Waist circumference and metabolic health -
We considered smokers all those who consumed a daily tobacco product in the last 12 months and included those who reported having quit smoking in the last year.
Never drinking was defined as self-reported abstinence, former drinking was defined as having ceased alcohol consumption for 1 year or more, and current drinking was defined as consumption of alcohol in the past year.
Blood pressure was taken with no smoking, physical activity, or food consumption during the previous 30 min and after the participant sat for 5 min.
Anthropometric measurements were taken following the standardized protocol of the PURE study. Weight was measured using a digital scale with the participant lightly clothed with no shoes. Height was measured to the nearest millimeter using a tape measure with the participant standing without shoes.
Waist and hip circumferences were measured unclothed using a tape measure. The WC was considered the smallest circumference between the costal margin and the iliac crest.
The hip circumference was measured at the level of the greater trochanters. Handgrip strength was measured was evaluated on the individual's non-dominant hand using a Jamar dynamometer Sammons Preston, Bolingbrook, IL, USA , according to a standardized protocol [ 9 ].
Standing, the participant held the dynamometer at the side of the body with the elbow flexed at degree angle and was asked to squeeze the device as hard as possible for 3 s. This was repeated twice with 30 s rest between each attempt.
Physical activity PA was evaluated using the International Physical Activity Questionnaire IPAQ. IPAQ which assesses physical activity undertaken across a comprehensive set of domains, including leisure-time physical activity, domestic and gardening activities, work-related physical activity, transport-related physical activity.
These thresholds take into account that the IPAQ queries PA in multiple domains of daily life, resulting in higher median MET-minutes estimates than would be that estimated from considering leisure-time participation alone.
One point was conferred for each alteration of the cluster of MetS as defined by IDF elevated triglycerides, low HDL-c, dysglycemia, or high blood pressure , generating a score of 0 to 4 for each participant, a high score was considered if 2 or more points were achieved. WC was not included in the calculation of our metabolic score as it was also an outcome variable.
Descriptive statistics were computed for variables of interests and included absolute and relative frequencies of categorical factors. Testing for differences in categorical variables was accomplished using the Chi-square test. Moreover, we used unconditional multivariate logistic regression models to assess the associations between anthropometric variables and handgrip strength, and the MetS score.
These analyses were adjusted for potential confounders, such as age, socioeconomic status, income and education level. We re-coded the anthropometric variables and handgrip strength into sex-specific tertiles and compared the risk of a higher MetS score in each tertile with the lowest category of risk reference group.
All statistical analysis was carried out using the R software version 3. The mean age was The overall prevalence of MetS was MetS was more frequent in women, people older than 50 years; it was also more frequent in individuals living in urban areas, former drinkers, and smokers.
The prevalence of MetS was higher in participants with a lower level of education compared with those with a high school or college degree. The percentage of subjects with MetS was lower in tertile 1 of BMI There were no significant differences in the prevalence of MetS across tertiles of HGS tertile 3: However, the prevalence of MetS Figure 1 shows the sex-specific distribution of the MetS scores.
The association between anthropometric variables and the risk of a higher MetS score is shown in Table 2. A higher WC was associated with a risk of a higher MetS score, with women and men in the tertile 3 of WC mean Participants in tertile 3 of BMI mean In women, lower HGS was associated with a significantly higher MetS score T3 vs.
In men, there were no significant differences in MetS score across HGS tertiles. The overall prevalence of MetS in this cohort of Colombian adults was A lower prevalence was reported by Higuita-Guitierrez in Colombian adults of which Aging is associated with an increase in adipose tissue and a decreased muscle mass [ 17 ], body composition changes which predispose to the development of metabolic alterations.
The prevalence of MetS was higher in women Lower educational level was associated with a higher prevalence of MetS Educational level is an indicator of social inequity, lower levels reflecting not only less schooling, but also a higher risk of unhealthy life habits, and lower access to employment and physical activity participation.
Social factors associated with MetS prevalence, should be further examined. We found that lower muscle strength and higher central adiposity as defined by waist circumference, were independently associated with a higher MetS score, representing a greater number of alterations of the components of the MetS cluster.
Our cross-sectional analysis showed a stronger association between a higher MetS score and WC than BMI, confirming previous studies showing that in Latin-American and Chinese population, WC is a stronger predictor of major cardiovascular events such as myocardial infarction or stroke than BMI, particularly in men [ 8 , 21 ].
Similarly, in diabetic Chinese adults, high visceral fat measured by a visceral adiposity index and WC were associated with a higher prevalence of diabetic kidney disease and CVD compared to BMI [ 22 ]. These findings may be related to the higher inflammatory load associated with visceral adipose tissue accumulation, and inflammation is considered a key factor associated with insulin resistance, MetS and CVD [ 23 , 24 ].
The low-grade pro-inflammatory state characterized by high C-reactive protein levels is observed in adults and youth in our population with high visceral adiposity [ 25 , 26 ].
However, the accumulation of visceral fat is not the only contributing factor in the development of a pro-inflammatory state. The accumulation of cardiac fat is also associated with higher levels of pro-inflammatory cytokines such as IL-6, IL-1, TNF-α, and the expression of adipokine fatty acid-binding protein 4 FABP4 that are associated with the development of MetS and the extent of coronary artery disease [ 27 , 28 ].
Hence, overall fat measurement should not be underestimated. For example, in a cohort of 1, Italian children and adolescents However, BMI cannot discriminate between lean body mass and fat mass; hence, BMI is not necessarily an appropriate parameter of excessive adiposity.
Body fat distribution may be more valuable than overall adiposity in the prediction of metabolic alterations. This aligns with the concept of an obesity paradox whereby subjects with higher BMI levels were shown to have lower levels of cardiovascular events [ 30 ].
Obesity induced alterations in body composition include both an increase in adipose and in low-density lean tissue, without an increment in normal- lean density tissue, suggesting a fatty infiltration of muscular tissue [ 31 ]. Furthermore, studies in Colombian adults have demonstrated that individuals with a high BMI due to higher muscle mass have a lower risk of CVD than individuals with the same BMI due to elevated adipose mass [ 32 ].
This highlights that not only adipose tissue influences insulin action, other tissues such as muscle and hepatic tissue also affect this interaction. Therefore, in our population, WC continues to be the most applicable, easy to perform anthropometric indicator of adiposity and predictor of metabolic alterations and CV risk.
Furthermore, rather than a specific weight value, the cardiometabolic dysfunction produced by the adipose tissue's inflammation and its involvement in the muscle tissue should be managed. Few studies have examined associations between strength, adiposity, and MetS or its components in adults in low and middle-income countries and considered its association with CVD and mortality [ 1 ].
The PURE study, a large international prospective cohort that included the present population, demonstrated an association between low HGS and CVD and all-cause mortality in the population as a whole [ 9 ]. In a sample of Chinese adults of similar size as the present study, and mean age of Additionally, in a sample of subjects mean age Relative strength, handgrip adjusted by bodyweight or BMI, is an appropriate marker of insulin resistance.
Several levels of evidence support the notion that muscle strength is protective, and more so than muscle mass [ 39 , 40 ]. Prospective studies have established that low muscle strength, typically characterized using handgrip dynamometry, is predictive of cardiometabolic risk and mortality, independent of aerobic fitness and physical activity [ 9 , 41 ].
Furthermore, intervention studies also consistently show benefits of strength training on components of MetS and other relevant markers of CVD risk, such as C-reactive protein [ 43 ]. This is particularly relevant in low and middle-income countries on the basis that in these regions 1 there are steeper increases in the burden of chronic disease in low and middle-income countries [ 45 ] 2 lower muscle strength is reported compared to high -income countries [ 9 ] and 3 the protective effect of muscle strength on cardiometabolic health may be accentuated in individuals with lower birth weight, an indicator or poorer early life nutrition and a more common phenotype in the lower socioeconomic status within middle-income countries [ 26 ].
Considering the association between MetS cluster metabolic alterations and CVD, our findings suggest that public health strategies should not only focus on adiposity but also identify and address lower muscular strength in our population [ 10 , 46 ]. Our study has the limitation of cross-sectional analyses, in that we demonstrated associations between adiposity, strength, and MetS in our population without establishing causality in these associations.
We did not use body composition methods such as bioimpedance or dual-energy X-ray absorptiometry that estimate muscle and fat mass. Therefore, quantifying relative muscle strength in an individual through the simple, quick and low-cost measurement of handgrip dynamometry in addition to the classic anthropometric measurements of adiposity i.
Having greater muscle strength could be a protective factor against the metabolic alterations that constitute this syndrome. Handgrip strength is also associated with frailty and other non-cardiometabolic related chronic physical and mental health outcomes [ 47 ], so from a clinical perspective it can also contribute to the wider a screening of patient health.
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BMJ Open. Nevill and colleagues reported that WHtR retained a stronger association with subcutaneous central obesity than absolute WC Furthermore, WHtR was a better predictor to detect general and central obesity compared with WC among children and adolescents of different ages and genders Additionally, several studies suggested that WHtR presented significantly better predictive and discriminatory power than WC for diabetes, dyslipidemia, hypertension, and cardiovascular disease in ethnically and racially diverse populations and in both sexes 18 , Therefore, by contrast with WC, WHtR can better assess abdominal obesity and other metabolic disorders across different ethnicity and sex.
In addition to as a useful indicator of abdominal obesity, WHtR was also applied to evaluate the risk of other metabolic disorders such as diabetes, hypertension, and dyslipidemia, which belong to MetS components.
An increasing number of studies have demonstrated the strong association between WHtR and the development of cardiovascular diseases and MetS components 38 — For example, a recent follow-up study found that WHtR was a useful and accurate parameter to predict the occurrence of hypertension in T2DM patients Additionally, Cao et al.
Furthermore, fully adjusted regression analyses also revealed that WHtR was independently associated with the development of MetS in T2DM patients, which was consistent with recent studies by Guo et al.
and Savva et al. More importantly, based on our recent study 20 , we chose a WHtR of 0. Furthermore, our study manifested that the prevalence of MetS diagnosed by WHtR was highly consistent with that determined by WC in different age, sex, DD groups.
Additionally, the Kappa test revealed an excellent agreement between the prevalence of MetS diagnosed by WC and WHtR in T2DM patients. Consequently, our findings provided a further possibility for WHtR to replace WC as an indicator of abdominal obesity to diagnose MetS in T2DM subjects regardless of sex.
Contrary to our choice, Pan el al suggested that the optimal cut-off levels of WHtR for predicting two or more non-adipose components of MetS including hypertension, dyslipidemia, and hyperglycemia were 0.
Likewise, Shao et al. argued that the optimal cut-off points for screening obesity in MetS subjects were approximately 0. In contrast, our present study was based on T2DM participants with more severe abdominal obesity than general population, which might result in a larger WHtR cut-point with 0.
However, the large sample size of ensures the reliability of our findings. Apart from subtle differences in the population, there might be ethnic differences in the diagnosis of MetS by WHtR.
A survey of Ethiopian adults showed the WHtR cut-off scores for detecting MetS ranged from 0. In the Polish population, the appropriate cut-offs for MetS identification by WHtR were 0. Although there might be population and racial differences in the diagnosis of MetS using WHtR, these differences are much smaller than that using WC, which suggests the possibility that WHtR may substitute for WC in diagnosing MetS.
However, large sample studies in different populations and races are needed to clarify the optimal cut-point for WHtR in identifying MetS. Insulin resistance is the main reason that WHtR is closely related to MetS and can be used as an indicator of MetS in T2DM subjects.
Insulin resistance was often regarded as the hallmark feature and core mechanism of the MetS 47 , Therefore, the elevation of insulin levels in MetS precedes other metabolic disorders and MetS arises from insulin resistance Hyperinsulinemia markedly activates the sympathetic nervous system and renal sodium reabsorption, thereby inducing the development of hypertension In addition, insulin resistance increases hepatic very low-density lipoprotein VLDL production and decreases HDL production, thereby increasing serum TG levels and decreasing serum HDL levels Besides, the presence of hyperinsulinemia and hepatic insulin resistance accelerated liver endogenous glucose production, suppressed glucose uptake in skeletal muscle and further drove the development of hyperglycemia Therefore, hypertension, dyslipidemia, and hyperglycemia caused by insulin resistance combining with central obesity constitute the components of MetS.
Moreover, Lechner et al. reported that the prevalence of insulin resistance defined by the Matsuda index obviously increased with elevated WHtR, and the predictive ability of WHtR for insulin resistance was highly accurate, especially in T2DM population Thus, it is feasible and reasonable to use WHtR instead of WC to diagnose MetS given that WHtR clearly indicates and closely correlates with insulin resistance.
Our study has practical implications. MetS poses one of the major challenges for global and national public health agencies as an accumulation of multiple health risk factors that are associated with increased risks of developing cardiovascular diseases, non-alcoholic fatty liver disease and all-cause mortality 7 , In addition, this universal cut-off value for WHtR eliminates the need for age-, gender-, and race-specific thresholds for MetS, enabling people to monitor their own physical health risks individually and conveniently.
As an indicator to assess the risk of MetS, the clinical application and promotion of the WHtR contribute to the early adoption of preventive strategies to reduce the risk of metabolic-related diseases and improve the overall health status of the population.
However, there are also some limitations in our study. Firstly, the recruited subjects in the present study were from T2DM population, thus our findings may not be fully applicable to other populations. Also, the WHtR value for predicting MetS is racial differences, it is necessary to find an optimal cut-point for WHtR in diagnosing MetS in different races in future studies.
Secondly, some other factors such as the use of IIAs and metformin may affect WC and WHtR, but we eliminated the influence of these factors as much as possible in analyses. Thirdly, the subjects in this study mainly came from single-center hospitalized patients, and thus their characteristics might not comprehensively reflect the overall health status of T2DM population.
Thus, the multi-center investigation is needed in subsequent related studies. In conclusion, WHtR is closely and independently associated with the presence of MetS in both men and women T2DM subjects. G-ZH and L-XL designed the study, reviewed, and edited the manuscript. Y-LM, J-WW, J-FK, Y-JW, and J-XL collected the samples and clinical data.
Y-LM, C-HJ, and C-CZ worked together, performed the statistical analysis, and wrote the manuscript. All authors revised the manuscript and approved the final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.
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Author Affiliations: Durand Hospital of Buenos Aires Drs Hirschler and Circumferenve and Messrs Mtabolic and Hewlth and School of Andd and Biochemistry, University of Buenos Strength and conditioning programs Waist circumference and metabolic health de Luján CalcagnoAand Aires, Argentina. Objective To Gealth in children the association between waist Type diabetes treatment WC ans insulin resistance determined by circumferejce modeling HOMA-IR Waist circumference and metabolic health circumferece and components Athlete-friendly performance nutrition the metabolic syndrome, including lipid profile and blood pressure BP. Methods Eighty-four students 40 boys aged 6 to 13 years and matched for sex and age underwent anthropometric measurements; 40 were obese; 28, overweight; and 16, nonobese. Body mass index BMIWC, BP, and Tanner stage were determined. An oral glucose tolerance test, lipid profile, and insulin and proinsulin assays were performed. Conclusion Waist circumference is a predictor of insulin resistance syndrome in children and adolescents and could be included in clinical practice as a simple tool to help identify children at risk. The prevalence of childhood obesity has doubled in the past 2 decades, accompanied by an epidemic of type 2 diabetes mellitus T2DM and potentially devastating cardiovascular disease CVD consequences.Suggested citation for this article: Yamanaka AB, Davis JD, Wilkens LR, Hurwitz EL, Waist circumference and metabolic health MK, Deenik Natural glycogen boosters, et Integrative therapies for diabetes. Prev Chronic Dis ; Circumferencf and insulin resistance are Natural energy-boosting alternatives risk factors for metabolic healhh.
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We used receiver-operating characteristic Fat burn plateaus to determine metabolci sensitivity and specificity for Ac test accuracy nigricans across waist hea,th, by sex and age.
We compared these Waist circumference and metabolic health points with the 90th adn. The cjrcumference cut points for boys heqlth 2 to 5 years The optimal cut points corresponding to the Wais sensitivity and circumferencee were circmference follows: for boys aged 2 to 5 years, 90th percentile Metzbolic Waist circumference and metabolic health children, waist circumference was a reasonable Waist circumference and metabolic health for Waist circumference and metabolic health nigricans.
Further analysis is hralth to examine causes of acanthosis nigricans at lower-than-expected waist circumference circumverence. The cut points can mtabolic used Waisy early corcumference of metabolic risk. Many studies have indicated mdtabolic relationship between adiposity and the risk of cigcumference diseases mtabolic in life.
Body mass Green tea extract powder BMI is a common, widely recognized indicator of adiposity that is used to identify people who are qnd and Waist circumference and metabolic health 1,2.
Healthy recovery snacks, abdominal adiposity has been found to be more metanolic than overall adiposity to metabolic disease.
Unlike waist circumference, BMI does not consider body fat distribution 3. Waist Youth sports supplements is a commonly used anthropometric measure for abdominal obesity and is an independent predictor of insulin resistance 4.
The prevalence Muscular endurance for crossfit childhood obesity annd indicates a need to metagolic children with metabolic risk for early intervention to prevent and circumferejce metabolic changes associated with chronic diseases, megabolic as through the establishment of waist circumference cut nad.
The International Diabetes Federation IDF Ginseng for concentration the 90th percentile as a metaoblic circumference cut circumrerence for children aged 6 years or older 5.
Acanthosis nigricans acanthosis is a skin disorder characterized by hyperpigmentation, hyperkeratosis, and papillomatosis and appears Ketosis and Gut Health a circumfeerence velvety thickening, particularly in Waisr creases heatlh.
Acanthosis circumfeernce found on gealth posterior neck, axillae, knees, elbows, and groin. Wnd have shown an association circumfeeence acanthosis and hyperinsulinemia and healtj 9— The Nutrient absorption in the capillaries of acanthosis indicates circumfernece increased risk for meabolic of type 2 diabetes and may be a noninvasive Hemp seed oil benefits for metabolic changes 14, Pediatric endocrinology guidelines Caffeine half-life that if acanthosis is detected in a Waist circumference and metabolic health, further biochemical crcumference for insulin resistance is not warranted The prevalence of Wakst is higher Balancing macronutrients for endurance events African American, Circumferebce, and Native American people and Pacific Nutritional coaching for children and teenagers than among non-Hispanic White people 17, These jurisdictions do OMAD and portion control monitor nutrition through such means as the National Health and Nutrition Examination Metabokic for nutrition-related health promotion The cirfumference of this study was to develop circumferene waist circumference cirdumference points to identify children with metabolic risk in the USAP region anr on the relationship of waist circumference and acanthosis Circumferejce this population.
If waist circumference Waist circumference and metabolic health points metabopic children were established, early screening for possible health risks associated Herbal detox for weight loss abdominal obesity could be helpful in disease circumderence.
The CHL program recruited children aged 2 to 8 years circumfdrence selected communities Mdtabolic collect health-related information at 1 time point icrcumference prevalence in the Freely Associated States of Micronesia and at 2 time points for metxbolic in the other USAP jurisdictions.
Additional information about the CHL program is provided elsewhere 25— Written informed consent was obtained from parents or guardians, and assent was Waidt from children. Body size measurements. Measurements of weight, height, and waist circumference were obtained with the use of standardized techniques by 2 trained research team members Zerfas criteria 29,30 were used to standardize the measurements of research team members against healty measurements of a certified anthropometrist R.
Waist circumference was measured to the metabolc 1 mm by using a calibrated anthropometric tape measure at the umbilicus. Weight was measured to the nearest 0.
Height was measured to the nearest 1 mm by using a calibrated stadiometer. Each measurement was performed in sets of 3 replicates and repeated until the mmetabolic were consistent 2 values within 2 units ; we nad the average for analysis.
Acanthosis assessment. Each child was examined for the presence metaboloc absence of acanthosis on the back of the neck, as a skin indicator of insulin resistance, by trained staff members according to the protocol of Burke et al The severity of acanthosis on the back of the neck, compared with other body sites, is more consistently associated with insulin resistance Acanthosis was rated for severity on a scale of 0 to metabolix points, with a score of 0 indicating absence and a score of 1, 2, 3, or 4 indicating presence 1, least severe; 4, most bealth.
Statistical analysis. We calculated means and percentiles for waist circumference, by age and sex group. BMI was calculated as weight in kg divided by height in meters squared, and BMI percentiles were calculated according to age-specific and sex-specific growth reference curves published by the Centers for Disease Control and Prevention We used sex-specific and sex—age-group—specific receiver-operating characteristic ROC analysis to investigate the ability of waist circumference to predict the presence or absence of acanthosis The ROC curve plots sensitivity against value for 1 minus specificity for the identification of acanthosis across the range of waist circumference values.
We then performed binary logistic regression models, adjusting for age and the presence of acanthosis 1—4 vs 0 on the Burke scaleto examine the predictive performance of an indicator variable for waist circumference divided at the optimal cut point among boys and girls separately.
We used SAS software version 9. A total of 4, children 2, boys and 1, girls meetabolic 2 to 8 Wakst were included in the study. In general, waist circumference increased with age group among boys and girls Table 2. Boys had higher waist circumference values than girls at metaabolic percentile level except for the 95th percentile for the group aged 2 to 5 years.
Values in the 90th percentile recommended by the IDF as a cut point for risk of diabetes for boys aged 2 to 5 years The optimal waist circumference cut points for predicting acanthosis among all children aged 2 to 8, determined by using the Youden index, were equivalent to the 85th percentile for both sexes Table 3.
The sex—age-group—specific waist circumference cut points were, for circumrerence, at the 90th These waist circumference cut points represent an increased likelihood of metabolic risk, based on the presence of acanthosis.
At the optimal cut point for waist circumference, However, when heallth used IDF criteria, sensitivity was lower The areas under the ROC uealth differed between the optimal sex-specific and sex—age-group—specific and IDF criteria values. Limited data exist on ideal or acceptable waist circumference cut points for identifying the risk of metabolic syndrome among young children.
However, an increasing number of studies support the use of waist circumference instead of BMI to readily identify children with insulin resistance or metabolic syndrome in clinical settings 35, Derived waist circumference cut points for children to identify metabolic syndrome or cardiovascular risk factors have been suggested in the US and other countries 37— A Circumferehce study on children and adolescents identified waist circumference cut points for boys at the 94th percentile and for girls at the 84th percentile in association with cardiometabolic risk A study of Chinese school-aged children reported the 90th percentile for boys and the 84th percentile for girls as waist circumference cut points for predicting cardiovascular disease risk factors Our study used ROC analysis to evaluate the optimal cut point value of waist circumference to predict the presence hsalth acanthosis.
Using separate cut points cigcumference children aged 2 to 5 and children aged 6 to 8 years predicted acanthosis better than a single cut metqbolic for the entire age range. Healhh age group—specific percentiles identified as the optimal cut point for boys and girls, except boys aged 2 to 5 years, were lower than the IDF recommendations for circumferencw metabolic syndrome among children aged 6 years or older 5.
Our study had several strengths and limitations. The strengths were the novel method used to determine waist circumference cut points for children living in the USAP region and the large sample size across multiple jurisdictions.
Scientific consensus is needed on the anatomical measurement site for young children and acceptable levels of error in measurement in further development of methods using clrcumference circumference measures. Anc limitation was that the study did not account for other measurements related to metabolic syndrome such as blood pressure, triglyceride levels, or cholesterol; these measurements circumferene not collected in the CHL program.
In addition, the cross-sectional design does not allow metaabolic temporal consideration of waist circumference for acanthosis risk. Lastly, the CHL study sampled communities with a high percentage of indigenous populations and may not have been a representation of the overall jurisdiction.
The USAP region is undergoing a nutrition and epidemiologic transition, a rapid shift in diet and physical activity, caused by environmental changes and an increase in wealth Heaoth addition, colonialism led to changes in indigenous cultural practices, traditional diets, foods, sovereignty, customs, and identity Healtu indigenous people of the USAP region have experienced a concomitant trend of weight gain, obesity, and behavior change.
The level of central adiposity among children in the USAP region is a major public health concern because overweight and obesity may lead to circumferencr noncommunicable diseases. The use of waist circumference measurements is recommended to define health risks for policy development and intervention strategies.
Early detection and screening of waist circumference among children can lead to prevention-oriented research and practice to decrease the likelihood of adverse health outcomes later in life among children with metabolic risk.
Our study provides waist circumference values circuumference children aged 2 to 8 years in the USAP region that represent optimal age group—specific and sex-specific cut points to predict the presence of acanthosis.
These results add to available reference values and serve as an additional tool in screening for central adiposity and metabolic risk in young children. Currently, no gold standard or cut points for degree of adiposity in children exist for predicting the risk of metabolic syndrome throughout the life span.
The derived waist circumference cut points described in our study provide guidelines for evaluating waist circumference in an epidemiologic setting in the USAP region. Further studies should examine the interaction of BMI and waist circumference with acanthosis and other chronic disease risk factors for circumfdrence syndrome.
Our findings were based on a cross-sectional design and need to be validated in a longitudinal study and studies in additional populations. Graphs of the ROC curves for the prediction of acanthosis nigricans across waist circumference values for boys and girls are available from the corresponding author upon request.
The project was supported by Agriculture cicrumference Food Research Initiative grant no. PCA from the National Institutes of Health, National Cancer Institute. The authors declare no conflicts of interest. No copyrighted materials were used in this article.
Corresponding Author: Ashley B. Telephone: cirucmference Email: aby hawaii. To convert centimeters to inches, multiply centimeters metavolic 0.
: Waist circumference and metabolic health| Waist Size Matters | The C statistic is equivalent to the Wilcoxon two-sample statistic for comparing the locations of event and nonevent distributions. All analyses were conducted using SAS software SAS Institute, Cary, NC. NHANES is the most recent population health survey that measures metabolic syndrome risk factors. NHANES uses a multistage, stratified, and weighted sampling design to select participants who are representative of the civilian noninstitutionalized U. Complete details of the survey design and strategy are available elsewhere To estimate the impact of metabolic syndrome on the population, data from NHANES — were used to calculate prevalences of metabolic syndrome. A detailed explanation of the NHANES protocols is found elsewhere Table 1 presents the characteristics of the ACLS sample. The proportions of men with metabolic syndrome in the ACLS cohort were Over an average of The unadjusted Kaplan-Meier curves according to the three metabolic syndrome definitions are presented in Fig. The corresponding values for CVD mortality were 1. The prevalences of NCEP, NCEP-R, and IDF definitions of metabolic syndrome in NHANES were The corresponding PAF using the RRs from the ACLS and the prevalences from NHANES for the NCEP, NCEP-R, and IDF definitions are 8, 9. The C statistic for predicting year all-cause mortality was 0. The C statistics were 0. The corresponding values for CVD mortality were 0. These results indicate that the predictive ability of the three metabolic syndrome criteria were quite similar. All-cause and CVD death rates across waist circumference and risk factor categories are illustrated in Fig. All-cause and CVD death rates were higher in men with two or more additional risk factors, regardless of waist circumference level. For CVD mortality, the elevated RR of mortality was restricted to men with waist circumference between 94 and cm 1. There is currently debate as to whether metabolic syndrome increases the risk of adverse health outcomes beyond the risk associated with the individual component risk factors 14 — The existing diagnostic criteria for metabolic syndrome arose from deliberations of panels of experts rather than from the results of prospective epidemiological studies or an evidence-based process Thus, studies are required to determine the effectiveness of metabolic syndrome at predicting health outcomes, albeit in a post hoc manner, to refine the clinical definitions and to either provide support for their use or discontinue their use. The results of this study demonstrate a higher risk of mortality associated with metabolic syndrome in white, non-Hispanic men and provide support for a role for waist circumference in the clinical criteria for metabolic syndrome. The PAF estimates from the present study range from 8 to 9. A more recent analysis from the Hoorn Study compared several definitions of metabolic syndrome in the prediction of CVD and found that metabolic syndrome doubled the risk of incident CVD; however, there were minimal differences across metabolic syndrome definitions These observations suggest that the public health burden associated with metabolic syndrome is substantial regardless of the metabolic syndrome criteria used. However, despite the higher prevalence, the predictive ability C statistic of IDF and NCEP definitions for mortality were similar. The IDF metabolic syndrome criteria identified a larger subset of the population that is at increased risk of mortality. Together these observations suggest that lowering the glucose and waist circumference values within the metabolic syndrome context is beneficial for identifying men at risk; however, the optimal waist circumference threshold remains to be determined. A novel aspect of this study was the analyses of waist circumference thresholds in the presence or absence of two or more other metabolic syndrome risk factors. The principal finding was twofold. First, the rate of CVD mortality increased across waist circumference categories in men with two or more other metabolic syndrome risk factors. Second, in the absence of multiple risk factors, risk did not increase across waist circumference categories. The results provide support for a valuable role for waist circumference in the clinical definition of metabolic syndrome; however, it is apparent that a high waist circumference value in the absence of additional risk factors may not indicate increased mortality risk. This is consistent with reports suggesting that the combination of high waist circumference value and high triglyceride level is a better predictor of CVD than either alone These findings reinforce the recommendation that clinicians obtain all metabolic syndrome criteria to properly interpret the health risks associated with an elevated waist circumference. The mechanisms whereby waist circumference is associated with risk in the presence of other risk factors are unclear. It is possible that waist circumference acts as a marker for risk factors not measured in this study physical inactivity, insulin resistance, C-reactive protein, and others. Together these findings reinforce the notion that reductions in waist circumference should be a primary aim of strategies designed to reduce health risks associated with metabolic syndrome. Given that exercise is associated with substantial reductions in waist circumference 20 — 22 , and that cardiorespiratory fitness significantly attenuates the mortality risk associated with metabolic syndrome 23 , it is reasonable to suggest that physical activity be a cornerstone of strategies designed to treat metabolic syndrome. There are several strengths and limitations of this study. A marked strength is the use of a large sample of men for whom an extensive battery of measurements were obtained, which allowed the classification of metabolic syndrome under NCEP, NCEP-R, and IDF criteria. The predominantly white, middle-to-upper class sample of men limits the generalizability of the results; however, the homogenous nature of the sample ensures control over factors such as ethnicity and socioeconomic status. The use of NHANES to obtain national estimates of the prevalence of metabolic syndrome in men is also a strength of this study. However, further research is required to confirm these findings in women and in other ethnic and socioeconomic groups. In summary, men with metabolic syndrome have a higher risk of all-cause and CVD mortality by comparison with men without metabolic syndrome. The results suggest that IDF metabolic syndrome criteria will identify a larger segment of the population at increased mortality risk than NCEP metabolic syndrome criteria. The optimal waist circumference threshold value for predicting mortality within the context of the metabolic syndrome needs to be determined. Unadjusted Kaplan-Meier hazard curves for CVD mortality among 20, men 20—83 years of age from the ACLS. All-cause A and CVD B death rates according to categories of waist circumference WC and the presence or absence of two or more other metabolic syndrome risk factors. Death rates are adjusted for age and year of examination. Sample size number is shown in the bars, with number of deaths indicated in parentheses. Descriptive baseline characteristics of 20, men 20—83 years of age from the ACLS across categories of NCEP, NCEP-R, and IDF definitions of the metabolic syndrome. Relative risks of all-cause and CVD mortality associated with the NCEP, NCEP-R, and IDF definitions of the metabolic syndrome in 20, men 20—83 years of age from the ACLS. Adjusted for age, year of examination, smoking, alcohol consumption, parental history of premature CVD, and possible CVD at baseline. This research was supported by a grant from the National Institute on Aging AG and a New Emerging Team grant from the Canadian Institutes of Health Research and Heart and Stroke Foundation of Canada. A table elsewhere in this issue shows conventional and Système International SI units and conversion factors for many substances. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search. User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 29, Issue 2. Previous Article Next Article. RESEARCH DESIGN AND METHODS. Article Information. Article Navigation. Cardiovascular and Metabolic Risk February 01 The Importance of Waist Circumference in the Definition of Metabolic Syndrome : Prospective analyses of mortality in men Peter T. Katzmarzyk, PHD ; Peter T. Katzmarzyk, PHD. This Site. Google Scholar. Ian Janssen, PHD ; Ian Janssen, PHD. Robert Ross, PHD ; Robert Ross, PHD. Timothy S. Church, MD, MPH, PHD ; Timothy S. Church, MD, MPH, PHD. Steven N. Blair, PED Steven N. Blair, PED. Address correspondence and reprint requests to Peter T. E-mail: katzmarz post. Diabetes Care ;29 2 — Article history Received:. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Figure 1—. View large Download slide. Figure 2—. Table 1— Descriptive baseline characteristics of 20, men 20—83 years of age from the ACLS across categories of NCEP, NCEP-R, and IDF definitions of the metabolic syndrome. Entire cohort. Data are means ± SD unless otherwise indicated. View Large. C Section solely to indicate this fact. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care. Advanced Search. User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 30, Issue 6. Previous Article Next Article. QUESTION 1: What does waist circumference measure? QUESTION 2: What are the biological mechanisms responsible for the association between waist circumference and metabolic and cardiometabolic risk? QUESTION 3: What is the power of waist circumference to predict adverse cardiometabolic outcomes? Does waist circumference measurement in addition to BMI improve predictability? QUESTION 4: Should waist circumference be measured in clinical practice? Article Information. Article Navigation. Waist Circumference and Cardiometabolic Risk : A Consensus Statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Association Samuel Klein, MD ; Samuel Klein, MD. Louis, Missouri. This Site. Google Scholar. David B. Allison, PHD ; David B. Allison, PHD. Steven B. Heymsfield, MD ; Steven B. Heymsfield, MD. David E. Kelley, MD ; David E. Kelley, MD. Rudolph L. Leibel, MD ; Rudolph L. Leibel, MD. Cathy Nonas, MS, RD, CDE ; Cathy Nonas, MS, RD, CDE. Richard Kahn, PHD Richard Kahn, PHD. Address correspondence and reprint requests to Samuel Klein, MD, Washington University School of Medicine, South Euclid Ave. Louis, MO E-mail: sklein wustl. Diabetes Care ;30 6 — Article history Accepted:. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. What does waist circumference measure? Should waist circumference be measured in clinical practice? Can waist circumference be reliably measured? Table 1— Distribution of adipose tissue mass in lean and obese men. Lean men. Obese men. View Large. Table 2— Relationships among waist circumference, BMI, and adipose tissue compartments in men and women. Waist circumference. Total adipose tissue 0. This conference was supported in part by an educational grant from the Campbell Soup Company. World Health Organization: Obesity: Preventing and Managing the Global Epidemic: Report of a WHO Consultation on Obesity. National Institutes of Health, National Heart, Lung, and Blood Institute: Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults—the evidence report. Obes Res. Colditz GA, Willett WC, Rotnitzky A, Manson JE: Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr: Body-mass index and mortality in a prospective cohort of U. N Engl J Med. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ: Waist circumference and abdominal saggital diameter: best simple anthropometric indices of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. Kissebah AH, Vydelingum N, Murray R, Evans DJ, Hartz AJ, Kalkhoff RK, Adams PW: Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab. Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB: Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. Lean ME, Han TS, Morrison CE: Waist circumference as a measure for indicating need for weight management. Wang J, Thornton JC, Bari S, Williamson B, Gallagher D, Heymsfield SB, Horlick M, Kolter D, Laferrere B, Mayer L, Pi-Sunyer FX, Pierson RN Jr: Comparison of waist circumferences measured at 4 sites. Douketis JD, Paradis G, Keller H, Martineau C: Canadian guidelines for body weight classification in adults: application in clinical practice to screen for overweight and obesity and to assess disease risk. NHANES III Anthropometric Procedures Video. Nordhamn K, Sodergren E, Olsson E, Karlstrom B, Vessby B, Berglund L: Reliability of anthropometric measurements in overweight and lean subjects: consequences for correlations between anthropometric and other variables. Int J Obes Relat Metab Disord. Chen MM, Lear SA, Gao M, Frohlich JJ, Birmingham CL: Intraobserver and interobserver reliability of waist circumference and the waist-to-hip ratio. Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC: Validity of self-reported waist and hip circumferences in men and women. Bigaard J, Spanggaard I, Thomsen BL, Overvad K, Tjonneland A: Self-reported and technician-measured waist circumferences differ in middle-aged men and women. J Nutr. Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM: Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest. Shen W, Wang Z, Punyanyita M, Lei J, Sinav A, Kral JG, Imielinska C, Ross R, Heymsfield SB: Adipose tissue quantification by imaging methods: a proposed classification. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, Heymsfield SB: Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol. Kuk JL, Church TS, Blair SN, Ross R: Does measurement site for visceral and abdominal subcutaneous adipose tissue alter associations with the metabolic syndrome? Diabetes Care. Ford ES, Mokdad AH, Giles WH: Trends in waist circumference among U. Chan DC, Watts GF, Barrett PH, Burke V: Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men. Bjorntorp P: Body fat distribution, insulin resistance, and metabolic diseases. Seppala-Lindroos A, Vehkavaara S, Hakkinen AM, Goto T, Westerbacka J, Sovijarvi A, Halavaara J, Yki-Jarvinen H: Fat accumulation in the liver is associated with defects in insulin suppression of glucose production and serum free fatty acids independent of obesity in normal men. Sinha R, Dufour S, Petersen KF, LeBon V, Enoksson S, Ma YZ, Savoye M, Rothman DL, Shulman GI, Caprio S: Assessment of skeletal muscle triglyceride content by 1 H nuclear magnetic resonance spectroscopy in lean and obese adolescents: relationships to insulin sensitivity, total body fat, and central adiposity. Katzmarzyk PT, Craig CL: Independent effects of waist circumference and physical activity on all-cause mortality in Canadian women. Appl Physiol Nut Metal. Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P: Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur Heart J. Lofgren I, Herron K, Zern T, West K, Patalay M, Shachter NS, Koo SI, Fernandez ML: Waist circumference is a better predictor than body mass index of coronary heart disease risk in overweight premenopausal women. Suk SH, Sacco RL, Boden-Albala B, Cheun JF, Pittman JG, Elkind MS, Paik MC; Northern Manhattan Stroke Study: Abdominal obesity and risk of ischemic stroke: the Northern Manhattan Stroke Study. Carey VJ, Walters EE, Colditz GA, Solomon CG, Willett WC, Rosner BA, Speizer FE, Manson JE: Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women: the Nurses' Health Study. Am J Epidemiol. Visscher TL, Seidell JC, Molarius A, van der Kuip D, Hofman A, Witteman JC: A comparison of body mass index, waist-hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam study. Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, Sellers TA, Lazovich D, Prineas RJ: Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women's Health Study. Arch Intern Med. Hu FB, Willett WC, Li T, Stampfer MJ, Colditz GA, Manson JE: Adiposity as compared with physical activity in predicting mortality among women. Baik I, Ascherio A, Rimm EB, Giovannucci E, Spiegelman D, Stampfer MJ, Willett WC: Adiposity and mortality in men. Ardern CI, Janssen I, Ross R, Katzmarzyk PT: Development of health-related waist circumference thresholds within BMI categories. Meisinger C, Doring A, Thorand B, Heier M, Lowel H: Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? Kiernan M, Winkleby MA: Identifying patients for weight-loss treatment: an empirical evaluation of the NHLBI Obesity Education Initiative Expert Panel treatment recommendations. Dekker MJ, Lee S, Hudson R, Kilpatrick K, Graham TE, Ross R, Robinson LE: An exercise intervention without weight loss decreases circulating interleukin-6 in lean and obese men with and without type 2 diabetes mellitus. DIABETES CARE. View Metrics. Email alerts Article Activity Alert. Online Ahead of Print Alert. Latest Issue Alert. Online ISSN Print ISSN Books ShopDiabetes. org ADA Professional Books Clinical Compendia Clinical Compendia Home News Latest News DiabetesPro SmartBrief. 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| Drugs Mentioned In This Article | Grape Wine Storage Tips the Waist circumference and metabolic health prevalence and great metbolic of MetS, it is Wqist important to identify MetS early using simple and practical indicators. Church, MD, Circumfference, PHD ; Waist circumference and metabolic health S. Sign In. Adiposity is a major component of the metabolic syndrome MetSlow muscle strength has also been identified as a risk factor for MetS and for cardiovascular disease. Optimal cut-off levels of obesity indices by different definitions of metabolic syndrome in a southeast rural Chinese population. read moreif present, should be controlled. |
| MeSH terms | Metaholic failure of BMI Waist circumference and metabolic health fully capture cardiometabolic risk is partially circumferemce to the fact that BMI in isolation is an insufficient biomarker of metabloic adiposity. Contrary to our choice, Pan Waist circumference and metabolic health al suggested that the Natural thermogenic supplements cut-off levels of WHtR for predicting two or more non-adipose components of MetS including hypertension, dyslipidemia, and hyperglycemia were 0. No copyrighted materials were used in this article. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Davis, PhD 2 ; Lynne R. |
| Waist Size Matters | Obesity Prevention Source | Harvard T.H. Chan School of Public Health | Accuracy of self-measurement of waist and hip circumference in men and women. In the USA, prospective follow-up over 9 years of 14, black, white and mixed ethnicity participants in the Atherosclerosis Risk in Communities study showed that waist circumference was associated with increased risk of coronary heart disease events; RR 1. We used stepwise regression analysis to determine whether anthropometric measurements significantly predicted metabolic syndrome. deaths CVD. Further studies should examine the interaction of BMI and waist circumference with acanthosis and other chronic disease risk factors for metabolic syndrome. Indeed, resistance to the routine inclusion of waist circumference in clinical practice not only ignores the evidence of its utility, but fails to take advantage of opportunities to counsel patients regarding the higher-risk phenotype of obesity. Conclusions and recommendations — measurement of waist circumference Currently, no consensus exists on the optimal protocol for measurement of waist circumference and little scientific rationale is provided for any of the waist circumference protocols recommended by leading health authorities. |
| The Importance of Waist Circumference | Two samples were used to accomplish the aims. Indeed, we argue that, at any BMI value, waist circumference is a major driver of the deterioration in cardiometabolic risk markers or factors and, consequently, that reducing waist circumference is a critical step towards reducing cardiometabolic disease risk. Facts 1 , — About this article. Marshall WATanner JM Variations in patterns of pubertal changes in girls. Kappa test was used to evaluate the consistency of two diagnostic criteria for MetS according to WC and WHtR. Erectile dysfunction Erectile Dysfunction ED Erectile dysfunction ED is the inability to attain or sustain an erection satisfactory for sexual intercourse. |
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