TL;DR: A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation, and twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability.
Abstract: Objective To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors. Subjects and methods Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (rg) and environmental (re) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs (intrapair BMI difference ≥ 3 kg/m2). Results Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (rg = 0.40), glycoprotein (rg = 0.37), serum triglycerides (rg = 0.36), BCAAs (rg = 0.30–0.40), HDL particle diameter (rg = − 0.33) and HDL cholesterol (rg = − 0.30). The effect of acquired obesity within the discordant MZ pairs was particularly strong for atherogenic lipoproteins. Conclusions A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability.
Obesity is often accompanied by a cluster of metabolic abnormalities including insulin resistance, atherogenic dyslipidemia and chronic low-grade inflammation.
It cannot by itself identify obese individuals who remain metabolically healthy and normal weight individuals who present disturbed lipid or glucose metabolism and increased cardiovascular risk [2,3].
More recently, the focus has widened to cover a more global serum metabolomics profile, which has predicted the incidence of cardiovascular events [6], type 2 diabetes [7] and allcause mortality [8] in prospective cohort studies.
Heritability estimates were moderate and ranged between 23% and 55% for amino acids and other small-molecule metabolites and were higher for serum lipid (range: 48%–62%) and lipoprotein (range: 50%–76%) concentrations demonstrating a genetic basis for individual differences in serum metabolite levels [12].
Previous twin studies have documented a moderate overlap of both genetic and unique environmental factors that contribute to adiposity and lipid traits [14,15].
2.1. The Twin Cohorts
The questionnaires were sent to twin individuals at age 12 and subsequent follow-up assessments were made when the twins were aged 14, 17 and as young adults (mean age 22 years).
Zygosity was determined initially by a validated questionnaire method and then confirmed by genetic analysis of polymorphic markers at the Paternity Testing unit, National Institute for Health and Welfare, Helsinki, Finland.
A venous blood sample for serum metabolite (NMR) analyses was taken in the morning of the assessment.
WCwasmeasured to the nearest millimeter midway between the spina iliaca superior and the lower rib.
2.2. TwinFat — the Sub-sample With Detailed Adiposity Measures
The TwinFat sample was enrolled from the FT12 and FT16 cohorts based on the twins’ BMI at the fourth wave of the data collection.
One twin had type 2 diabetes and used metformin and insulin.
All other participants were healthy (based on medical history, clinical examination, and structured psychiatric interview), were normotensive, and did not use any medications except oral contraceptives.
Fat percentage was calculated as fatmass/(fatmass + leanmass + bonemineral content) for the total body and android and gynoid fat mass and fat percentage were determined from a total body scan as described by Wiklund et al. [17].
2.3. The NMR Metabolomics Platform
All serum samples were analyzed using the same highthroughput NMR metabolomics platform.
The sample preparation and NMR spectroscopy methods have been described in detail elsewhere [20,21].
The NMR metabolomics methodology provides quantitative information on lipoprotein subclass and particle concentrations, serum FAs including, e.g. omega-3 and omega-6 FAs and low-molecular-weight metabolites such as amino acids, 3-hydroxybutyrate, and glycoprotein.
The total number of metabolites analyzed in the current study was 56.
These measures describe the main metabolic pathways.
2.4. Statistical Analyses
Descriptive characteristics of the study samples were expressed as mean ± SE.
Pearson correlations were used to determine correlations between body composition and serum metabolites.
Based on these results, the authors decided to present the results for men and women combined in order to increase the statistical power and to simplify the presentation of the results.
BMI measures from the ones of heavier co-twins’, irrespective of the magnitude of the intrapair difference in weight.
This number was used to correct for multiple testing using the Bonferroni method.
4. Abdominal Obesity and Serum Metabolites: Bivariate Models
The sex-, age- and cohort-adjusted phenotypic correlations between WC and circulating serum metabolites in individual twins are presented in Fig.
The actual Pearson correlation coefficients are shown in ESM Table 1.
4.1. Phenotypic Correlations
WC was significantly correlated with 50 out of the 56 investigated serum metabolites towards an unfavorable metabolic profile.
Specifically, abdominal obesity was positively correlated with concentrations of triglycerides and very low-density lipoprotein (VLDL) particles, low-density lipoprotein (LDL) particles, and concentrations of small high-density lipoprotein (HDL) particles, as well as total cholesterol, LDL-C, intermediate density lipoprotein cholesterol (IDL-C), Apolipoprotein (Apo) B and the ApoB to ApoA1 ratio.
Serum FAs as a proportion of total FAs were inversely, albeit weakly correlated with WC, except for the positive correlation with monounsaturated fatty acids .
WC was not significantly correlated with the following metabolites: histidine, glycine, lactate, acetate, creatinine and urea.
The largest positive correlation was seen with the ApoB to ApoA1 ratio (r = 0.35) followed by glycoprotein (r = 0.33) and the largest negative correlationwas observedwith HDL particle diameter (r = −0.33) followed by the concentration of large HDL particles and the HDL-C to LDL-C ratio (r = −0.31 for both).
4.2. Genetic and Environmental Correlations
To further investigate the relationship between abdominal obesity and the circulating serum metabolites, the authors decomposed these phenotypic correlations into their genetic and environmental components (Fig. 1, ESM Table 1).
The significant genetic and environmental correlations which were observed between WC and many metabolites indicate that the genetic and environmental factors that influenceWC overlap partly with those that influence the serum metabolites.
The strength of the correlations was weak to modest suggesting that only a fraction of the genetic and environmental variation is common to abdominal obesity and the serum metabolites.
Exceptions were the metabolites ApoA1, BCAA and phenylalanine, for which genetic correlations were stronger than unique environmental correlations.
This indicates that shared genetic factors largely explain the significant associations between abdominal obesity and these serum metabolites.
5. Obesity-related Measures and Serum Metabolites: Detailed Phenotyping in the TwinFat Sub-sample
Sex- and age-adjusted phenotypic correlations between detailed obesity-related measures and serum metabolites in the TwinFat subsample are presented as a heatmap in Fig.
Overall (BMI, % body fat) and abdominal obesity (WC), android fat% and android to gynoid ratio correlated positively with an unfavorable lipoprotein profile (i.e. increased VLDL, IDL, LDL and small HDL particle concentrations, IDL- and LDL-C, triglycerides, ApoB and ApoB to ApoA1 ratio and reduced large HDL particle concentration, HDL-C and diameter).
The strongest correlations were observed between measures of abdominal obesity and small VLDL particle concentration, HDL diameter and triglycerides (r ≥ 0.4).
Measures of obesity were not significantly correlated with saturated fatty acids (SFAs) but positively with MUFAs (rmax = 0.45) and negatively.
ApoA1
BMI and measures of abdominal obesity correlated inversely with glutamine (rmax = −0.26).
Measures of insulin resistance (i.e. fasting insulin and HOMA-IR) showed similar associations to the metabolites as overall and abdominal obesity although many correlations were stronger (e.g. medium VLDL: r = 0.58, triglycerides: r = 0.56 and glycoprotein: r = 0.48–0.50).
The same analysis that was performed in individual twins was repeated using within-pair differences (Δ) in MZ twin pairs, thus being able to control for familial and genetic confounding.
The correlations between Δia fat and Δliver fat and ΔLDL particle concentrations, ΔLDL-C, ΔApoB, ΔApoB to ApoA1 ratio, ΔHDL diameter, Δacetoacetate, and Δ3-hydroxybutyrate were stronger than in individual-level analysis.
5.1. BMI-discordant Monozygotic Co-twins
The authors further studied the role of environmental factors by comparing the serummetabolites between 33 BMI-discordant MZ twin pairs.
In obese co-twins the authors indeed found significantly increased levels of thosemetabolites that correlated strongest with obesity measures above (ESM Table 2).
Obese co-twins had lower concentrations of HDL particles, HDL-C and a smaller HDL diameter.
The ratios HDL to LDL, ApoB to ApoA1 and PUFA to total FAs were lower in the obese than lean cotwins.
6. Discussion
Abdominal obesity, insulin resistance and low-grade inflammation were associated with higher concentrations of atherogenic lipoproteins, aromatic and BCAA, glycoprotein and lower concentrations of PUFAs and glutamine.
Previous twin studies have reported that genetic correlations are stronger between physiologically similar phenotypes, such as triglycerides and HDL-C, and weaker for other pairs of phenotypes, such as obesity and blood pressure [24,25] or obesity and lipid traits [15,26].
In the present analysis, the authors extend these earlier observations by quantifying the genetic and environmental factors that underlie abdominal obesity and a large number of metabolites, which are potential novel biomarkers of metabolic diseases.
The authors found that obesity and insulin resistance were associated with increased concentrations of MUFAs and lower concentrations of PUFAs, in particular omega-6 FAs and to a lesser extend omega-3 FAs.
Potential shortcomings of the study must also be acknowledged.
Author Contributions
JK, KHP and AR collected the data and recruited study subjects.
LHB and SK researched data and wrote the manuscript.
JR and AOA assisted in statistical analyses.
All authors reviewed and edited the manuscript and approved the final version of the manuscript.
KHP is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Acknowledgments
Data collection in FinnTwin16 and FinnTwin12 was supported by the National Institute of Alcohol Abuse and Alcoholism (grants AA-12502 and AA-09203 to R. J. Rose).
The Finnish Funding Agency for Technology and Innovation and the Strategic Research Funding from the University of Oulu to MAK.
Sigrid Juselius (MAK), Helsinki University Central Hospital (NL, AH, AR, KHP), Jenny and Antti Wihuri (LHB), Novo Nordisk (KHP), the Orion and Farmos Research (SK), Jalmari and Rauha Ahokas (LHB, KHP), Biomedicum Helsinki (LHB), 1.3 milj, also known as Grants from following Foundations.
Klubi-klubben (LHB), Ida Montin (SK), Paavo and Eila Salonen (SK), the Finnish Foundation for Cardiovascular Research (SK, KHP) and a grant from Central Finland Health District Science committee (SK).
Conflict of Interest
AJK, PS andMAK are shareholders of Brainshake Ltd, a startup company offering NMR-based metabolite profiling.
Total and regional fat distribution is strongly influenced by genetic factors in young and elderly twins.
Serum fatty acid composition predicts development of impaired fasting glycaemia and diabetes in middle-aged men.
TL;DR: The differential factors contributing to the metabolic heterogeneity of obesity are outlined by discussing the behavioral, genetic, phenotypical, and biological aspects associated with each of the two metabolic phenotypes (MHO and MUO) and their clinical implications.
Abstract: Obesity-related disease complications reduce life quality and expectancy and increase health-care costs. Some studies have suggested that obesity not always entails metabolic abnormalities and increased risk of cardiometabolic complications. Because of the lack of universally accepted criteria to identify metabolically healthy obesity (MHO), its prevalence varies widely among studies. Moreover, the prognostic value of MHO is hotly debated, mainly because it likely shifts gradually towards metabolically unhealthy obesity (MUO). In this review, we outline the differential factors contributing to the metabolic heterogeneity of obesity by discussing the behavioral, genetic, phenotypical, and biological aspects associated with each of the two metabolic phenotypes (MHO and MUO) of obesity and their clinical implications. Particular emphasis will be laid on the role of adipose tissue biology and function, including genetic determinants of body fat distribution, depot-specific fat metabolism, adipose tissue plasticity and, particularly, adipogenesis. Finally, the emerging role of gut microbiota in obesity and adipose tissue dysfunction as well as the search for novel biomarkers for the obesity-related metabolic traits and associated diseases will be briefly presented. A better understanding of the main determinants of a healthy metabolic status in obesity would allow promotion of this favorable condition by targeting the relevant pathways.
TL;DR: An analysis of mortality in the older cohort from 1975 to 2009 indicates that the mortality of adult twins (as individuals) does not differ from the population at large.
Abstract: In 2002 and 2006, review papers have described the Finnish Twin Cohort and studies conducted on these population-based, longitudinal data sets with extensive follow-up data. Three cohorts have been established: the older twin cohort in the 1970s, and the Finntwin12 and Finntwin16 studies initiated in the 1990s. The present review provides on update on the latest data collections conducted since the previous review. These cover the fourth waves of data collection in the older cohort (twins born before 1958) and Finntwin12 (twins born 1983-1987). The fifth wave of data collection in Finntwin16 (twins born 1975-1979) also included assessments of their spouses/partners. An analysis of mortality in the older cohort from 1975 to 2009 indicates that the mortality of adult twins (as individuals) does not differ from the population at large. Based on the cohorts, many sub-studies with more detailed phenotyping and collection of omics data have been conducted or are in progress. We also contribute to numerous national and international collaborations.
TL;DR: It is found that BCAA is a useful biomarker for early detection of IR and later diabetic risk and factors influencing BCAA level can be divided into four parts: race, gender, dietary patterns, and gene variants.
Abstract: Recent studies have shown the positive association between increased circulating BCAAs (valine, leucine, and isoleucine) and insulin resistance (IR) in obese or diabetic patients. However, results seem to be controversial in different races, diets, and distinct tissues. Our aims were to evaluate the relationship between BCAA and IR as well as later diabetes risk and explore the phenotypic and genetic factors influencing BCAA level based on available studies. We performed systematic review, searching MEDLINE, EMASE, ClinicalTrials.gov, the Cochrane Library, and Web of Science from inception to March 2016. After selection, 23 studies including 20,091 participants were included. Based on current evidence, we found that BCAA is a useful biomarker for early detection of IR and later diabetic risk. Factors influencing BCAA level can be divided into four parts: race, gender, dietary patterns, and gene variants. These factors might not only contribute to the elevated BCAA level but also show obvious associations with insulin resistance. Genes related to BCAA catabolism might serve as potential targets for the treatment of IR associated metabolic disorders. Moreover, these factors should be controlled properly during study design and data analysis. In the future, more large-scale studies with elaborate design addressing BCAA and IR are required.
125 citations
Cites background from "Abdominal obesity and circulating m..."
...[25] consisting of 286 subjects (MZ: 136, DZ: 150) showedHOMA-IR correlated significantly with higher valine, leucine, and aromatic amino acids (AAA) as well as lipid profiles (r = 0....
TL;DR: Observations are necessary to interpret data gathered by genome-wide association studies of polymorphisms and DNA methylation in MZ twins to provide new insights into the mechanisms of disease discordance in twins beyond strong associations such as those with HLA alleles.
Abstract: Autoimmunity and chronic inflammation recognize numerous shared factors and, as a result, the resulting diseases frequently coexist in the same patients or respond to the same treatments. Among the convenient truths of autoimmune and chronic inflammatory diseases, there is now agreement that these are complex conditions in which the individual genetic predisposition provides a rate of heritability. The concordance rates in monozygotic and dizygotic twins allows to estimate the weight of the environment in determining disease susceptibility, despite recent data supporting that only a minority of immune markers depend on hereditary factors. Concordance rates in monozygotic and dizygotic twins should be evaluated over an observation period to minimize the risk of false negatives and this is well represented by type I diabetes mellitus. Further, concordance rates in monozygotic twins should be compared to those in dizygotic twins, which share 50% of their genes, as in regular siblings, but also young-age environmental factors. Twin studies have been extensively performed in several autoimmune conditions and cumulatively suggest that some diseases, i.e. celiac disease and psoriasis, are highly genetically determined, while rheumatoid arthritis or systemic sclerosis have a limited role for genetics. These observations are necessary to interpret data gathered by genome-wide association studies of polymorphisms and DNA methylation in MZ twins. New high-throughput technological platforms are awaited to provide new insights into the mechanisms of disease discordance in twins beyond strong associations such as those with HLA alleles.
TL;DR: This review substantiates the understanding that tissue‐specific dysfunction of the BCAA‐catabolic enzymes and accumulating intermediary metabolites could act as better surrogates of metabolic imbalances, highlighting the biochemical communication among the nutrient triad of BCAAs, glucose, and fatty acid.
Abstract: Beyond their contribution as fundamental building blocks of life, branched-chain amino acids (BCAAs) play a critical role in physiologic and pathologic processes. Importantly, BCAAs are associated with insulin resistance, obesity, cardiovascular disease, and genetic disorders. However, several metabolome-wide studies in recent years could not attribute alterations in systemic BCAAs as the sole driver of endocrine perturbations, suggesting that a snapshot of global BCAA changes does not always reveal the underlying modifications. Because enzymes catabolizing BCAAs have a unique distribution, it is plausible that the tissue-specific roles of BCAA-catabolic enzymes could precipitate changes in systemic BCAA levels, flux, and action. We review the genetic and pharmacological approaches dissecting the role of BCAA-catabolic enzyme dysfunctions. We summarized emerging evidence on BCAA metabolic intermediates, tissue specificity of BCAA-catabolizing enzymes, and crosstalk between different metabolites in driving metabolic maladaptation in health and pathology. This review substantiates the understanding that tissue-specific dysfunction of the BCAA-catabolic enzymes and accumulating intermediary metabolites could act as better surrogates of metabolic imbalances, highlighting the biochemical communication among the nutrient triad of BCAAs, glucose, and fatty acid.-Biswas, D., Duffley, L., Pulinilkunnil, T. Role of branched-chain amino acid-catabolizing enzymes in intertissue signaling, metabolic remodeling, and energy homeostasis.
TL;DR: The correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
Abstract: The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
29,217 citations
"Abdominal obesity and circulating m..." refers background in this paper
TL;DR: Findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.
Abstract: Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics) We investigated whether metabolite profiles could predict the development of diabetes Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS) Cases and controls were matched for age, body mass index and fasting glucose Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile) The results were replicated in an independent, prospective cohort These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment
2,487 citations
"Abdominal obesity and circulating m..." refers background in this paper
...[7] Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al....
[...]
...It has been known for many years that obesity is associated with an increase in these amino acids [7,40]....
[...]
...More recently, the focus has widened to cover a more global serum metabolomics profile, which has predicted the incidence of cardiovascular events [6], type 2 diabetes [7] and allcause mortality [8] in prospective cohort studies....
TL;DR: WC, and not BMI, explains obesity-related health risk; for a given WC value, overweight and obese persons and normal-weight persons have comparable health risks, however, when WC is dichotomized as normal or high, BMI remains a significant predictor of health risk.
Abstract: Background: The addition of waist circumference (WC) to body mass index (BMI; in kg/m 2 ) predicts a greater variance in health risk than does BMI alone; however, whether the reverse is true is not known. Objective: We evaluated whether BMI adds to the predictive power of WC in assessing obesity-related comorbidity. Design: Subjects were 14 924 adult participants in the third National Health and Nutrition Examination Survey, grouped into categories of BMI and WC in accordance with the National Institutes of Health cutoffs. Odds ratios for hypertension, dyslipidemia, and the metabolic syndrome were compared for overweight and class I obese BMI categories and the normal-weight category before and after adjustment for WC. BMI and WC were also included in the same regression model as continuous variables for prediction of the metabolic disorders. Results: With few exceptions, overweight and obese subjects were more likely to have hypertension, dyslipidemia, and the metabolic syndrome than were normal-weight subjects. After adjustment for WC category (normal or high), the odds of comorbidity, although attenuated, remained higher in overweight and obese subjects than in normal-weight subjects. However, after adjustment for WC as a continuous variable, the likelihood of hypertension, dyslipidemia, and the metabolic syndrome was similar in all groups. When WC and BMI were used as continuous variables in the same regression model, WC alone was a significant predictor of comorbidity. Conclusions: WC, and not BMI, explains obesity-related health risk. Thus, for a given WC value, overweight and obese persons and normal-weight persons have comparable health risks. However, when WC is dichotomized as normal or high, BMI remains a significant predictor of health risk. Am J Clin Nutr 2004;79: 379 – 84.
1,912 citations
"Abdominal obesity and circulating m..." refers background in this paper
...As previously reported for standard lipid measures [17,31], abdominal obesity and insulin resistance were the strongest correlates of an atherogenic lipoprotein profile including triglycerides and triglycerides in VLDL and IDL, LDL-C, IDL-C and ApoB, VLDL and LDL particle subclasses of all sizes as well as small HDL particle subclasses....
TL;DR: Although waist circumference is a better marker of abdominal fat accumulation than the body mass index, an elevated waistline alone is not sufficient to diagnose visceral obesity and it is proposed that an elevated fasting triglyceride concentration could represent a simple clinical marker of excess visceral/ectopic fat.
Abstract: There is currently substantial confusion between the conceptual definition of the metabolic syndrome and the clinical screening parameters and cut-off values proposed by various organizations (NCEP-ATP III, IDF, WHO, etc) to identify individuals with the metabolic syndrome. Although it is clear that in vivo insulin resistance is a key abnormality associated with an atherogenic, prothrombotic, and inflammatory profile which has been named by some the "metabolic syndrome" or by others "syndrome X" or "insulin resistance syndrome", it is more and more recognized that the most prevalent form of this constellation of metabolic abnormalities linked to insulin resistance is found in patients with abdominal obesity, especially with an excess of intra-abdominal or visceral adipose tissue. We have previously proposed that visceral obesity may represent a clinical intermediate phenotype reflecting the relative inability of subcutaneous adipose tissue to act as a protective metabolic sink for the clearance and storage of the extra energy derived from dietary triglycerides, leading to ectopic fat deposition in visceral adipose depots, skeletal muscle, liver, heart, etc. Thus, visceral obesity may partly be a marker of a dysmetabolic state and partly a cause of the metabolic syndrome. Although waist circumference is a better marker of abdominal fat accumulation than the body mass index, an elevated waistline alone is not sufficient to diagnose visceral obesity and we have proposed that an elevated fasting triglyceride concentration could represent, when waist circumference is increased, a simple clinical marker of excess visceral/ectopic fat. Finally, a clinical diagnosis of visceral obesity, insulin resistance, or of the metabolic syndrome is not sufficient to assess global risk of cardiovascular disease. To achieve this goal, physicians should first pay attention to the classical risk factors while also considering the additional risk resulting from the presence of abdominal obesity and the metabolic syndrome, such global risk being defined as cardiometabolic risk. (Arterioscler Thromb Vasc Biol 2008;28:1039-1049)
1,398 citations
"Abdominal obesity and circulating m..." refers background in this paper
...[47] Despres JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al....
[...]
...It has been suggested that gluteofemoral fat functions as a ‘metabolic sink’ to buffer excess energy, thereby preventing ectopic and visceral fat deposition [47]....
TL;DR: The WHR and waist circumference are independently associated with risk ofCHD in women and were independently strongly associated with increased risk of CHD also among women with a BMI of 25 kg/m2 or less.
Abstract: Context.—Obesity is a well-established risk factor
for coronary heart disease (CHD), but whether regional fat distribution
contributes independently to risk remains unclear.Objective.—To compare waist-hip ratio (WHR) and waist
circumference in determining risk of CHD in women.Design and Setting.—Prospective cohort study among US
female registered nurses participating in the Nurses' Health Study
conducted between 1986, when the nurses completed a questionnaire, and
follow-up in June 1994.Participants.—A total of 44,702 women aged 40 to 65
years who provided waist and hip circumferences and were free of prior
CHD, stroke, or cancer in 1986.Main Outcome Measures.—Incidence of CHD (nonfatal
myocardial infarction or CHD death).Results.—During 8 years of follow-up 320 CHD events (251
myocardial infarctions and 69 CHD deaths) were documented. Higher WHR
and greater waist circumference were independently associated with a
significantly increased age-adjusted risk of CHD. After adjusting for
body mass index (BMI) (defined as weight in kilograms divided by the
square of height in meters) and other cardiac risk factors, women with
a WHR of 0.88 or higher had a relative risk (RR) of 3.25 (95%
confidence interval [CI], 1.78-5.95) for CHD compared with women with
a WHR of less than 0.72. A waist circumference of 96.5 cm (38 in) or
more was associated with an RR of 3.06 (95% CI, 1.54-6.10). The WHR
and waist circumference were independently strongly associated with
increased risk of CHD also among women with a BMI of 25
kg/m2 or less. After adjustment for reported hypertension,
diabetes, and high cholesterol level, a WHR of 0.76 or higher or waist
circumference of 76.2 cm (30 in) or more was associated with more than
a 2-fold higher risk of CHD.Conclusions.—The WHR and waist circumference are
independently associated with risk of CHD in
women.
1,234 citations
"Abdominal obesity and circulating m..." refers background in this paper
...[5] Rexrode KM, Carey VJ, Hennekens CH,Walters EE, Colditz GA, Stampfer MJ, et al....
[...]
...Indeed, observational studies have shown that abdominal obesity, especially excess visceral [4,5], together with liver fat [3] accumulation is the main driver of cardiometabolic risk factors and disease independently of BMI....