Certain obese individuals have adiponectin levels similar to those found in normal BMI subjects; this is associated with the metabolically healthy obese phenotype.
Common polymorphisms in the fat mass and obesity-associated gene (FTO) have shown strong association with obesity in several populations. In the present study, we explored the association of FTO gene polymorphisms with obesity and other biochemical parameters in the Mexican population. We also assessed FTO gene expression levels in adipose tissue of obese and nonobese individuals. The study comprised 788 unrelated Mexican-Mestizo individuals and 31 subcutaneous fat tissue biopsies from lean and obese women. FTO single-nucleotide polymorphisms (SNPs) rs9939609, rs1421085, and rs17817449 were associated with obesity, particularly with class III obesity, under both additive and dominant models (P = 0.0000004 and 0.000008, respectively). These associations remained significant after adjusting for admixture (P = 0.000003 and 0.00009, respectively). Moreover, risk alleles showed a nominal association with lower insulin levels and homeostasis model assessment of B-cell function (HOMA-B), and with higher homeostasis model assessment of insulin sensitivity (HOMA-S) only in nonobese individuals (P dom = 0.031, 0.023, and 0.049, respectively). FTO mRNA levels were significantly higher in subcutaneous fat tissue of class III obese individuals than in lean individuals (P = 0.043). Risk alleles were significantly associated with higher FTO expression in the class III obesity group (P = 0.047). In conclusion, FTO is a major risk factor for obesity (particularly class III) in the MexicanMestizo population, and is upregulated in subcutaneous fat tissue of obese individuals.
Aims To select a core list of standard outcomes for diabetes to be routinely applied internationally, including patientreported outcomes. Methods We conducted a structured systematic review of outcome measures, focusing on adults with either type 1 or type 2 diabetes. This process was followed by a consensus-driven modified Delphi panel, including a multidisciplinary group of academics, health professionals and people with diabetes. External feedback to validate the set of outcome measures was sought from people with diabetes and health professionals. Results The panel identified an essential set of clinical outcomes related to diabetes control, acute events, chronic complications, health service utilisation, and survival that can be measured using routine administrative data and/or clinical records. Three instruments were recommended for annual measurement of patient-reported outcome measures: the WHO Well-Being Index for psychological well-being; the depression module of the Patient Health Questionnaire for depression; and the Problem Areas in Diabetes scale for diabetes distress. A range of factors related to demographic, diagnostic profile, lifestyle, social support and treatment of diabetes were also identified for case-mix adjustment. Conclusions We recommend the standard set identified in this study for use in routine practice to monitor, benchmark and improve diabetes care. The inclusion of patient-reported outcomes enables people living with diabetes to report directly on their condition in a structured way.
IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.