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Obesity

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...

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Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic

Mohammad Y Anwar¹, Heather Highland¹, Victoria Lynn Buchanan¹

¹Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Obesity (Silver Spring, Md.)

|November 5, 2024

Summary

This summary is machine-generated.

Machine learning identified two distinct subgroups of individuals with obesity. One subgroup showed higher inflammation and blood glucose, while the other had elevated cholesterol, suggesting different cardiometabolic disease risks.

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Area of Science:

Metabolomics and Proteomics
Machine Learning in Health Research
Cardiometabolic Disease Subtyping

Background:

Obesity confers variable risk for cardiometabolic diseases.
Identifying distinct patient subgroups is crucial for personalized medicine.

Purpose of the Study:

To apply an integrative multi-omics approach to identify subgroups within the obese population.
To characterize distinct cardiometabolic disease patterns associated with these subgroups.

Main Methods:

Unsupervised clustering using machine learning on proteomics and metabolomics data from 243 individuals (Multi-Ethnic Study of Atherosclerosis cohort).
Functional characterization of omics pathways contributing to clusters.
Multivariate regression to assess cardiometabolic trait differences between clusters.

Main Results:

Two distinct clusters (iCluster1 and iCluster2) were identified.
iCluster2 exhibited higher BMI, fasting blood glucose, and inflammation.
iCluster1 was linked to higher total and HDL cholesterol, with associated cell growth and lipogenesis pathways.

Conclusions:

The identified clusters may represent different stages of obesity-related pathology or distinct underlying mechanisms.
Factors such as dietary patterns or differential metabolic damage rates could explain cluster differences.
Further research is needed to elucidate the precise mechanisms driving these distinct cardiometabolic profiles in obesity.