### Waist circumference and body shape -

Work with a primary care provider to determine the best method for you to reduce your waist circumference if you're over the recommended guidelines. There are many measures of overall health and wellness. Waist circumference happens to be just one. It is not the be-all, end-all metric, but it can be a helpful clue in determining your long-term health.

If you are concerned about your waist measurement, consult with a healthcare provider about safe ways to lose weight and reduce your risk of chronic disease. Smith U. Abdominal obesity: a marker of ectopic fat accumulation. J Clin Invest. Centers for Disease Control and Prevention.

Assessing your weight. Hajian-Tilaki K, Heidari B. Is waist circumference a better predictor of diabetes than body mass index or waist-to-height ratio in Iranian adults?

Int J Prev Med. Gutin I. In BMI We Trust: Reframing the Body Mass Index as a Measure of Health. Soc Theory Health. Ross R, Neeland IJ, Yamashita S, et al.

Waist circumference as a vital sign in clinical practice: A consensus statement from the IAS and ICCR working group on visceral obesity.

Nat Rev Endocrinol. American Heart Association. About metabolic syndrome. The Diabetes Prevention Program Research Group. Long-term safety, tolerability, and weight loss associated with metformin in the diabetes prevention program outcomes study.

Diabetes Care. American Diabetes Association. Healthy weight loss. Feller S, Boeing H, Pischon T. Body mass index, waist circumference, and the risk of type 2 diabetes mellitus: implications for routine clinical practice.

Dtsch Arztebl Int. National Institute of Diabetes and Digestive and Kidney Disorders. Weight-control information network. Okauchi Y, Kishida K, Funahashi T, et al. Changes in serum adiponectin concentrations correlate with changes in BMI, waist circumference, and estimated visceral fat area in middle-aged general population.

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Read more on Heart Foundation website. Together, body mass index BMI and waist size can help work out whether your weight is within the healthy range and whether you are at risk of some chronic conditions. Find out what each means and how to use them.

Read more on Department of Health and Aged Care website. Find out how much weight you should expect to gain at each stage of pregnancy, based on your BMI, and tips on what to eat and how to exercise while pregnant.

Read more on Queensland Health website. Obesity is a major issue in our society, but exercise must be carefully managed. Find out how here. Read more on Exercise and Sports Science Australia ESSA website. Read more on Better Health Channel website. Duromine is a weight loss medicine containing phentermine.

It is used in conjunction with diet and exercise in people who are obese. Read more on myVMC — Virtual Medical Centre website. As your baby grows, you will gain weight. How much you gain depends on your weight before pregnancy.

Lean more about healthy weight gain in pregnancy. Overweight and obesity is a public health issue and major risk factor for ill-health, including heart disease, type 2 diabetes and some cancers.

Fat is stored throughout the body and that it produces chemicals and hormones which can be toxic to the body. View our facts on toxic fate to find out more. Read more on LiveLighter website. Being a healthy weight is important to prevent liver disease and, if you already have disease, to prevent it getting worse.

If you have fatty liver disease, one of the best things you can do is lose weight. Read more on Liver Foundation website. Read more on Ausmed Education website. Read more on Baker Heart and Diabetes Institute website. Read more on RACGP - The Royal Australian College of General Practitioners website.

Reproduced with permission from The Royal Australian College of General Practitioners. Diabetes is a group of disorders and the 10th leading cause of deaths in Australia.. Authors' conclusions: There is moderate quality evidence that aquatic exercise may have small, short-term, and clinically relevant effects on patient-reported pain, disability, and QoL in people with knee and hip OA.

Read more on Cochrane Australia website. Every woman of reproductive age should be considered for preconception care C.

Unplanned weight loss occurs when a client experiences an unintentional reduction in body mass. According to the National Aged Care Mandatory Quality Indicator Program, there are two categories of unplanned weight loss: significant unplanned weight loss and consecutive unplanned weight loss.

Authors' conclusions: The evidence suggests that KDs could demonstrate effectiveness in children with drug-resistant epilepsy, however, the evidence for the use of KDs in adults remains uncertain.

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For more information sgape PLOS Subject Areas, click here. Obesity, znd quantified in Carbohydrate metabolism and nutrition of Body Mass Index BMI exceeding threshold values, Carbohydrate metabolism and nutrition considered a leading cause of premature Waist circumference and body shape worldwide. Bpdy given body ahape BMIit is recognized that risk is Metabolism and blood sugar control affected by body shape, particularly as a marker of abdominal fat deposits. Waist circumference WC is used as a risk indicator supplementary to BMI, but the high correlation of WC with BMI makes it hard to isolate the added value of WC. We considered a USA population sample of 14, non-pregnant adults from the National Health and Nutrition Examination Survey NHANES — with follow-up for mortality averaging 5 yr deaths. We developed A Body Shape Index ABSI based on WC adjusted for height and weight:. ABSI had little correlation with height, weight, or BMI.### Waist circumference and body shape -

To determine your waist circumference, measure around the smallest part of your waist, or about one inch above your navel. For your hip circumference, measure the widest part of your hip. Divide the waist measure by the hip measure to get your waist-to-hip ratio WHR.

Health risk: Most of the fat in an apple-shaped person is distributed around the internal organs in the abdomen area. This can be associated with a greater risk of heart disease, diabetes and stroke.

If you are a pear type, your hip section is likely to be wider than your upper body, with most of the fat deposited around your thighs, hips and buttocks region.

Health risk: Having a pear body shape indicates a lower metabolic risk compared to an apple body shape. Maintain a well-balanced diet that is appropriate to sustain a healthy body weight. Cut back on saturated fats found in high-fat dairy and red meat, and hydrogenated oils found in processed foods.

Instead, opt for a low-fat diet that includes low-fat dairy foods such as milk, yogurt and cottage cheese. This can help break down stubborn fat cells in your hips and thighs.

Do regular exercise of moderate intensity at least minutes a day, at least five times per week. See the previous page to learn what is the ideal waist size health-wise for men and women. What Causes Middle Age Weight Gain? Unlock Your Body's Fat Burning Potential.

Which Exercise Burns More Fat? Resistance Training or Aerobics? Tips to Boost Metabolism. Complete Guide to Healthy Weight Loss. Brisk Walking: How to Do It Right and Effectively.

Font size. News Video. Set font size. How Body Shape Affects Your Health Risk. How Body Shape Affects Your Health Risk Your body shape can provide an insight into your health risks. Dr Tan Hong Chang, Senior Consultant from the Department of Endocrinology at Singapore General Hospital SGH , explains why waist size matters.

Total Shares. Determining your body shape type and health risks Your body shape can provide you with insight into your health risks. Apple-shaped body — WHR above 0. Pear-shaped body — WHR below 0. Tips on how to influence your body shape type Your body shape is determined by a number of factors.

Approximating the obtained regression coefficients with ratios of small integers, we have, 2. We defined A Body Shape Index ABSI to be proportional to the ratio of actual WC to the WC expected from the regression allometry: 3.

The sample mean and standard deviation of ABSI thus defined is. Correlation coefficients of ABSI with height, weight, BMI and WC in the NHANES sample are shown in Table 1. It can be seen that most variability in WC reflects variability in BMI and that unlike BMI, WC also has some correlation with height , consistent with earlier findings [24] — [26].

On the other hand, ABSI shows little correlation with height, weight, or BMI. Its correlation with WC is modest , since most variability in WC is correlated with BMI and therefore excluded from ABSI. To control for age and sex differences in mean ABSI, we entered it into proportional hazards regression for mortality as a z score: 4 where the population ABSI mean and standard deviation depend on age and sex.

To estimate and , we first computed the sample mean and standard deviation for each age, separately for males and females and using the NHANES sample weights markers in Figure 1a-b. Then we smoothed the and curves for each sex using Tikhonov regularization with a regularization matrix that approximates a second derivative operator and a regularization parameter chosen so that the mean square residual between the curve and the sample values, scaled by the estimated standard error of the sample values, is equal to 1 [27].

These smoothed values curves in Figure 1 were used for converting ABSI to z scores following Eq. Individuals age 85 and over for whom the exact age was not available were not included in the smoothing, and their ABSI values were converted to z scores using the sample mean and standard deviation asterisks near right edges of panels in Figure 1.

The age and sex specific and used for computing ABSI z scores are tabulated as Table S1. Markers show the sample quantities for each age; the smooth curves shown were used to convert values to z scores. Units are for ABSI, for BMI, and for WC. Mean ABSI increased steadily from midlife into old age Figure 1a.

Mean ABSI was consistently higher in males than females after young adulthood Figure 1a , while the scatter in ABSI at a given age was greater in females than in males Figure 1b.

The age- and sex-specific BMI and WC means Figure 1c,e , calculated using the same approach, showed different behavior than ABSI, falling after about age Mean WC was higher in males while mean BMI was higher in females, consistent with the higher mean ABSI in males compared to females.

As with ABSI, variability in BMI and WC was higher in females than in males Figure 1d,f. To quantify the association of baseline ABSI with death rate, we employed Cox proportional hazard modeling for mortality with age as the time scale [28].

In this approach, log death rate is modeled as a nonparametric function of age plus fitted coefficients that multiply the values of predictors, such as baseline ABSI. Predictors may be entered as continuous variables or discretized into two or more categories such as quantiles of ABSI , depending on their nature and the desired model.

ABSI and the other anthropometric variables BMI, WC were entered as z scores relative to age- and sex-specific normals, obtained as described above, to avoid confounding by age and sex differences in body size and shape.

Two types of models were employed. Comparing the unadjusted and adjusted model coefficients showed to what extent the mortality risk associated with higher ABSI, BMI, or WC changed when these other risk factors are controlled for. The additional factors considered were sex, ethnicity, smoking status, presence of diabetes, blood pressure, and serum cholesterol.

Sex, ethnicity, smoking status, and presence of diabetes were all entered as binary variables. Ethnicity was entered as 1 for blacks and 0 for all others, since we found that blacks had significantly elevated death rates compared to the other four ethnicities, whose death rates were not significantly different from each other.

Systolic and diastolic blood pressure and total and HDL cholesterol levels were each entered as z scores relative to age- and sex-specific normals, obtained as described for ABSI. Because not all individuals with anthropometric measurements also had the other data needed for the adjusted analysis, the unadjusted analyses were run twice — once for the full sample with available anthropometry and once restricted to the sample used for the adjusted analysis.

We also determined the mortality risk associated with ABSI, BMI, and WC for subgroups of the NHANES sample, in order to test the robustness and range of applicability of coefficients determined for the entire sample. Subgroups included males and females; people younger and older than 65 yr at baseline; the three largest ethnic groupings whites, blacks, and Mexicans ; and people with BMI above and below the age- and sex-specific mean.

As another check of whether these attributes impact the association with mortality, we checked the significance in the proportional hazard model of interaction terms of ABSI BMI, WC with sex, age, ethnicity variables white, black, or Mexican , and BMI.

To address the question of whether ABSI predicts medium-term as compared to short-term mortality, we conducted an additional analysis where the modeled follow-up period started 3 yr after the baseline, thus excluding from consideration all deaths within 3 yr of examination.

In Cox proportional hazard modeling, the relationship between hazard here, death rate and continuous variables, such as ABSI here, is most commonly estimated on the assumption that the logarithm of the hazard is a linear function of the variable; this yields a single regression coefficient that summarizes the strength of the relationship between the variable and log hazard.

A recommended test of this linearity assumption is to fit an alternative model where the dependence of log hazard on the variable is described by a smoothing spline, with the degree of smoothing determined to optimize the Akaike Information Criterion [29] , [30].

Linearity is rejected if the nonlinear terms in the fitted smoothing spline are different from zero with low p value. Our testing showed that the linearity assumption did not hold for ABSI, BMI or WC. In showing results from the models described above, we retained the linearity assumption for all three variables to facilitate comparing mortality hazards across populations and population subgroups.

In separate analyses, we also fit smoothing splines to the association with mortality risk of ABSI, BMI, and WC in order to visualize it as accurately as possible.

To quantify in a simpler form the nonlinear relationship between ABSI BMI, WC and log mortality, we also carried out analyses where risk was computed separately for each quintile of the ABSI BMI, WC z score, relative to the middle quintile. A measure of the fraction of the total population mortality hazard predicted by high values of ABSI BMI, WC was calculated as.

Uncertainty in this expression was approximated as being due only to uncertainty in the numerator. While converting variables to z scores before entering them into a hazard regression model may be methodologically preferable given the nonlinear effects of age and sex on mean ABSI, BMI, and WC Figure 1 , we also conducted the same proportional hazard modeling using the original variables, rather than z scores, as predictors.

For these analyses, sex was included as a predictor even for the unadjusted models, in order to control for the sex differences in ABSI, BMI, and WC distributions. Proportional hazard modeling, including differential sample weighting and adjustment for the cluster survey design, was carried out using the survey package in the computer language R [31].

For all analyses, two-tailed was taken as the threshold for statistical significance. Table 2 shows the impact of ABSI z score, as a continuous variable, on death rate, along with results for BMI and WC z scores.

Because the proportional hazard regression coefficients for ABSI, BMI, and WC showed little impact — generally shifting by less than their standard error — from either restricting the sample to those with data for the other risk factors sex, ethnicity, smoking, diabetes, blood pressure, and serum cholesterol; middle column of Table 2 or from adjusting for the other risk factors right column of Table 2 , we carried out the analyses described below with unadjusted models.

Further, high ABSI predicts similar elevation of relative mortality hazard for younger age yr at baseline and older age yr individuals, with narrower confidence intervals for the older group because of their much higher absolute death rate over the follow-up period Table 3.

ABSI predicted mortality among individuals with above-mean BMI about as well as it did for individuals with below-mean BMI Table 3. Among the three main ethnic groups in the sample, ABSI predicted mortality in both whites and blacks, while ABSI was not a significant predictor of mortality in Mexicans Table 3.

These conclusions from subgroup analysis were largely borne out by checking the significance of interaction terms added to the Cox proportional hazard model. ABSI age, ABSI sex, and ABSI BMI interactions were not significant, confirming that high ABSI predicts mortality across these categories.

By contrast, the impact of increasing BMI and WC depended strongly on BMI Table 3 , consistent with U-shaped relationships where lower weight would increase mortality at low BMI and decrease it at high BMI. Interaction with ABSI of an indicator variable for white ethnicity were significantly positive, implying that whites with high ABSI show greater relative risk elevation than other USA ethnicities.

High ABSI continued to be a significant predictor of death even when the first 3 yr of the follow-up period were excluded Table 3 , suggesting that the correlation of higher ABSI with death rate is not merely due to a propensity of acutely ill people to have high ABSI.

To examine the correlation of different levels of ABSI with death rate, we stratified the population into quintiles by ABSI z score, where the middle third quintile included those near the population mean ABSI, and conducted proportional hazard modeling with ABSI quintiles, rather than ABSI z score, as the predictor variables, with hazard ratios expressed relative to the middle quintile.

We found that people with low ABSI first and second quintiles had nonsignificantly decreased mortality risk relative to the middle quintile, while ABSI in the fourth and fifth quintiles was associated with progressively and significantly increased mortality risk Table 4.

Similar analyses were conducted with BMI and WC quintiles. BMI and WC in the first quintile were both associated with significantly greater mortality hazard than for the middle quintile.

Significantly increased death rate compared to the middle quintile was also seen for WC and BMI in the fifth quintile Table 4. Comparing the excess mortality hazard from high ABSI top two quintiles with that posed by high BMI and WC as a fraction of the total population death rate, we found that — of the population mortality hazard was attributable to high ABSI, compared to — for BMI and — for WC.

Plotting estimated mortality risk by percentile from models with ABSI, BMI, or WC as continuous predictor variables Figure 2 , with smoothing splines used to represent their nonlinear associations with mortality, gives results consistent with the quantile analyses.

While risk increases progressively with increasing ABSI, BMI and WC risk is lowest near the population median and increases for both high and low values.

Estimates are from proportional hazard modeling where log mortality hazard is a smoothing-spline function in ABSI, BMI, or WC. Corresponding population percentiles are given in the top axis; the range shown is the 1st through 99th percentiles. The vertical axis is logarithmic. Figure 3 shows the estimated relative mortality risk taking both body size BMI and shape ABSI z score into account.

These estimates are based on the lack of interaction we found between BMI and ABSI as predictors of mortality, so that the estimated mortality risk given both values is the product of that due to each separately Figure 2a, b. The ranges of BMI and ABSI shown correspond to the 1st through 99th percentiles.

The contour interval is 0. We found that our main results continued to hold when the original variables, rather than their z scores, were used as mortality predictors.

Log mortality risk increased steadily with ABSI, while decreasing for increasing BMI and WC up to values around the population median Figure 4. The association of ABSI with mortality hazard remained after adjusting for other known risk factors Table 5.

This is the same as Figure 2 , but with ABSI, BMI, or WC, rather than their z scores, used as predictors. The newly developed and applied ABSI is based on WC, weight and height, where high ABSI indicates that WC is higher than expected for a given height and weight and corresponds to a more central concentration of body volume.

Applying ABSI along with BMI as a predictor variable separates the influence of the component of body shape measured by WC from that of body size. Our finding that higher ABSI predicts mortality hazard is thus quite analogous to the outcome of analyses which have adjusted WC for BMI without invoking ABSI.

Thus, an analysis of mortality outcomes in an elderly yr USA cohort found that including both BMI and WC as continuous variables in a Cox proportional hazard model for mortality results in a direct correlation between WC and mortality and an inverse correlation between BMI and mortality [12].

In a large multination European cohort, stratifying by BMI category transformed the curve of mortality risk as a function of WC from U-shaped to more linear, similar to our curve of mortality risk as a function of ABSI quantile [14].

Our work also follows on findings that dividing WC by height increases its ability to predict cardiometabolic risk factors [32] — [35]. Some conceptual advantages of introducing ABSI are that it accounts for the sublinear increase of WC with BMI i. along with the nonlinear association of WC with height, and that using it instead of WC avoids inflation of regression uncertainty associated with the near collinearity of WC and BMI.

A logical next step would be to investigate the association of ABSI with longer-term mortality risk, as well as its ability to predict morbidity and impaired quality of life.

What aspect of human physiology measured by ABSI accounts for its association with death rate? At a given height and weight, high ABSI may correspond to a greater fraction of visceral abdominal fat compared to peripheral tissue.

As mentioned in the Introduction, excess visceral fat has been associated with a variety of potentially adverse metabolic changes. Equally important may be that individuals with high ABSI have a smaller fraction of mass as limb muscle; lean tissue mass and limb circumference have been shown to have strong negative correlations with mortality risk [7] , [36].

For example, we found that ABSI is positively correlated to trunk fat mass as estimated from X-ray scans between z score of trunk fat mass adjusted for height and weight and ABSI z score but negatively correlated with limb lean mass , consistent with the above hypotheses; by contrast, WC has only weak associations with both trunk fat and limb lean mass after these are adjusted for height and weight for both , suggesting that it is a less consistent indicator of changes in body shape and composition not reflected in height and weight.

We found that both low and high BMI increased the mortality hazard compared to near-median BMI U-shaped curve for mortality hazard vs. BMI and WC in Figure 2. In the studied population, the hazard sustained by low BMI quantiles appears to be at least as great than that sustained by corresponding high BMI quantiles, consistent with the nonsignificantly negative linear regression coefficient for mortality hazard on BMI z score Table 2.

Figure 4b. Similarly, the 40thth percentile range of population WC was 94— cm for men and 88—97 cm for women, above most suggested cut-off points for higher mortality hazard [18]. These results add to many previous studies that show high population mortality hazard even in developed countries from underweight compared to overweight, particularly among the elderly and chronically ill [15] , [40] — [45] , supporting a rethinking of BMI-based obesity thresholds [46].

However, since high ABSI appears to identify increased mortality risk independent of BMI, it could complement either low or high BMI in risk assessment, as illustrated in Figure 3. In addition to WC, inverse hip circumference, or waist to hip ratio, have been suggested as alternative measures of body shape that predict mortality better than BMI [18] , [47].

It is theorized that gluteofemoral fat may benefit health by removing free fatty acids from the bloodstream [48]. Different studies have reached a range of conclusions about whether WC [14] , [16] or waist to hip ratio [13] , [17] is a better predictor of mortality; a meta-analysis of British studies found them to be equally good predictors [49].

A recent prospective analysis from Mauritius found that higher WC and lower hip circumference both correlated with greater mortality risk, while BMI did not correlate with mortality risk [50].

An analysis of an earlier NHANES cohort NHANES III, examined — found that neither low nor high waist to hip ratio significantly affected mortality hazard compared to an intermediate reference level, where levels were defined by analogy with WHO obesity categories [41].

That study also found that waist to hip ratio, despite its nondimensional form, was significantly correlated to BMI , and we may hypothesize that adjusting hip circumferences or waist to hip ratios for height and weight, as done here for WC, would make them more useful as predictors of mortality hazard.

The significance of hip circumference or waist to hip ratio cannot be evaluated with NHANES — data because hip circumference was not measured, though it is possible that adjusting WC for height and weight may indirectly provide similar information to waist to hip ratio — i.

a wider waist for given height and weight may imply narrower hips, and vice versa. This prospective study does not directly address whether interventions aimed at reducing ABSI would reduce mortality risk, independent of weight change, for which large randomized controlled trials would be necessary.

If ABSI does reflect malleable body shape and composition attributes, however, we may speculate that the effectiveness of weight loss interventions in improving health outcomes would be affected by how they impact WC relative to weight, since ABSI varies with the ratio.

Lifestyle change that reduces ABSI, such as an exercise program that builds skeletal muscle, may yield health benefits independent of the amount of weight loss; indeed, exercise has been shown to have beneficial health impacts for obese individuals, including reductions in WC and hence ABSI , even when weight loss does not occur [51].

Weight loss programs including either low-calorie diets or exercise can also reduce WC, along with BMI, enough to reduce ABSI [52] , [53].

As other possible applications, the strong association of ABSI with mortality may be of interest to actuaries [54] , and may be used as a selection criterion for enrollment in clinical trials desired to have higher power to detect mortality outcome differences with given sample size.

In particular, WC should be measured according to the NHANES protocol [22] in order to meaningfully compare ABSI with the population normals given here, even though in general the association of WC with health outcomes seems independent of the specified measurement protocol [55].

In summary, body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical measurements.

This table contains the mean and standard deviation of ABSI by age and sex for NHANES —, based on all individuals with available data except pregnant women and weighted to represent the larger USA population which NHANES was intended to sample.

The smoothed means and standard deviations shown were used to generate ABSI z scores. The table contains 11 space-separated columns in plain text. The layout is: Column 1: Age 85 includes those older than Column 2: Number of males with available data at this age.

Column 3: ABSI mean for males at the given age. Column 4: ABSI standard deviation for males at the given age. Column 5: Smoothed ABSI mean for males at the given age. Column 6: Smoothed ABSI standard deviation for males at the given age.

Columns 7— Same as columns 2—6, but for females. We thank Steven Heymsfield, Michael Kleerekoper, James Levine, and Tom Rifai for valuable discussions and encouragement. Conceived and designed the experiments: NYK JCK.

Performed the experiments: NYK JCK. Analyzed the data: NYK JCK. Wrote the paper: NYK JCK. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Background Obesity, typically quantified in terms of Body Mass Index BMI exceeding threshold values, is considered a leading cause of premature death worldwide.

Methods and Findings We considered a USA population sample of 14, non-pregnant adults from the National Health and Nutrition Examination Survey NHANES — with follow-up for mortality averaging 5 yr deaths.

We developed A Body Shape Index ABSI based on WC adjusted for height and weight: ABSI had little correlation with height, weight, or BMI. Conclusions Body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical measurements.

Funding: The authors have no funding or support to report. Introduction According to the World Health Organization WHO , overweight and obesity are increasing in prevalence and rank fifth as worldwide causes of death among risk factors, behind high blood pressure, tobacco use, high blood glucose, and physical inactivity.

Methods Description of Data We employed public-use releases of baseline interview and medical examination and mortality outcome data from the National Health and Nutrition Examination Survey NHANES — Construction of the Body Shape Index We performed linear least-squares regression on as a function of and for the entire nonpregnant adult sample.

Approximating the obtained regression coefficients with ratios of small integers, we have, 2 We defined A Body Shape Index ABSI to be proportional to the ratio of actual WC to the WC expected from the regression allometry: 3 The sample mean and standard deviation of ABSI thus defined is.

Download: PPT. Conversion to z Scores To control for age and sex differences in mean ABSI, we entered it into proportional hazards regression for mortality as a z score: 4 where the population ABSI mean and standard deviation depend on age and sex. Figure 1. Mean and standard deviation of ABSI, BMI, and WC by age and sex.

Mortality Hazard Modeling To quantify the association of baseline ABSI with death rate, we employed Cox proportional hazard modeling for mortality with age as the time scale [28]. Results Higher Mortality Hazard for Increasing ABSI Table 2 shows the impact of ABSI z score, as a continuous variable, on death rate, along with results for BMI and WC z scores.

Mortality Hazard by ABSI Quantile To examine the correlation of different levels of ABSI with death rate, we stratified the population into quintiles by ABSI z score, where the middle third quintile included those near the population mean ABSI, and conducted proportional hazard modeling with ABSI quintiles, rather than ABSI z score, as the predictor variables, with hazard ratios expressed relative to the middle quintile.

Figure 2. Mortality hazard by ABSI, BMI, and WC z score relative to age and sex specific normals. Figure 3. Estimated mortality hazard relative to the population mean by combination of BMI and ABSI z score.

Discussion The newly developed and applied ABSI is based on WC, weight and height, where high ABSI indicates that WC is higher than expected for a given height and weight and corresponds to a more central concentration of body volume.

Supporting Information. Table S1. s TXT. Acknowledgments We thank Steven Heymsfield, Michael Kleerekoper, James Levine, and Tom Rifai for valuable discussions and encouragement. Author Contributions Conceived and designed the experiments: NYK JCK.

Error: This is required. Error: Not circumcerence valid value. Carbohydrate metabolism and nutrition Herbal energy supplements find your Circummference using the healthdirect BMI Waisr. The Waist circumference and body shape can give you an idea of any health risks related to your BMI or waist circumference. It also offers information based on your personal results. Your BMI is a guide to tell you if you are the correct weight for your height. Your BMI can give an indication of your chance of developing weight-related disease such as diabetes.
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