Impact of inflammatory markers on the relationship between sleep quality and diabetic kidney disease.

PURPOSE: Both poor sleep and diabetic kidney disease are closely associated with inflammation. However, the correlation between poor sleep and diabetic kidney disease has not been well clarified. Thus, the aim of this study was to determine the mediating role that inflammatory markers play in the pathogenic effect of poor sleep on the severity of diabetic kidney disease (DKD). METHODS: A cross-sectional survey was conducted on 336 patients with type 2 diabetes (T2D). DKD was diagnosed according to the guidelines of the National Kidney Foundation-Kidney Disease Outcome Quality Initiative (NKF-K/DOQI). The Pittsburg Sleep Quality Index (PSQI) score was applied to assess patients for the quality of their sleep. Patients with a PSQI score of more than 5 were assigned to the poor sleep group, and the rest of the patients were assigned to the good sleep group. Circulating levels of six inflammatory biomarkers related to poor sleep and DKD were measured. RESULTS: The prevalence of DKD was higher in patients with poor sleep quality than in those with good sleep quality (42% vs. 25%, P = 0.002). After adjustment, poor sleep quality (PSQI score OR 1.075 [95%CI 1.018-1.135], P = 0.009) remained independently associated with DKD. PSQI score was found to be positively related to fibroblast growth factor (FGF23), interleukin 6 (IL-6), P-selectin, and intercellular adhesion molecule-1 (ICAM-1) (P < 0.01), rather than fibrinogen and C-reactive protein (CRP) in linear regression models. As revealed by multiple mediation analysis, FGF23 and IL-6 mediated 26% and 23% of the relationship between PSQI score and urinary microalbumin (UMA), respectively. Similarly, the FGF23 and ICAM-1, instead of IL-6 and P-selectin, mediated 32% and 24% of the association between PSQI and estimated glomerular filtration rate (eGFR), respectively. CONCLUSIONS: Poor sleep quality is independently associated with DKD. These results suggest that inflammatory markers contribute to a pathogenic connection between poor sleep and DKD.

miRNA­342 suppresses renal interstitial fibrosis in diabetic nephropathy by targeting SOX6.

Diabetic kidney disease (DKD) is one of the major microvascular complications in patients with type 1 and/or type 2 diabetes, the first cause of end­stage renal disease (ESRD) in several countries and regions. However, the pathogenesis of DKD and the mechanisms through which it leads to ESRD remain unknown. Thus, in this study, we aimed to elucidate some of these mechanisms. The expression of microRNA (miRNA or miR)­342­3p and SRY­box 6 (SOX6) in the renal tissues of mice with DKD and mouse renal mesangial cells (MCs) was determined by RT­qPCR and western blot analysis. The diabetic kidney environment was established using high­glucose medium. SOX6 was verified as a target gene of miR­342­3p by dual­luciferase activity assay. In addition, western blot analysis was employed to determine the changes in the levels of several biomarkers of fibrosis [transforming growth factor (TGF)­ß1, fibronectin (FN), collagen IV (referred to as C­IV) and phosphatase and tensin homolog (PTEN)]. Compared with THE control mice, the expression of miR­342­3p in the kidney tissues of mice with DKD was downregulated, whereas that of SOX6 was upregulated. The same phenomenon was observed in the MCs cultured in high­glucose medium. Subsequently, miR­342­3p inhibited SOX6 expression, promoted cell proliferation and inhibited the apoptosis of MCs. Moreover, the overexpression of miR­342­3p suppressed high glucose­induced renal interstitial fibrosis. In addition, it was found that miR­342­3p inhibited SOX6 expression by binding to the 3'­UTR of SOX6. On the whole, the findings of this study demonstrate that miR­342­3p suppresses the progression of DKD by inducing the degradation of SOX6. Thus, the miR­342­3p/SOX6 axis may serve as a novel therapeutic target in the treatment of DKD.

Impact of inflammatory markers on the relationship between sleep quality and incident cardiovascular events in type 2 diabetes.

AIMS: To analyze relevance of sleep quality with CVD in T2D patients and determine whether inflammation prompted by poor sleep has impact on the CVD. METHODS: 332 T2D patients were recruited and their sleep qualities were evaluated by PSQI. The patients with PSQI score >7 were in the poor sleep group, and the others were in the good sleep group. Plasma samples of the patients were obtained to measure inflammatory markers. Correlation analyses and regression analyses were performed to examine the cross-sectional relationships among the poor sleep, inflammatory markers and CVD. RESULTS: The morbidity of CVD was significantly higher in the poor sleep patients compared to the good sleep patients (P=0.000). PSQI score ORs were both >1 for CVD in model 1 and model 2 (P<0.05). PSQI score were positively related to IL-6 and ICAM-1(P<0.05), negatively to FBI (P<0.05), but not related to CRP in linear regression models. Multiple logistic regression analysis showed IL-6 and ICAM-1, but not FBI and CRP, were related to CVD (P<0.05). CONCLUSIONS: Poor sleep is regarded as a plausible risk factor for CVD in T2D patients, and may be mediated by certain inflammatory markers.

Interaction of sleep quality and sleep duration on glycemic control in patients with type 2 diabetes mellitus.

BACKGROUND: Copious evidence from epidemiological and laboratory studies has revealed that sleep status is associated with glucose intolerance, insulin resistance, thus increasing the risk of developing type 2 diabetes. The aim of this study was to reveal the interaction of sleep quality and sleep quantity on glycemic control in patients with type 2 diabetes mellitus. METHODS: From May 2013 to May 2014, a total of 551 type 2 diabetes patients in Tianjin Metabolic Diseases Hospital were enrolled. Blood samples were taken to measure glycosylated hemoglobin (HbA1c), and all the patients completed the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) questionnaire to evaluate their sleep status. "Good sleep quality" was defined as PQSI <5, "average sleep quality" was defined as PQSI 6-8, and "poor sleep quality" was defined as PQSI >8. Poor glycemic control was defined as HbA1c ≥7%. Sleep quantity was categorized as <6, 6-8, and >8 hours/night. Short sleep time was defined as sleep duration <6 hours/night. RESULTS: In the poor glycemic control group, the rate of patients who had insufficient sleep was much higher than that in the other group (χ(2) = 11.16, P = 0.037). The rate of poor sleep quality in poor glycemic control group was much greater than that in the average control group (χ(2) = 9.79, P = 0.007). After adjusted by gender, age, body mass index, and disease duration, the adjusted PSQI score's OR was 1.048 (95% CI 1.007-1.092, P = 0.023) for HbA1c level. The sleep duration's OR was 0.464 (95% CI 0.236-0.912, P = 0.026) for HbA1c level. One-way analysis of variance showed that the poor sleep quality group had the highest homeostasis model assessment-insulin resistance (P < 0.01). CONCLUSIONS: Inadequate sleep, in both quality and quantity, should be regarded as a plausible risk factor for glycemic control in type 2 diabetes. Poor sleep might bring much more serious insulin resistance and could be the reason for bad glycemic control. A good night's sleep should be seen as a critical health component tool in the prevention and treatment of type 2 diabetes. It is important for clinicians to target the root causes of short sleep duration and/or poor sleep quality.