Visceral adiposity is associated with metabolic profiles predictive of type 2 diabetes and myocardial infarction.

Background: Visceral fat (VF) increases risk for cardiometabolic disease (CMD), the leading cause of morbidity and mortality. Variations in the circulating metabolome predict the risk for CMD but whether or not this is related to VF is unknown. Further, CMD is now also present in adolescents, and the relationships between VF, circulating metabolome, and CMD may vary between adolescents and adults.
Methods: With an aim to add understanding to the metabolic variations in visceral obesity, we tested associations between VF, measured directly with magnetic resonance imaging, and 228 fasting serum metabolomic measures, quantified with nuclear magnetic resonance spectroscopy, in 507 adults (36-65 years) and 938 adolescents (12-18 years). We further utilized data from published studies to estimate similarities between VF and CMD-associated metabolic profiles.
Results: Here we show that VF, independently of body mass index (BMI) or subcutaneous fat, is associated with triglyceride-rich lipoproteins, fatty acids, and inflammation in both adults and adolescents, whereas the associations with amino acids, glucose, and intermediary metabolites are significant in adults only. BMI-adjusted metabolomic profile of VF resembles those predicting type 2 diabetes in adults (R 2 = 0.88) and adolescents (R 2 = 0.70), and myocardial infarction in adults (R 2 = 0.59) and adolescents (R 2 = 0.40); this is not the case for ischemic stroke (adults: R 2 = 0.05, adolescents: R 2 = 0.08). Conclusions: Visceral adiposity is associated with metabolomic profiles predictive of type 2 diabetes and myocardial infarction even in normal-weight individuals and already in adolescence. Targeting factors contributing to the emergence and maintenance of these profiles might ameliorate their cumulative effects on cardiometabolic health.

Introduction

Obesity has emerged as a global health problem in adults as well as youth[1,2]. Excess adiposity increases the risk for multiple disorders, including cardiometabolic disease (CMD)[3]. Obesity and CMD are innately related with multiple aberrations in the levels of circulating metabolites: metabolomic studies have shown that obesity is causally related to deviations in blood lipids, glucose, and amino acids[4], and that similar metabolic deviations predict the development of CMD including type 2 diabetes (T2D) and incident cardiovascular disease[5-7].

The distribution of body fat is a key modulator of obesity-related risk for CMD; individuals who store body fat within the abdominal cavity rather than elsewhere in the body are at greater risk[8,9]. The previous metabolomic studies have estimated obesity indirectly using body mass index (BMI)[4] and there is only a limited number of reports directly assessing visceral fat (VF)[10]. Further, the previous studies predominantly investigated adult populations[10] and the evidence in youth is lacking.

To add understanding to VF-related changes in systemic metabolism, we investigated the associations between magnetic resonance imaging (MRI)-quantified VF volume and blood metabolome in a two-generational study of adolescents (average age 15 years) and adults (average age 49 years). We further tested VF-by-age group interactions for each metabolite to examine the impact of age group on the associations. In secondary analyses, we tested for sex-specific models as previous literature suggests that there are sex differences in VF accumulation[11], in the circulating metabolome[12], as well as CMD prevalence and mortality[13]. Finally, to provide insights into whether similar metabolic pathways may be involved in VF and CMD, we compared the metabolomic association profile of VF with those predicting T2D, myocardial infarction (MI), and ischemic stroke (IS) reported in published studies[6,7].

Our results suggest that VF, independently of general obesity estimated by BMI, associates with aberrations in lipid metabolism in both generations and, in adults only, also with aberrations in amino acid and glucose metabolism. These VF-associated metabolomic variations highly resemble those predicting T2D and MI, which further suggest that visceral adiposity may be detrimental for cardiometabolic health not only in adults but also in adolescents.

Methods

Study population

Here we studied 956 adolescents (12–19 years of age) and 598 parents (36–65 years of age) from the Saguenay Youth Study, a two-generational population-based cohort from the Saguenay-Lac Saint-Jean region of Quebec, Canada (details described in ref. [14]). Individuals with self-reported lipid disease (15 adolescents and 14 adults) or on lipid-lowering medications (3 adolescents and 77 adults) were excluded from the analyses. The final sample included 938 adolescents and 507 adults. All adults and adolescents provided written consent and assent, respectively. The study was approved by the Research Ethics Boards of the Integrated Health and Social Services Centres of the Saguenay-Lac St. Jean, Chicoutimi, Canada (2002-023, 2003-005, and 2011-008) and of the Hospital for Sick Children in Toronto, Canada (#1000032154 and #1000068660).

Measurements

Adiposity

VF and subcutaneous fat (SF) were measured from T1-weighted MRI of the abdomen acquired on a Gyroscan NT 1.0-T scanner (Philips Healthcare, Best, Netherlands) in adolescents, and on an Avanto 1.5-T scanner (Siemens, Erlangen, Germany) in adults. A 10-mm-thick axial slice at the level of the umbilicus was selected to quantify VF and SF, in cubic centimeters, with a semiautomatic method described previously[14] (Fig. 1a). BMI was calculated as body weight in kilograms (0.1-kg precision) divided by height in meters squared (0.1-cm precision).

Fig. 1

Associations between visceral fat measured directly with magnetic resonance imaging (MRI) and selected key metabolomic measures in adolescents and adults.

a Visceral fat, which is fat around internal organs, and subcutaneous fat, which is fat outside the abdominal cavity and underneath the skin, were quantified at the level of the umbilicus with MRI; native and fat-segmented images are shown. b Associations between visceral fat and metabolomic measures were studied using linear regression models in up to 507 adults and 938 adolescents. All variables were inverse rank-transformed to normality and adjusted for age, sex, age-by-sex interaction, genetic relatedness, and family environment. Visceral fat was additionally adjusted for height. In further models, visceral fat was additionally adjusted for body mass index (BMI) or subcutaneous fat (SF). The effect sizes and corresponding 95% confidence intervals (CI) are in standard deviation (SD) units. The 14 lipoprotein subfractions are 6 very-low-density lipoproteins (VLDL), 1 intermediate-density lipoprotein (IDL), 3 low-density lipoproteins (LDL), and 4 high-density lipoprotein (HDL) subfractions. The lipoproteins are sorted by size from extra-extra-large (XXL) to extra-large (XL), large (L), medium (M), small (S) and extra-small (XS). SFA saturated fatty acids, MUFA mono-unsaturated fatty acids, PUFA polyunsaturated fatty acids, Omega-6 omega-6 fatty acids, LA linoleic acid, Omega-3 omega-3 fatty acids, DHA docosahexaenoic acid, and GlycA glycoprotein acetyls.

Serum metabolomic profiling

Overnight fasted serum samples were analyzed using high-throughput nuclear magnetic resonance (NMR) spectroscopy (Nightingale Health Ltd, Helsinki, Finland). The platform (2016 quantification version) assesses 228 metabolic measures, including lipoproteins and lipids within 14 lipoprotein subfractions, which are 6 very-low-density lipoprotein (VLDL), 1 intermediate-density lipoprotein (IDL), 3 low-density lipoprotein (LDL), and 4 high-density lipoprotein (HDL) subfractions. The lipoprotein lipids within these subfractions include triglycerides (TGs), cholesteryl esters (CEs), free cholesterol and phospholipids. The platform also quantifies apolipoprotein B and apolipoprotein A1, as well as multiple measures of fatty acids (FAs), breakdown products of FAs, glucose and intermediary metabolism, amino acids, and a marker of inflammation, glycoprotein acetyls (GlycA). The details of the method have been described elsewhere[15,16]. Shortly, a serum sample volume of 350 μl was used for the analysis. Before the NMR measurements, the samples were mixed with a sodium phosphate buffer and moved to the NMR tubes using automated liquid handlers. The metabolomic quantifications were completed using 500 MHz and 600 MHz spectrometers (Bruker AVANCE III and Bruker AVANCE III HD) equipped with a robotic sample changer that enabled sample handling in a cooled (+6 °C) temperature. Lipoprotein and low-molecular-weight metabolites were measured from the original serum samples. Subsequently, the same samples went through a lipid extraction procedure enabling the measurements of lipid data. The downstream processing of the NMR spectra was done using automated computational algorithms, the intellectual property rights of which belong to the Nightingale Health Ltd. The output provided by the company is a list of concentrations of different metabolic measures complemented with selected ratios.

Statistical analyses

Prior to association testing, all variables were transformed using the rank-based inverse normal transformation[17] and adjusted for age, sex, age-by-sex interaction, genetic relatedness and shared family environment; VF and SF were additionally adjusted for height. Adjustments were done using “lmekin” function from “coxme” R package[18], which allows inclusion of genetic relationship matrix in the model.

In primary association analyses, we tested associations of VF with 228 metabolomic measures by fitting linear models separately in adolescents and adults: in these models, each of the metabolomic measures served as an outcome and VF served as an explanatory variable. Due to the high correlation of the metabolomic measures, the number of independent tests is lower than the number of tested measures. To conduct multiple testing corrections, we carried out a principal component analysis of the metabolomic data[19], which is useful in estimating the number of independent tests when correlated data are analyzed. We found that the first 24 and 25 principal components explained 95% of variation among the 228 metabolomic measures in adolescents and adults, respectively (Fig. S1). Accordingly, we set our statistical significance threshold at p < 0.001 (0.05/(24 + 25) to correct for 24 + 25 independent tests in two generations.

In secondary association analyses, we tested whether the metabolomic associations of VF remain after additional adjusting for BMI or SF. We also fitted models in a pooled sample to test for adiposity-by-age group interactions in order to explore whether the metabolomic associations of the adiposity traits differ between adolescents and adults. To study if the associations differ by sex, we also fitted the regression models in sex-specific subsamples—these models were adjusted for age, height, genetic relatedness, and shared family environment.

Finally, we tested—in both adolescents and adults—if the BMI-adjusted metabolomic profile of VF is similar to the BMI-adjusted metabolomic profiles of T2D, MI and IS from previous open-access studies of adults using the same Nightingale NMR-based metabolomics platform we used in the present study[6,7]. Data for 87 metabolomic variables that were available in all studies were used to determine correlations between the effect estimates of VF and the effects estimates derived from odds ratios of T2D, MI and IS. First, odds ratios (and corresponding 95% confidence intervals (CIs)) were log-transformed to get all these values on the beta estimate scale and, subsequently, each beta (and CI) was scaled to the effect on VLDL-TG to facilitate comparison of the effect similarities regardless of the magnitude of the absolute effect that may vary due to differences in study settings; VLDL-TG was selected to be the scaling factor because it is among the metabolic measures most strongly associated with VF and it shows a positive association also with T2D, MI, and IS. These scaled beta estimates were used to determine correlations (Pearson’s r) between the metabolomic profiles of VF vs. T2D, MI and IS. We further derived standard errors for each association as follows: SE = (CI95upper–CI95lower)/(2*1.96). Using the scaled beta estimates and derived standard errors, we estimated for each metabolic measure if the difference between the effects of VF vs. CMD (T2D, MI, or SI) is significant: Z = (beta1–beta2)/sqrt(se1^2 + se2^2). The p values for Z were derived from the normal distribution. We further re-evaluated the similarities between metabolomic profiles of VF and CMD in sensitivity analyses using a subset 15 of noncorrelated metabolic measures identified using hierarchical clustering (R function “hcut”).

Table 1

Basic characteristics of studied participants.

Characteristic	Adolescents (n = 938)	Adults (n = 507)	pdifference
Male, %	47.5	44.6	0.3
Age, years	14.6 ± 1.8	49.0 ± 4.8	p < 2e−308
Height, cm	163.2 ± 9.6	167.1 ± 8.5	6.8e−24
Weight, kg	58.7 ± 15.1	78.1 ± 16.7	5.4e−110
BMI, kg/m2	21.8 ± 4.5	27.9 ± 5.4	1.4e−114
Overweight or obesea, %	28.1	68.2	7.7e−49
Visceral Fat, cm3	22.1 ± 19.1	83.0 ± 60.3	4.4e−159
Subcutaneous fat, cm3	126.1 ± 102.0	283.9 ± 148.3	2.7e−130
Total TG, mmol/l	0.9 ± 0.4	1.4 ± 0.7	3.0e−57
LDL-Cb, mmol/l	1.2 ± 0.4	1.6 ± 0.5	1.9e−79
HDL-C, mmol/l	1.3 ± 0.3	1.4 ± 0.4	5.7e−4
Glucose, mmol/l	3.6 ± 0.3	4.1 ± 0.8	3.9e−112

Proportions (%) or means ± standard deviations are shown.

aIn adults, “overweight or obese” refers to BMI ≥ 25 kg/m2 and, in adolescents, the threshold was set to BMI ≥ 1 SD (equivalent to BMI ≥ 25 kg/m2 at 19 years) determined using the WHO BMI Z-score calculator, which takes into account age, sex, height and weight[21].

bLevels of NMR-based LDL-C are lower than the LDL-C levels measured with common assays.