Plasma Metabolomic Profiling in 1391 Subjects with Overweight and Obesity from the SPHERE Study

Abstract

Overweight and obesity have high prevalence worldwide and assessing the metabolomic profile is a useful approach to study their related metabolic processes. In this study, we assessed the metabolomic profile of 1391 subjects affected by overweight and obesity, enrolled in the frame of the SPHERE study, using a validated LC-MS/MS targeted metabolomic approach determining a total of 188 endogenous metabolites. Multivariable censored linear regression Tobit models, correcting for age, sex, and smoking habits, showed that 83 metabolites were significantly influenced by body mass index (BMI). Among compounds with the highest association, aromatic and branched chain amino acids (in particular tyrosine, valine, isoleucine, and phenylalanine) increased with the increment of BMI, while some glycerophospholipids decreased, in particular some lysophosphatidylcholines (as lysoPC a C18:2) and several acylalkylphosphatidylcholines (as PC ae C36:2, PC ae C34:3, PC ae C34:2, and PC ae C40:6). The results of this investigation show that several endogenous metabolites are influenced by BMI, confirming the evidence with the strength of a large number of subjects, highlighting differences among subjects with different classes of obesity and showing unreported associations between BMI and different phosphatidylcholines.

Keywords: body mass index; metabolomics; obesity; overweight; plasma metabolome.

Figure 1

Volcano plot representing the results of the Tobit linear regression models considering the metabolites (dependent variables) in relation to BMI (independent variable), adjusted for age, sex, and smoking habit. Each dot represents a metabolite and they are displayed based on the % variation (∆% = (exp(β) − 1) × 100) (x-axis) and the negative logarithm (base 10) of the FDR p-value (y-axis). The upper dashed line represents an FDR p-value equal to 0.0001, while the lower dashed line represents an FDR p-value equal to 0.05.

Figure 2

Boxplots summarizing the distribution, for study subjects divided in four different classes of BMI, of the 16 metabolites with the lowest FDR p-value in the Tobit regression models. The box contains 50% of the observations, with the median dividing the box in two areas and the upper and lower hinge representing the 25th and 75th percentile of the distribution. Outside the box, the upper whisker extends from the hinge to the highest value no further than 1.5 times the interquartile range (IQR) from the hinge. The lower whisker extends from the hinge to the smallest value at most 1.5 times the IQR of the hinge. Data beyond the whiskers are plotted individually and represented as dots.

Figure 3

Network analysis performed considering metabolites as nodes and correlation coefficients (r) obtained from each pair of metabolites as edges. The Fruchterman–Reingold force-directed layout algorithm was used, and the edge weights were set on the value of r. Only statistically significant correlations with r > 0.4 were considered and metabolites with no connection were removed.

Figure 4

Network analysis performed considering metabolites as nodes and correlation coefficients (r) obtained from each pair of metabolites as edges. The Fruchterman–Reingold force-directed layout algorithm was used, and the edge weights were set on the value of r. Only statistically significant correlations with r > 0.7 were considered and metabolites with no connection were removed.

Figure 5

Heat map showing metabolite levels among subjects. The subjects were sorted by BMI. Only the most significant metabolites in the Tobit models for BMI (FDR p-value < 0.0001) were considered and they were grouped with a cluster analysis. Dendrograms built with Euclidean distances related to the cluster analysis are reported above.

Figure 6

Plot relative to the pathway analysis displaying each altered pathway as a dot, ordered for pathway impact (x-axis and size) and negative logarithm (base 10) of the p-value (y-axis and color). The pathway analysis was performed with regressions between metabolites and the BMI of subjects, a GlobalTest was selected as pathway enrichment analysis, an out-degree centrality was chosen as pathway topology analysis, and the SMPDB Homo sapiens library was chosen as pathway library.