genetically unresolved cases.

Introduction

In skeletal muscles, the sarcoplasmic reticulum (SR) is surrounded by specialised invaginations of the sarcolemma in the form of terminal cisternae and transverse tubules (T-tubules). The juxtaposition of a T-tubule with two terminal cisternae forms the triad. 1 In the triads, proteins, notably the dihydropyridine receptor (DHPR) in the T-tubule and the ryanodine receptor (RYR) in the SR, maintain Ca 2+ homeostasis and are crucial in excitation-contraction (EC) coupling. 2

Disturbed EC coupling and Ca 2+ homeostasis, along with secondary abnormalities, including structural alterations of T-tubules, triad structure and function 2 3 are the pathomechanisms of myopathies associated with variants in genes encoding proteins critical to EC coupling, including RYR1 , CACNA1S , ORAI1 , STAC3 , STIM1 , MTM1 , DNM2, TRDN and BIN1 . These disorders are collectively referred to as triadopathies. 3

Junctophilins are key proteins responsible for triad structure formation and maintenance in striated muscle. 4 There are three junctophilin genes. JPH1 is predominantly expressed in skeletal muscles, while JPH2 is expressed in cardiac and skeletal muscles and JPH3 specifically in the brain. 5 6 In the skeletal muscle triad, JPH1 interacts with RYR1 aiding in the release of Ca 2+ ( figure 1A ). In vitro, downregulation or loss of junctophilins can result in defective triads and dysregulated Ca 2+ homeostasis due to mislocalisation of RYR1 and DHPR. 7 8 Jph1 knockout (KO) mice die shortly after birth, with ultrastructural analysis showing defective and reduced triads along with structurally abnormal SR. 4

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JPH1 -related myopathy in four families. (A) Schematic representation of interaction of junctophillin-1 (JPH1) and ryanodine receptor type 1 (RYR1) at the neuromuscular triad. Flow of Ca 2+ is indicated from the sarcoplasmic reticulum to the sarcoplasm through RYR1. (B) Bilateral ptosis and ophthalmoplegia and (C) kyphoscoliosis in patient F1-II.1. (D) Facial weakness and ophthalmoplegia and (E) dorsal scoliosis, lumbar lordosis and winged scapulae in patient F2-II.1. (F) Pedigrees of the four consanguineous families included in this study, family 1 and 2 are of European origin, family 3 is of Khmer origin and family 4 is of Middle Eastern origin. Genotypes are shown for the identified JPH1 variants. (G) A scheme of identified pathogenic variants in JPH1 and their position on the JPH1 gene model.

Here, we report four unrelated probands with strikingly similar phenotypes involving facial and ocular muscle weakness caused by homozygous null variants in JPH1 . Our deep phenotyping and novel genetic findings expand the spectrum of congenital myopathies caused by defects in triad proteins and provide evidence for the first time that loss of JPH1 results in a skeletal muscle disease and should be classified as a triadopathy.

Patients and clinical examinations

Blood samples were collected from four unrelated affected patients and seven additional asymptomatic family members. Consanguinity was known, or suspected, for all the families. Patients’ biomaterials for diagnostic purposes were collected after written informed consent was obtained from the patients or their legal guardians by the referring clinicians.

All four probands underwent clinical neuromuscular examination. Ancillary tests, including electrophysiological examinations (nerve conduction studies and needle electromyogram) and serum creatine kinase levels were obtained in all patients.

Molecular genetics

Genomic DNA was isolated from blood cells of probands and available family members, using standard techniques.

Exome sequencing (ES) from the genomic DNA of F1-II.1 and F2-II.1 and F4-III.1 was carried out by the Broad Institute Genomics Platform using an 8 MB targeted Illumina exome capture. PCR-free libraries were prepared from the genomic DNA of F3-I.1, F3-I.2 and F3-II.1. Short-read (sr) genome sequencing (GS) was performed on NovaSeq 6000 (Illumina, San Diego, California, USA) with pair-end 150 bp reads at the Kinghorn Centre for Clinical Genomics (Garvan Institute of Medical Research, New South Wales, Australia).

Single nucleotide variant (SNV) analysis for the four families was performed using seqr , 9 hosted by the Centre for Population Genomics, a collaboration between Garvan Institute of Medical Research (Sydney, Australia) and the Murdoch Children’s Research Institute (Melbourne, Australia).

ES and srGS results were analysed and SNV/indels were filtered using a minor allele frequency ≤0.0001 in the Genome Aggregation Database V.2.1.1 (hg19) and V.3.1.2 (hg38).

Variants in JPH1 are annotated on NM_020647.2 and NP_065698.1. All identified variants were also evaluated for current American College of Medical Genetics and Genomics (ACMG) pathogenicity annotations using VarSome, 10 Alamut (Alamut Visual Plus V.1.6.1, SOPHiA GENETICS) and Mutalyzer. 11

Muscle biopsy, immunohistochemical and imaging studies

Snap-frozen muscle biopsy samples were obtained from three affected patients (F1-II.1: quadriceps, F2-II.1: deltoid, F3-II.1: right upper arm). Routine muscle histopathological studies were performed, including H&E, modified Gomori’s trichrome and NADH tetrazolium reductase staining. 12 DAB immunostaining was performed using mouse monoclonal antimyotilin (clone RSO34, 1:20, LEICA Biosystems Newcastle, UK) and mouse monoclonal antidesmin (clone D33, 1:70, Richard-Allan Scientific, USA), with Mouse ExtrAvidin Peroxidase Staining Kit (EXTRA2, Merck KGaA, Darmstadt, Germany). Microscopic images were obtained using a NIKON ECLIPSE Ci microscope equipped with an OLYMPUS ColorView II camera.

For patient F3-II:1, ultrathin resin sections with a thickness of 70–80 nm were prepared for electron microscopy and examined with an FEI Morgagni 268 Transmission Electron Microscope operating at 80 kV. Electron micrographs were obtained using the Olympus-SIS Morada digital camera (Olympus Soft Imaging Solutions, Münster, Germany).

RNA-sequencing

Total RNA was extracted from patient F3-II:1 and control skeletal muscle biopsies (~15–50 mg) using the RNeasy Fibrous Tissue Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Strand-specific Poly-A+RNA libraries were prepared from extracted RNA using the Agilent SureSelect XT library preparation kit (Agilent, Santa Clara, California, USA). QC was performed using TapeStation 4200 (Agilent) and Qubit 4 Fluorometer (Thermo Fischer Scientific, Waltham, Massachusetts, USA), as well as QC sequencing on an Illumina iSeq 100 flowcell (Illumina, San Diego, California, USA). These strand-specific libraries were sequenced on an Illumina NovaSeq 6000 to produce paired-end 150 bp reads and an average of 50 million read pairs per sample. Adaptor sequences were removed and demultiplexed FASTQ files were provided by Genomics WA (Western Australia) for download and further analysis. FASTQ files were processed, including read quality control and alignment, using the nf-core/rnaseq pipeline ( https://nf-co.re/rnaseq ), V.3.8.1. Trimmed reads were aligned to the NCBI GRCh38 human reference genome using STAR V.2.7.10a 13 (STAR, RRID: SCR_004463). We used DROP V.1.0.3, 14 as previously described 15 to analyse aberrant gene expression among a cohort of 129 skeletal muscle RNA-sequencing (RNA-seq) datasets from rare muscle disease patients and unaffected controls. DROP leverages OUTRIDER, 16 which uses a denoising autoencoder to control co-variation before fitting each gene over all samples via negative binomial distribution. Multiple testing correction was done across all genes per sample using DROP’s in-built Benjamini-Yekutieli’s false discovery rate method. Plots were prepared using R (V.4.1.3) in RStudio. The splicing pattern and expression of JPH1 in F3 was visualised using Integrative Genomics Viewer (IGV) 17 and plots were created using ggsashimi. 18

Data sharing statement

ES and srGS data of probands and family members are available on seqr . All relevant clinical data are shared as part of this study.

Identified variants in JPH1 have been submitted to ClinVar with accession numbers SCV004228294–SCV004228296 and SCV004697810.

Code for generating plots is available at: https://github.com/RAVING-Informatics/jph1-cm

Clinical findings in patients with JPH1 -related myopathy

The clinical findings of all four probands are summarised in table 1 . In general, the four probands had a remarkably similar presentation with global distribution of muscle weakness and generalised muscle wasting, with F3-II.1 notably exhibiting thin muscle bulk. They showed facial weakness accompanied by bilateral ptosis and ophthalmoplegia (patient F1-II.1, figure 1B ; patient F2-II.1, figure 1D ), a nasal voice and dysphagia. They also presented with myalgia, exercise intolerance and fatigability. Reduced forced vital capacity was prominent in F1-II.1 (19% of predicted value), who needed non-invasive ventilation. The patients also showed kyphoscoliosis (patient F1-II.1, figure 1C ) lordosis and scoliosis (patient F2-II.1, figure 1E ). None of the patients showed any cardiac involvement or intellectual impairment. First clinical assessments indicated either a novel congenital myopathy or a congenital myasthenic syndrome-like phenotype.

• View inline

Clinical, histopathological and MRI details of patients included in the study

Identification of deleterious variants in JPH1

Analysis of ES (F1, F2 and F4) and srGS (F3) data were initially negative for approximately 600 genes known to cause a neuromuscular phenotype. 19 20 Subsequently, we identified four unique homozygous protein truncating variants in JPH1 : two in exon 1, c.373delG, p.(Asp125Thr fs *30) and c.354C>A, p.(Tyr118*), in F1 and F2, respectively and two in exon 4, c.1738delC, p.(Leu580Trp fs* 16) and c.1510delG; p.(Glu504Ser fs *3) in F3 and F4, respectively ( figure 1F, G , online supplemental figure ). Using VarSome and Alamut, we assessed the pathogenicity of the identified variants. Since all four variants would result in null alleles and were absent in the reference population databases, they fulfilled the PVS1 (very strong) and PM2 (supporting) criteria of the ACMG guidelines, resulting in classification of the variants as ‘likely pathogenic/pathogenic’.

Supplemental material

Muscle pathology associated with biallelic loss-of-function jph1 variants.

Muscle biopsies from patients F2-II.1 and F3-II.1 revealed a striking pattern of type 1 myofiber predominance ( figure 2A ). No other characteristic features were observed; there was no increase in internally or centrally located nuclei ( figure 2A ), or staining suggestive of cores( figure 2C ) or nemaline bodies. Electron microscopy analysis of the muscle biopsy of F3-II.1 showed ultrastructural defects including some focal and possibly non-specific Z-band streaming, slightly reduced number of triads and structurally abnormal SR which appeared dilated ( figure 2D–F ).

Muscle pathology of patient F3-II.1. (A) H&E showing preserved muscle structure. (B) Immunohistochemistry for fast myosin heavy chain (stained with DAB (brown)) and eosin showing type 1 myofiber predominance. Most of the atrophic myofibers stain as type 2 but the normal chequerboard distribution of fibre types appears relatively preserved (C) NADH staining. No cores, or minicores are present. Electron microscopy (patient F3-II.1) showing some focal Z-band streaming. The observed area of Z-line streaming is near the sarcolemma, this finding in this instance may be non-specific (D); reduced triads with dilated sarcoplasmic reticulum (white arrowheads) (E,F).

Analysis of skeletal muscle RNA-seq data from proband F3-II.1

OUTRIDER analysis detected under expression of JPH1 as an outlier ( figure 3A ) in F3-II.1 (Z=−8.48, p-adj=1.15×10 –8 ). Based on normalised gene counts, JPH1 expression in F3-II.1 was the lowest at 1167.87 compared with the other 129 patients with muscle disease and healthy controls (log 2 fold change=−2.84). IGV analysis ( figure 3B ) and sashimi plots ( figure 3C ) confirmed the low expression of the gene.

Splicing and gene-expression in skeletal muscle from a patient with JPH1 -related myopathy. (A) Volcano plot showing results from the OUTRIDER analysis. JPH1 is indicated as an outlier in red colour. (B) Visualisation of RNA-sequencing (RNA-seq) data in Integrative Genomics Viewer comparing JPH1 expression with a control. (C) ggsashimi plot analysis of JPH1 from RNA-seq data of F3-II.1. JPH1 expression and splicing patterns of the patient are shown in grey colour, compared with other NMD patients in red (n=39) and unaffected controls in blue (n=6). (D) Normalised expression of genes encoding for other triad proteins is presented as box plots. Median and quartile values are shown, with whiskers reaching up to 1.5 times the IQR. Expression levels from individual samples in the cohort are shown with jitter points and that of F3-II.1 is represented with red colour. The violin plot illustrates the distribution of data in each cohort. The scaled Y-axis shows normalised counts. NMD, neuromuscular disease.

Since, a reduced expression of JPH1 could affect other T-tubule proteins, we additionally analysed the expression of other genes encoding components of the triad and T-tubules, including: RYR1 , CACNA1S , JPH2 , MTM1 , DNM2 , BIN1 , STAC3 , ORAI1 , STIM1 , CAV3 and TTN ( figure 3D ). There were no differences in expression levels for any of these genes of interest in JPH1 -related myopathy compared with healthy control muscle or patients with other forms of neuromuscular diseases.

Our results demonstrate that loss-of-function variants in JPH1 , coding for junctophillin-1, result in a congenital myopathy, characterised by global distribution of muscle weakness and wasting, but with prominent facial muscle weakness, bilateral ptosis, exercise intolerance and fatigability.

In skeletal muscles, junctophilins have a regulatory and maintenance function with other triad proteins, including assembly of Ca 2+ release complex and organisation of the Store Operated Ca 2+ entry pathway through interaction with other T-tubule proteins including RYR1, DHPR and CAV3. 2

All four probands showed prominent myalgia, along with exercise intolerance and fatigability. These features are commonly seen in other triadopathies, such as tubular aggregate myopathies caused by pathogenic variants in STIM1 . 3

In our patients, we observed homozygous null variants in JPH1 resulting in no expression of complete transcript suggesting no viable production of JPH1. This is well reflected in our morphological and ultrastructural studies which concur with Jph1 KO mice. EM analysis of muscle biopsy of F3-II.1 showed a reduced number of triads. Light microscopy analysis of F2-II.1 and F3-II.1 showed predominance of type 1 myofibers. Generally, triad abundance varies in different myofiber types in skeletal muscle due to the distinct Ca 2+ requirements of EC coupling of the functionally different myofiber types. Myofibers under higher contraction load require more triads due to the greater and faster Ca 2+ influx and efflux requirements. Type 1 myofiber predominance is also observed in other triadopathies, including RYR1 , DNM2 , BIN1 and MTM1 -associated congenital myopathies. 21–24

EM analysis of muscle biopsy of F3-II.1 also showed dilated SR. Disorganisation of triads and swelling of SR was observed in mutant muscles of Jph1 KO mice. 4 The swelling of SR can be attributed to SR Ca 2+ overloading and has been seen in mice lacking both ryr1 and ryr3 . 25 Likewise, in human muscles lacking JPH1, SR Ca 2+ overloading could cause similar abnormalities due to reduced triad junctions potentially hindering DHPR-mediated activation of RYR. Further characterisation of the spectrum of pathologies associated with JPH1 -related myopathy will be needed as additional patients are identified.

Congenital myopathies arising due to pathogenic variants in genes encoding components of the triads or proteins involved in triad formation and maintenance, including RYR1 and STAC3, share many clinical features. 26 27 These include hypotonia and axial weakness, which often tends to be static or slowly progressive, facial and bulbar weakness, resulting in dysphagia and dysarthria, ocular weakness, including ptosis and ophthalmoplegia and respiratory insufficiency. Joint contractures may be present at birth. 26 27

Previously, deficiency of Jph1 in mouse models was shown to result in neonatal death. This was attributed to failure in suckling, as a newborn due to weak contractile activity of jaw muscles and weak pharyngo-oesophageal or diaphragm muscles. 4 The myofibers of these Jph1 KO mice were morphologically normal, and analysis of muscle histology did not detect obvious abnormalities. Ultrastructural analysis using electron microscopy, however, revealed that Jph1 KO neonates had swollen SR and defective and highly reduced triads. These observations suggested that loss of JPH1 clearly affects triad formation in skeletal muscles. 4

Additionally, reduced JPH1 expression has been associated with defective triad formation and disturbed Ca 2+ homeostasis due to mislocalisation of RYR1 and DHPR. 7 8 28 While the overall disease presentation is similar in Jph1 KO mice and JPH1 patients, none of our patients had severe muscle weakness or a dystrophic phenotype as seen in neonatal mice.

Analysis of RNA-seq data showed that mRNA expression of other key genes of the triad are unaltered in JPH1 patient’s skeletal muscle compared with healthy controls and other neuromuscular disease biopsies. This is perhaps not surprising given the relatively mild phenotype observed in these patients, compared with affected individuals with bi-allelic loss-of-function variants in CACNA1S, RYR1 or STAC3 .

This would suggest that the loss of JPH1 observed in our patients due to homozygous null variants, affects the triad formation and maintenance. The exact pathomechanism of how loss of JPH1 and normal expression of other triad genes contribute to the phenotype, remains to be understood.

Our results, show for the first time that bi-allelic null variants in JPH1 cause a congenital myopathy characterised by prominent facial and ocular muscle weakness. Hence, JPH1 should be included in genetic screenings of unsolved patients with similar clinical presentation.

Ethics statements

Patient consent for publication.

Consent obtained from parent(s)/guardian(s).

Ethics approval

This study was approved by the Human Research Ethics Committee, University of Western Australia, the National Research Ethics Service (NRES) Committee North East-Newcastle & North Tyneside 1 (reference 08/H0906/28) and Prince Sultan Military Medical City (PSMMC) IRB Committee, Riyadh, Saudi Arabia (reference IRB-PSMMC-934). This study was performed according to the Declaration of Helsinki. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

Sequencing was conducted in the Genomics WA Laboratory in Perth, Australia. BioPlatforms Australia, State Government Western Australia, Australian Cancer Research Foundation, Cancer Research Trust, Harry Perkins Institute of Medical Research, Telethon Kids Institute and the University of Western Australia support this facility. We gratefully acknowledge the Australian Cancer Research Foundation and the Centre for Advanced Cancer Genomics for making available Illumina Sequencers for the use of Genomics WA. We acknowledge Tuomo Polvikoski for histopathology advice. We also acknowledge Elyshia Rowles and Rhonda Taylor for technical support.

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GR and VS are joint senior authors.

X @mriduljohari, @mapifres, @Gianina_Natoli

MJ and AT contributed equally.

Contributors Guarantor: MJ; conceptualisation of the study: GR, VS; project administration: GR, VS; funding acquisition: MJ, AT, GR, VS; supervision: GR, VS; patient samples and data collection: AC, TR, JV, PM, EH, CM-B, KH, AMA, MA-O, RM; data analysis and curation: MJ, AT, CF; methodology: MJ, AT, CF, LD, JD, KH, AMA, MA-O; visualisation: MJ, CF; writing the original draft: MJ, AT, CF; review and editing of the manuscript: MJ, AT, TR, AC, EH, JV, GR, VS.

Funding This work is supported by The Fred Liuzzi Foundation (GR/MJ), the Association Française contre les Myopathies (AFM Téléthon, The French Muscular Dystrophy Association, grant award number: 24438 to MJ) and an Australian NHMRC Ideas Grant (APP2002640). LD is supported by an Australian Government Research Training Programme (RTP) Scholarship at the University of Western Australia. AT and VS have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 779257 (Solve-RD). They are supported by the NIHR Newcastle Biomedical Research Centre. MYO-SEQ was funded by Sanofi Genzyme, Ultragenyx, LGMD2I Research Fund, Samantha J. Brazzo Foundation, LGMD2D Foundation and Kurt+Peter Foundation, Muscular Dystrophy UK and Coalition to Cure Calpain 3. Analysis was provided by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG) and was funded by the National Human Genome Research Institute, the National Eye Institute, and the National Heart, Lung, and Blood Institute grant UM1 HG008900, and in part by National Human Genome Research Institute grant R01 HG009141. PM and JV received support from Fundacion Isabel Gemio, Spain. GR is supported by an Australian NHMRC Fellowship (APP2007769).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Open access
• Published: 29 August 2024

Diagnostic challenges and outcome of fatty acid oxidation defects in a tertiary care center in Lebanon

• Rose T. Daher 1   na1 ,
• Katia El Taoum 2   na1 ,
• Jinane Samaha 3 &
• Pascale E. Karam   ORCID: orcid.org/0000-0003-0164-4935 2 , 3  

Orphanet Journal of Rare Diseases volume  19 , Article number:  315 ( 2024 ) Cite this article

Metrics details

Fatty acid oxidation defects are rare autosomal recessive disorders with variable clinical manifestations and outcome. Early detection by systematic neonatal screening may improve their prognosis. Long-term outcome studies of these disorders in the Middle East and North Africa region are limited. The purpose of this study is to report the diagnostic challenges and outcome of fatty acid oxidation defects in a major tertiary care center in Lebanon, a resource-constrained country in the Middle East.

A retrospective review of charts of all fatty acid oxidation defects sequential patients diagnosed and followed at our center was conducted. Collected data included: parental consanguinity, age at diagnosis, clinical presentation, biochemical profile, confirmatory diagnosis, treatment and outcome. A genotype–phenotype correlation was also performed, when available.

Seven types of fatty acid oxidation defects were identified in a total of 34 patients from 21 families. Most families (79%) were consanguineous (first-degree cousins). The majority were diagnosed when clinically symptomatic (78%), at various ages between 10 days and 19 years (average: 2 years). Follow-up duration spanned between 2 months and 15 years (average: 5 years). The remainder of the patients were detected while still asymptomatic by systematic neonatal screening (9%) or due to positive family history (9%). The most common defect was carnitine transporter deficiency (50%) with an exclusive cardiac presentation related to a founder variant c.981C > T, (p.Arg254*) in the SLC22A5 gene. Medium chain acyl-CoA dehydrogenase deficiency was found in 13% only, which could be explained by the absence of systematic neonatal screening. Rare gene variants were detected in very long chain and multiple acyl-CoA dehydrogenase deficiency. The worse prognosis was observed in very long chain acyl-CoA dehydrogenase deficiency. The overall survival at last follow-up reached 75% with a complete reversal of symptoms with treatment in most patients (63%), despite their late diagnosis.

Conclusions

Our experience highlights the diagnostic challenges and outcome of fatty acid oxidation defects in a resource-constrained country with high consanguinity rates. Physicians’ awareness and systematic neonatal screening are key for diagnosis. Larger genotype–phenotype studies are still needed to understand the natural history of these rare diseases and possibly improve their outcome.

Introduction

Fatty acid oxidation (FAO) defects are a group of rare autosomal recessive metabolic diseases caused by enzymatic deficiency of fatty acids transport, β-oxidation, or electron transfer in the mitochondria. These mainly include: (1) carnitine cycle defects: carnitine transporter defect (CTD) or primary carnitine deficiency, carnitine palmitoyl-CoA transferase-I (CPT-I), carnitine-acylcarnitine translocase (CACT), and carnitine palmitoyl-CoA transferase II (CPT-II) deficiencies, (2) β-oxidation defects: very long chain acyl-CoA dehydrogenase (VLCAD), long chain hydroxyacyl-CoA dehydrogenase (LCHAD), mitochondrial trifunctional protein (MTP), medium chain acyl-CoA dehydrogenase (MCAD) and short chain acyl-CoA dehydrogenase (SCAD) deficiencies, (3) electron transfer defects affecting electron transfer flavoprotein and electron transfer flavoprotein ubiquinone oxidoreductase, leading to multiple acyl-CoA dehydrogenase (MAD) deficiency [ 1 ]. The most common long chain FAO (LC-FAO) defects include carnitine cycle (CTD, CPT-I, CACT, CPT-II), mitochondrial β-oxidation (VLCAD, LCHAD, MTP) and electron transfer MAD deficiencies [ 2 ].

At times of prolonged fasting, fatty acids represent the main source of energy for major organs like the liver, brain, heart and skeletal muscles. Hence, most FAO defects share clinically similar presentations with various degrees of severity. These include hepatomegaly, psychomotor delay, heart failure symptoms, myalgia and exercise intolerance, while peripheral neuropathy and retinopathy may be observed specifically in LCHAD and MTP deficiencies [ 1 ]. Furthermore, life-threatening complications and even death can rapidly occur in all patients [ 2 ].

Biochemical investigations may show hypoketotic hypoglycemia, hyperammonemia, lactic acidosis, elevated liver enzymes and/or creatine phosphokinase during acute metabolic decompensations [ 3 ]. Diagnosis relies on specific abnormal patterns of blood acylcarnitine and/or urine organic acids profiles. Further confirmation can be achieved by molecular genetic testing and/or enzymatic assays [ 4 ]. Treatment is mainly preventive, based on avoidance of hypoglycemia during prolonged fasting or catabolic stress. Dietary management and supplementation with L-carnitine and/or triheptanoin and/or riboflavin are tailored depending on the FAO defect type [ 5 ].

Early recognition of these fatal disorders is crucial for preventive and timely treatment [ 1 ]. The introduction of expanded neonatal screening by Tandem Mass Spectrometry in high-income countries in the early 1990’s unveiled a highly heterogeneous incidence of FAO defects ranging between 0.9 and 15.2 per 100,000 [ 6 ]. In some Arab countries with high rates of consanguinity, like in Qatar, FAO defects incidence reaches 28/100,000 [ 7 ]. In Lebanon, in the absence of a systematic expanded neonatal screening program in the country, an estimated incidence of 6.4/100,000 was reported [ 8 ]. The importance of systematic neonatal screening for FAO defects, mainly for MCAD deficiency and some long-chain fatty acid oxidation defects was shown to improve the outcome [ 2 , 9 ]. Long-term outcome studies of FAO defects are available from various high-income countries, like Canada [ 2 ], United States [ 10 ], some European countries [ 11 ], and Eastern Asia [ 12 , 13 ]. However, studies from the Middle East and North Africa are scarce, with few reports from Saudi Arabia [ 14 , 15 ].

The aim of this 15-year retrospective study is to report the diagnostic challenges and long-term outcome of FAO defects in a major tertiary care center in Lebanon, a resource-constrained country in the Middle East.

Materials and methods

A retrospective chart review of all sequential patients with FAO defects followed at the American University of Beirut Medical Center, between February 2008 and February 2023, was conducted.

Collected data for each FAO defect type included initial clinical presentation, diagnosis, treatment and outcome. Clinical manifestations were categorized into cardiac, hepatic, neurological, or sudden infant death syndrome. Expanded neonatal screening and acylcarnitine profile on dried blood spots, plasma total and free carnitine levels, urine organic acids chromatography, genetic testing and/or enzyme assay on fibroblasts were all referred to established laboratories outside Lebanon. A genotype–phenotype correlation was also performed, when available. The outcome was determined based on the last clinical evaluation and classified as either asymptomatic or symptomatic with cardiac, and/or hepatic, and/or neurological complications, and/or death.

Microsoft Excel version 2208 was used for data analysis. This study was approved by the Institutional Review Board at the American University of Beirut, Lebanon.

A total of 34 patients from 21 families were diagnosed with FAO defects and followed at the American University of Beirut Medical Center, during the study period (Tables  1 , 2 ).

Seven types of FAO defects were identified: (1) carnitine cycle (CT and CPT-IA), (2) β-oxidation (VLCAD, MTP, MCAD, and SCAD), and (3) electron transfer (MAD) deficiencies (Additional file 1 ; Table S1). The most frequent disorder was CTD followed by VLCAD and MCAD deficiencies (Fig.  1 ). A few patients were detected by neonatal screening (two CTD, one VLCAD, three MCAD, and two SCAD deficiency patients). The two SCAD deficiency patients were excluded from the aggregate data, as SCAD deficiency is currently considered as a pure biochemical finding with no phenotypic expression, and its clinical relevance is controversial [ 16 ]. Among the studied 19 families, the majority were diagnosed when clinically symptomatic (78%). The age at onset varied between two days of life and 14 years (average 2 years) whereas age at diagnosis ranged from 10 days to 19 years (average: 3 years). Follow-up duration varied between two months and 15 years (average: 5 years). Most families (79%) were consanguineous (first-degree cousins). Confirmatory diagnosis was achieved in 89% of the families, by enzymatic assays in fibroblasts or by molecular testing using single gene sequencing for CTD patients and exome sequencing for the others.

Distribution of fatty acid oxidation defects in a total of 32 Lebanese patients. CTD -carnitine transporter defect, CPT-IA-carnitine palmitoyl-CoA transferase-IA deficiency, VLCAD-Very long chain acyl-CoA dehydrogenase deficiency, MTP- Mitochondrial trifunctional protein deficiency, MCAD- Medium chain acyl-CoA dehydrogenase deficiency, SCAD- Short chain acyl-CoA dehydrogenase deficiency, MAD-Multiple acyl-CoA dehydrogenase deficiency

Three CTD and two VLCAD deficiency patients were considered as possibly affected, based on their clinical presentation and suggestive repeatedly abnormal biochemical profiles.

The treatment was based on avoidance of prolonged fasting in all FAO types. Dietary fat restriction was recommended for patients with long chain fatty acid oxidation defects such as CPT, VLCAD, and MTP deficiencies as well as for MAD deficiency. Supplementation with oral L-carnitine and/or coenzyme Q10 and/or riboflavin was prescribed depending on each FAO defect type. The overall mortality was 25%: the highest in VLCAD deficiency reaching 67% (four out of six patients), followed by MTP deficiency in 33% (one out of three patients) and CTD in 19% (three out of 16 patients).

Carnitine cycle defects (Table  1 )

Carnitine transporter defect

The majority of CTD patients (75%, 12/16) presented exclusively with dilated cardiomyopathy (92%,11/12). Sudden infant death was the primary manifestation in one patient (N4). The age at onset varied from 6 months to 10 years (average 2 years 5 months). Four patients (N7, N11, N15, and N16) were identified by screening while asymptomatic, due to a positive history of affected siblings. All patients had decreased plasma total carnitine between 9.0 and 31 µmol/L (reference range: 33–72 µmol/L) and free carnitine between 3.7 and 24.7 µmol/L (reference range: 27–59 µmol/L). The diagnosis was confirmed by identification of homozygous pathogenic variants in the SLC22A5 gene. Three patients from one family (F8) did not undergo molecular testing and were considered as possibly affected by CTD, based on their plasma carnitine levels and/or clinical presentation. All CTD patients were treated with oral L-carnitine supplementation (100 mg/kg/day in three divided doses) with complete resolution of the cardiomyopathy within six months of therapy, regardless of their age at diagnosis.

Carnitine palmitoyl-CoA transferase- IA deficiency

CPT-IA deficiency was diagnosed in two patients, at 6.5 and 4 years of age, following episodes of hypoketotic hypoglycemia and hepatomegaly, during intercurrent febrile illnesses, occurring since 2 and 3 years of age, respectively. The diagnosis was suspected on acylcarnitine profile and confirmed by enzyme assay on fibroblasts. Both patients remained asymptomatic after dietary management and recommendations to avoid prolonged fasting.

β-oxidation defects (Table  2 )

Very long chain acyl-coa dehydrogenase deficiency.

VLCAD deficiency was identified in six patients (18%) from three families. The first patient (N19) from family F11 was not screened neonatally, despite a positive family history of two siblings who were lost to sudden infant death syndrome. He presented at day 2 of life with hypoketotic hypoglycemia and seizures. Echocardiography performed at day 5 of life revealed a mild biventricular hypertrophy. Qualitative acylcarnitine profile on dried blood by mass spectrometry showed highly elevated C14:1 and C16. The diagnosis was confirmed by enzyme assay on fibroblasts. He was treated with low-fat diet and medium chain triglycerides supplementation. In addition to the neurological and cardiac involvement, the patient developed recurrent episodes of rhabdomyolysis since 5 years of age requiring repeated hospitalizations. At last assessment at the age of 15 years, he had severe developmental delay, epilepsy, myopathy, and hypertrophic cardiomyopathy with no evidence of arrythmias. The 2 siblings (N20 and N21) of patient N19 were presumptively considered as suffering from VLCAD deficiency, in view of their clinical presentation with unexplained sudden infant death and the positive family history.

In the second family (F12), patient N22 was diagnosed with hypertrophic cardiomyopathy at one month of age. Family history was positive for sudden infant death at 2 months of age. Acylcarnitine profile showed elevated C14:1 and C14:2 in conjunction with a very low free carnitine level. VLCAD deficiency was suspected. Dietary treatment and L-carnitine supplementation at 50 mg/kg/day in three divided doses was initiated to normalize plasma carnitine levels. Genetic testing could not be performed, and the patient was lost to follow-up at 3 months of age.

Patient N23 was also presumptively considered to have VLCAD deficiency in view of the family history and the unexplained sudden death at 2 months of age.

Patient N24, from family F13, presented at 2 days of life with hypoketotic hypoglycemia before neonatal screening results were reported. She developed hypertrophic cardiomyopathy at one month and died at 3 months of age due to cardiac failure. Exome sequencing post-mortem revealed compound heterozygous variants (one pathogenic and one variant of unknown significance) in the ACADVL gene.

Mitochondrial trifunctional protein deficiency

MTP deficiency was identified in three patients (9%), born after uncomplicated pregnancies without signs of maternal HELLP (Hemolysis, Elevated Liver enzymes, Low Platelets) syndrome. Patient N25 had a late-onset presentation at 12 years of age, while the two other patients were symptomatic by one year of age. All three patients had the neuromyopathic phenotype. Molecular genetic testing of HADHA gene revealed homozygous likely pathogenic variant in F14 and a variant with conflicting classifications of pathogenicity in F15. Patients were treated with a long-chain fat-restricted diet with medium chain triglycerides supplementation and low-dose L-carnitine at 25 mg/kg/day in three divided doses to maintain normal plasma carnitine levels. In family F15, acylcarnitine profiles tested while patients were on treatment came back normal (Table  2 ). One patient (N27) died at 9 months of age during an intercurrent respiratory infection with rhabdomyolysis. The surviving two patients suffer from progressive myopathic deterioration and peripheral neuropathy.

Medium chain Acyl-CoA dehydrogenase deficiency

Three out of four patients (12%) diagnosed with MCAD deficiency were detected by neonatal screening while still asymptomatic. Interestingly, one patient (N28) had a history of undiagnosed “hepatitis” at 2 years of age, and he was retrospectively diagnosed at 10 years of age after detection by systematic neonatal screening of an affected sibling (N29). The acylcarnitine profile in all patients revealed an increase in C6, C8 and C10:1. Molecular testing by exome sequencing identified homozygous pathogenic variants in the ACADM gene in all patients. At last follow-up, all patients remained asymptomatic on preventive treatment (Additional file 1 ).

Short chain acyl-CoA dehydrogenase deficiency

SCAD deficiency was detected in two patients by systematic neonatal screening. Urine organic acids chromatography showed elevated excretion of ethylmalonic acid and methylsuccinic acid.

Plasma acylcarnitine profile showed elevated butyryl-isobutyryl carnitine (C4) (Table  2 ). Genetic testing in patient N32 detected a homozygous benign variant in ACADS gene. Both patients remained asymptomatic without any treatment.

Electron transfer defects (Table  2 )

Multiple acyl-coa dehydrogenase deficiency.

One patient with late-onset MAD deficiency presented at 14 years of age with progressive muscle weakness associated with episodes of acute rhabdomyolysis. Acylcarnitine profile showed increased C6, C8 and C10. The diagnosis was confirmed at 19 years of age by exome sequencing, revealing compound heterozygous pathogenic and likely pathogenic variants in the ETFDH gene. The patient was treated with a combination of riboflavin at 300 mg daily, L-carnitine at 50 mg/kg/day in three divided doses, and coenzyme Q10 at 200 mg daily in two divided doses. A significant improvement in the myopathy was noted within one month of initiation of therapy with no recurrence of acute rhabdomyolysis episodes at last follow-up, at 23 years of age.

Diagnosis and outcome of FAO disorders remain challenging with scarce data in the literature from resource-constrained countries [ 17 ]. Early detection of these defects by expanded neonatal screening has been shown to reduce mortality and morbidity rates [ 1 , 10 ]. In Lebanon, despite high rates of consanguinity [ 18 ] linked to autosomal recessive disorders like fatty acid oxidation defects, neonatal screening is not mandatory and is selectively offered in some hospitals [ 8 , 19 ]. Few patients (9%, 3/32) were detected by systematic neonatal screening or due to positive family history (9%, 3/32) while still asymptomatic. Usually, MCAD deficiency is reported as the most common FAO disorder detected by neonatal screening [ 20 ]. In this Lebanese series of patients, the majority (79%) were diagnosed upon clinical manifestations at various ages, in contrast to 37% (14/38) of symptomatically identified patients in Canada, for example [ 2 ]. As a result, CTD rather than MCAD deficiency was the most frequent clinically identified disorder (50% vs 13%).

Half of the patients with LC-FAO defects were still available between 5 to 15 years for follow-up, similarly to a larger study of 426 patients in the United States [ 17 ]. A shorter follow-up duration (average 2.4 years) was reported from low- to middle-income countries [ 6 ].

The overall survival in our cohort of 32 patients reached 75% at last follow-up, with a five-year survival of 53% despite the late diagnosis of most cases, in comparison to 52% in a large French pediatric cohort [ 21 ].

Genotype–phenotype correlation, when available, revealed rare variants, sometimes related to a founder effect in the highly consanguineous Lebanese population.

Interestingly, the phenotypic presentation of CTD was exclusively an isolated dilated cardiomyopathy in all cases. No muscular, hepatic or neurological symptoms were noted. The genotypic predominance of the nonsense variant, c.981C > T: (p.Arg254Ter) in the SLC22A5 gene, already reported in three Lebanese families [ 22 , 23 ] and further identified in two others in this study, is in line with a founder effect linked to this phenotypic expression. Few CTD patients (19%) were considered as possibly affected based on their clinical and biochemical profile as reported in other series [ 24 ] in the absence of genetic testing. Although most of the cases were late-diagnosed and had profoundly decreased free plasma carnitine levels, the cardiomyopathy was totally reversed following L-carnitine supplementation.

VLCAD deficiency patients were all symptomatic before one month of age with a family history of sudden infant death by 2 months of age. They exhibited the worst prognosis and the highest mortality, similar to previous reports [ 21 ]. One patient carried compound heterozygous variants in ACADVL gene: a pathogenic variant c.711_712delTG, (p.Cys237Trpfs*15), recently reported by Arunath et al. [ 25 ] in a South Asian patient with a similar phenotype, and a variant of unknown significance c.1393A > C, (p.Asn465His).

MTP deficiency patients had variable ages at onset with no history of maternal HELLP syndrome. Typical phenotypes were observed with chronic peripheral neuropathy in surviving patients [ 26 , 27 ]. Both families carried homozygous HADHA missense variants confirmed by parental testing.

While acylcarnitine profile in patient N25 from family F14 was suggestive of MTP deficiency, it was normal in both patients in family F15. Acylcarnitine profiles were tested while patients N26 and N27 were on treatment, outside any metabolic decompensation. In recent reviews on fatty acid oxidation disorders, Vianey-Saban et al. (2023) [ 16 ] along with Spiekerkoetter and Vockley (2022) [ 28 ] report that acylcarnitines may be normal in patients with neuromyopathic presentation, similarly to these two siblings in family F15.

The variant c.955G > A, (p.Gly319Ser)in HADHA gene, identified by whole exome sequencing in family F15, was recently described as a “variant of conflicting classifications of pathogenicity” [ 29 ]. Homozygosity for this variant was confirmed by parental testing. Both parents were heterozygote carriers of the variant c.955G > A, (p.Gly319Ser) in HADHA gene. In addition, in silico parameters were all suggestive of a disease-causing variant:

Polymorphism Phenotyping: probably damaging, Align-GVGD (Grantham Variation Grantham Deviation): C55 (C0: least likely to interfere with function, C65: most likely to interfere with function), SIFT (Sorting Intolerant From Tolerant): deleterious, and Mutation Taster: disease causing. Furthermore, no further variant clinically relevant to the described phenotype was found. A neuropathy panel gene testing came out negative. Further clinical reports or functional studies are still needed to confirm the conflicting effect of the c.955G > A, (p.Gly319Ser) variant in HADHA gene.

All MCAD deficiency patients remained asymptomatic after diagnosis. The homozygous pathogenic variant c.985A > G, usually reported to cause enzymatic deficiency of less than 1% with a severe phenotype [ 30 ], did not lead to similar outcome in affected patients from two Lebanese families.

SCAD patients displayed a biochemical phenotype without any clinical expression, reflecting the benign effect of the detected variant c.625G > A, (p.Gly209Ser) in exon 6 of ACADS gene [ 31 ]. This variant is considered as a “susceptibility” variant, requiring other genetic or environmental factors to cause symptoms [ 31 ]. Homozygous patients for this variant may have a higher incidence of increased excretion of ethylmalonic acid [ 32 ], even though they are asymptomatic, similarly to our SCAD deficiency patients (N32, N33).

Late-onset MAD deficiency was diagnosed in one patient harboring compound heterozygous variants, c.1130 T > C; c.1529C > T, (p.Leu377Pro); (p.Leu510Pro) in the ETFDH gene. The previously unreported variant, c.1529C > T, (p.Leu510Pro) was considered likely pathogenic according to the American College of Medical Genetics and Genomics. Despite the lack of a clear genotype–phenotype correlation with riboflavin responsiveness [ 33 , 34 ], the c.1130 T > C, (p.Leu377Pro) variant was previously described in another late-onset MAD deficiency case showing dramatical improvement upon coenzyme Q10 and riboflavin supplementation [ 35 ], like our patient.

In conclusion, our experience highlights the diagnostic challenges and outcome of FAO deficiency patients in a resource-constrained country. The outcome of other defects, mainly VLCAD remains guarded despite early detection.

CTD, the most frequently encountered FAO defect had a favorable outcome even in late-diagnosed patients. The identification of a mild variant c.981C > T, ( p.Arg254*) in the SLC22A5 gene may explain the observed good outcome in CTD, despite the absence of systematic neonatal screening for this disorder.

In countries with limited resources like Lebanon, the implementation of systematic neonatal screening would allow earlier identification of FAO defects demonstrating a good outcome with treatment, like MCAD deficiency. Furthermore, an increased awareness among physicians of the suggestive clinical presentations of FAO defects and the appropriate diagnostic testing may allow timely recognition of these disorders. The choice of advanced biochemical testing including total and free plasma carnitine, blood acylcarnitine profile, and urine organic acids chromatography relies on the physicians’ diagnostic acumen. Molecular testing is key for an accurate diagnosis despite the cost incurred by families, in the absence of a third-party payer for such testing. Larger genotype–phenotype studies of FAO defects are still needed, especially in highly consanguineous populations. Nevertheless, performing a single gene or panel sequencing in these populations poses the difficulty of ruling out a possible dual diagnosis in the same patient. Hence, exome or even genome sequencing may overcome such limitation and confirm the diagnosis. Genotype–phenotype correlations would enable further detection and understanding of the natural history of these defects, thus tailoring the prevention and management of these rare disorders accordingly.

Availability of data and materials

Data sharing not applicable. All available data was included in the study.

Abbreviations

Fatty acid oxidation

Carnitine palmitoyl-CoA transferase-I

Carnitine-acylcarnitine translocase

Carnitine palmitoyl-CoA transferase II

Very long chain acyl-CoA dehydrogenase

Long chain hydroxyacyl-CoA dehydrogenase

Mitochondrial trifunctional protein

Medium chain acyl-CoA dehydrogenase

Short chain acyl-CoA dehydrogenase

Multiple acyl-CoA dehydrogenase

Long chain -fatty acid oxidation

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Rose T. Daher and Katia El Taoum these authors contributed equally to this work.

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Department of Pathology and Laboratory Medicine, American University of Beirut Medical Center, Beirut, Lebanon

Rose T. Daher

Department of Pediatrics and Adolescent Medicine, American University of Beirut Medical Center, Beirut, Lebanon

Katia El Taoum & Pascale E. Karam

Inherited Metabolic Diseases Program, Department of Pediatrics and Adolescent Medicine, American University of Beirut Medical Center, Beirut, Lebanon

Jinane Samaha & Pascale E. Karam

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Writing first draft: RTD, KET and PK; Data collection: KET and JS; interpretation of data and statistical analysis: RTD, KET and PK; Study concept, design and critical revision: PK. All authors read and approved the final article.

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Supplementary Information

Additional file 1. supplementary table s1.

Molecular profile of patients with fatty acid oxidation defects diagnosed and followed at a tertiary care center in Lebanon. RefSeq- Reference sequence accession number, CTD-carnitine transporter defect, VLCAD-very long chain acyl-CoA dehydrogenase deficiency, MTP- mitochondrial trifunctional protein deficiency, MCAD-medium chain acyl-CoA dehydrogenase deficiency, SCAD-short chain acyl-CoA dehydrogenase deficiency, MAD-multiple acyl-CoA dehydrogenase deficiency.

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Daher, R.T., Taoum, K.E., Samaha, J. et al. Diagnostic challenges and outcome of fatty acid oxidation defects in a tertiary care center in Lebanon. Orphanet J Rare Dis 19 , 315 (2024). https://doi.org/10.1186/s13023-024-03325-4

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ABO and Rhesus blood group variability and their associations with clinical malaria presentations

• Enoch Aninagyei 1 ,
• Pearl Sedinam Agbenowoshie 1 ,
• Praise Mawuena Akpalu 1 ,
• Selina Blefono Asiewe 1 ,
• Regina Yayra Menu 1 ,
• Fred Gbadago 2 &
• Richard Harry Asmah 1  

Malaria Journal volume  23 , Article number:  257 ( 2024 ) Cite this article

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Plasmodium falciparum infection is associated with the human ABO blood group. However, there is a paucity of data on the role that ABO and Rhesus blood groups play in malaria clinical presentations. Therefore, the objective of this study was to assess the association of human ABO blood groups and the Rhesus blood (Rh) types with the severity of malaria.

This cross-sectional study was carried out at the Suhum Government Hospital in the Eastern region of Ghana. Conveniently, study participants with malaria, diagnosed by microscopy, were selected into the study. Subsequently, their ABO and Rh blood groups were determined (Accucare ABO/Rh monoclonal antibodies, Chennai, India). Malaria severity was assessed using the criteria for assessing severe malarial anaemia published by the World Health Organization. According to the criteria, severe malarial anaemia was classified as having haemoglobin (Hb) < 5 g/dL for children < 12 years and in patients ≥ 12 years, Hb level < 7 g/dL, with parasitaemia > 10,000/µL in both cases. Severe malarial anaemia was also classified as having plasma bilirubin > 50 µmol/L with parasitaemia ≥ 100,000/µL, for all ages. Chi square statistical analysis was used to test the association between the blood groups and the clinical or laboratory findings, while multivariate analysis was performed to identify which blood groups were more vulnerable to develop severe malarial anaemia.

Of the total number of the study participants (n = 328), most of the patients had blood group O Rh positive (35.7%) while few of them had blood group AB Rh negative (2.1%). The types of Rhesus did not associate with malaria. However, compared to blood group O, the odds of developing severe malarial anaemia, in children < 12 years and in patients ≥ 12 years, were 16 times and 17.8 times higher among patients with blood group A, respectively. Furthermore, the odds of having bilirubin level > 50 µmol/L with parasitaemia ≥ 100,000 /µL was 10 times higher among patients with blood groups A and 2.6 times higher in patients with blood group B, compared to blood group O. Finally, in patients with blood group A majority (71.6%) of them developed high temperature (> 37.5 °C) while 43.3% of them vomited and had diarrhoea. However, pallor (group B = 46.2% vs group A = 37.3%), fever (group B = 84.6% vs group A = 79.1%) and nausea (group B = 46.2% vs group A = 25.4%) were more frequent in patients with blood group B than A.

Conclusions

This study found that people with blood groups A and B were severely affected by malaria, with group A being the most vulnerable. It is recommended that blood group assessment be performed for all patients with malaria. Patients found to have blood group A or B must be promptly and efficiently managed to avoid the development of severe malaria anaemia.

Studies conducted elsewhere on patients with malaria have shown that there is an association between malaria and blood groups of an individual [ 1 ]. These studies have indeed shown that people with the blood group O have a selective evolutionary advantage over malaria compared to people with non- O blood groups (A, B and AB) [ 2 ]. The mechanism by which blood group O confers selective resistance to malaria, while individuals with non- O blood groups show selective vulnerability, has been attributed to rosette formation [ 3 ]. Rosetting is a mechanism in which an infected erythrocyte with Plasmodium falciparum adheres to non-infected erythrocytes [ 4 ]. Therefore, in patients with malaria with the non-O blood group, invasion of uninfected red blood cells is enhanced, compared to the other red cell polymorphs. Rosetting may be mediated by a membrane protein, including PfEMP1 [ 5 ], RIFIN and STEVOR [ 3 ], which are expressed on the surface of the infected host cell. Rosettes formed by non-O blood groups are resilient to disruption [ 6 ].

However, there are several unanswered questions and unclear links about the various blood groups contribute to severe anaemia presentations. Furthermore, the role of Rhesus (Rh) types in P. falciparum infection remains unanswered, since available data on this subject are equivocal [ 7 ].

Despite the comparative vulnerability of blood groups A, B and AB to malaria, the association of these blood groups with malaria severity has not yet been fully studied. Plasmodium parasites are known to cause fever in patients with malaria [ 8 ]. However, it was interesting to note that the degree of fever, assessed by measuring body temperature, does not always correlate with Plasmodium parasitaemia. Furthermore, patients with malaria had a different set of clinical signs and symptoms, even with similar parasitaemia [ 9 , 10 ]. Based on the foregoing, it was hypothesized that host genetic variants may influence clinical malaria presentations. To date, it remains unknown whether blood group variability plays a role in the observed differences in similar levels of parasitaemia. This evidence gap merits investigation. Therefore, the study aimed to explore and determine the association of malaria severity with the ABO and Rh blood groups. In this study, the severe malarial anaemia was defined using strict definitions of the World Health Organization (WHO) [ 11 ]. Per these definitions, the estimation of haemoglobin, plasma bilirubin and parasite count are required to assess severe malaria anaemia.

Study design, site, and participant recruitment

This cross-sectional study was done at the Suhum Government Hospital in the Suhum municipality (Latitude: 6.0379101, longitude: -0.4456668) in the Eastern Region of Ghana. Study participants were patients of all ages who had tested positive for malaria, by microscopy. The hospital is located in a malaria endemic district, where malaria is among the top five diseases recorded at the outpatient department in the study hospital. The hospital also serves as referral facility for other health facilities, both public and private. The healthcare professionals manage malaria according to the Ministry of Health guidelines [ 12 ]. Prospective study participants were patients with malaria classified as uncomplicated by the attending medical officer. After prior informed consent, the study participants were recruited. Study participants were selected based on their availability to the researcher.

Inclusion and exclusion criteria

Participants included in the study were individuals infected symptomatically with malaria, caused by P. falciparum , and had consented to participate in the study. Individuals who did not provide written consent, pregnant women and infants, were excluded from the study. Pregnant women are prone to both physiological and pathological anaemia of other aetiology and in infants it was difficult getting the desired volume of blood for the haematological and the bilirubin assays. In addition, patients who had tested for malaria only by rapid test kit were excluded. Furthermore, patients who had other common diseases (HIV and hepatitis B/C) and other red blood cell genetic disorders (sickle cell disease and G6PD deficient) were excluded from the study, because their co-morbidity could affect the outcome of the investigations. Finally, the microscopically detected Plasmodium parasites other than the P. falciparum species were also excluded from this study.

Sample size determination

Using the Cochrane’s formula, n = z 2 p(1–p)/d2, where n is the sample size, z is the confidence level at 95% (standard value of 1.96), d is the margin of error at 5% (standard value of 0.05), a minimum of 296 malaria patients were used, given that the prevalence of malaria in the Eastern region of Ghana among individuals suspected of malaria was 26% [ 13 ].

Collection of blood samples

With the consent of the patient, blood samples were collected by a trained Ghana Health Service phlebotomist. The area to be sampled (the antecubital fossa) was disinfected with 70% ethanol and allowed to air dry. Using a 23-gauge syringe, whole blood (approximately 4 ml) was collected into an EDTA tube and gently mixed. The punctured side was covered with cotton wool and covered with a phlebotomy plaster.

Laboratory diagnosis of malaria and confirmation of causative species

Malaria was diagnosed by microscopy. In doing that, a thick blood film was prepared using approx. 6µL of whole blood. The smear was air dried, stained with 10% Giemsa for 10 min and any parasite thereof was quantified according to WHO protocol [ 14 ]. Quantification was done by counting the number of parasites per at least 200 white blood cells. The number of parasites counted was then divided by the number of white blood cells counted, and the resulting figure was multiplied by 8000 (that is, the estimated total of white blood cells per microlitre of blood) as used elsewhere [ 15 ]. Subsequently, P. falciparum was specified by detecting the histidine-rich protein 2 (Pfhrp2) with CareStart TM rapid diagnostic test kit (Somerset NJ, USA). Pfhrp2 antigens are specific to P. falciparum . The confirmation of species was carried out placing exactly 5 µL of blood in the sample column and four drops of sample buffer placed in the appropriate column. The results were read after 15 min as recommended by the manufacturer.

Blood group and Rhesus factor determinations

Blood group and Rhesus factor typing were performed using Accucare ABO/Rh monoclonal antibodies (Chennai, India). The tile method was used. In summary, approximately 50 µL of antibodies was mixed with approximately 20 µL of whole blood, for about 30 s. After which the reactions were read. Samples with visible agglutinations were considered positive for the respective antigen and vice versa. Blood samples from known blood groups (A Rh D positive, B Rh D positive, and O Rh negative) were used as controls.

Haemoglobin estimation

Haemoglobin estimation was performed using a fully automated haematology analyser (Urit 5160; Guangzhou, China). The haemoglobin concentration was measured using the cyanide-free colorimetric method.

Bilirubin estimation

Bilirubin estimation was performed by using a Biobase Biochemistry analyser (Guangzhou, China) and Elitech reagents obtained locally but manufactured by the Elitech Group (Allées de Dublin, France). Total bilirubin was estimated on the basis of the modified Malloy-Evelyn endpoint method based on the following principle. Sulfanilic acid reacts with sodium nitrite to form diazotized sulfanilic acid. In the presence of accelerator (cetrimide), conjugated and unconjugated bilirubin reacts with diazotized sulfanilic acid to form azobilirubin. The increase in absorbance at 546 nm is proportional to the bilirubin concentration.

Data and statistical analyses

IBM SPSS data analysis software (version 27) was used to analyse the data. The demographic characteristics, clinical, laboratory findings and the various blood groups of study participants were presented as percentages, using the total number of participants as the denominator. Further, the Chi-square test was used to test the association between the laboratory findings, the different blood groups, and the association between the clinical presentations and the various blood groups. However, Fisher’s exact test was used when the frequencies were less than 5. Further multinomial logistic regression analysis was conducted to verify the relationship between the multinomial outcome variable “blood groups and Rhesus types” and haemoglobin, bilirubin and parasite count variables that showed significance during the chi-square analysis. Blood group O was set as the reference for the blood groups for all regressions. The logistic regression analysis allowed for identifying significant predictors and quantifying their effects on the likelihood of a blood group being associated with severe malaria. Statistical significance was established at p < 0.05.

Demographic characteristics of study participants

The study recruited 328 participants, of whom 67.1% were females and the males were 32.9%. The age range of the participants was < 1 and 89 years. The mean age of the participants was 19.4 years and the standard deviation was 20. Most of the participants, 142/328 (43.3%) were between < 1 and 9 years old. Additionally, the majority of the participants 170/328 (51.8%) were under marital age with 68/328 (20.7%) in high school. Other details of the study participants are indicated in Table  1 .

Clinical and laboratory findings of study participants

Most of the study participants (61.9%) enrolled in the study were recruited from the outpatient department of the hospital. Overall, the mean temperature ± standard deviation of the study participants was 37.4 °C ± 0.8. Furthermore, almost 40% of malaria patients recorded temperatures above 37.5 °C (severe hyperthermia). Surprisingly, 35.4% of malaria patients were hypothermic (temperatures below 37 °C). The overall mean level of haemoglobin was 9.5 g/dL ± 2.7. Among the study participants, 31.1% had no anaemia (Hb level > 11.0 g/dL) whereas 18.0%, 29.3% and 21.6% of the patients with malaria were mildly, moderately and severely anaemic, respectively. Using 17 µmol/L as the cut-off point of the normal human bilirubin level, the majority (69.8%) of the participants with malaria had elevated plasma bilirubin. The majority of the participants (53.0%) also had parasitaemia levels between 10,000 and 100,000 parasites/µL of blood. The signs and symptoms of malaria presented by the majority of the participants were fever (78.4%), chills (67.7%), and headache (59.5%). The other details are shown in Table  1 .

Profiling of the ABO and Rhesus blood groups of study participants

A higher proportion of study participants (45.1%) were of blood group O with patients with blood group AB in the minority (10.7%). Furthermore, the majority of study participants 266/328 (81.1%) were Rhesus positive. Finally, in defining ABO / Rh types, most of the study participants (35.7%) were O positive in the blood group and few of them were AB negative (2.1%). The frequencies found in the other blood groups are presented in Table  1 .

Association of laboratory findings with ABO blood groups in patients with malaria

The three laboratory variables used to assess the association between ABO variability and severe malarial anaemia were haemoglobin level, bilirubin level (jaundice), and parasite density (hyperparasitaemia). Analysis of the data revealed that in patients with malaria, anaemia, jaundice, and hyperparasitaemia are associated with the blood group. Significantly, a higher number of malaria patients with blood group O (42.5%) had no anaemia, while mild anaemia was associated with patients with blood group AB. Furthermore, most of the malaria patients with blood group B (37.2%) were moderately anaemic while those with blood group A (32.8%) were severely anaemic. Furthermore, hyperbilirubinaemia was significantly higher in patients with malaria with blood group A (85.1%, p = 0.001). The Rh types did not associate with anaemia or jaundice, except for Rh positive that associated with hyperparasitaemia (17.4%, p = 0.036) (Table  2 ).

Association of clinical presentations to ABO blood groups in patients with malaria

Table 3 represents the association between the malaria clinical presentations and human blood types. Hyperthermia (> 37.5 °C) was significantly higher in blood group A (71.6%), while hypothermia was common in blood group O (48.6%). It was also observed that Rhesus types did not associate with degree of body temperature. Chills, headaches, fatigue, and muscle ache were not associated with any blood group. However, pallor (46.2%), fever (84.6%) and nausea (46.2%) significantly associated with blood group B, while vomiting (43.3%) and diarrhoea (43.3%) were commonly observed in patients with blood group A. Aside from pallor that associated with Rh positivity (p = 0.015), none of the clinical presentations was associated with any of the Rh types.

Relationship between ABO/Rh groups and severe forms of malaria

The World Health Organization (WHO) has a number of definitions to classify severe malarial anaemia. One of these definitions includes haemoglobin concentration < 5 g/dL together with parasite count > 10,000µL for children < 12 years of age. For patients with malaria ≥ 12 years, the same parasitaemia range together with haemoglobin concentration is < 7 g/dL is diagnostic. In Table  4 , the prevalence of severe malarial anaemia among children classified as having uncomplicated malaria was 8% (26/328). In children, severe malarial anaemia associated with blood group B, while Rh types did not. In patients 12 years or above, the prevalence of severe malarial anaemia among those classified as having uncomplicated malaria was 4.3% (14/328). The incidence of severe malarial anaemia in patients ≥ 12 years associated with blood groups A and B, as well as Rh positivity. Using bilirubin as a criterion, WHO defined severe malarial anaemia as having bilirubin > 50 µmol/L with parasitaemia > 100,000 /µL. With this criteria, 17% (56/328) of the study participants with malaria were severe. Severe malarial anaemia was observed in patients with blood group A whereas Rh types did not associate with severe malarial anaemia (Table  4 ).

Prediction of malaria severity using ABO blood groups

The association of blood groups with the severity of malaria was compared to that of blood group O (reference blood group) (Table  4 ). Compared to blood group O, the odds of blood groups B (aOR = 1.6, 95% CI 0.6–2.7) and AB (aOR = 1.1, 95% CI 0.7–1.9) developing severe malarial anaemia in children under 12 years of age were higher but did not reach a significant level. However, children less than 12 years old with blood group A are approximately 16 times (p = 0.0005) more likely to develop severe malarial anaemia compared to those with the blood group O. For patients ≥ 12 years with malaria, the odds of developing severe malarial anaemia were higher among those with blood groups AB (aOR = 4.4, 95% CI 2.7–6.1, p = 0.0095) and A (aOR = 17.8, 95% CI 12.6–31.2, p = 0.0030), compared to blood group O. Using the bilirubin > 50 µmol/L with parasitaemia ≥ 100,000 /µL criteria, the odds of developing severe malarial anaemia were higher among patients with blood groups A (aOR = 10, 95% CI: 6.5–19.8, p < 0.0001) and B (aOR = 2.6, 95% CI: 1.1–6.0), p = 0.0232), compared to blood group O. However, individuals with the blood groups AB are less likely to develop severe malarial anaemia compared to those with the blood group O.

Variabilities in blood groups play an important role in the pathogenesis of malaria. Several studies have linked malaria to non-O blood group [ 7 , 16 , 17 ]. Among the non-O group, malaria incidence is higher among individuals with blood group B [ 16 , 18 ], some studies have also reported a higher probability of malaria incidence in blood group AB [ 19 , 20 ]. Despite these associations, the impact of ABO and Rhesus (Rh) blood types on the severity of malaria has not been explored, especially in the Ghanaian population.

Among the participants with malaria studied, most of them were blood group O, followed by blood groups B, A and AB. The individuals who were in the O blood group were 54.9%. Among non-O blood groups, individuals with blood group B were more (43.3%), closely followed by blood group B (37.2%). This observation is consistent with previous publications on this subject matter, elsewhere [ 18 , 19 ] and in Ghana [ 21 ]. However, among the general Ghanaian population, blood group O dominates, followed by blood group A, B and blood group AB in the minority [ 22 ].

The association of blood group B or A with malaria is attributable to resetting, which is enhanced in these blood groups compared to blood group O [ 3 ]. Rosette formation allows uninfected red blood cells to be attracted to infected red cells, mediated by the parasite protein called Plasmodium falciparum erythrocyte membrane protein (PfEMP1) [ 23 ]. Hyperparasitaemia was observed to be significantly higher in blood group A compared to the other blood groups. However, it was surprising to observe that low parasitaemia was higher in blood group B compared to blood group O. This observation could be due to the cytoadherence of the parasites in high parasitaemic situations in patients with blood group B, which could reduce the peripheral blood density of the parasites [ 23 , 24 ]. Low parasitaemia after cytoadherence could be the case because even though low parasitaemia was associated with blood group B, more than 30% of them were moderately or severely anaemic, compared to blood group O. Notwithstanding the above, the effect of blood group variations on malaria parasite cytoadherence should be proven in a future study. In contrast, in group A blood, hyperparasitaemia corresponded to the severity of the anaemia, with concomitant hyperbilirubinaemia. Hyperparasitaemia and severe anaemia are linked due to the ability of the parasite to haemolyse infected cells, mediated by enhanced resetting. Hyperbilirunemia is a direct consequence of intravascular haemolysis [ 24 ].

Furthermore, hyperthermia was observed at higher rates in patients with blood group A. This is explained by the associated hyperparasitaemia and severe anaemia. Hyperthermia occurs when infected red cells rupture and uninfected cells are being invaded [ 25 ]. This phenomenon is mediated by cytokines such as tumor necrosis factor (TNF) and interleukins (IL) 2 and IL6 [ 26 ]. Whereas pallor and nausea associated with blood group B of patients with malaria, vomiting, and diarrhoea associated with blood group A. According to the findings of this study, malaria in individuals in blood group A is a medical emergency. This is because the co-occurrence of vomiting and diarrhoea will eventually lead to dehydration, if parenteral fluids are not administered immediately. This will lead to hypovolemic shock and electrolyte imbalance, the result of which is mostly fatal. Due in part to hypoperfusion and hypoxia of the tissue. If left untreated, hypovolemic shock can cause ischemic injury to vital organs, leading to multi-organ failure [ 27 ].

In children, severe malarial anaemia due to low haemoglobin and parasite count > 10,000 per µL was significantly higher in blood group B, while in patients over 12 years of age, severe malarial anaemia was significantly higher in both groups A and B. On the other hand, severe malarial anaemia defined by hyperbilirubinaemia together with parasite count > 100,000 parasites/µL was significantly higher in individuals with blood group A. Therefore, individuals with blood groups A and B are vulnerable to developing severe malarial anaemia compared to blood groups O and AB. For these reasons, blood group assessments should be added to the list of investigations requested for patients suspected of malaria. This will help to promptly and adequately manage individuals who are likely to be severely affected by the disease, to ensure better treatment outcomes.

The majority of the participants studied in this publication were Rh positive (81.1%). However, Rh status was not associated with anaemia, jaundice, temperature, and severity of malaria. Furthermore, none of the clinical presentations associated with Rh status, except pallor. A review by Rattanapan et al. [ 28 ] found an inconsistencies in the vulnerability to severe malaria between individuals with Rh positive and negative. The review analyzed data from 36 eligible papers. Overall, 44.4% of the studies revealed that Rh positive individuals had a lower proportion of malaria than Rh negative individuals, while the remaining studies revealed a higher or no difference in the proportion of malaria between Rh positive and negative. The study then concluded that having Rh positive or Rh negative did not influence the development of severe malaria. This study adds to previous publications that Rh status did not have much impact on malaria clinical presentations.

Even though low levels of haemoglobin, high bilirubin levels with their attendant high parasitaemia were observed in blood groups A and B individuals with malaria, it must be stated that these levels could be confounded by various factors. Low levels of circulating erythropoietin is associated with low red blood cell and haemoglobin counts [ 29 ]. Further, bilirubin levels have also been found to be elevated in liver diseases [ 30 ]. In addition, anti-malaria immunity is likely to influence hyperparasitaemia seen in the study participant [ 31 ]. In addition, other factors such as poor nutrition, lower socioeconomic status and longer duration of symptoms were likely to affect the levels of the biomolecules reported in this study.

Limitations

The study participants were not tested for alpha thalassaemia. The G6PD screening was performed using the sodium nitrite-methylene blue method, which may not be of higher sensitivity, especially at lower haemoglobin levels. Other entero and haemoparasites, such as hookworms, filarial worms, and Babesia parasites, which could affect haemoglobin and bilirubin levels, were not screened. In addition, effect modifiers such as low levels of circulating erythropoietin, liver diseases, incompetent or low anti-malaria immunity, poor nutrition, lower socioeconomic status and longer duration of symptoms could directly or indirectly affect the levels of haemoglobin, bilirubin and/or the parasite counts recorded in this study. Finally, due to the convenient sampling technique employed, the outcome is limited to the population studied.

Out of the 328 participants, severe malarial anaemia was observed in 8% (26/328) children less than 12 years while in patients above 12 years or above, 4.3% (14/328) had severe malaria anaemia. Using hyperbilirubinaemia as a criterion, 17% (56/328) had severe malaria. When these low numbers were distributed among the blood group types, some of the frequencies were very low. However, the odds ratios were determined based on these figures. It was observed that, the odds of developing severe malarial anaemia, in children less than 12 years, was about 16 times higher, in patients with blood group A compared to patients with blood group O, with malaria. For patients ≥ 12 years, the odds of developing severe malarial anaemia was 4.4 times and 17.8 times higher among patients with blood groups A and AB, respectively, compared to patients with blood O. Finally, using the hyperbilirubinaemia with parasitaemia 100,000 /µL criteria, the chances of developing severe malaria were 10 times higher among patients with blood groups A and 2.6 times higher in patients with blood group B, compared to blood group O.

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Acknowledgements

The authors wish to acknowledge the Management of Suhum Government Hospital for allowing samples used for this study to be collected within a short time. Additionally, the role the Medical Laboratory Scientists played in the laboratory analysis merits commendation.

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Enoch Aninagyei, Pearl Sedinam Agbenowoshie, Praise Mawuena Akpalu, Selina Blefono Asiewe, Regina Yayra Menu & Richard Harry Asmah

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EA and RHA conceived, supervised and provided resources the study. PSA, PMA and SBA participated in participant recruitment and sample collection. RYM and FG performed laboratory analysis. EA wrote the initial draft. However, all authors approved the manuscript.

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Aninagyei, E., Agbenowoshie, P.S., Akpalu, P.M. et al. ABO and Rhesus blood group variability and their associations with clinical malaria presentations. Malar J 23 , 257 (2024). https://doi.org/10.1186/s12936-024-05081-z

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DOI : https://doi.org/10.1186/s12936-024-05081-z

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