Diagnostic Performance Of Body Mass Index Research Articles

BMI in Evaluating Obesity of Contemporary College Students

Background: Changes in lifestyle and reduction in physical activity have led to a continuous increase in the number of obese people in my country, but the criteria for determining obesity are different. Body mass index (BMI) is the most commonly used indicator for obesity research. Objective: To evaluate the authenticity, reliability and profitability of the obesity determination standard of body mass index (BMI) of college students, using the body fat rate obesity determination standard proposed by the National Center for Disease Control and Prevention as the gold standard, and to discuss BMI as a diagnostic performance and suitable cut-off value for excess body fat percentage. Methods: 1208 college students (557 males and 651 females) were tested, the height and weight were measured to calculate BMI, and the electrical impedance method was used to measure the percentage of body fat (BF%) and the ROC curve cutoff value to construct the BMI obesity diagnosis. standard. Results: The authenticity evaluation of BMI as an obesity screening standard showed high specificity and false negative rate, but low false positive rate and sensitivity; especially for women, the sensitivity was 0.05. Males had higher crude concordance and Youden index than females. When the overall cut-off value of ROC curve analysis was 23.05kg/m², the specificity and sensitivity were high; when the cut-off values for men and women were 23.67kg/m² and 23.35kg/m², the sensitivity was low, the specificity was high, and the misdiagnosis rate was low; BF% was used as the When determining the criteria, the diagnostic performance of BMI and male and female groups was good. Conclusion: When the BMI criterion is 28kg/m², the overall sensitivity is low and male is higher than that of female; the overall specificity is high and the misdiagnosis rate is low. When the BMI obesity diagnosis value is 23.05kg/m², it can effectively improve the sensitivity, reduce the missed diagnosis rate, reduce the missed diagnosis of "occult obesity", improve the evaluation of obesity in the population, and miss the opportunity for intervention.

Obesity, but not glycemic control, predicts liver steatosis in children with type 1 diabetes

ObjectiveNonalcoholic fatty liver disease (NAFLD), the most common liver disease in children, is strongly associated with obesity and insulin resistance. Although type 1 diabetes (T1D) is characterized by insulin deficiency, increasing obesity rates among children with T1D is a major risk factor for NAFLD in this patient population. Predisposing factors for NAFLD in children with T1D are not known. Study designThis is a cross-sectional study comparing children with T1D across the range of body mass index (BMI) to the BMI-matched obese group without T1D. Hepatic steatosis was semi-quantitatively measured via the vibration-controlled transient elastogram (VCTE) method. Linear regression analysis was performed to assess the relationship between controlled-attenuated parameter (CAP) scores and clinical parameters. Receiver-operator curve (ROC) analysis was used to evaluate the diagnostic performance of several clinical parameters against NAFLD status determined via CAP. ResultsTwo-thirds of subjects with obesity had CAP scores suggestive of NAFLD, while 16 % (n = 6) of T1D patients without obesity had elevated CAP. Obese subjects were different from non-obese subjects in many laboratory and clinical characteristics, regardless of T1D status. CAP score was significantly associated with BMI, HDL-Cholesterol (HDL-c), and HbA1c in all subjects as well as the T1D-only subgroup. Among subjects with obesity only, age, HDL-cand ALT were the most significant predictors. Diagnostic performance of BMI, HDL-c, and BMI/HDL ratio were in the good to the excellent range for predicting NAFLD among all subjects, while performance varied for T1D-only or obesity-only groups. ConclusionThe clinical and imaging findings of children with T1D and obesity are comparable to non-diabetic children with a similar degree of obesity. Obesity is the major risk factor for NAFLD in pediatric T1D. BMI, HDL-c, and BMI/HDL ratio may be helpful markers to determine further workup for NAFLD in children with T1D, particularly those with obesity.

Body weight at 1.5- and 3-year health checks and body fat at 14 years of age: a population-based retrospective cohort study using dual-energy X-ray absorptiometry

BackgroundIn Japan, height and weight measurements, taken for all children at birth and 1.5- and 3-year health checks, are recorded in the Mother and Child Health (MCH) Handbook, as required by the law. The present population-based retrospective cohort study aimed to evaluate the diagnostic performance of height and weight records in the Handbook for predicting excessive adiposity in adolescents.MethodsThe source population consisted of 8th grade students (800 students aged 14 years) registered at two public junior high schools. Of these, we excluded students who were born at a gestational age < 37 weeks or > 42 weeks. The present analyses included 435 participants who provided complete information. Body mass index (BMI) was calculated using height and weight records. Body fat mass at 14 years of age was measured by dual-energy X-ray absorptiometry (DXA). Diagnostic performance of BMI calculated from the MCH Handbook records to discriminate between the presence and absence of excessive adiposity at 14 years of age was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was used to quantify the diagnostic accuracy of BMI.ResultsWith regard to the prediction of excessive fat at 14 years of age, AUCs and 95% confidence intervals for BMI at 1.5 and 3 years of age were greater than 0.5. Meanwhile, the AUC of BMI at birth was not significantly greater than 0.5.ConclusionThe present study findings indicate that BMI values calculated using MCH Handbook data have potential ability to distinguish between the presence and absence of excessive fat at 14 years of age.

Benchmarking the Discriminatory Performance of Body Mass Index and Body Fat for Obesity Detection in Patients Treated by Intragastric Balloon.

The accuracy of body mass index (BMI) in detecting obesity in patients treated by intragastric balloon (IGB) remains still speculative. We aimed to determine the discriminatory performance of BMI as an estimate of excess body fat (%BF) in an IGB-treated population. Retrospective analysis of prospectively collected data of 476 patients who completed the 6-month IGB treatment period and were subjected to body composition analysis. We evaluated the relationship between BMI and %BF or lean mass and the diagnostic performance of BMI ≥ 30 kg/m2 for adipose tissue detection, stratified by age (< 40 and ≥ 40 years) and gender. Moreover, we identified anthropometric and body composition parameters serving as predictors of obesity according to %BF-based criteria (> 25% in men or > 35% in women). Gender emerged as an effect modifier in the quadratic polynomial relationship between BMI and %BF (R2 = 0.849 for men, R2 = 0.715 for women), while BMI was linearly associated with %BF in both age groups (R2 = 0.435 for men, R2 = 0.474 for women). BMI was strongly correlated with both %BF (r = 0.67) and lean mass (r = - 0.65). The overall area under the ROC curve for BMI ≥ 30 kg/m2 to detect %BF was 0.87 (95%CI 0.85-0.90). A regression model including lean mass, total body water, age, BMI, and female gender explained 0.970 of the variance in %BF. The discriminatory performance of BMI as an estimate of excess body fat is enhanced by the implementation of gender- and age-specific BMI thresholds for defining obesity, in IGB-treated patients.

Frequency of Prediabetes According to Optimal Cut-Points for Body Mass Index in Saudi Population

Background and Objective: The prevalence and incidence of prediabetes are increasing worldwide. Body mass index (BMI) cut-off for prediabetes can vary. The objective of this study is to identify the optimal BMI cut-off that is associated with prediabetes. Methods: For the present study, we analyzed participants who are equal to or older than 18 years old. A total of 5498 were selected to be enrolled for the present study. All patients were from the population of the primary health and diabetic Centers at King Fahad Armed Forces Hospital. Participants were defined as having prediabetes according to selfreport, clinical reports, use of antidiabetic agents and HbA1c (5.7-6.4). All data were collected by personal interview and on the basis of a review of electronic medical data. Weight (kg) and height (cm) were measured by physician and nurse interviewers and recorded. The optimal sensitivity and specificity using different BMI cut-off values to predict the presence of prediabetes were examined by receiver operating characteristic curve analysis. Results: Of the 5498 participants analyzed, 2049 (37.3%) were male and 3449 (62.7%) were female with female to male ratio 1.7:1. Age was 42.7 ± 15.8 (minimum 18 years and maximum 105 years ) and males were significantly older than females (45.3 ± 16.6 vs. 41.1 ± 15.1 respectively, p&lt;0.0001). BMI was 29.6 ± 6.9Vwhere females had higher BMI than males (30.1 ± 7.5 vs. 28.8 ± 5.5 respectively, p&lt;0.0001). Prediabetes was present in 579 (10.5%), 89 (15.4%) were male and 490 (84.6%) were female with female to male ratio 5.5:1. Diagnostic performance of BMI in detecting prediabetes using optimal BMI cut-off values based on the shortest distance in ROC curve showed BMI values ranged from 28.50 to 29.50 in total population, 27.50 to 28.50 in male and from 28.50 to 29.50 in female. The AUC was 0.560 (95% CI, 0.503- 0.617) in male and 0.517 (95% CI, 0.490-0.544) in female. The predictive value of BMI in detecting prediabetes using BMI cut-off values based on the lowest significant association between BMI and the risk factors from the logistic regression analysis showed that the risk of prediabetes was significantly increased at BMI values as low as 21 kg/m2 and has not been increased as BMI increased. Applying this criterion to identify the cut-off values resulted in improvements in sensitivity, false negative rate and worsening in specificity and false positive rate. Using these lower BMI cut-offs resulted in a very small false negative rate ranging from 0.07 to 0.09. A significant positive association for total population was observed with BMI values starting at 21 kg/m2 and non significant association with BMI values for both genders. Conclusion: The diagnostic usefulness of BMI alone in defining obesity as risk for prediabetes is limited among men and women Saudi adults.

Diagnostic accuracy of body mass index to identify obesity in older adults: NHANES 1999-2004.

Body composition changes with aging lead to increased adiposity and decreased muscle mass, making the diagnosis of obesity challenging. Conventional anthropometry, including body mass index (BMI), while easy to use clinically may misrepresent adiposity. We determined the diagnostic accuracy of BMI using dual-energy X-ray absorptiometry (DEXA) in assessing the degree of obesity in older adults. The National Health and Nutrition Examination Surveys 1999-2004 were used to identify adults aged ⩾60 years with DEXA measures. They were categorized (yes/no) as having elevated body fat by gender (men: ⩾25%; women ⩾35%) and by BMI ⩾25 and ⩾30 kg m(-)(2). The diagnostic performance of BMI was assessed. Metabolic characteristics were compared in discordant cases of BMI/body fat. Weighting and analyses were performed per NHANES (National Health and Nutrition Examination Survey) guidelines. We identified 4984 subjects (men: 2453; women: 2531). Mean BMI and % body fat was 28.0 kg m(-2) and 30.8% in men, and 28.5 kg m(-)(2) and 42.1% in women. A BMI ⩾30 kg m(-)(2) had a low sensitivity and moderately high specificity (men: 32.9 and 80.8%, concordance index 0.66; women: 38.5 and 78.5%, concordance 0.69) correctly classifying 41.0 and 45.1% of obese subjects. A BMI ⩾25 kg m(-2) had a moderately high sensitivity and specificity (men: 80.7 and 99.6%, concordance 0.81; women: 76.9 and 98.8%, concordance 0.84) correctly classifying 80.8 and 78.5% of obese subjects. In subjects with BMI <30 kg m(-)(2), body fat was considered elevated in 67.1% and 61.5% of men and women, respectively. For a BMI ⩾30 kg m(-)(2), sensitivity drops from 40.3% to 14.5% and 44.5% to 23.4%, whereas specificity remains elevated (>98%), in men and women, respectively, in those 60-69.9 years to subjects aged ⩾80 years. Correct classification of obesity using a cutoff of 30 kg m(-)(2) drops from 48.1 to 23.9% and 49.0 to 19.6%, in men and women in these two age groups. Traditional measures poorly identify obesity in the elderly. In older adults, BMI may be a suboptimal marker for adiposity.

Performance of body mass index in predicting diabetes and hypertension in the Eastern Province of Saudi Arabia.

BACKGROUND AND OBJECTIVES:Body mass index (BMI) is the most widely used measure to define obesity and predict its complications, such as diabetes and hypertension, but its accuracy and usefulness in Saudi subjects is unknown. This study aimed to assess the validity of standard BMI cut-point values in the Saudi population.SUBJECTS AND METHODS:197 681 adults participated in a cross-sectional study to detect diabetes and hypertension in the Saudi Eastern province in 2004/2005, with blood pressure, fasting blood sugar, height and weight measurements taken. Sensitivities, specificities, areas under the curves, predictive values, likelihood ratios, false positive, false negatives and total misclassification ratios were calculated for various BMI values determined from receiver operating characteristic (ROC) curves. The significance of the association between risk factors and BMI was assessed using regression analysis.RESULTS:For the definition of overweight, ROC curve analysis suggested optimal BMI cut-offs of 28.50 to 29.50 in men and 30.50 to 31.50 in women, but the levels of sensitivity and specificity were too low to be of clinical value and the overall misclassification was unacceptably high across all the selected BMI values (>0.80). The relationship between BMI and the presence of diabetes and/or hypertension was not improved when a BMI of 25 was used. Using regression analyses, the odds ratios for hypertension and/or diabetes increased significantly from BMI values as low as 21-23 with no improvement in the diagnostic performance of BMI at these cutoffs.CONCLUSION:In Saudi population, there is an increased risk of diabetes and hypertension relative to BMI, starting at a BMI as low as 21 but overall there is no cutoff BMI level with high predictive value for the development of these chronic diseases, including the WHO definition of obesity at BMI of 30.