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Beyond Body Mass Index: Advantages of Abdominal Measurements for Recognizing Cardiometabolic Disorders.

ABSTRACT: The clinical recognition of cardiometabolic disorders might be enhanced by anthropometry based on the sagittal abdominal diameter (SAD; also called "abdominal height") or waist circumference rather than on weight. Direct comparisons of body mass index (BMI, weight/height(2)) with SAD/height ratio (SADHtR) or waist circumference/height ratio (WHtR) have not previously been tested in nationally representative populations.Nonpregnant adults without diagnosed diabetes (ages 20-64 years; n = 3071) provided conventional anthropometry and supine SAD (by sliding-beam caliper) in the 2011-2012 US National Health and Nutrition Examination Survey. Population-weighted, logistic models estimated how strongly each anthropometric indicator was associated with 5 cardiometabolic disorders: Dysglycemia (glycated hemoglobin ?5.7%), HyperNonHDLc (non-high-density-lipoprotein [HDL] cholesterol ?4.14 mmol/L, or taking anticholesteremic medications), Hypertension (systolic blood pressure ?140 mm Hg or diastolic blood pressure ?90 mm Hg, or taking antihypertensive medications), HyperALT (alanine transaminase ?p75 [75th percentile, sex-specific]), and HyperGGT (gamma-glutamyltransferase ?p75 [sex-specific]).After scaling each indicator, adjusted odds ratios (aORs) tended to be highest for SADHtR and lowest for BMI when identifying each disorder except dysglycemia. When SADHtR entered models simultaneously with BMI, the aORs for BMI no longer directly identified any condition, whereas SADHtR identified persons with HyperNonHDLc by aOR 2.78 (95% confidence interval [CI], 1.71-4.51), Hypertension by aOR 2.51 (95% CI, 1.22-5.15), HyperALT by aOR 2.89 (95% CI, 1.56-5.37), and HyperGGT by aOR 5.43 (95% CI, 3.01-9.79). WHtR competed successfully against BMI with regard to Dysglycemia, HyperNonHDLc, and HyperGGT. c-Statistics of SADHtR and WHtR were higher than those of BMI (P <.001) for identifying HyperNonHDLc and HyperGGT.Among nonelderly adults, SADHtR or WHtR recognized cardiometabolic disorders better than did the BMI.

SUBMITTER: Kahn HS

PROVIDER: S-EPMC5292922 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Beyond Body Mass Index: Advantages of Abdominal Measurements for Recognizing Cardiometabolic Disorders.

Kahn Henry S HS Bullard Kai McKeever KM

The American journal of medicine 20150821 1

<h4>Background</h4>The clinical recognition of cardiometabolic disorders might be enhanced by anthropometry based on the sagittal abdominal diameter (SAD; also called "abdominal height") or waist circumference rather than on weight. Direct comparisons of body mass index (BMI, weight/height(2)) with SAD/height ratio (SADHtR) or waist circumference/height ratio (WHtR) have not previously been tested in nationally representative populations.<h4>Methods</h4>Nonpregnant adults without diagnosed diabe ...[more]

PMID: 26302146

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Project description:ObjectiveThe worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.MethodsThe cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.ResultsBMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r?=?0.78 versus r?=?0.51), but not in sex-stratified analyses (men, r?=?0.63 versus r?=?0.79; women, r?=?0.69 versus r?=?0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.ConclusionsBAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI's relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.

Project description:Background Metabolic syndrome is associated with high risk of cardiovascular disease, although risk may differ according to the specific conditions present and variability in those conditions. Methods and Results We defined obesity (body mass index [BMI] ≥30 kg/m2) and metabolic health (<2 nonobesity National Cholesterol Education Program Adult Treatment Panel III conditions) among 3632 Framingham Heart Study offspring cohort participants (mean age, 50.8 years; 53.8% women) who were followed up from 1987 to 2014. We defined participants whose variance independent of the mean for a metabolic syndrome-associated measure was in the top quintile as being "variable" for that measure. Variable metabolic health was defined as ≥2 variable nonobesity metabolic syndrome components. We investigated the interaction between obesity and metabolic health in their associations with cardiometabolic disease and cardiovascular disease using Cox proportional hazards regression. In addition, we estimated the associations of BMI variability and variable metabolic health with study outcomes within categories of obesity and metabolic health status, respectively. We observed 567 incident obesity (41 439 person-years), 771 incident metabolically unhealthy state (25 765 person-years), 272 incident diabetes mellitus (56 233 person-years), 503 incident hypertension (12 957 person-years), 589 cardiovascular disease (60 300 person-years), and 195 chronic kidney disease (47 370 person-years) events on follow-up. Obesity and being metabolically unhealthy were independently and positively associated with all outcomes. BMI variability, compared with stable BMI, was associated with 163%, 67%, 58%, and 74% higher risks of having obesity, becoming metabolically unhealthy, having diabetes mellitus, and having hypertension, respectively, among nonobese participants. Variable metabolic health, compared with stable metabolic health, was associated with a 28% higher risk of cardiovascular disease, among metabolically healthy participants. Conclusions We did not observe evidence for a positive interaction between obesity and metabolic health status with regard to study outcomes. BMI and metabolic health variability are associated with adverse health outcomes.

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Beyond Body Mass Index: Advantages of Abdominal Measurements for Recognizing Cardiometabolic Disorders.

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Beyond Body Mass Index: Advantages of Abdominal Measurements for Recognizing Cardiometabolic Disorders.

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