OBJECTIVE: To determine values of simple anthropometric measurements which are associated with the presence of @entity6 , @entity101 and dyslipidaemia and to assess anthropometric cut-off values for predicting the likelihood of these chronic conditions in a Mexican population. DESIGN AND SETTING: The data were obtained from PRIT (Prevalence of Cardiovascular Risk Factors in General Hospital Workers) surveys from 1994 to 2000 adjusted to the structure of the overall Mexican population. SUBJECTS: A total of 2426 @entity1 and 5939 @entity1 aged 38.99+/-7.11 and 39.11+/-14.25 y, respectively. MEASUREMENTS: The optimal sensitivity and specificity of using various cut-off values of BMI (body mass index), WHR (waist-to-hip ratio), WC (waist circumference) and WTH (waist-to-height ratio) to predict @entity6 ( @entity6 ), @entity101 ( @entity101 ), or dyslipidaemia were examined by receiver operating characteristic curve (ROC) analysis. The likelihood ratios for having @entity6 , @entity101 and dyslipidaemia in subjects with various cut-off values of BMI, WHR, WC and WTH were calculated. Multiple step-wise logistic regression analysis was used to examine the independent relationship between the anthropometric indexes, age and smoking, and the odds ratio of having chronic conditions. RESULTS: The BMI cut-off to predict @entity6 , @entity101 , or dyslipidaemia varied from 25.2 to 26.6 kg/m2 in both @entity1 and @entity1 . The optimal WC cut-offs were 90 cm in @entity1 and 85 cm in @entity1 . The WHR cut-off was about 0.90 in @entity1 and 0.85 in @entity1 , and the optimal WTH cut-off was 52.5 in @entity1 and varied from 53 to 53.5 in @entity1 . The cut-off levels for WC, WHR and WTH corresponded to the inflexion points in the likelihood ratio graphs. In the case of BMI likelihood ratio graphs, we found a significant increase in the risk for chronic conditions from 22 to 23 BMI levels in both genders. Logistic regression analyses disclosed that only BMI and age were included in all the models as well as the influence of smoking in @entity6 and dyslipidaemia in @entity1 . CONCLUSION: Although these results may not be readily applied to the rest of the Mexican population or to other Hispanic populations, they point to the necessity of similar studies with large randomized samples to find the cut-off levels for chronic conditions in different populations.

Anthropometric indexes in the prediction of @entity6 , XXXX and dyslipidaemia in a Mexican population.

@entity1 :: ('9606', 'Species') :: ['women', 'men'] @entity101 :: ('MESH:D006973', 'Disease') :: ['HT', 'hypertension'] @entity6 :: ('MESH:D003920', 'Disease') :: ['diabetes', 'DM', 'type 2 diabetes mellitus']
@entity101 :: (MESH:D006973,Disease) :: ['hypertension']
