Nutrition-/diet-induced changes in gene expression in pancreatic β-cells

Downregulation of Kcnq1ot1 attenuates β-cell proliferation and insulin secretion via the miR-15b-5p/Ccnd1 and Ccnd2 axis

AIM

To examine the effect of lncRNA Kcnq1ot1 on pancreatic β cells in the development of diabetes.

METHODS

The expression levels of Kcnq1ot1 were detected in the islets of diabetes mouse models and the serum of patients with type 2 diabetes by qRT-PCR. CCK8, Ki67 staining, immunohistochemical analyses, glucose-stimulated insulin secretion and intraperitoneal glucose tolerance test were performed to detect the effect of Kcnq1ot1 on β-cell proliferation and insulin secretion in vitro and in vivo. The relationship between Kcnq1ot1 and miR-15b-5p was predicted by bioinformatics prediction, which was confirmed by luciferase reporter assay.

RESULTS

Kcnq1ot1 was more abundant in the pancreas. The expression of Kcnq1ot1 was decreased in the islets of db/db mice and diet-induced obese mice and in the serum of patients with type 2 diabetes. Silencing Kcnq1ot1 inhibited the β-cell proliferation concomitant with a reduction in the levels of Ccnd1 and Ccnd2. Insulin synthesis and secretion were impaired, along with the decreased expression of Ins1, Ins2, and insulin-related transcription factors. Moreover, Kcnq1ot1 knockdown in vivo reduced glucose tolerance and decreased insulin secretion, consistent with the reduction in the relative islet area and Ki67-positive β-cells detected by immunochemistry and immunofluorescence staining, respectively. Mechanistically, Kcnq1ot1 directly targeted miR-15b-5p which regulated β-cell proliferation and insulin secretion through Ccnd1 and Ccnd2. Notably, the suppression of miR-15b-5p attenuated the inhibition of Min6 proliferation and insulin production induced by Kcnq1ot1 knockdown.

CONCLUSION

Kcnq1ot1 regulated β-cell proliferation and insulin secretion via the miR-15b-5p/Ccnd1 and Ccnd2 axis, which is worthy of further investigation considering its potential in diabetes treatment.

Effect of low-intensity ergometer aerobic training on glucose tolerance in severely impaired nondiabetic stroke patients

OBJECTIVE

To investigate whether low-intensity ergometer aerobic training has beneficial effect on glucose tolerance in nondiabetic patients with severely impaired stroke.

METHODS

Fifty-four severely impaired stroke survivors were recruited and randomly assigned to the experimental group and control group. They have no diabetes history with fasting plasma glucose less than 7 mmol/L. Both groups participated in a 6-week rehabilitation training program with low-intensity ergometer aerobic training added only in the experimental group 3 times per week. Primary outcome variables were fasting glucose, fasting insulin, 2-hour glucose, and homeostasis model assessment-insulin resistance (HOMA-IR) in oral glucose tolerance test before and after intervention.

RESULTS

Before intervention, 36 of 54 (66.7%) were diagnosed with impaired glucose status or diabetic glucose tolerance totally. The average 2-hour plasma glucose level was 9.14 ± 1.39 mmol/L. After intervention, aerobic training significantly improved fasting insulin (from 8.51 ± 2.01 μU/mL to 7.11 ± 2.02 μU/mL), 2-hour glucose level (from 9.13 ± 1.14 mmol/L to 7.22 ± 1.23 mmol/L), and HOMA-IR (from 1.62 ± 1.01 to 1.29 ± .79) in the intervention group compared with the control group (P < .05). Aerobic training also significantly improved their glucose tolerance state (P < .05).

CONCLUSIONS

Preliminary findings suggest that abnormal glucose tolerance may be highly present among severely impaired nondiabetic stroke patients and low-intensity ergometer aerobic training may have beneficial role in improving glucose tolerance.

The role of FOXO1 in β-cell failure and type 2 diabetes mellitus

Over the past two decades, insulin resistance has been considered essential to the aetiology of type 2 diabetes mellitus (T2DM). However, insulin resistance does not lead to T2DM unless it is accompanied by pancreatic β-cell dysfunction, because healthy β cells can compensate for insulin resistance by increasing in number and functional output. Furthermore, β-cell mass is decreased in patients with diabetes mellitus, suggesting a primary role for β-cell dysfunction in the pathogenesis of T2DM. The dysfunction of β cells can develop through various mechanisms, including oxidative, endoplasmic reticulum or hypoxic stress, as well as via induction of cytokines; these processes lead to apoptosis, uncontrolled autophagy and failure to proliferate. Transdifferentiation between β cells and α cells occurs under certain pathological conditions, and emerging evidence suggests that β-cell dedifferentiation or transdifferentiation might account for the reduction in β-cell mass observed in patients with severe T2DM. FOXO1, a key transcription factor in insulin signalling, is implicated in these mechanisms. This Review discusses advances in our understanding of the contribution of FOXO1 signalling to the development of β-cell failure in T2DM.

Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research

Systems epidemiology applied to the field of nutrition has potential to provide new insight into underlying mechanisms and ways to study the health effects of specific foods more comprehensively. Human intervention and population-based studies have identified i) common genetic factors associated with several nutrition-related traits and ii) dietary factors altering the expression of genes and levels of proteins and metabolites related to inflammation, lipid metabolism and/or gut microbial metabolism, results of high relevance to metabolic disease. System-level tools applied type 2 diabetes and related conditions have revealed new pathways that are potentially modified by diet and thus offer additional opportunities for nutritional investigations. Moving forward, harnessing the resources of existing large prospective studies within which biological samples have been archived and diet and lifestyle have been measured repeatedly within individual will enable systems-level data to be integrated, the outcome of which will be improved personalized optimal nutrition for prevention and treatment of disease.