Associations between adipose tissue volume and small molecules in plasma and urine among asymptomatic subjects from the general population

Abstract

Obesity is one of the major risk factor for cardiovascular and metabolic diseases. A disproportional accumulation of fat at visceral (VAT) compared to subcutaneous sites (SAT) has been suspected as a key detrimental event. We used non-targeted metabolomics profiling to reveal metabolic pathways associated with higher VAT or SAT amount among subjects free of metabolic diseases to identify possible contributing metabolic pathways. The study population comprised 491 subjects [mean (standard deviation): age 44.6 yrs (13.0), body mass index 25.4 kg/m² (3.6), 60.1% females] without diabetes, hypertension, dyslipidemia, the metabolic syndrome or impaired renal function. We associated MRI-derived fat amounts with mass spectrometry-derived metabolites in plasma and urine using linear regression models adjusting for major confounders. We tested for sex-specific effects using interactions terms and performed sensitivity analyses for the influence of insulin resistance on the results. VAT and SAT were significantly associated with 155 (101 urine) and 49 (29 urine) metabolites, respectively, of which 45 (27 urine) were common to both. Major metabolic pathways were branched-chain amino acid metabolism (partially independent of insulin resistance), surrogate markers of oxidative stress and gut microbial diversity, and cortisol metabolism. We observed a novel positive association between VAT and plasma levels of the potential pharmacological agent piperine. Sex-specific effects were only a few, e.g. the female-specific association between VAT and O-methylascorbate. In brief, higher VAT was associated with an unfavorable metabolite profile in a sample of healthy, mostly non-obese individuals from the general population and only few sex-specific associations became apparent.

Figure 1

Standardized β-estimates from linear regression analyses with the amount of visceral (VAT; left panel) or subcutaneous (SAT; right panel) adipose tissue as exposure and plasma metabolites as outcome conducting either the whole population (square), only men (circle) or women (diamond). Displayed are only metabolites which were annotated and significant (controlling the false discovery rate (FDR) at 5%) in at least one of the subsets (indicated by darker colors). Metabolites printed in bold showed a nominal significant (p < 0.05) interaction term between VAT or SAT and sex. Regression models were adjusted for age, (sex), smoking behavior, alcohol consumption, LDL-cholesterol, systolic blood pressure, and estimated glomerular filtration rate. The Venn diagram displays the overlap in associated metabolites, including unknown(*) compounds.

Figure 2

Standardized β-estimates from linear regression analysis with the amount of visceral (VAT; left panel) or subcutaneous (SAT; right panel) adipose tissue as exposure and urine metabolites as outcome conducting either the whole population (square), only men (circle) or women (diamond). Displayed are only metabolites which were annotated and significant (controlling the false discovery rate (FDR) at 5%) in at least one of the subsets (indicated by darker colors). Metabolites printed in bold showed a nominal significant (p < 0.05) interaction term between VAT or SAT and sex. Regression models were adjusted for age, (sex), smoking behavior, alcohol consumption, LDL-cholesterol, systolic blood pressure, and estimated glomerular filtration rate. The Venn diagram displays the overlap in associated metabolites, including unknown(*) compounds. PDG = 5beta-pregnan-3alpha,21-diol-11,20-dione 21-glucosiduronate.

Figure 3

Subnetwork of the derived GGM with emphasize on cortisol as well as piperine and related compounds (e.g. X – 11593 putatively O-methylascorbate). On each node the results from linear regression analyses for visceral fat were mapped for the whole population (black), only women (light grey) or men (dark grey) as portion of the associations strength given as –log₁₀(FDR-value). Significant results in at least one population, false discovery rate (FDR) below 5%, were highlighted by colors. Node sizes were chosen as maximum association strength. The prefix P denotes plasma metabolites whereas U indicates urine metabolites. Edges represent significant partial correlations (par. cor.) between metabolites. Type and color represent metabolite and fluid dependencies. Regression models were adjusted for age, (sex), smoking behavior, alcohol consumption, LDL-cholesterol, systolic blood pressure, and estimated glomerular filtration rate.

Figure 4

Comparison of the effect sizes (95%-CI indicated by lines) from linear regression models using visceral (VAT, upper panel) or subcutaneous (SAT, lower panel) adipose tissue and metabolite levels as outcome before (x-axis) and after (y-axis) further adjustment for the homeostatic model of insulin resistance (HOMA-IR). Model 1 was adjusted for age, sex, smoking behavior, alcohol consumption, low-density liporotein cholesterol, systolic blood pressure, and estimated glomerular filtration rate. Metabolites meeting statistical significance in both models (false discovery rate <5%) are indicated by darker colors and the number is given in brackets. Metabolites with strong attenuation of effect sizes (>50%) have been annotated. The solid line indicates the fit of an ordinary linear regression model between effect estimates from both models. The dotted line would indicate identity of effect estimates.