Metabolomic correlates of central adiposity and earlier-life body mass index

Abstract

BMI is correlated with circulating metabolites, but few studies discuss other adiposity measures, and little is known about metabolomic correlates of BMI from early life. We investigated associations between different adiposity measures, BMI from childhood through adulthood, and metabolites quantified from serum using 1H NMR spectroscopy in 900 British men and women aged 60–64. We assessed BMI, waist-to-hip ratio (WHR), android-to-gynoid fat ratio (AGR), and BMI from childhood through adulthood. Linear regression with Bonferroni adjustment was performed to assess adiposity and metabolites. Of 233 metabolites, 168; 126; and 133 were associated with BMI, WHR, and AGR at age 60–64, respectively. Associations were strongest for HDL, particularly HDL particle size—e.g., there was 0.08 SD decrease in HDL diameter (95% CI: 0.07–0.10) with each unit increase in BMI. BMI-adjusted AGR or WHR were associated with 31 metabolites where there was no metabolome-wide association with BMI. We identified inverse associations between BMI at age 7 and glucose or glycoprotein at age 60–64 and relatively large LDL cholesteryl ester with postadolescent BMI gains. In summary, we identified metabolomic correlates of central adiposity and earlier-life BMI. These findings support opportunities to leverage metabolomics in early prevention of cardiovascular risk attributable to body fatness.

The metabolic profile of waist to hip ratio–A multi-cohort study

“…A great number of studies have investigated the metabolic profile of obesity, as reviewed in [7]. Some studies have also evaluated the metabolomics of fat distribution [8][9][10][11][12][13][14][15], but to the best of Twenty-four validated metabolites were associated with WHR in men only (Table 3). Of those, five showed an interaction with sex with p<0.05 (one GPE and four amino acid derivatives).…”

“…A great number of studies have investigated the metabolomic profile of obesity, and a meta-analysis of 11 studies found high levels of branched-chain and aromatic amino acids, certain fatty acids and reduced levels of acylcarnitines and lysophosphatidylcholines to be the most common metabolic alterations in obese individuals [7]. There are also studies on the metabolomic profile of an altered fat distribution [8][9][10][11][12][13]. However, only a few studies have tried to disentangle if the metabolic profile of a disadvantageous fat distribution was different from that found in general obesity [14,15].…”

T/A Polymorphism of the FTO Gene and Adiposity in Young Mongolian Men

“…These were caused by an increase in the values of the traits re ecting the endomorphic component of the body composition, between the normal weight and obese groups. Indices which indicate the development of abdominal obesity and visceral fat deposition increased signi cantly with increasing in BMI among those in our survey, portending increased risks of cardiometabolic diseases in participants with high BMI 3,17 . It is well known that waist circumference, waist-to-hip circumference ratio, waist-to-height ratio, used to assess central (abdominal) obesity, are closely associated with metabolic markers, which in turn indicate the development of cardiometabolic diseases based on general obesity estimated by BMI 18 .…”

“…Their contribution to total mortality is projected to further rise to 69% by 2030 1 . Such diseases are mainly caused by overweight and obesity, which in turn are controlled by endogenous (genetic, ethnic, sex and age) and exogenous (socio-economic, environmental, lifestyle, and Changes in body composition associated with increased fat mass and accumulated visceral fat, as well as abdominal fat deposition, cause persistent pathological changes in lipid and glucose metabolism 2,3 . Studies conducted in Mongolia over the past decades con rm the global trend of the increasing proportion of overweight and obese people.…”

Relationship between FTO gene polymorphism with central obesity in ethnic Mongolians

“…Èçìåíåíèÿ ìîðôîëîãè÷åñêèõ õàðàêòåðèñòèê è ñîñòàâà òåëà, ñâÿçàííûå ñ óâåëè÷åíèåì aeèðîâîé ìàññû, íàêîïëåíèåì âèñöåðàëüíîãî aeèðà, à òàêaeå òðóíêàëüíûé õàðàêòåð aeèðîîòëîaeåíèÿ âûçûâàþò ñòîéêèå ïàòîëîãè÷åñêèå èçìåíåíèÿ ëèïèäíîãî è óãëåâîäíîãî îáìåíîâ, êîòîðûå ïðèâîäÿò ê ðàçâèòèþ êàðäèîìåòàáîëè÷åñêèõ çàáîëåâàíèé [Guan et al, 2016;Wulaningsih et al, 2019]. Èññëåäîâàíèÿ, ïðîâåäåííûå çà ïîñëåäíèå äåñÿòèëåòèÿ â Ìîíãîëèè, ïîäòâåðaeäàþò ãëîáàëüíóþ òåíäåíöèþ ïî óâåëè÷åíèþ äîëè íàñåëåíèÿ ñ èçáûòî÷íûì âåñîì è îaeèðåíèåì.…”