Metabolic Physiological Networks: The Impact of Age.

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, and waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, and creatinine), along with vital signs (axillar temperature, systolic, and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network's topology that reflect changes in the system-wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, i.e., the density of triangles in the network, can provide topological indicators of health that assess the whole state of the system. Overall, our findings show the importance of visualizing health as a network of organs and/or systems, and highlight the importance of triglycerides, insulin, uric acid and glucose as key biomarkers in the prevention of the development of metabolic disorders.

The Impact of Education and Age on Metabolic Disorders.

Metabolic disorders, such as obesity, elevated blood pressure, dyslipidemias, insulin resistance, hyperglycemia, and hyperuricemia have all been identified as risk factors for an epidemic of important and widespread chronic-degenerative diseases, such as type 2 diabetes and cardiovascular disease, that constitute some of the world's most important public health challenges. Their increasing prevalence can be associated with an aging population and to lifestyles within an obesogenic environment. Taking educational level as a proxy for lifestyle, and using both logistic and linear regressions, we study the relation between a wide set of metabolic biomarkers, and educational level, body mass index (BMI), age, and sex as correlates, in a population of 1,073 students, academic and non-academic staff at Mexico's largest university (UNAM). Controlling for BMI and sex, we consider educational level and age as complementary measures-degree and duration-of exposure to metabolic insults. Analyzing the role of education across a wide spectrum of educational levels (from primary school to doctoral degree), we show that higher education correlates to significantly better metabolic health when compared to lower levels, and is associated with significantly less risk for waist circumference, systolic blood pressure, glucose, glycosylated hemoglobin, triglycerides, high density lipoprotein and metabolic syndrome (all p < 0.05); but not for diastolic blood pressure, basal insulin, uric acid, low density lipoprotein, and total cholesterol. We classify each biomarker, and corresponding metabolic disorder, by its associated set of statistically significant correlates. Differences among the sets of significant correlates indicate various aetiologies and the need for targeted population-specific interventions. Thus, variables strongly linked to educational level are candidates for lifestyle change interventions. Hence, public policy efforts should be focused on those metabolic biomarkers strongly linked to education, while adopting a different approach for those biomarkers not linked as they may be poor targets for educational campaigns.

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.

An Analysis of Real, Self-Perceived, and Desired BMI: Is There a Need for Regular Screening to Correct Misperceptions and Motivate Weight Reduction?

We study the relationship among real, self-perceived, and desired body mass index (BMI) in 21,288 adults from the Mexican National Health and Nutrition Survey 2012, analyzing the effect of sex and diagnosis of obesity/overweight by a healthcare professional. Self-perceived and desired BMI are analyzed via a figure rating scale question and compared to real BMI. Only 8.8 and 6.1% of the diagnosed and non-diagnosed obese, respectively, correctly identify themselves as such. For the obese, 20.2% of non-diagnosed and 12.7% of diagnosed perceive themselves as normal or underweight, while 49.1 and 37% of these are satisfied with their perceived BMI. Only 7.8% of the obese, whose real and perceived BMI coincide, have a desired BMI equal to their perceived one. In contrast, 43.2% of the obese, whose perceived BMI is normal, have a desired BMI the same as their perceived one. Although the average desired body figure corresponds to the normal BMI range, misperceptions of BMI correlate strongly with the degree of satisfaction associated with perceived BMI, with larger misperceptions indicating a higher degree of satisfaction. Hypothesizing that the differences between real, perceived, and desired weight are a motivator for weight change, one potential intervention could be the periodic assessment of real, perceived, and desired BMI in order to correct misleading weight misperceptions that could potentially obstruct positive behavioral change.

Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach.

Data mining and knowledge discovery as an approach to examining medical data can limit some of the inherent bias in the hypothesis assumptions that can be found in traditional clinical data analysis. In this paper we illustrate the benefits of a data mining inspired approach to statistically analysing a bespoke data set, the academic multicentre randomised control trial, U.K Glucose Insulin in Stroke Trial (GIST-UK), with a view to discovering new insights distinct from the original hypotheses of the trial. We consider post-stroke mortality prediction as a function of days since stroke onset, showing that the time scales that best characterise changes in mortality risk are most naturally defined by examination of the mortality curve. We show that certain risk factors differentiate between very short term and intermediate term mortality. In particular, we show that age is highly relevant for intermediate term risk but not for very short or short term mortality. We suggest that this is due to the concept of frailty. Other risk factors are highlighted across a range of variable types including socio-demographics, past medical histories and admission medication. Using the most statistically significant risk factors we build predictive classification models for very short term and short/intermediate term mortality.