Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity

• Objective: This longitudinal study used metabolomics and systems epidemiology tools (Self-Organizing Map, SOM) on over 12,800 participants to dissect the complex temporal associations between Body-Mass Index (BMI), Waist-Hip Ratio (WHR), and the circulating metabolome.
• Key Finding: The study revealed two dynamically different metabolic patterns for increasing obesity:
  1. An increase in BMI that was not accompanied by an increase in fatty acid (FA) saturation was associated with a relatively favorable metabolic profile.
  2. An increase in WHR (central adiposity) was uniformly associated with a single, highly adverse metabolic profile, characterized by worse glucose metabolism, inflammation, and high amino acid levels.
• Implication: The results suggest that obesity is metabolically heterogeneous and that WHR is a more consistent marker of severe adverse metabolic risk than BMI alone, emphasizing the need to consider dynamic metabolic changes for personalized risk assessment.

PubMed: 36823293 DOI: 10.1038/s41366-023-01281-w Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Findings: Two Distinct Metabolic Patterns of Obesity

This longitudinal metabolomics study dissected the complex temporal associations between Body-Mass Index (BMI), Waist-Hip Ratio (WHR), and the circulating metabolome. The core finding is that there are two dynamically different metabolic patterns associated with increasing obesity over time, which are strongly influenced by the specific obesity metric used (BMI vs. WHR).

1. BMI Trajectories: Metabolic Health vs. Risk

The longitudinal analysis (using the NFBC1966 cohort with data spanning 15 years) identified two main metabolic patterns related to BMI:

• “Metabolically Healthy” BMI: An increase in BMI that was not accompanied by an increase in fatty acid (FA) saturation (the degree to which FAs are saturated) was associated with a favorable lipid profile, better glucose metabolism, and lower inflammatory markers.
• “High-Risk” BMI: An increase in BMI that was accompanied by an increase in FA saturation was linked to an adverse metabolic profile, including elevated very-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) cholesterol, increased insulin resistance, and higher inflammation markers.

2. WHR Trajectories: Highly Adverse Profile

In contrast to BMI, an increase in Waist-Hip Ratio (WHR) over time was uniformly associated with a single, highly adverse metabolic profile, regardless of the accompanying changes in FA saturation. WHR, a measure of central adiposity, was linked to: * Significantly worse glucose metabolism (insulin resistance). * Higher levels of inflammatory markers. * Higher concentrations of amino acids (specifically branched-chain amino acids, BCAA), which are known markers of insulin resistance.

Methods and Study Design

Cohorts and Data

• Longitudinal Cohorts: Northern Finland Birth Cohort (NFBC1966, n=3,117) with two time points (age 31 and 46), allowing for the calculation of BMI and WHR trajectories (changes over time).
• Cross-sectional Cohort: FINRISK (n=9,708) for initial data-driven subgrouping.
• Metabolomics: Quantification of 174 circulating metabolic measures (including lipids, fatty acids, and amino acids) using proton nuclear magnetic resonance (\(^1\)H-NMR) spectroscopy.

Systems Epidemiology Tools

1. Self-Organizing Map (SOM): An unsupervised machine learning algorithm used on the cross-sectional data to simplify the high-dimensional metabolomics data into four discrete, biologically interpretable metabolic subgroups (A, B, C, D).
2. Longitudinal Modeling: The study used the continuous trajectory of BMI and WHR, along with the metabolomic subgroups, to identify the specific metabolic changes associated with the development of obesity over the 15-year follow-up.

Conclusions and Implications

The study demonstrates that obesity is not a metabolically uniform state. The use of longitudinal metabolomics and systems epidemiology tools (like SOMs) is essential for dissecting the heterogeneity of obesity.

The key clinical implication is that WHR and BMI trajectories should be viewed as distinct risk factors: * WHR (central adiposity) is a more consistent marker of a severe, underlying adverse metabolic risk. * BMI alone may mask distinct metabolic phenotypes, and its risk assessment should be refined by incorporating the associated fatty acid saturation profile.

The findings support moving beyond simple single-time-point measurements to focus on dynamic changes in metabolic profiles for personalized risk assessment and intervention in obesity-related diseases.