Context: In the ob/ob mice, keeping adiponectin concentrations in the physiological range (through overexpression of this gene in the adipose tissue) results in expansion of fat mass and protection against metabolic co-morbidities. Objective: The aim of the study was to test in humans whether plasma adiponectin levels, similar to those found in lean subjects, are associated with the metabolically healthy obese phenotype. Design and Setting: A cross-sectional analysis was performed of a cohort of obese and nonobese subjects aged 18–70 yr. A medical history was taken, and glucose, plasma lipids, and total adiponectin were measured. Participants: We studied 189 men and 527 women. The majority were obese (n = 470, 65.6%). The metabolically healthy obese phenotype was found in 38 men and 133 women. This is defined as a body mass index (BMI) above 30 kg/m² plus high-density lipoprotein cholesterol of at least 40 mg/dl in the absence of type 2 diabetes and arterial hypertension. Results: Twenty percent of the cases with a BMI above 40 kg/m² had adiponectin concentrations above the median value of normal BMI subjects. Adiponectin levels above 12.49 mg/liter in obese women (odds ratio, 3.02; 95% confidence interval, 1.95–4.67; P < 0.001) and above 8.07 mg/liter in obese men (odds ratio, 2.14; 95% confidence interval, 1.1–4.06; P = 0.01) increased the probability of being metabolically healthy. The association remained significant (β, 0.673 ± 0.205, P < 0.001) in a logistic regression model (r² = 0.25, P < 0.001) after controlling for the confounding effect of age, insulin, and waist circumference. Conclusions: 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 Mexican‐Mestizo 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 patient‐reported 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.

Introduction Previous 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 methods We 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. Results SNNN 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). Conclusions Diabetes 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.

Background: Mobile health (mHealth) has been hailed as a potential gamechanger for non-communicable disease (NCD) management, especially in low- and middle-income countries (LMIC). Individual studies illustrate barriers to implementation and scale-up, but an overview of implementation issues for NCD mHealth interventions in LMIC is lacking. This paper explores implementation issues from two perspectives: information in published papers and field-based knowledge by people working in this field. Methods: Through a scoping review publications on mHealth interventions for NCDs in LMIC were identified and assessed with the WHO mHealth Evidence Reporting and Assessment (mERA) tool. A two-stage web-based survey on implementation barriers was performed within a NCD research network and through two online platforms on mHealth targeting researchers and implementors. Results: 16 studies were included in the scoping review. Short Message Service (SMS) messaging was the main implementation tool. Most studies focused on patient-centered outcomes. Most studies did not report on process measures and on contextual conditions influencing implementation decisions. Few publications reported on implementation barriers. The websurvey included twelve projects and the responses revealed additional information, especially on practical barriers related to the patients’ characteristics, low demand, technical requirements, integration with health services and with the wider context. Many interventions used low-cost software and devices with limited capacity that not allowed linkage with routine data or patient records, which incurred fragmented delivery and increased workload. Conclusion: Text messaging is a dominant mHealth tool for patient-directed of quality improvement interventions in LMIC. Publications report little on implementation barriers, while a questionnaire among implementors reveals significant barriers and strategies to address them. This information is relevant for decisions on scale-up of mHealth in the domain of NCD. Further knowledge should be gathered on implementation issues, and the conditions that allow universal coverage.