Predicting & Preventing Cardiometabolic Diseases Through Metabolic Phenotypes

Table of Contents

Go ahead and eat that chocolate bar!

I bet you didn’t expect to hear that, did you? But what if I told you that by personalizing your medication and overall treatment through metabotypes, it might be okay to do so with your specific phenotype (set of characteristics)?

By identifying at-risk groups, scientists can provide preventative measures and apply current treatment methods in metabolomics, such as strokes — a silent disease. Every year in Canada, there are over 50,000 new strokes. That’s one stroke every 10 minutes.

Metabotyping in Cardiometabolic Diseases: What does that mean?

The underlying idea behind metabotyping is to identify metabolic phenotypes based on diet, anthropometric, clinical parameters, metabolomics data, and the gut microbiome. This idea’s critical application falls under tailoring an optimal diet to fit each metabotype and help understand biomarkers' influence.

Hold on a sec, who here remembers what biomarkers are? What do they have to do with metabotyping?

Picture yourself working on, let’s say, a big Biology Project that’s due in a few days. Your younger sibling comes into the room and nags you to play with you. Five minutes later, another sibling comes to nag you to play with them. Once more, fifteen minutes later, another cousin comes into nag you to play with them. You understand what would happen after this, right? They would continue to continually pressure you to play, the environment around you, AKA your house, would continuously pressure you. Well, you aren’t the only one that experiences this, so do biomarkers and, in general, your body overall.

The biomarker is you and the annoying siblings, pressures like diet changes, unhealthy diets, gut microbiome, genes, medication, environment, lifestyle and general understanding/education. Now, like me a few weeks ago, you might be wondering: HOW the heck are we supposed to study biomarkers and drug analysis when we can’t trust biomarkers? Figure out the big picture on how exactly we influence them, aka our metabotype!

Where does this play into cardiometabolic diseases like strokes?

A stroke is a sudden interruption in the blood supply of the brain. Most strokes are caused by an abrupt blockage of arteries leading to the brain (ischemic stroke). Other strokes are caused by bleeding into brain tissue when a blood vessel bursts (hemorrhagic stroke). Because stroke occurs rapidly and requires immediate treatment, stroke is also called a brain attack. When a stroke’s symptoms last only a short time (less than an hour), this is called a transient ischemic attack (TIA) or mini-stroke.

Why strokes specifically? I live 5,633 miles away from my grandfather, and when he had a stroke in August of 2020, I didn’t know anything, neither did my grandma or any of my family members, and we were all left with shock. The shock that it happened. The wonder that we didn’t notice symptoms. The surprise that there was no way to foresee it happening.

Why can’t we identify and pinpoint the biomarkers?

That’s what makes it so scary, and that’s why we need metabotypes. To differentiate between you and the next person, to STOP the epidemic of cardiometabolic diseases. In a book I read recently, Homo Deus by Yuval Noah Harrari pinpoints the fact that famine was humanity’s worst enemy for thousands of years. “Until recently, most humans lived on the very edge of the biological poverty line, below which people succumb to malnutrition and hunger.” Now, “in most countries, today, overeating has become a far worse problem than famine.” While the 1% are eating pricey salads, 99% are eating cheap “food.”

“How will this even help me?”

— my 10-year-old sister, after studying the Canada Food Guide and merely colouring and drawing things she should eat instead of her regular meals (healthy alternatives).

Diet is among the most important modifiable lifestyle factors contributing to cardiometabolic disease risk. Changes in a diet can delay or even prevent the onset of disease. While national and international dietary guidelines promote health and prevent the disease from a population perspective, data from Europe, the United States, and Australia have clearly shown that these guidelines are poorly adhered to.

We need to find a cure to the next thing on humanity’s list of things to do, cardiometabolic diseases, and it can’t start with a few general biomarkers. Biomarkers must be specific. They must match the type. Hello! Let’s start with metabotypes!

This article is a synthesis of 3 research papers exploring prototyping applications of metabotyping in precision nutrition and precision medicine. The research papers progress from oldest to newest and end off with takeaways. See above for a thorough table of contents!

Research Paper 1 (2019): “Paving the Way to Precision Nutrition Through Metabolomics” by Abdellah Tebani and Soumeya Bekri

This paper was the first on the chronological time scale, with its publication dating back to 2019. The key ideas reflect on slowly progressing the metabotype concept’s problem, the outside influences.

Integrating + Understanding Genetic Influence on Metabotypes

Different studies confirm the complex interactions that exist among food components, dietary patterns and epigenetic modifications. In this paper, it is reported the strong influences genetic variants have on metabotypes. This idea cited further in the paper explains the interconnection present between metabolomics and gene regulation through epigenetics. This idea confirms that integrating metabolomics and other omics might be of great interest to future biomarker + metabotype researchers.

Given this biological complexity, systematically tracking relationships between disease through multi-omic lenses is very challenging. So, innovative systematic strategies are urgently needed to unveil the role of metabotypes in disease development, ultimately design dietary recommendations to promote health and well-being or cure diseases.

Experiments + Figures: What was done?

Featured above is a table of dietary biomarkers investigated by suing cohort studies (metabotype-like profiles). There were four groups of cohorts:

  1. Group: 33 males and 35 females; 58–60 years old
    Platforms: Untargeted Mass Spectrometry (MS) Metabolomics
    Goal: Develop a data-driven procedure to discover urine biomarkers able to indicate habitual exposure to different foods.
    Takeaways: Specific metabolites as dietary biomarkers of oily fish (methyl-histidine) and coffee (dihydrocaffeic acid derivatives).
  2. Group: 1,257 females and 790 males; 35–64 years old (including 801 type 2 diabetes cases.) More on this study in Research Paper 3.
    Platform: Targeted MS Metabolomics
    Goal: Identify blood metabolites that possibly relate to red meat consumption to the occurrence of type 2 diabetes.
    Takeaways: Six biomarkers were associated with red meat consumption and diabetes risk. (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1).
  3. Group: 275 males and females; 55–80 years
    Platform: Untargeted MS Metabolomics
    Goal: Whole-grain bread consumption biomarkers.
    Takeaways: Pyrraline, riboflavin, 3-indole carboxylic acid, glucuronide, 2,8-hydroxyquinoline glucuronide, and N-α-acetyl citrulline were also tentatively identified.
  4. Group: 275 males and females; 55–80 years
    Platform: Untargeted MS Metabolomics
    Goal: Characterize dietary walnut fingerprinting.
    Takeaways: 18 metabolites, including markers of fatty acid metabolism, ellagitannin-derived microbial compounds, and intermediate metabolites of the tryptophan/serotonin pathway.

Although focused on specified nutritional biomarkers, these experiments allowed for a dataset of the biomarkers radiated by specified goals like, for example, walnuts.

Key Findings: What can be concluded?

NO! Metabolomics still faces a challenge. The biggest challenge is the integration between the other omics and phenotypic data. However, standardization and normalization of metabolomics data are mandatory to effectively implement its functional and operational integration. Doing so will allow us to enhance our knowledge of diet-health relationships and through that particular metabotypes.

Research Paper 2 (2020): “Perspective: Metabotyping — Potential Personalized Nutrition Strategy for Precision Prevention of Cardiometabolic Diseases” by Marie Palmas et al.

This paper was the second on the chronological time scale, with its publication dating back to 2020. The key ideas reflect on identifying what’s preventing metabotypes from progressing and going from a “new concept” to the concept in cardiometabolic precision prevention.

What is Stopping Metabotyping From Going From A “New Concept” to “The Concept”

What if disease-associated metabotypes-that is, metabotypes present in those populations at high risk for cardiometabolic diseases- will affect the response to a specific diet. Gut Microbiota is a crucial determinant and a modifier of metabotypes, as well as habitual diet, phenotype, anthropometric measures, and biochemicals and clinical markers. In other words, the interaction between the host (you) and exogenous exposures (e.g., diet, drugs, and gut microbiota) is a crucial factor for dietary response and identifying certain metabotypes.

Experiments + Figures: What was done?

  1. Study #1: Men and women from a large Irish study, for example, were grouped according to their plasma fatty acid profile, producing four groups that varied in terms of metabolic syndrome components, anthropometric indicators, dietary habits and demographics. More on this later.
  2. Study #2: In another study, urinary metabolites associated with diabetes were used to separate diabetic and non-diabetic patients into four novel classes. The variations in plasma glucose levels led the authors to speculate on the differences in diabetes control and potential complications for the two metabotypes in diabetic patients. From a nutritional perspective, the study’s authors identified two metabolite-based metabotypes and evaluated their respective responses to a dietary intervention aimed at weight loss.
  3. Study #3: In another recent study, two distinct subgroups of monozygotic twin pairs associated with cardiometabolic risk factors, high-density lipoprotein cholesterol, and BMI were identified using metabolic agents.

In summary, balanced, overweight males and females were classified into two distinct metabotypes based on plasma glucose concentrations of metabolites linked to lipid metabolism. Individuals belonging to various metabotypes often differentiated in terms of amino acid and carbohydrate metabolism, post-prandial glucose and insulin levels, liver lipid content, intra-abdominal fat mass, and eating habits.

When faced with a 12-week weight loss study with decreased caloric intake, only those with more disease-associated metabotypes reported an increase in glucose and insulin levels at the end of the intervention; thus, the study identified a responsive response non-responsive metabotype.

Key Findings: What can be concluded?

Once they have been described and validated, biomarkers suitable for large-scale applications are required. The authors encourage consideration of the evaluation and validation of metabotype biomarkers in future metabotyping studies.

Blood biomarkers can be useful since they can be tested repeatedly and continuously. In contrast, other biological samples (e.g. fecal samples) may be more challenging to obtain at specified time points and time intervals.

On the other hand, future studies could determine whether the classification of individuals based on a small number of anthropometric, clinical or other easily assessed parameters alone might be appropriate to classify specific diets that would be suitable for particular metabotypes.

Research Paper 3 (2020): “Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study” by Anna Riedl et al.

This paper was the third on the chronological time scale, with its publication dating back to 2020. The key ideas reflect on evaluating and studying the German KORA Cohort study, specifically the Irish population. Additionally, I tried to incorporate this paper because it is merely factual data, so it required me to do most of the analysis and comprehension.

Evaluating Metabotype Concepts Application in German KORA FF4 Cohort Study

Considering that groups are different in their dietary patterns, the vision is to develop a complete customized prevention model with relevant dietary changes. However, further validation and recognition of differences in lifestyle-disease associations between metabotypes is vital for creating targeted disease prevention strategies.

See the image above for examples of identifying high-risk groups, separated by the three clusters the study participants were separated in. Pay close attention to the third cluster as most of the “inactive” and smokers were placed there.

Experiments + Figures: What was done?

This study’s study population was from the health examinations conducted in Augsburg in Southern Germany between 2013 and 2014. In this survey/health examination, there were a crazy 4261 participants aged 25–74 years in the first examination, 3080 individuals in the second one and 2279 individuals in the last one.

All these studies were invited to the study centre for a standardized physical examination and a computer-assisted personal interview, both conducted by trained staff.

To ensure comparability with previous studies on metabotyping, the same size samples were used. (KORA F4: n=1768, KORA FF4 study: n= 2279.)

1.2 Assessment of Demographic, Anthropometric and Lifestyle Data for Metabotype Characterization

Demographic and lifestyle data were assessed in standardized face-to-face computer-assisted interviews and self-administered questionnaires in the KORA FF4. This data included sex, age, education, physical activity, smoking status and Body Max Index (BMI).

1.3 Statistical Analysis

All statistical analyses were performed using the statistical software package RStudio version 1.0.136 that uses R version 3.2.2 (R Development Core Team, 2010, Statistical significance was determined as a p<0.005.

Key Findings: What can be concluded?

The table above shows the total study population’s demographic characteristics and each of the three metabotype clusters identified in the KORA F4 study. The total study population aged 32–77 years comprised nearly equal proportions of men and women. They were separated into 3 clusters (groups of metabotypes).

Of 1744 participants, 590 (33.8%) were assigned to cluster 1, 813(46.6%) to cluster 2 and 341 (19.6%).

The proportion of men was higher in clusters 2 (58.7%) and 3 (60.7%) compared to cluster 1(27.5%). Cluster 3 was characterized as the cluster with the highest median age of 62.0 years (range = 45–77 years) and BMI of 29.7 kg m−2(range = 21.5–47.6 kg m−2), as well as the highest proportions of physically inactive individuals (50.1%) and smokers (19.9%).

In other words, the most populated cluster, cluster 3, had the highest percentage of individuals physically inactive and smokers, while the first two clusters had minimal amounts.

Predictions for the Future

One of the biggest challenges of pushing metabotyping into the future is the progress of personalized and precision nutrition (PN). Overall it’s hard to put theory into practice. All of these studies progress through randomized controlled trials (RCTs) with clinical points. Essentially, it’s like observing and not really getting in on the action.

There are many inevitable changes before going head straight into metabotyping. The biggest one? The fact that EVERYONE is so different in terms of lifestyle changes (nutrition, physical activity, etc.)

The above image perfectly exemplifies this idea of many factors (determinants) playing a key role in identifying metabotypes. There are so many influences, like we talked about in the example above. This lack of foundation and stability in the results is what evidently creates context gaps with study information. If you aren’t sure of your study and results, how are you supposed to progress into it?

Context Gaps

Going forward with metabotyping in the future will require new perspectives, fresh pairs of eyes. Setting strong theoretical foundations by identifying key individual attributes that drive medicine's personalization is a long process.

Additionally, evidence for the cost-effectiveness of well-designed interventional studies is also key. Now also comes into play the idea of applying this almost imaginative like idea into the real world.

The introduction of a regulatory framework is mandatory to gain the trust of regulatory health professionals and policymakers.

Conclusion: What now?

These context gaps must be filled by multidisciplinary teams consisting of clinicians, behavioural psychologists, nutritionists, computer scientists and biomedical scientists. As well as the notion of intersecting the “omics” family. Also known as genomics, proteomics, transcriptomics, metabolomics, etc.

Like the clusters of metabotypes, the scientific community must come together to overcome the context potholes and drive smoothly down this bright looking road!


  • Metabotyping is the concept of grouping individuals with similar metabolomic characteristics.
  • Metabotyping is currently being projected to be utilized as a form of identifying and providing preventative measures to clusters or groups of individuals considered at “risk.”
  • There are many context gaps to fill, for instance, dietary changes that need to be assessed.
  • Building a robust theoretical foundation will allow scientists to fill the context gaps and solve the biggest limitation/challenge of all, putting theory into practice.

Cited Research Papers

Ciao, I’m Anastasija, a 15 y/o interested in the intersection between bioinformatics and biotechnology! Check out my website, connect with me on LinkedIn and follow me on Twitter!



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Anastasija Petrovic

I’m Anastasija, a 17-year-old interested in the intersection between biotechnology and bioinformatics. I also write about mindsets and emerging technologies!