Classification of diagnostic subcategories for obesity and diabetes based on eating patterns.

AIM: To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutritional studies. METHODS: Statistical analysis of self-reported eating patterns based on designated food groups is conducted. Predictive models for health status based on dietary patterns are built using a naïve Bayes classifier. RESULTS: Clear patterns emerge in the model building where adults are categorised as having obesity, diabetes or both. The model for diabetics showed the greatest degree of predictability, producing sensitivity results 2.4 times higher than the average, using score decile testing. The models for people with obesity and for those with both obesity and diabetes both reported sensitivity doubling the average. Coverage also showed greatest response for the diabetic model, the first decile containing 24% of all diabetics. CONCLUSIONS: Classifier models using dietary habits as inputs succeed in subcategorising Mexican adults based on health status. Diabetics are associated with a very different, and more appropriate dietary pattern (significantly less sugar consumption) for their condition, relative to the non-diagnosed group. Adults with obesity are also associated with a very different, but inappropriate (higher overall consumption), dietary pattern. We hypothesise that obesity, unlike diabetes, is not seen as a sufficiently serious condition, leading to an inadequate response to the diagnosis. Furthermore, data mining techniques can provide new results in nutritional studies.

Anthropometric measurements and mortality in frail older adults.

BACKGROUND: As the number of older adults increases, so does the number of frail older adults. Although anthropometry has been widely used as a way to stratify the overall mortality risk of a person, the significance of these measurements becomes blurred in the case of frail older adults who have changes in body composition. Therefore, the aim of this study is to determine the association of anthropometric measurements (body mass index, knee-adjusted height body mass index, waist-to-hip ratio and calf circumference) with mortality risk in a group of older Mexican adults. METHODS: This is a longitudinal analysis of the Mexican Health and Aging sub-sample (with biomarkers, n = 2573) from the first wave in 2001, followed-up to the last available wave in 2015. Only frail 50-year or older adults (Frailty Index with a cut-off value of 0.21 or higher, was used) were considered for this analysis (n = 1298). A survival analysis was performed with Kaplan-Meier curves and Cox regression models (unadjusted and adjusted for confounding). Socio-demographic, health risks, physical activity and comorbidities were variables used for adjusting the multivariate models. RESULTS: From the total sample of 1298 older adults, 32.5% (n = 422) died during follow-up. The highest hazard ratio in the adjusted model was for calf circumference 1.31 (95% confidence interval 1.02-1.69, p = 0.034). Other measurements were not significant. CONCLUSIONS: Anthropometric measurements have different significance in frail older adults, and these differences could have implications on adverse outcomes. Calf circumference has a potential value in predicting negative health outcomes.

The effect of a medical opinion on self-perceptions of weight for Mexican adults: perception of change and cognitive biases.

BACKGROUND: This study analysed the relationship between perceived and actual Body Mass Index (BMI) and the effect of a prior identification of obesity by a medical professional for adults using difference in response for two distinct BMI self-perception questions. Typically, self-perception studies only investigate the relation with current weight, whereas here the focus is on the self-perception of weight differences. METHODS: A statistical approach was used to assess responses to the Mexican ENSANUT 2006 survey. Adults in the range of BMI from 13 to 60 were tested on responses to a categorical question and a figure rating scale self-perception question. Differences in response by gender and identification of obesity by a medical professional were analysed using linear regression. RESULTS: Results indicated that regardless of current BMI and gender, a verbal intervention by a medical professional will increase perceived BMI independently of actual BMI but does not necessarily make the identified obese more accurate in their BMI estimates. A shift in the average self-perception was seen with a higher response for the identified obese. A linear increase in perceived BMI as a function of actual BMI was observed in the range BMI < 35 but with a rate of increase much less than expected if weight differences were perceived accurately. CONCLUSIONS: Obese and overweight Mexican adults not only underestimated their weight, but also, could not accurately judge changes in weight. For example, an increase of 5 kg is imagined, in terms of self-image, to be considerably less. It was seen that an identification of obesity by a health care professional did not improve ability to judge weight but, rather, served as a new anchor from which the identified obese judge their weight, suggesting that even those identified obese who have lost weight, perceive their weight to be greater than it actually is. We believe that these results can be explained in terms of two cognitive biases; the self-serving bias and the anchoring bias.