Association between serum uric acid levels and cardiovascular risk among university workers from the State of Mexico: a nested case–control study

Background: Recent evidence suggests that serum uric acid (SUA) can be an inexpensive and easy-to-obtain indicator of cardiovascular risk (CR). This is especially important in developing countries with high prevalence of cardiovascular disease. We examined the association between SUA levels and 10-year global CR among university workers from the State of Mexico, Mexico. Methods: A case–control study nested within a cohort was conducted between 2004 and 2006. Anthropometric measures, lifestyle variables, family background and CR factors were assessed. The analysis estimated odds ratios using conditional logistic regression. Results: The study included 319 cases with CR and 638 controls. Subjects in the upper tertile of SUA had 48.0% higher odds of having an elevated CR than those in the lower tertile (OR = 1.48, 95% CI: 1.04 - 2.10) in the crude analysis, but the association was non-significant when adjusting for other covariates. Among physically inactive individuals, being in the third tertile of SUA doubled the odds of high CR, compared with those who perform physical activity three or more hours per week being in the first tertile of SUA (OR = 2.35, 95% CI: 1.24 - 4.45). Conclusion: Serum concentration of uric acid is associated with 10-year global CR among individuals with high levels of physical inactivity

A distinct metabolic signature predicts development of fasting plasma glucose

ABSTRACT: BACKGROUND: High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called `omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. METHODS: We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. RESULTS: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. CONCLUSIONS: We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods

Oppression of Obesity

This dissertation study investigated the cultural influences and oppressive effects of obesity among 10 highly visible adults in contemporary USA media. The following research questions guided the study: (a) What are the influential cultural factors affecting the increase of obesity in the USA? (b) What are the oppressive effects of contemporary cultural biases perpetuated toward overweight/obese/fat people in the USA? Participant characteristics assumed to indicate the cultural factors of influence on the development of obesity as well as the lived experiences of oppression were identified through the analysis of participant responses to constructed interview questions. The following cultural studies tenets guided the construction of the questions: (a) articulation; (b) hegemony; (c) ideology; and (d) representation. Phenomenological analyses indicated that there are multiple cultural factors that influence the development of obesity and that the lived experience of oppression is common among those who are obese, regardless of race, socioeconomic status, or gender. The study indicated that the oppression of obesity began early in the participants lives and continued into adulthood, at home, at school, and in the workplace. The analyses also revealed that the study participants were vulnerable to the effects of key cultural factors that affect the development of obesity in contemporary society. Specifically, these influences include the following: (a) misleading advertising; (b) sedentary lifestyles; and (c) increased consumption of processed foods