Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Abstract

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Keywords: MC4R; health; human genetics; metabolomics; obesity; polygenic risk scores; rare variants.

Graphical abstract

Figure 1

Pathway Categories of Metabolites Associated with BMI (A and B) Shown are the pathway categories of (A) the 307 metabolites significantly associated with BMI and (B) the 49-metabolite signature. (C) The values of each of the 49 BMI-associated metabolites are plotted with a Loess curve against the BMI for time point 1 in TwinsUK. Only unrelated individuals of European ancestry are included, and the small number of individuals with BMI below 20 (n = 31) or above 40 (n = 10) are removed to keep the ends of the graphs from being skewed. The apparent inversion of the relationship between one cofactor/vitamin metabolite and BMI at higher BMIs is an artifact that is corrected once morbidly obese individuals are included.

Figure 2

Variables Associated with BMI and Predicted BMI from the Metabolome (A) Correlation between ridge regression model prediction of BMI and actual BMI for all unrelated individuals of European ancestry in the TwinsUK and Health Nucleus dataset. The identification of outliers is defined below: the pink box shows individuals with a much lower predicted BMI (mBMI) than actual BMI, and the yellow box shows individuals with a much higher mBMI than actual BMI. (B) Factors associated with being an mBMI outlier. Participants were split into five groups: those whose metabolome accurately predicted their BMI (residual after accounting for age, sex, and BMI between −0.5 and 0.5) whose BMIs were either normal (18.5–25), overweight (25–30), or obese (>30), and those whose metabolome predicted a substantially higher mBMI than the actual BMI (residual < −0.5) or a substantially lower mBMI than the actual BMI (residual > 0.5). All y axis values are scaled to a range from 0 to 1 to allow comparison across groups. (C) The same process is used to show DEXA imaging values associated with metabolic BMI outliers. The mBMI ≫ BMI and mBMI ≪ BMI groups had a comparable measured BMI and age; however, these two groups were statistically significantly different from each other (p < 0.01) for all modalities except blood pressure, LDL, total cholesterol, and polygenic risk score. Additionally, the mBMI ≫ BMI group was statistically significantly different from normal weight, metabolically healthy people (p < 0.01) for all traits (except LDL), while mBMI ≪ BMI individuals were only statistically different in diastolic blood pressure (p = 0.005). In contrast, outliers with mBMI ≪ BMI were always statistically significantly different from obese, metabolically obese individuals (except systolic blood pressure, LDL, and total cholesterol), while mBMI ≫ BMI individuals were only statistically different in percent body fat (p = 4.4 × 10⁻⁶). LDL, low-density lipoprotein; HDL, high-density lipoprotein. The correlations of each modality with mBMI can be found in Table S2. Whiskers of boxplot extend to the most extreme points no greater than 1.5 times the interquartile range (distance between the first and third quartiles).

Figure 3

Body Composition Profiles from Dixon Magnetic Resonance Imaging for Four Outlier Individuals (A) Correlation between ridge regression model prediction of BMI and actual BMI for all unrelated individuals of European ancestry in the TwinsUK and Heath Nucleus dataset. Outliers highlighted in (B) and (C) are marked with corresponding colors. All individuals highlighted are from the outlier mBMI ≫ BMI or mBMI ≪ BMI categories shown in Figure 2. (B) Body composition profiles (red, visceral adipose tissue; yellow, subcutaneous adipose tissue; cyan, muscle). (C) Waist to hip cross-sections (hip, mid femoral head; waist, top of ASIS).

Figure 4

Progression of Different mBMI/BMI Categories (A) Alluvial plot showing the proportion of participants who remained in the same weight category or transitioned to a different weight category over the course of the 8–18 years of the TwinsUK study. Red individuals have an obese metabolome, orange individuals have an overweight metabolome, and gray individuals have a normal metabolome. (B) Alluvial plot showing the proportion of participants who remained in the same mBMI category or transitioned to a different mBMI category over the course of the 8–18 years of the TwinsUK study. Red individuals begin the study with an obese BMI, orange overweight, and gray normal weight. (C) Bar graph showing baseline rates of stroke, infarction, angina, angioplasty, any of the above, and blood pressure medication use at baseline for TwinsUK (with colors indicating groups as in D; n = 1,573 for information on cardiovascular and stroke events and 379 for information on blood pressure medication). Those with mBMI ≪ BMI had none of these conditions at baseline except stroke, while those with mBMI ≫ BMI had rates similar to those of overweight and obese individuals. (D) Survival plot showing age until cardiac event (infarction, angina, or angioplasty). The plot is divided into those whose mBMI corresponds with their BMI (normal weight (n = 598), overweight (n = 461), and obese (n = 192) categories) as well as the two outlier groups: those with mBMI ≪ BMI (n = 158) and those with mBMI ≫ BMI (n = 164) (p = 0.003 for a difference between these categories in cardiovascular outcomes).

Figure 5

Genetic Risk Compared to BMI-Relevant Variables (A) Correlation between polygenic risk score (PG) category, MC4R carrier status, and BMI and anthropomorphic and clinical measurements for all unrelated individuals of European ancestry in the TwinsUK and HN dataset. All y axis values are scaled to a range from 0 to 1 to allow comparison across groups. (B) The same process is used to show blood work and DEXA imaging values. While there was a trend for genetic risk to be associated with various measurements, the polygenic risk score achieved nominal p < 0.05 for BMI, waist/hip ratio, android/gynoid ratio, and triglycerides, and MC4R carrier status achieved nominal p < 0.05 for BMI. LDL, low-density lipoprotein; HDL, high-density lipoprotein. Whiskers of boxplot extend to the most extreme points no greater than 1.5 times the interquartile range (distance between the first and third quartiles).