Identification of risks of herdsmen's grassland transfer behavior in Xinjiang and Inner Mongolia and a possible risk prevention mechanism.

Effective identification of the risks of grassland circulation is an important prerequisite for improving the management of the grassland rental market. In this study, the potential risks in the game among the subjects of grassland transfer are analyzed through the lens of repeated game theory. With the help of interpretive structural modeling, we analyze the relationships and hierarchy among the risk factors in grassland circulation. We find that social, economic, and ecological risks are the main potential risks of grassland circulation, and there are strong correlations among risk factors. A risk hierarchy analysis shows that social risk is at the upper level, economic risk is at the middle level, ecological risk is at the bottom level, and there are cross-layer effects among the various risk levels. Contract risk, social security risk, and wealth gap risk are the core risk factors that trigger the risks associated with grassland transfer. Therefore, the first priority of the pasture manager (i.e., the government) should be to regulate the grassland circulation market and strengthen supervision and punishment of defaulting subjects. Second, a social security system in which the herdsmen and the government are both invested should be established. Third, the government should strengthen the construction of a "rule of law" system instead of "rule of man," to avoid the loss of government credibility caused by rent-seeking.

Circulating C1q/TNF-related protein isoform 15 is a marker for the presence of metabolic syndrome.

BACKGROUND: C1q/TNF-related protein isoform 15 (CTRP15) has been reported to be related to glucose and lipid metabolism, but the results are inconsistent. Metabolic syndrome (MetS) is a cluster of metabolic disorders. The aim of this study is to determine circulating CTRP15 levels in individuals with MetS and investigate the association among circulating CTRP15 and markers for MetS as well as insulin resistance. METHODS: A total of 341 individuals were recruited for this cross-sectional study. These subjects were screened for MetS. Serum CTRP15 concentrations were measured by ELISA. RESULTS: Serum CTRP15 levels were significantly higher in MetS individuals relative to those of the healthy individuals. Circulating CTRP15 correlated positively with WHR, BMI, SBP, FAT %, 2 h-BG, FIns, 2 h-Ins, TG, FFA, HbA1c, HOMA-IR, and AUCglucose , while negatively with HDL-C and ISI. Multiple linear regression showed that HOMA-IR and HDL-C are independently related factors influencing serum CTRP15 concentrations. In addition, binary logistic regression analysis showed that serum CTRP15 concentrations were significantly related to MetS. When the mean concentrations of circulating CTRP15 in MetS subjects were stratified by the number of components of the MetS, circulating CTRP15 was found to increase progressively with increasing number of the MetS components. Finally, ROC curve analysis showed that the best cutoff values for circulating CTRP15 to predict MetS and insulin resistance were 63.6 and 64.0 µg/L. CONCLUSIONS: Serum CTRP15 concentrations were associated with the key components of MetS and insulin resistance.

Myonectin Predicts the Development of Type 2 Diabetes.

Context: Myonectin has been identified as a myokine, expressed predominantly in skeletal muscle. However, its clinical implications are largely unknown. Objective: The aim of this study is to investigate the relationship between myonectin (C1q tumor necrosis factor-α-related protein isoform 15) and type 2 diabetes mellitus (T2DM) in cross-sectional and interventional studies. Design: In a separate study, oral glucose tolerance tests, a 45-minute bout of exercise, lipid infusions, and euglycemic-hyperinsulinemic clamps (EHCs) were performed to investigate the association of myonectin with homeostasis model assessment of insulin resistance (HOMA-IR) and T2DM. Circulating myonectin was measured by enzyme-linked immunosorbent assay. Patients: One hundred four newly diagnosed T2DM (nT2DM), 109 impaired glucose tolerance (IGT), and 128 healthy individuals were recruited for this study. Results: nT2DM and IGT subjects had higher circulating myonectin concentrations than normal subjects (82.3 ± 47.6 and 68.9 ± 46.6 vs. 45.2 ± 23.5 µg/L, P < 0.05 or P < 0.01). It was also found that in nT2DM individuals, circulating myonectin was higher than in IGT subjects. Plasma myonectin correlated positively with waist/hip ratio, percentage of body fat, triglyceride, fasting blood glucose, 2-hour blood glucose after glucose overload, fasting insulin, hemoglobin A1c, and HOMA-IR and negatively with the insulin sensitivity index in all of the study population. Multivariate logistic regression analysis revealed that circulating myonectin levels were significantly correlated with IGT and T2DM. A 45-minute bout of exercise did not change circulating myonectin levels in healthy, young individuals. Circulating myonectin levels were not significantly altered in response to an oral glucose challenge or EHC. In addition, acute elevated free fatty acid levels induced by lipid infusion had no effects on circulating myonectin. Conclusions: These data suggest that myonectin may be a useful marker in predicting the development of prediabetes and diabetes.

Potassium measurements and risk of type 2 diabetes: a dose-response meta-analysis of prospective cohort studies.

OBJECTIVE: To clarify the relationship between serum, dietary, and urinary potassium and the risk of type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We searched PubMed and EMBASE through January 6, 2017 for studies reporting risk estimates on the association of potassium measurements and the risk of T2DM. The summary risk estimates were obtained through a random-effects model. Dose-response analysis was conducted. RESULTS: Eight studies involving 5,053 cases and 119,993 individuals were included. A trend toward significance was found in the highest versus lowest meta-analysis on serum potassium and T2DM risk (RR = 0.79; 95% CI 0.60-1.04); moreover, the RR per 1 mmol/L increase in serum potassium was 0.83 (95% CI 0.73-0.95). A non-significant association of dietary potassium and T2DM risk was detected (RR for the highest versus lowest category: 0.93; 95% CI 0.81-1.06; RR for every 1000mg increase per day: 1.00, 95% CI 0.96-1.05). A similar non-significant association was found for urinary potassium and T2DM risk (RR for the highest versus lowest category: 0.83; 95% CI 0.39-1.75; RR per 10 mmol increase: 1.00; 95% CI 0.95-1.05). Evidence of a linear association between serum, dietary, and urinary potassium and the risk of T2DM was found (all Pnon-linearity > 0.05). CONCLUSIONS: Low serum potassium increases the risk of T2DM in a linear dose-response manner; nevertheless, neither dietary potassium nor urinary potassium shows any association with the risk of T2DM. However, these findings should be interpreted with caution due to limited studies.