Table 1.
Descriptive statistics of subjects stratified according to intraperitoneal/abdominal fat ratio.
Figure 1.
Statistically significant Spearman correlation map between CT scan and DXA body composition parameters (95% confidence interval).
IPVF and Log10 IPVF correlated similarly to fat body composition parameters. Log10 IPVF showed a strong association with android/gynoid fat ratio (r = 0.48, p = 0.0015), Log10 VAT/SAT (r = 0.72, p<0.001) and Log10 VAT/total abdominal fat (r = 0.71, p<0.001), subcutaneous fat (r = 0.58, p<0.001), and some dependencies with lean mass parameters. Log10 values of VAT/SAT and VAT/total abdominal fat ratios were poorly correlated with most body composition parameters, except for arms lean mass (r = 0.43, p = 0.0056; r = 0.42, p = 0.0070), whilst VAT/SAT correlated with subcutaneous fat (r = 0.58, p = 0.0489), and android/gynoid fat ratio (r = 0.48, p = 0.0453). NB: Blue denotes negative correlation, orange denotes positive correlation, and black denotes no correlation.
Figure 2.
Statistically significant Spearman correlation map between body fat composition parameters and clinical measures (95% confidence interval).
Log10 values of IPVF, VAT/SAT, VAT/total abdominal fat were strongly associated with HOMA-IR (r = 0.39, p = 0.015; r = 0.56, p<0.001; r = 0.55, p<0.001) and fasting insulin (r = 0.35, p = 0.0275; r = 0.49, p = 0.0017; r = 0.48, p = 0.0020). Strong associations were observed with ALAT (r = 0.39, p = 0.0128; r = 0.37, p = 0.0175; r = 0.38, p = 0.0167) and ALAT/ASAT ratio (r = 0.44, p = 0.0044; r = 0.35, p = 0.0268; r = 0.35, p = 0.0302). IPVF and Log10 values of IPVF correlated with waist (r = 0.55, p<0.001; r = 0.35, p = 0.04) and waist/hip ratio (r = 0.69, p<0.001; r = 0.52, p = 0.0017), but not Log10 values of VAT/SAT and VAT/total abdominal fat. NB: Blue denotes negative correlation, orange denotes positive correlation, and black denotes no correlation.
Table 2.
Insulin and glucose response to oral glucose tolerance test according to intraperitoneal/abdominal fat ratio.
Figure 3.
Plot describing metabolite importance and robustness in predicting visceral fat adiposity as assessed by Random forest analysis using metabolic data collected at V0 and V2.
Visceral adiposity was associated with increasing concentrations of amino acids (glutamine, leucine/isoleucine, phenylalanine and tyrosine), lysophosphatidylcholine LPC 24∶0 and diacyl phospholipids (PC 30∶0, PC 34∶4). In addition, visceral adiposity was marked by a depletion in ether lipid species PC-O 36∶3, PC-O 40∶3, PC-O 40∶4, PC-O 40∶6, PC-O 42∶2, PC-O 42∶3, PC-O 42∶4, PC-O 44∶3, PC-O 44∶4, PC-O 44∶6, and two diacyl phosphocholines (PC 42∶0 and PC 42∶2). To reflect the weight of the selected biomarkers in the classification of visceral adiposity, a pooled mean decrease of accuracy for each compound was calculated from 10000 forest generations. Higher variable importance corresponds to higher values of pooled mean decrease in accuracy. Key: IPVF, intraperitoneal fat volume; LPC, Lysophosphatidylcholines; PC, Phosphatidylcholines; PC-O, 1-O-alkyl-2- acylglycerophosphocholines; Ratio1, intraperitoneal/subcutaneous fat ratio; Ratio 2, intraperitoneal/abdominal fat ratio. Assignment of PC-O species is made on the assumption that only even numbered carbon chains are present. A potential overlap between PC species containing odd-chain fatty acids and even-chained PC-O species cannot be excluded with low mass resolution.
Table 3.
Metabolite variations across subjects stratified according to intraperitoneal/abdominal fat ratio.
Figure 4.
Bar plots describing metabolite variations in the study population stratified in four quartiles according to visceral fat adiposity (intraperitoneal fat) at V2.
Statistical significance is reported in Table S3. Key: PC-O, 1-O-alkyl-2- acylglycerophosphocholines. Assignment of PC-O species is made on the assumption that only even numbered carbon chains are present. A potential overlap between PC species containing odd-chain fatty acids and even-chained PC-O species cannot be excluded with low mass resolution.
Figure 5.
Spearman correlation network between blood plasma metabolic markers highlighting strong functional relationships between phospholipids and eicosanoid metabolic remodelling.
Non significant correlations and those between 0.4 and −0.4 were removed to reduce the number of edges and facilitate visualization.