Abstract
The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations.
Highlights
A pandemic of obesity exists that is associated with a number of serious and debilitating diseases, including diabetes mellitus type 2 (DM2) and metabolic syndrome (MS), a condition which is characterized by central obesity, glucose intolerance, hypertension, and hyperlipidemia [1]
The prevalence of DM2 was significantly lower in the Index Group (IG; 5%), it was equivalent in the cross validation (CV) and endocrine practice (EP) groups at 13% and 10% respectively
Correlations between anthropometric variables and dual energy x-ray absorptometry (DXA)-derived body compartments showed that waistline was not correlated to the total fat mass, but rather to trunk fat (TF) and to fat free mass (FFM) (Table 2)
Summary
A pandemic of obesity exists that is associated with a number of serious and debilitating diseases, including diabetes mellitus type 2 (DM2) and metabolic syndrome (MS), a condition which is characterized by central obesity, glucose intolerance, hypertension, and hyperlipidemia [1]. Results of a number of prospective studies show that identification of individuals at high risk for metabolic disease allows for direct life style or medical interventions that delay or prevent the progression of DM2 [4,5] and MS [6,7]. Because the prevalence of DM2 and MS within large populations in the developed and developing world countries is so high, there is a need for simple, effective screening methods to identify those individuals who likely have disease. Those at high risk could be offered follow-up diagnostic testing to determine definitively the degree of metabolic dysfunction and offered appropriate treatments
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