Eur J Nutr. Among the factors that affect fst composition, aging may be the most frustrating. Misra A, Wasir JS, Pandey RM. Lancet—

Factors influencing body fat percentage -

In all four groups, waist circumference was more strongly correlated with abdominal fat, particularly the ip and retroperitoneal compartments, as measured by MRI, than was BMI. Spearman correlation coefficients r s of BMI and waist circumference with generalized and regional body fat.

In both Black and White men, percent total body fat correlated strongest with HOMA among the different risk factors. The same was true for Black and White women except that CRP was as highly correlated as HOMA. In general, body fat compartments were not more highly correlated univariately with risk factors than was percent total body fat except for abdominal sc fat in Black women.

Table 3 also gives partial correlation coefficients for each after adjustment for percent total body fat. Partial correlation coefficients for subcompartments of truncal fat often showed modest additional and significant positive correlations with risk factors after adjustment for percent total body fat.

In contrast, lower body fat was negatively correlated with risk factors when adjusted for percent total body fat. Univariate and partial correlations of percent body fat, simple measurements of body fat, and percent regional fat compartments with four metabolic risk factors. BMI and waist circumference correlated with risk factors at least as strongly as did percent total body fat, and they generally added incremental partial correlation after adjustment for percent total body fat.

For HOMA, waist-to-hip ratios did not show a stronger correlation with risk factors than did BMIs or waist circumferences except for systolic blood pressure in Black men and women.

Because of the widely held view that ip visceral adiposity has a powerful influence on metabolic risk factors, multiple regression analysis was performed to examine the effect of ip fat Tables 4 and 5 for men and women, respectively.

In this analysis, percent total body fat explained more of the variance in insulin levels than for other risk factors, except for CRP in women model 1. If all compartments of adipose tissue were to be equal in their metabolic activity, none of them would show an additional effect beyond that of percent total body fat.

In models 2—6, addition of one other body fat parameter often explained a significantly higher proportion of the variance in several of the risk factors. But to focus on ip fat, we added it as a second independent variable in model 6 and as a third predictor variable models 7—9.

For insulin and CRP, the contribution of percent ip fat was noted in some groups but was not as consistent or substantial. Multiple regression analysis of metabolic risk factors with body fat variables in men.

Model 1 has one independent variable, percent total fat. Models 2—6 have two independent variables, and models 7—9 have three independent variables.

The dependent variables were log transformed for the regression analysis. R b is reported as a percentage: R b × Multiple regression analysis of metabolic risk factors with body fat variables a in women.

Models 2—6 have two independent variables, and Models 7—9 have three independent variables. Although metabolic risk factors are continuous variables, for clinical purposes they often are presented as categorical variables 1. Therefore, in Fig.

In men, the median number of risk factors was not increased until the waist circumference exceeded cm. In women, additional risk factors appeared at 88 cm.

No significant difference in the relationship between number of risk factors and waist circumference was seen between the two races. Number of categorical metabolic risk factors according to waist circumference category in four groups.

This study aimed to place the relation between body fat content and distribution and metabolic risk factors on a quantitative basis according to gender and ethnicity.

The Results detail the findings; they will be generalized and translated clinically to the extent possible in this section. There is a prevailing view that Black women have more body fat than White women 34 — We found that Black women had significantly higher BMIs than did White women, but percentages of body fat were similar.

Higher BMIs in Black women may result from differences in fat-free mass e. muscle and bone. Mean BMIs were similar for White and Black men, yet percent total body fat was significantly higher in White men.

Thus, any higher prevalence of metabolic disorders, such as diabetes 38 , in the black population cannot be ascribed to a higher percent body fat. Well-known differences in percent body fat and fat distribution between genders were observed.

But importantly, both Black and White women on average had greater masses of total fat and truncal fat than did their male counterparts. Women, moreover, had approximately twice the lower body fat of men. These findings raise the possibility that women in general have a fat-storage capacity in sc adipose tissue exceeding that of men.

Total body fat percentage correlated positively with all studied risk factors, but most strongly for insulin resistance, shown by fasting insulin and HOMA, and for CRP in women. Still, less than one third of the variation in risk factor levels could be explained by percent total body fat.

Other influences therefore must modify the severity of metabolic risk factors even in the presence of excess body fat. Among these may be the distribution of body fat.

Upper body fat, especially truncal fat, is reported to worsen risk factors 3 — 5 , 7 — We found that after adjustment for percent total body fat, percent truncal fat indeed showed positive partial correlation with the risk factors. Thus, percent truncal fat acts on risk factors beyond percent total body fat.

Whether this incremental effect can be ascribed mainly to sc fat in the trunk or abdomen or to ip fat is disputed 3 — 5 , 7 — We observed that both abdominal sc fat and ip fat carried incremental prediction over total body fat, but the relative importance of the two could not be ascertained simply by partial correlations.

Because of the widely held view that ip fat is uniquely important for metabolic risk 5 , 6 , 18 , 23 — 25 , we examined regression models in which percent ip fat was added as second and third predictive variables. This suggests that ip fat in fact has predictive power beyond total fat and truncal sc fat.

For other risk factors, i. insulin levels and CRP, percent ip fat contributed independently in some groups but was not as consistent. These analyses reveal the complexity of attempting to ascribe independent predictive power to multiple collinear variables, but with this said, it appears that ip fat does impart independent prediction for some risk factors, especially dyslipidemia.

Even so, across the full range of risk-factor levels, all fat parameters together still account for a minority of variation in levels. A potentially important observation is that percent lower body fat generally was, independent of percent total fat, negatively correlated with risk factors in both men and women.

The mechanism of this seemingly protective effect is worthy of consideration. One intriguing possibility is that the presence of plentiful amounts of lower-body adipose tissue serves as a fat reservoir to guard against ectopic fat deposition in visceral depots, liver, and muscle.

Support for this concept comes from the Hoorn Study 40 , which observed that fat mass in the legs was negatively correlated with glucose and HOMA levels.

If lower body fat is protective, excess upper body fat may be a sign of a deficient lower-body fat reservoir and hence an indicator of predisposition of ectopic fat accumulation.

Measurement of waist circumference may be a simple means to determine whether adipose tissue stores are metabolically overloaded. An important finding in both Black and White men was that waist circumference was equally, if not more strongly, correlated with the other body fat parameters, including percent total body fat and body fat subcompartments than was BMI.

In men, waist circumference was highly correlated with truncal fat by DXA and with total abdominal fat by MRI but less well with ip fat. Nonetheless, for both Black and White men, waist circumference was generally superior to BMI as a measure of body fat content and body fat distribution.

In contrast, in women who in general have more lower-body fat, BMI was better than waist circumference as an indicator of percent total body fat, although not of abdominal fat compartments.

Overall, BMI was not a better predictor of risk factors than was waist circumference, particularly for dyslipidemia in which waist circumferences was more robust.

Waist-to-hip ratio did not predict risk factors better than waist circumference. For clinical purposes, waist circumference appears to be as good as if not better than body fat compartmentalization for evaluating metabolic risk in both men and women.

Both body fat contents and metabolic risk factors are continuous variables. Yet it must be noted that for all risk factors, body fat parameters accounted for less than one third of their variability. Such might denigrate the importance of body fat for metabolic risk.

On the other hand, these findings may be an incomplete picture. For example, for clinical purposes, both body fat measures and risk factors are typically expressed as categorical variables. This allows for easier identification of persons at higher risk.

The same was true for women, starting at a waist circumference of at least 88 cm. They also suggest that thresholds of obesity are required for metabolic risk factors to become clinically significant.

These findings support the currently recommended thresholds for defining abdominal obesity 1 , 2 in the United States. The authors acknowledge the valuable comments of Drs. Helen Hobbs and Jonathan Cohen on this manuscript. This work was supported by the Donald W.

Reynolds Cardiovascular Clinical Research Center at Dallas and General Clinical Research Center Grant MO1-RR, the Moss Heart Foundation, and a Veterans Affairs Merit grant.

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Influence of Body Fat Content and Distribution on Variation in Metabolic Risk. Gloria Lena Vega , Gloria Lena Vega. Reynolds Cardiovascular Clinical Research Center and the Department of Internal Medicine G.

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Defining the genetic architecture of the predisposition to obesity: a challenging but not insurmountable task external icon. Am J Clin Nutr ; Choquet H, Meyre D. Genetics of obesity: what have we learned?

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Gloria Lena Advanced weight loss supplements, Beverley Adams-Huet, Factrs Peshock, Fxt Willett, Brijen Shah, Scott M. Influencinv Several reports indicate that the body fat Factosr, especially prrcentage fat, predict metabolic risk better Factors influencing body fat percentage total body fat. Factors influencing body fat percentage objective of the influemcing was to determine whether this can be perentage and generalized throughout the population. Participants: A representative sample of Black and White women and men of the Dallas Heart Study participated in the study. Design: We measured the fat in total body, trunk, and lower body with dual-energy x-ray absorptiometry and in abdominal compartments sc, ip, and retroperitoneal with magnetic resonance imaging. Other measurements included body mass index BMIwaist circumference, blood pressure, plasma lipids, glucose, insulin including homeostasis modeland C-reactive protein. Results: In all groups, total body fat correlated positively with key metabolic risk factors, i. You know that body Factors influencing body fat percentage percentage is a better marker of health than weight alone. Learn the Lean Muscle Diet Factorz affect body composition, Creatine for improving memory a few that are, Fators, not rat your control. Despite the negative associations, body fat is essential-we need a certain amount for proper hormone function and to protect our organs and provide insulation. Yet a higher percentage of body fat can come with health risks. Understand your total body composition and the factors that affect it so you can take steps to stay healthy.