Brain insulin resistance is a key pathological feature contributing to obesity, diabetes, and neurodegenerative disorders, including Alzheimer’s disease (AD). Besides the classic transcriptional mechanism mediated by hormones, posttranscriptional regulation has recently been shown to regulate a number of signaling pathways that could lead to metabolic diseases. Here, we show that microRNA 7 (miR-7), an abundant microRNA in the brain, targets insulin receptor (INSR), insulin receptor substrate 2 (IRS-2), and insulin-degrading enzyme (IDE), key regulators of insulin homeostatic functions in the central nervous system (CNS) and the pathology of AD. In this study, we found that insulin and liver X receptor (LXR) activators promote the expression of the intronic miR-7-1 in vitro and in vivo, along with its host heterogeneous nuclear ribonucleoprotein K (HNRNPK) gene, encoding an RNA binding protein (RBP) that is involved in insulin action at the posttranscriptional level. Our data show that miR-7 expression is altered in the brains of diet-induced obese mice. Moreover, we found that the levels of miR-7 are also elevated in brains of AD patients; this inversely correlates with the expression of its target genes IRS-2 and IDE. Furthermore, overexpression of miR-7 increased the levels of extracellular Aβ in neuronal cells and impaired the clearance of extracellular Aβ by microglial cells. Taken together, these results represent a novel branch of insulin action through the HNRNPK–miR-7 axis and highlight the possible implication of these posttranscriptional regulators in a range of diseases underlying metabolic dysregulation in the brain, from diabetes to Alzheimer’s disease.

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BackgroundSelenium and single-nucleotide-polymorphisms in selenoprotein genes have been associated to diabetes. However, the interaction of selenium with genetic variation in diabetes and oxidative stress-related genes has not been evaluated as a potential determinant of diabetes risk.MethodsWe evaluated the cross-sectional and prospective associations of plasma selenium concentrations with type 2 diabetes, and the interaction of selenium concentrations with genetic variation in candidate polymorphisms, in a representative sample of 1452 men and women aged 18–85 years from Spain.ResultsThe geometric mean of plasma selenium levels in the study sample was 84.2 µg/L. 120 participants had diabetes at baseline. Among diabetes-free participants who were not lost during the follow-up (N=1234), 75 developed diabetes over time. The multivariable adjusted odds ratios (95% confidence interval) for diabetes prevalence comparing the second and third to the first tertiles of plasma selenium levels were 1.80 (1.03, 3.14) and 1.97 (1.14, 3.41), respectively. The corresponding hazard ratios (95% CI) for diabetes incidence were 1.76 (0.96, 3.22) and 1.80 (0.98, 3.31), respectively. In addition, we observed significant interactions between selenium and polymorphisms in PPARGC1A, and in genes encoding mitochondrial proteins, such as BCS1L and SDHA, and suggestive interactions of selenium with other genes related to selenoproteins and redox metabolism.ConclusionsPlasma selenium was positively associated with prevalent and incident diabetes. While the statistical interactions of selenium with polymorphisms involved in regulation of redox and insulin signaling pathways provide biological plausibility to the positive associations of selenium with diabetes, further research is needed to elucidate the causal pathways underlying these associations.

Inorganic arsenic exposure may be associated with diabetes, but the evidence at low-moderate levels is not sufficient. Polymorphisms in diabetes-related genes have been involved in diabetes risk. We evaluated the association of inorganic arsenic exposure on diabetes in the Hortega Study, a representative sample of a general population from Valladolid, Spain. Total urine arsenic was measured in 1451 adults. Urine arsenic speciation was available in 295 randomly selected participants. To account for the confounding introduced by non-toxic seafood arsenicals, we designed a multiple imputation model to predict the missing arsenobetaine levels. The prevalence of diabetes was 8.3%. The geometric mean of total arsenic was 66.0 μg/g. The adjusted odds ratios (95% confidence interval) for diabetes comparing the highest with the lowest tertile of total arsenic were 1.76 (1.01, 3.09) and 2.14 (1.47, 3.11) before and after arsenobetaine adjustment, respectively. Polymorphisms in several genes including IL8RA, TXN, NR3C2, COX5A and GCLC showed suggestive differential associations of urine total arsenic with diabetes. The findings support the role of arsenic on diabetes and the importance of controlling for seafood arsenicals in populations with high seafood intake. Suggestive arsenic-gene interactions require confirmation in larger studies.