SBP illnesse blood pressure, LDL low-density lipoprotein, TG triglycerides. McArdle, William D. Less favorable body composition and adipokines in South Asians compared with other US ethnic groups: results from the MASALA and MESA studies.

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Sign up to receive industry and product news. Payment Methods. Follow Us. Linkedin Facebook Instagram Youtube. LBWEB Terms of Service for End User. Calculate BMI by dividing weight in pounds lbs by height in inches in squared and multiplying by a conversion factor of For adults 20 years old and older, BMI is interpreted using standard weight status categories.

These categories are the same for men and women of all body types and ages. BMI is interpreted differently for children and teens, even though it is calculated using the same formula as adult BMI.

The CDC BMI-for-age growth charts take into account these differences and visually show BMI as a percentile ranking. These percentiles were determined using representative data of the US population of 2- to year-olds that was collected in various surveys from to Obesity among 2- to year-olds is defined as a BMI at or above the 95 th percentile of children of the same age and sex in this to reference population.

For example, a year-old boy of average height 56 inches who weighs pounds would have a BMI of For more information and to access the CDC Growth Charts. For adults, the interpretation of BMI does not depend on sex or age.

Read more about interpreting adult BMI. The correlation between the BMI and body fatness is fairly strong 1,2,3,7 , but even if two people have the same BMI, their level of body fatness may differ The accuracy of BMI as an indicator of body fatness also appears to be higher in persons with higher levels of BMI and body fatness While, a person with a very high BMI e.

According to the BMI weight status categories, anyone with a BMI between 25 and However, athletes may have a high BMI because of increased muscularity rather than increased body fatness. In general, a person who has a high BMI is likely to have body fatness and would be considered to be overweight or obese, but this may not apply to athletes.

People who have obesity are at increased risk for many diseases and health conditions, including the following: 10, 17, For more information about these and other health problems associated with obesity, visit Health Effects.

A comparison of the Slaughter skinfold-thickness equations and BMI in predicting body fatness and cardiovascular disease risk factor levels in children. et al. Body fat throughout childhood in healthy Danish children: agreement of BMI, waist circumference, skinfolds with dual X-ray absorptiometry.

Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Comparison of dual-energy x-ray absorptiometric and anthropometric measures of adiposity in relation to adiposity-related biologic factors.

Association between general and central adiposity in childhood, and change in these, with cardiovascular risk factors in adolescence: prospective cohort study. BMJ , , p. Estimates of excess deaths associated with body mass index and other anthropometric variables.

Relation of body mass index and skinfold thicknesses to cardiovascular disease risk factors in children: the Bogalusa Heart Study. Comparison of bioelectrical impedance and BMI in predicting obesity-related medical conditions. Silver Spring , 14 3 , pp. Managing Overweight and Obesity in Adults: Systematic Evidence Review from the Obesity Expert Panel [PDF — 5.

Vital Health Stat. Beyond body mass index. To assess the independent relevance of body composition measures, models of WC were additionally adjusted for BMI, and models of fat mass and appendicular lean mass were mutually adjusted.

There were no violations of model assumptions. Analyses were conducted using Stata version 15 Stata Corp, TX, United States and figures were constructed using R 3. The mean age was Similar to women, Chinese men had the lowest fat mass For appendicular lean mass, small differences were reported across ethnic groups in TMC, although Indian men and women had the lowest means.

Fat mass for a given BMI was generally equivalent across ethnicities for women Fig. Adjusted means of fat mass, lean mass and waist circumference by body mass index BMI deciles across ethnicities, adjusted for age and height lean mass only.

Small increases in LDL-C were similar across all male ethnic groups, but strongest in Chinese women 0. The association of BMI with triglycerides was notably weaker in both Indian men and women compared to the other groups ~0.

Chinese and Malay men and Indian women reported similarly strong associations between BMI and HbA1c ~0. SBP systolic blood pressure, LDL low-density lipoprotein, TG triglycerides.

Associations are fully adjusted for age, height, education, physical activity, smoking status, alcohol intake. However, the absolute mean changes were marginally weaker between fat mass and SBP than for BMI e. Conversely, the average increase in mean LDL-C was nearly twice as strong for fat mass as for BMI for most ethnic groups e.

Associations are fully adjusted for age, height, education, physical activity, smoking status, alcohol intake and lean mass. Associations between appendicular lean mass adjusted for fat mass and cardiovascular risk factors Fig.

Higher appendicular lean mass was positively associated with triglycerides and inversely associated with LDL-C to a similar extent across all sex and ethnic groups, except for White women.

Associations between appendicular lean mass and HbA1c were null for most sex and ethnic groups except for Malay 0. Associations are fully adjusted for age, height, education, physical activity, smoking status, alcohol intake and fat mass.

After mutually adjusting for BMI, however, the associations between WC and SBP were largely or wholly attenuated for all ethnic groups Fig. Associations between WC, LDL-C and triglycerides were not substantively affected by adjustment for BMI.

However, adjustment for BMI had diverse effects on the associations between WC with HbA1c across sex and ethnic groups. Associations were wholly attenuated for Chinese participants, partly attenuated for Malay participants, and strengthened for Indian men but unaffected for Indian women.

Overall, fully adjusted associations between WC, SBP and lipids tended to be strongest in the Chinese groups and weakest in the Indian groups, whereas this pattern was reversed for HbA1c. Models are presented without and with mutual adjustment for body mass index. In the largest ethnic comparison of adiposity, body composition and cardiovascular risk factors study to date, we observed distinctly different patterns with CVD risk factors across ethnic groups despite generally small differences in body composition at a given BMI.

BMI and fat mass had similar positive associations with SBP and HbA1c although stronger overall in Malaysian ethnicities than White ; but the associations with lipids were generally stronger for fat mass.

A notable exception was for Indian men and women for whom there was little association of either BMI or fat mass with triglycerides. Contrasting associations across CVD risk factors were observed for appendicular lean mass, with no evidence in men of differences across ethnic groups.

However, among women, associations with appendicular lean mass were particularly strong in Malay and Indian women, with positive associations that were greater than those for fat mass or BMI.

Adjustment for BMI did not impact associations between WC and lipids, but it largely attenuated associations with SBP and produced diverse effects on associations with HbA1c across the sex- and ethnic-groups. Previous research has documented different obesity-related risks across ethnic groups, with South Asians generally at a higher risk for diabetes but a lower risk for CVD than Caucasians at similar levels of BMI [ 2 , 3 , 12 ].

BMI has been criticised as a measure of adiposity since it does not indicate potentially important characteristics of body composition for disease risk, such as the proportion of fat and lean mass, or fat distribution [ 13 , 14 ].

However, this study observed distinctly different patterns of body composition and CVD risk factors across ethnic groups despite generally small differences in body composition at a given BMI.

Other research has also reported that Chinese men had stronger relationships with SBP, fasting glucose and blood lipids than White men for a given BMI, suggesting they were more prone to the metabolic effects of obesity [ 15 ]. Interestingly, the strong relationship between BMI and SBP for the Chinese in this study was still weaker than associations reported from large-scale studies of Chinese adults from mainland China 8.

Even though BMI as a measure of adiposity has been criticised for failing to distinguish between types of tissue mass, ethnic comparisons showed broadly similar patterns for fat mass and BMI although lipids were slightly more strongly associated with fat mass. Conversely, associations with appendicular lean mass were distinct from those reported with BMI and not consistently beneficial.

The positive association between lean mass and SBP has been documented before across White and Non-White ethnicities, but this study reported a novel finding that in Malay and Indian women the deleterious associations of SBP, triglycerides and HbA1c with appendicular lean mass were generally stronger than those with BMI or fat mass [ 18 , 19 ].

Previous research on a Malay population in Malaysia found higher metabolic risks at lower levels of BMI and WC than recommended by international diagnostic criterion, suggesting other elements of body composition were important for metabolic risk [ 20 ]. Current evidence is equivocal regarding the role of lean mass in cardiometabolic health, with large prospective studies reporting both increased and decreased risks of incident CVD with greater lean mass [ 14 , 21 , 22 ].

Theories suggest that muscle tissue is the main depot for glucose uptake and clearance, entailing that greater lean mass should improve insulin sensitivity.

However, meta-analyses of resistance training interventions in participants with diabetes indicated that improvements in glycaemic control were seen alongside improvements in strength, without gains in absolute lean mass [ 21 , 23 ].

This suggests future studies need to look more closely at muscle quality in relation to cardiovascular health, such as fibre typology and fat accumulation, particularly as previous research has reported that south Asians may have higher intermuscular fat than BMI-matched White or Chinese groups [ 21 , 24 ].

Differences in muscle quality may further differ by sex-specific ethnic groups, given the particularly strong associations of lean mass with triglycerides and HbA1c for Malay and Indian women in this study.

This could be an important source of heterogeneity for metabolic health that needs to be examined. Another novel finding from this study was that associations of WC with HbA1c were largely attenuated by adjustment for BMI in Chinese adults, but were less affected in the Malay and were strengthened in Indian men.

Few studies have compared associations of general and central adiposity across ethnicities. One study on adults from different ethnicities in the London SABRE study found that central adiposity, particularly visceral adipose tissue, was a stronger risk factor for diabetes in south Asian than European men [ 25 ].

Likewise, Indian men and women in this study had the strongest associations between WC and HbA1c of any ethnic group. Such differences may be due to adipocyte morphology, with suggestions that south Asians may have a lower capacity to store fat in subcutaneous fat depots, so excess fat more readily overflows into ectopic compartments that increase metabolic impairment [ 26 ].

However, this theory contradicts the markedly weaker relationships between adiposity and triglycerides for Indian men and women in this study, as an increase in liver fat accumulation is often accompanied by elevated triglycerides [ 27 ]. Such weak associations are also interesting as elevated triglycerides are generally associated with insulin resistance and diabetes, with Indian adults reporting elevated risks of both compared to other ethnicities [ 28 , 29 ].

In the future, incorporation of genetic data would help elucidate the independent relevance of different anthropometric and body composition measures across ethnic groups.

For example, a previous sub-study in TMC suggested there was a gradient in genetic risk scores for type II diabetes across ordered strata of BMI, with the genetic risk score having progressively larger effects across decreasing levels of BMI.

However, that study was too small to detect differences across ethnic groups and genetic evidence in multi-ethnic populations for other measures of body composition like ectopic fat is currently lacking [ 30 , 31 ]. A clear strength of this research is that it is the largest study to date with global multi-ethnic comparisons of detailed measures of body composition and cardiovascular risk factors, so chance findings due to small sample sizes between ethnic- and sex- specific groups is less likely.

Furthermore, the Malaysian and UK studies began recruitment around the same time, and had harmonised measurements on many covariates.

TMC collected fasting blood samples from their participants, whereas UK Biobank did not, which limits the comparisons of lipids between the two studies, although it still allows for comparisons within TMC. Additionally, while both studies assessed body composition using BIA, this was done using two different models, each with their own algorithms for estimating fat and lean mass.

However, since the two different BIA models were not able to be calibrated to a gold standard measure in this study, any inferences on body composition should be limited to within-cohort comparisons.

Furthermore, neither study was able to adjust the associations with BIA for hydration status, a key factor that can impact the measurements [ 32 ].

Future research should investigate if more detailed measurements of body composition across ethnicities, such as those from DXA, would produce similar associations. The data used in this study was cross-sectional, so temporality and causality cannot be inferred. Even though a comprehensive list of prevalent diseases were excluded in both datasets to limit reverse causality, there is still the possibility that prevalent subclinical disease could be influencing both CVD risk factors and body composition when measurements were taken.

Residual confounding is also possible due to both unmeasured confounders and to errors within measured confounders e. In particular, dietary intake was not adjusted for, but since sequential adjustment for other lifestyle factors had little impact on the associations data not shown , it is unlikely that adjustment for dietary intake would have made a substantive impact.

Different relationships with body composition could also be due to environmental differences between and within countries for ethnic groups. Overall, this study observed distinctly different patterns of adiposity and body composition with CVD risk factors across ethnic groups despite generally small differences in body composition at a given BMI.

Chinese men and women had a smaller BMI and less fat mass, but the strongest associations with many risk factors.

Meanwhile, Indian participants reported the strongest relationships between WC and HbA1c, particularly after adjustment for BMI, but notably weak associations between adiposity and triglycerides. There were consistently weak associations with appendicular lean mass across male ethnic groups, but positive relationships between lean mass and several risk factors were stronger in Malay and Indian women than for BMI.

Despite these distinct patterns across ethnic groups, it is still not clear why marked differences in the risks for diabetes or CVD for a given BMI have been observed in different ethnic groups. The limitations of BIA and the as-yet unclear mechanisms linking aspects of body composition to cardiovascular disease suggest that more detailed measurements of regional fat and lean mass across ethnicities needs to be undertaken.

Only once the mechanisms linking adiposity and body composition with disease aetiology are better understood can we start to engage with more targeted prevention strategies to help attenuate the increasing global burden of obesity-associated diseases.

The global burden of obesity-related disease has been increasing over the last three decades, but the metabolic risks associated with adiposity differ between populations and are not completely understood.

PubMed was searched for all papers up to July containing words related to 1 adiposity or body composition e. Studies were excluded if they studied children, adolescents, or elderly populations; and if they focused on weight maintenance, weight management or weight reduction.

No large-scale studies compared relative associations between ethnicities regarding anthropometry and body composition and cardiovascular disease CVD risk factors.

In the largest comparison to date of global multi-ethnic populations; with harmonised data on over 30, Malay, 25, Chinese, 10, Indian and , White Europeans; unique insights into metabolic health were observed. Chinese participants had lower absolute levels of adiposity but generally stronger deleterious relationships to CVD risk factors than Malay, Indian or White participants.

Those of Indian descent had markedly weaker relationships between adiposity and triglycerides, but the strongest relationship between waist circumference and HbA1c. Associations with appendicular lean mass were not consistently beneficial, particularly for Malay and Indian women, among whom there were positive relationships with systolic blood pressure, triglycerides and HbA1c that were stronger than those for BMI.

There were distinct patterns in adiposity and body composition and CVD risk factors across sex and ethnic groups that do not explain observed variation in CVD rates across populations.

The unclear mechanisms linking body composition to cardiovascular disease risk suggest that more detailed measurements of regional fat and lean mass across ethnicities needs to be undertaken. Only once the mechanisms underlying associations of adiposity and body composition with CVD are better understood can we start to engage with appropriately targeted prevention strategies to attenuate the increasing global burden of disease from obesity.

All results from this analysis are returned to UK Biobank within 6 months of publication, at which point they can be made available to other researchers upon reasonable request.

Data analysis in TMC is done by staff at Universiti Kebangsaan Malaysia but relevant tables and analytic code can be shared with researchers upon reasonable request.

The statistical analysis plan and analytic code are available upon request to the corresponding author. The GBD Obesity Collaborators. Health effects of overweight and obesity in Countries over 25 Years. N Engl J Med. Article Google Scholar. Jamal R, Syed Zakaria SZ, Kamaruddin MA, Abd Jalal N, Ismail N, Mohd Kamil N, et al.

Cohort profile: the Malaysian Cohort TMC project: a prospective study of non-communicable diseases in a multi-ethnic population. Int J Epidemiol.

Article PubMed Google Scholar. Gajalakshmi V, Lacey B, Kanimozhi V, Sherliker P, Peto R, Lewington S. Lancet Glob Health.

Article PubMed PubMed Central Google Scholar. Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of prospective studies in four continents.

Kurpad AV, Varadharajan KS, Aeberli I. The thin-fat phenotype and global metabolic disease risk. Curr Opin Clin Nutr Metab Care. UK Biobank. Body Composition Measurement.

Accessed 14th March Bosy-Westphal A, Müller MJ. Identification of skeletal muscle mass depletion across age and BMI groups in health and disease—there is need for a unified definition.

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