Body shape index -
Plug your measurements into the calculator the Krakauers have developed. The number you want to note is the relative risk from ABSI. A 1 means you're at average risk of death for your age.
Below 1 means a below-average risk; above 1 means greater risk. The good news: You can lower your ABSI by whittling your waist through diet and exercise.
Just be sure, as always, to consult your doctor before making dramatic lifestyle changes. From the April issue of O, The Oprah Magazine. NEXT STORY. More O Magazine. Could the Breakfast of Weight Loss Champions Be We calculated ABSI for both sexes and HI for women with coefficients from the National Health and Nutrition Examination Survey NHANES 12 , For HI in men, we used coefficients based on UK Biobank data, to avoid the inverse correlation between HI and BMI introduced by the coefficients from NHANES 14 :.
We standardised anthropometric indices to sex-specific z-scores when using them on a continuous scale value minus mean, divided by standard deviation, SD. Blood samples in UK Biobank were obtained throughout the day 8 am to 9 pm , with no specific requirements for fasting. Serum levels of biomarkers were measured on a Beckman Coulter AU analyser.
As liver function tests, we examined bilirubin total and direct, measured with colorimetric assays , and liver enzymes measured with enzymatic rate assays : aspartate aminotransferase AST , alanine aminotransferase ALT , gamma glutamyltransferase GGT , and alkaline phosphatase ALP.
As biomarkers of lipid metabolism, we examined high-density lipoprotein cholesterol HDL-C, enzyme immune-inhibition assay , low-density lipoprotein cholesterol LDL-C, enzymatic selective protection assay , triglycerides enzymatic assay , apolipoproteins A1 and B ApoA1, ApoB, immuno-turbidimetric assays.
As biomarkers of glucose metabolism, we examined serum glucose enzymatic assay and HbA1c measured in red blood cells with high-performance liquid chromatography on Bio-Rad VARIANT II Turbo analyser , which is not affected by fasting and provides information for glucose status over the last three months.
As inflammatory biomarkers, we examined white blood cell counts lymphocytes, monocytes, neutrophils and serum levels of C-reactive protein CRP, high sensitivity immuno-turbidimetric assay. Direct bilirubin was below the limit of detection for 7. We log-transformed all biomarker measurements, to mitigate the influence of right-skewed distributions, and calculated sex-specific z-scores, to provide a standardised scale for comparability.
To examine independent associations with waist and hip size, for each biomarker as an outcome, we used an additive model including ABSI, HI, and BMI on a continuous scale sex-specific z-scores , and covariates, interpreting the estimates as SD difference in biomarker levels per one SD increment of the anthropometric index.
To examine heterogeneity by body size, we used categories combining body size and body shape in the BMI-by-ABSI-by-HI cross-classification, which is equivalent to an interaction model. As in our previous study 10 , covariates evaluated at enrolment and used for adjustment of all models comprised height, age, weight change within the last year preceding enrolment, smoking status, alcohol consumption, physical activity, Townsend deprivation index tertiles , region of the assessment centre, and additionally time of blood collection, fasting time, use of nonsteroidal anti-inflammatory drugs NSAID, as these may affect inflammatory factors , paracetamol use as this may affect liver function tests , and in women also menopausal status, oral contraceptives use never, past , HRT use never, past , and age at the last live birth.
Covariates were mainly defined as previously 10 , with the study specific definitions explained in Supplementary Methods. We replaced missing values for covariates with the median sex-specific value or category, as missing information was limited Supplementary Table S2.
We examined the consistency of the associations with BMI, ABSI, or HI on a continuous scale across the available range of biomarker levels, using as a smoothing function generalised additive models with restricted maximum likelihood REML estimation package mgcv v1.
For each of the anthropometric indices we used residuals from multivariable linear regression models including the other two anthropometric indices and covariates.
We further examined non-linearity by including in fully adjusted models restricted cubic splines for one of the anthropometric indices on a continuous scale using function rcs from package rms v.
For biomarkers with plots suggesting change of direction in the associations towards the tails of the distributions, we additionally calculated SD differences with fully adjusted linear models for subgroups according to biomarker levels. As biomarkers reach clinically relevant levels towards the tails of their distributions, examining the central part of the distribution acts as a sensitivity analysis, excluding any underlying medical conditions contributing to the tails.
To evaluate the statistical significance of individual terms, we used Wald tests. To evaluate the contribution of body shape phenotypes overall, we used a likelihood ratio test, comparing a model including BMI categories and covariates with a model additionally including an ABSI-by-HI cross-classification.
To evaluate heterogeneity by BMI, we used a likelihood ratio test, comparing the additive model including the ABSI-by-HI cross-classification, BMI categories and covariates with the interaction model including the BMI-by-ABSI-by-HI cross-classification and covariates.
To evaluate non-linearity, we used a likelihood ratio test, comparing the additive model including BMI, ABSI, and HI on a continuous scale and covariates with the model replacing the linear term for one of the anthropometric indices with the corresponding restricted cubic splines.
To examine associations between biomarkers, we calculated partial Pearson correlation coefficients with adjustment for ABSI, HI, BMI continuous, z-scores , and covariates as above except for region of the assessment centre and age at the last live birth.
In sensitivity analyses, we examined the influence of covariates overall in unadjusted models including only BMI, ABSI, and HI , the influence of recent weight change excluding participants reporting weight loss or weight gain within the year preceding enrolment , and the influence of medication use and alcohol consumption, examining subgroups with no NSAID or paracetamol use, as well as subgroups with NSAID use, paracetamol use, and different quantities of alcohol consumption.
All subgroup analyses were based on fully adjusted models, omitting the corresponding variable, which defined the subgroups. We used R version 4.
This research was conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent has been obtained from all study participants.
The current study was approved by the UK Biobank access management board. Participants who had withdrawn consent by the time of the analysis were excluded from dataset. The study included , men and , women. The proportions of participants with NSAID or paracetamol use were comparable Table 1.
BMI was associated inversely with bilirubin, HDL-C, and ApoA1 and positively with all other biomarkers. The associations with BMI were more prominent in women compared to men for bilirubin, ALP, neutrophils, and CRP but were more prominent in men compared to women for GGT, AST, ALT, lymphocytes, and monocytes Fig.
ABSI was associated with all biomarkers in the same direction as BMI but more weakly, while HI was associated in the opposite direction and even more weakly than ABSI.
Mainly ABSI, in addition to BMI, was associated with bilirubin, ALP, monocytes, neutrophils, and CRP. Both ABSI and HI were associated more strongly in women compared to men with metabolic biomarkers and lymphocytes but were associated more strongly in men compared to women with monocytes, neutrophils, and CRP.
Associations with all three anthropometric indices were most prominent for GGT, ALT, HDL-C, ApoA1, triglycerides, and CRP Fig.
Associations of biomarkers with body size and body shape indices continuous. ABSI — a body shape index; ALP — alkaline phosphatase; ALT — alanine aminotransferase; ApoA1 — apolipoprotein A1; ApoB — apolipoprotein B; AST — aspartate aminotransferase; Bilirubin D — direct bilirubin; Bilirubin T — total bilirubin; BMI — body mass index; CI — confidence interval; CRP — C-reactive protein; GGT — gamma-glutamyltransferase; HbA1c — haemoglobin A1c glycated haemoglobin ; HDL-C — high-density lipoprotein cholesterol; HI — hip index; LDL-C — low-density lipoprotein cholesterol; SD — standard deviation.
Covariates included height, age at enrolment, weight change within the last year preceding enrolment, smoking status, alcohol consumption, physical activity, Townsend deprivation index, region of the assessment centre, time of blood collection, fasting time, use of nonsteroidal anti-inflammatory drugs, paracetamol use, and in women, menopausal status, hormone replacement therapy use, oral contraceptives use, and age at the last live birth.
Covariates are defined in Supplementary Methods. Numbers are shown in Supplementary Table S4. Combining dichotomised ABSI and HI in body shape phenotypes maximised the opposition of ABSI and HI, resulting in the largest difference between phenotypes with discordant waist and hip size Fig.
Neutrophil and monocyte counts resembled the pattern of CRP, while lymphocyte counts resembled the pattern of metabolic biomarkers Fig. Associations of biomarkers with body shape phenotypes.
Numbers are shown in Supplementary Table S5. Overall, the association patterns of biomarkers with body shape phenotypes were retained in all BMI categories Fig. Also in obese men, there was little evidence for association of bilirubin, LDL-C, or lymphocytes with body shape. In both men and women, associations with AST were similar to ALT but weaker.
Aminotransferases resembled the pattern of triglycerides, while GGT resembled more closely the pattern of HbA1c Fig. Heterogeneity of the associations of biomarkers with body shape phenotypes according to body size. Across biomarker levels, associations with anthropometric indices were largely consistent with the observations reported for the total dataset within the central part of the biomarker distributions but were weaker or absent towards the tails Fig.
Models with restricted cubic splines provided strong evidence for a plateau in the associations with BMI for high levels of lipid-related biomarkers and CRP, and in men also of GGT and ALT Supplementary Table S4 , Fig.
There was also a weaker evidence for a similar pattern of associations with ABSI, while the associations of BMI with HbA1c were J-shaped Supplementary Table S4 , Fig. Also in men, HDL-C above 1. Associations of biomarkers with body size and body shape indices across biomarker levels.
Vertical lines represent 2. Extreme values were removed, to avoid leverage on the estimates see details in Supplementary Table S6. For each of the anthropometric indices, we used residuals derived from a multivariable linear regression model including the other two anthropometric indices and covariates.
Covariates comprised height, age at enrolment, weight change within the last year preceding enrolment, smoking status, alcohol consumption, physical activity, Townsend deprivation index, region of the assessment centre, time of blood collection, fasting time, use of nonsteroidal anti-inflammatory drugs, paracetamol use, and in women, menopausal status, hormone replacement therapy use, oral contraceptives use, and age at the last live birth.
Independent of anthropometric indices and other covariates, total and direct bilirubin were correlated inversely with triglycerides and HbA1c and direct bilirubin was additionally correlated inversely with LDL-C and ApoB Fig. ALP and GGT were correlated positively with each other and with CRP.
GGT was also correlated positively with ALT and both were correlated positively with LDL-C, ApoB, and triglycerides, more strongly in men than in women. Triglycerides were correlated substantially inversely with HDL-C and more weakly with ApoA1, but were similarly positively correlated with LDL-C and ApoB.
HbA1c and glucose were mainly correlated positively with each other but not with lipid-related biomarkers. CRP was correlated positively with neutrophils but not with lymphocytes, while lymphocytes were correlated most strongly positively with monocytes Fig.
Correlations between biomarkers, independent of body size and body shape. ABSI — a body shape index; ALP — alkaline phosphatase; ALT — alanine aminotransferase; ApoA1 — apolipoprotein A1; ApoB — apolipoprotein B; AST — aspartate aminotransferase; Bilirubin D — direct bilirubin; Bilirubin T — total bilirubin; BMI — body mass index; CRP — C-reactive protein; GGT — gamma-glutamyltransferase; HbA1c — haemoglobin A1c glycated haemoglobin ; HDL-C — high-density lipoprotein cholesterol; HI — hip index; LDL-C — low-density lipoprotein cholesterol; Ly — lymphocytes; Mo — monocytes; Neu — neutrophils.
Values represent partial Pearson correlation coefficients men — bottom-left; women — top-right with adjustment for BMI, ABSI, HI, height, age at enrolment, Townsend deprivation index, time of blood collection, fasting time continuous , weight change within the last year preceding enrolment, smoking status, alcohol consumption, physical activity, use of nonsteroidal anti-inflammatory drugs, paracetamol use, and in women, menopausal status, hormone replacement therapy use, and oral contraceptives use.
Unadjusted models showed that adjustment for covariates had attenuated to some extent the associations with BMI and ABSI for most biomarkers, except for the associations of ABSI with HDL-C and ApoA1, which were stronger after adjustment, especially in women Supplementary Figure S2.
Subgroups with stable weight or without medication use represented the larger part of the dataset The fully adjusted subgroup models showed no material difference from the patterns described for the complete dataset, except from marginally stronger associations of BMI with lipid-related biomarkers and CRP, and in men also with GGT and ALT, in the subgroup with stable weight Supplementary Figure S4.
In subgroups with NSAID or paracetamol use, only the associations of BMI with HDL-C, ApoA1, and in men also with ALT, triglycerides, and CRP were marginally weaker Supplementary Figure S4. The association patterns with body shape phenotypes described for the complete dataset were largely retained, including in the subgroups with NSAID or paracetamol use Supplementary Figure S5.
In subgroups with higher alcohol consumption, associations of BMI with liver function tests, lipid-related biomarkers, and CRP were stronger than in the subgroup with low alcohol consumption, as were associations of ABSI with GGT, AST, and ALT, while associations of ABSI and HI with HDL-C and ApoA1, and in women with HbA1c, were weaker Supplementary Figure S6.
There was no material difference, however, in the association patterns with body shape phenotypes, except that in obese men with higher alcohol consumption was retained a positive association of ALT with ABSI, in addition to the inverse association with HI Supplementary Figure S7.
Inflammatory biomarkers, represented by CRP, neutrophils, and monocytes, as well as ALP, were only associated positively with waist and not with hip size, independent of positive associations with BMI, while bilirubin was associated inversely with BMI and waist size but not with hip size.
The patterns were similar for all BMI categories, except that ALT and lipid-related biomarkers were associated only with hip size in obese men and ALP was associated positively with hip size in obese women.
Studies examining associations of allometric body shape indices with metabolic and inflammatory biomarkers remain scarce and focused exclusively on ABSI, without considering HI. Positive associations with ABSI, as well as with BMI, have been reported for triglycerides and fasting glucose, together with inverse associations for HDL-C Men with large ABSI have additionally shown lower insulin sensitivity 21 , while women with large ABSI have shown a larger proportion of the pro-atherogenic small dense LDL particles Large ABSI has also been associated with higher CRP 21 , 23 , and in agreement with our findings, CRP has previously shown stronger positive associations with BMI in women compared to men Notably, however, several studies have reported an inferior discrimination of ABSI compared to BMI or traditional waist-circumference-based indices for components of the metabolic syndrome 24 , type 2 diabetes 16 , 25 , or high ALT levels This should not be surprising, given that BMI is associated with metabolic factors and conditions and any measure of waist size strongly correlated with BMI, such as waist circumference, would carry the same information as BMI, while ABSI is independent of BMI by design and should be interpreted not as an alternative but as a complement to BMI.
Previous studies have also extensively noted positive associations of ALT and GGT but not AST with BMI and waist circumference, unexplained by hepatitis or alcohol consumption, along with positive associations of ALT and GGT with triglycerides, LDL particles, fasting insulin, and insulin resistance, as well as inverse associations with HDL-C 26 , 27 , 28 , 29 , 30 , 31 , 32 , but no attention has been paid to hip size.
The similarities in the association patterns of ALT and GGT noted in our and previous studies would likely be related to their shared genetic background and the differences between obese men and women reported in our study would likely be related to the sexually dimorphic relative contribution of individual genes Physiologically, ALT participates in the glucose-alanine cycle, transferring ammonium groups from amino acids to pyruvate released from glycolysis in the muscle, thus producing alanine, which in the liver is converted back to pyruvate and is used to generate glucose in gluconeogenesis Correspondingly, intervention studies in humans support a more important role of insulin than lipids for ALT regulation, as carbohydrate restriction contributes to a greater reduction in ALT compared to fat restriction, despite similar weight reductions with alternative low-energy diets Nevertheless, statin administration in humans reduces ALT and GGT levels 36 , and in animal models, cholesterol-depleted but not cholesterol-loaded HDL particles induce ALT release from the liver to the circulation 37 , which is in agreement with the association patterns of ALT matching more closely lipid-related than glucose-related biomarkers in our study.
GGT, on the other hand, is a membrane-bound ectoenzyme, hydrolysing gamma-glutamyl bonds of glutathione and its S-conjugates with xenobiotics, and as such represents part of the cellular antioxidant system but can also have a pro-oxidative action and has been implicated in the pathogenesis of atherosclerosis via oxidation of LDL-C , inflammatory conditions, and cancer This suggests that in addition to factors related to or originating from VAT, there is also an involvement, at least in men, of a factor that either originates from gluteofemoral fat or determines its accumulation.
One such factor could be oestradiol originating from peripheral aromatisation in adipose tissue. This would be compatible with the sexual dimorphism of lipids described in our and other studies, with lower levels of triglycerides and ALT and higher HDL-C and ApoA1 in women compared to men Chronic low-grade inflammation is characteristic of obesity and can contribute to the generation for dysfunctional HDL particles 40 , In our study, however, hip size was associated only with glucose-related and lipid-related factors but not with CRP or neutrophils, highlighting differences in the underlying mechanisms.
Low-grade inflammation is mediated by macrophage infiltration of the adipose tissue Nevertheless, macrophages play a complex role, as their classical activation contributes to a pro-inflammatory phenotype, promoting the development of insulin resistance and type 2 diabetes, while their alternative activation improves insulin sensitivity The classical activation of macrophages can also be triggered by tissue infiltration with neutrophils, which thus contribute to the maintenance of chronic low-grade inflammation, in addition to their key role in the acute inflammatory response Correspondingly, mice with neutropenia have reduced liver lipogenesis and steatosis In humans, neutrophil counts are higher in hyperlipidaemia, hyperglycaemia, and insulin resistance even in healthy individuals 28 , 44 , and are accompanied with higher lymphocyte and monocyte counts and higher CRP levels in obesity and the metabolic syndrome 46 , An upregulation of genes related to neutrophil degranulation has also been reported in patients with cardiovascular diseases Neutrophil counts, however, are reduced after bariatric surgery proportional to the changes in BMI and insulin resistance, while lymphocyte counts are not affected materially and the response of monocytes varies according to the surgical technique The latter is in agreement with the similar associations with body shape for CRP, neutrophils, and partially for monocytes but not for lymphocytes observed in our study.
A likely mechanistic factor explaining the positive association of waist size with metabolic and inflammatory biomarkers would be cortisol, as glucocorticoids play a key role in the regulation of the anti-inflammatory response and the hypothalamus—pituitary—adrenal axis is dysfunctional in obesity, favouring VAT accumulation, metabolic alterations, and abdominal obesity In animal models, neutrophil infiltration of mouse liver and secretion of neutrophil elastase is accompanied with activation of clock genes and follows a circadian rhythm, with the lowest neutrophil counts in liver at lights-off time and the highest at lights-on time, corresponding to the circulating corticosterone levels 45 , In humans, glucocorticoids contribute to higher circulating neutrophil counts via increased release of polymorphonuclear cells from the bone marrow and from the marginalised pool cells attached to the endothelial surface , as well as by delayed apoptosis It remains unclear, however, what is the contribution of glucocorticoids to associations with hip size and how they interact with other factors related to body shape.
Bilirubin and ALP, similarly to inflammatory biomarkers, were associated only with BMI and waist and not with hip size, but unlike their concomitant increase in obstructions of the biliary tract, their associations with anthropometric indices were discordant.
In agreement with our findings, higher levels of the total and liver fraction of ALP in serum have been reported in obesity and it has been shown that tissue ALP is involved in lipid metabolism and adipokine synthesis 53 , Obesity is also associated with higher expression of leucocyte ALP ALPL gene in neutrophils, a marker of neutrophil activation 55 , which is compatible with an involvement of ALP in the inflammatory response, in agreement with the positive correlation of ALP with CRP and neutrophil counts observed in our study.
Further in agreement with our findings, lower bilirubin levels have been reported in obesity without metabolic complications 56 , as well as in type 2 diabetes and the metabolic syndrome Bilirubin also plays a protective role against liver lipid infiltration and the development of NAFLD 58 , and animal models have shown that biliverdin, a bilirubin precursor in the haem catabolic pathway, contributes to smaller adipocyte size and suppresses inflammatory factors, thus reducing insulin resistance Our study has shown that associations with body size and body shape related to obesity hold within the clinical reference ranges of biomarker levels, while for lower or higher levels, pathological conditions other than obesity would likely gain leverage.
Thus, severe chronic inflammatory conditions and liver damage can contribute to skeletal muscle wasting and cachexia 60 , potentially explaining the inverse associations of BMI with high ALT and CRP observed in our study in men and for high CRP, also in women. Further, the inverse associations of BMI with low neutrophil counts, most prominent in our study for women, could be related to a secondary autoimmune type neutropenia accompanying chronic inflammatory or autoimmune conditions Although high HDL-C is generally considered beneficial, U-shaped associations have been reported for HDL-C, with a positive association with ALT and AST at high HDL-C This, together with the positive association of high HDL-C and ApoA1 with ABSI observed in our study in men, suggests un underlying pathological condition for very high HDL-C levels, which merits further investigation.
Our study benefited from a very large sample size, which enabled us to examine in more detail some relatively small subgroups. There was also a detailed information for covariates, which permitted adjustment for major lifestyle and reproductive factors and minimised confounding.
The standardised anthropometric measurements, obtained by trained personnel, avoided bias from self-reported values. The standardised approach to biomarker measurements, with a unified and systematic quality control for all samples, minimised measurement errors. Due to limited numbers, however, we could not examine underweight or severe obesity, or ethnic variations, or pre-menopausal women, or younger men, or longitudinal associations, or association of biomarkers with imaging measurement of body composition, which were obtained a few years later for a small part of the UK Biobank cohort.
A misclassification of medication use is also possible, as the information was self-reported and was assembled from several questions. Importantly, our study was cross-sectional, and as such could not assess temporality or provide strong insights for potential causality.
Although we have removed participants with known underlying conditions potentially influencing body composition or contributing to weight change prevalent cancer and non-cancer illness or medication use at enrolment, or incident cancer and death within the first two years after enrolment , thus retaining only half of the original UK Biobank dataset, some possibility for reverse causality from subclinical or unreported conditions remains.
Nevertheless, body size and body shape and the underlying body composition, as well as biomarker levels, are endogenous factors. As such, they are likely interrelated in complex causal networks, rather than in linear causal pathways, with each other and with other endogenous factors such as sex steroids and glucocorticoids, as well as with exogenous and genetic factors.
In this context, our findings suggest that body size and body shape and their determining factors are more likely to be leading a direct association within the central part of the biomarker distributions. Towards the tails of the biomarker distributions, however, biomarkers and their determining factors and associated diseases are more likely to be contributing to reverse causality.
Finally, UK Biobank participants are not only relatively older, but have a healthier lifestyle and are not representative of the overall UK population This discrepancy would be aggravated further by the removal of participants with prevalent illnesses at enrolment or using medications.
In conclusion, glucose-related and lipid-related biomarkers are associated in opposite directions with waist and hip size, independent of overall body size, while inflammatory biomarkers are associated only with waist size, suggesting differences in the underlying mechanisms.
Associations with body size and body shape related to obesity remain consistent within the clinical reference ranges of biomarker levels, but are lost or change direction for low or high levels, potentially reflecting the influence of chronic inflammatory or autoimmune conditions.
Piché, M. Obesity phenotypes, diabetes, and cardiovascular diseases. Article PubMed CAS Google Scholar. Saltiel, A. Inflammatory mechanisms linking obesity and metabolic disease.
Article PubMed PubMed Central Google Scholar. Engin, A. Non-alcoholic fatty liver disease. Article CAS PubMed Google Scholar. Follow the Centers for Disease Control and Prevention guidelines to correctly measure your waist. Use a flexible measuring tape made of non-stretchable material. See here for additional instructions on how to properly measure waist circumference.
This calculator is intended for illustrative and educational purposes only and not for medical use. ABSI is designed to be used together with the more familiar body mass index BMI and not replace it. Top of Page.
menu FatCalc. Calculate Your ABSI A Body Shape Index Since the early s, Body Mass Index, or BMI, has become an almost universally used indicator of obesity with obesity being a leading cause of death worldwide. ABSI Calculator.
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Thank you for shaps nature. You are using Omega- for Parkinsons disease browser version Shxpe limited support for CSS. To Electrolyte Absorption the best invex, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Associations of liver, metabolic, and inflammatory biomarkers in blood with body shape are unclear, because waist circumference WC and hip circumference HC are dependent on overall body size, resulting in bias. Since the early s, Body Mass Index, or BMI, has inded an almost universally Body shape index indicator of obesity inddex obesity being ihdex leading cause of death worldwide. Recently a Stress management techniques for better concentration measure of obesity, Body shape index OBdy Body Shape Indexhas shown to be a better predictor of mortality. Use this calculator to calculate your ABSI. BMI doesn't tell you anything about where you're carrying your weight. Numerous studies have found that having an apple shaped body or carrying excess belly fat is riskier than having a pear shaped body or heavy bottom. Another flaw with BMI is that it does not distinguish between fat and muscle.Insex RIGHTS Bdoy. OWN: OPRAH Anti-aging innovations NETWORK. Fat burning supplements Next Indxe The Number Body shape index Bpdy to Know Omega- for Parkinsons disease Better Health.
Knowing your Body shape index sjape just Body shape index weight—may Omega- for Parkinsons disease the key to better Bodg. By Shapee DiNardo. For more than Shale years, scientists have searched for a simple formula for determining who's Gluten-free diet and mental health and who's not.
First there was body mass index Bpdy Body shape index, which compares weight with height, but doesn't account for lean muscle mass or bone density.
It also Sports nutrition for triathletes indicate where you carry your weight, an important factor in understanding your overall health.
As a result, doctors began embracing waist circumference Inded as oBdy measure of the dangerous body fat that xhape around the belly. But WC, too, can Bofy short—for ahape to inddex height into shapf equation: A 5'10" woman with a inch waist may shapd a very Body shape index health profile from a 5'2" woman with the same girth.
Given shapr rates of chronic Enhance insulin sensitivity associated Bodt obesity, Omega- for Parkinsons disease no wonder that the medical community has been laboring to come up with a better metric.
Now a father-and-son team—Jesse Krakauer, MD, an endocrinologist, and Nir Krakauer, PhD, an assistant professor of civil engineering at City College of New York—may have developed one: A Body Shape Index ABSIwhich takes into account weight, height and waist circumference.
Why ABSI May Be Better Than BMI In an initial study, Team Krakauer calculated the ABSI and BMI of more than 14, Americans of all shapes and sizes pregnant women excluded and found that high ABSI appears to be more accurate than high BMI at predicting mortality.
Those with the highest ABSI numbers had more than twice the risk of dying from any cause than those with the lowest. And the researchers found that even when people's BMI fell within the normal range, if they had a high ABSI, they could still be in the danger zone.
Why ABSI May Be Better Than WC Because ABSI accounts for height, it likely depicts body roundness in a more precise way than waist circumference can. What Does It Mean for You? Plug your measurements into the calculator the Krakauers have developed.
The number you want to note is the relative risk from ABSI. A 1 means you're at average risk of death for your age. Below 1 means a below-average risk; above 1 means greater risk.
The good news: You can lower your ABSI by whittling your waist through diet and exercise. Just be sure, as always, to consult your doctor before making dramatic lifestyle changes. From the April issue of O, The Oprah Magazine. NEXT STORY.
More O Magazine. Could the Breakfast of Weight Loss Champions Be Oz's 4-Step Weight Loss Plan You Can Do in Your Sleep. The Vaccine That Could Save Your Life. The Truth About Mammograms and Whether You Should Get One.
Oz: 4 Unexpected Signs You're at Risk of a Stroke.
: Body shape index| A Body Shape Index (ABSI) | Figure 3. You can use this shaps to estimate your body fat Body shape index and indec risk. Evening Standard Limited. Glucose was determined using the GOD-PAP method. An upregulation of genes related to neutrophil degranulation has also been reported in patients with cardiovascular diseases |
| ABSI Calculator | Boxy are defined in Supplementary Methods. We calculated Body shape index HRs using Shaoe proportional hazards models, as described for the first step above, with waist-by-BMI group as exposure variable and low-waist-normal-weight as reference. ORIGINAL RESEARCH article. Thomson, C. British Actuarial Journal 17— Advanced search. |
| Body shape index - Wikipedia | Gomez-Peralta, F. All measurements were performed Body shape index but kndex case of divergent results were repeated for the Bodh time. Our study was the first to determine the ability of ABSI to recognize AAC. Waist indices unadjusted for weight or BMI by design are correlated strongly with BMI. Our testing showed that the linearity assumption did not hold for ABSI, BMI or WC. Figure 2. |
| ABSI - A Body Shape Index | Neamat-Allah J, Wald D, Hüsing A, Teucher B, Wendt A, Delorme S, et al. Guidelines for the management of hypertension. Participants were asked to refrain from physical activity for 48 h before blood sampling. On the contrary, ABSI predictive ability was not better than BMI with respect to type 2 diabetes, hypertension and cardiovascular disease in Chinese and Iranian populations [ 25 - 27 ]. Analyzed the data: NYK JCK. Cardiovascular Anthropometry For Large Scale Population Studies. |
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