Search

The article focuses on exploring the relationship between C-peptide levels and low muscle mass in patients with type 2 diabetes mellitus (T2DM), highlighting the potential of C-peptide as a biomarker for identifying low muscle mass. Topics include the methodology of the study, the prevalence of low muscle mass among patients with varying C-peptide levels, and the association between serum C-peptide levels and low muscle mass after adjusting for potential confounders.

Background: Uncontrolled hyperglycemia, hypercholesterolemia, and hypertension are common in persons with diabetes.Objective: To compare the effectiveness of team-based care with and without a clinical decision support system (CDSS) in controlling glycemia, lipids, and blood pressure (BP) among patients with type 2 diabetes.Design: Cluster randomized trial. (ClinicalTrials.gov: NCT02835287).Setting: 38 community health centers in Xiamen, China.Patients: 11 132 persons aged 50 years or older with uncontrolled diabetes and comorbid conditions, 5475 receiving team-based care with a CDSS and 5657 receiving team-based care alone.Intervention: Team-based care was delivered by primary care physicians, health coaches, and diabetes specialists in all centers. In addition, a computerized CDSS, which generated individualized treatment recommendations based on clinical guidelines, was implemented in 19 centers delivering team-based care with a CDSS.Measurements: Coprimary outcomes were mean reductions in hemoglobin A1c (HbA1c) level, low-density lipoprotein cholesterol (LDL-C) level, and systolic BP over 18 months and the proportion of participants with all 3 risk factors controlled at 18 months.Results: During the 18-month intervention, HbA1c levels, LDL-C levels, and systolic BP significantly decreased by -0.9 percentage point (95% CI, -0.9 to -0.8 percentage point), -0.49 mmol/L (CI, -0.53 to -0.45 mmol/L) (-19.0 mg/dL [CI, -20.4 to -17.5 mg/dL]), and -9.1 mm Hg (CI, -9.9 to -8.3 mm Hg), respectively, in team-based care with a CDSS and by -0.6 percentage point (CI, -0.7 to -0.5 percentage point), -0.32 mmol/L (CI, -0.35 to -0.29 mmol/L) (-12.5 mg/dL [CI, -13.6 to -11.3 mg/dL]), and -7.5 mm Hg (CI, -8.4 to -6.6 mm Hg), respectively, in team-based care alone. Net differences were -0.2 percentage point (CI, -0.3 to -0.1 percentage point) for HbA1c level, -0.17 mmol/L (CI, -0.21 to -0.12 mmol/L) (-6.5 mg/dL [CI, -8.3 to -4.6 mg/dL]) for LDL-C level, and -1.5 mm Hg (CI, -2.8 to -0.3 mm Hg) for systolic BP. The proportion of patients with controlled HbA1c, LDL-C, and systolic BP was 16.9% (CI, 15.7% to 18.2%) in team-based care with a CDSS and 13.0% (CI, 11.7% to 14.3%) in team-based care alone.Limitation: There was no usual care control, and clinical outcome assessors were unblinded; the analysis did not account for multiple comparisons.Conclusion: Compared with team-based care alone, team-based care with a CDSS significantly reduced cardiovascular risk factors in patients with diabetes, but the effect was modest.Primary Funding Source: Xiamen Municipal Health Commission. [ABSTRACT FROM AUTHOR]

Background: The severity of liver fibrosis is an important predictor of death in patients with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM). However, there is still no definite conclusion on the relationship between triiodothyronine (T3) and the severity of liver fibrosis. Thus, the aim of this study was to analyze the correlation between T3 level and the severity of liver fibrosis. Methods: We performed a cross-sectional study of 2072 T2DM patients with normal thyroid function from January 2017 to January 2020. NAFLD fibrosis score (NFS), Fibrosis index based on the 4 factors (FIB-4) and BARD score (BARD) were used to assess the severity of fibrosis in T2DM patients, and linear regression analyses were used to determine the factors independently associated with liver fibrosis. Further experiments were performed to assess the impact of low T3 on fibrosis progression in mice model and explore possible mechanisms. Results: Free triiodothyronine (fT3) levels had significantly inverse correlations with NFS and FIB-4, and BARD in T2DM patients (P < 0.05). In multiple linear regression analyses, decreased fT3 level was an independent risk factor for the severity of liver fibrosis of T2DM patients (P < 0.01). Findings from in-vivo experiment using mice model proved that hypothyroidism mice had more severe of liver fibrosis than those mice with normal thyroid function. We also found that T3 could inhibit the profibrotic TREM2+CD9+ macrophage, which had been identified an important player in the progression of liver fibrosis. Conclusion: The findings from this study proved an inverse correlation between T3 level and the severity of liver fibrosis, and lower fT3 level within the normal range was an independent risk factor for severe liver fibrosis. [ABSTRACT FROM AUTHOR]

Aims: Nocturnal asymptomatic hypoglycemia (NAH) is a serious complication of diabetes, but it is difficult to be detected clinically. This study was conducted to determine the largest amplitude of glycemic excursion (LAGE) to predict the episodes of NAH in outpatients with type 2 diabetes. Methods: Data were obtained from 313 outpatients with type 2 diabetes. All subjects received continuous glucose monitoring (CGM) for consecutive 72 hours. The episodes of NAH and glycemic variability indices (glucose standard deviation [SD], mean amplitude of plasma glucose excursion [MAGE], mean blood glucose [MBG]) were accessed via CGM. LAGE was calculated from self-monitoring blood glucose (SMBG). Results: A total of 76 people (24.3%) had NAH. Compared to patients without NAH, patients with NAH showed higher levels of glucose SD (2.4 ± 0.9 mmol/L vs 1.7 ± 0.9 mmol/L, p <0.001), MAGE (5.2 ± 2.1 mmol/L vs 3.7 ± 2.0, p<0.001) and LAGE (4.6 ± 2.3 mmol/L vs 3.8 ± 1.9 mmol/L, p=0.007), and lower level of MBG (7.5 ± 1.5 mmol/L vs 8.4 ± 2.2 mmol/L, p=0.002). LAGE was significantly associated with the incidence of NAH and time below rang (TBR) in model 1 [NAH: 1.189 (1.027-1.378), p=0.021; TBR: 0.008 (0.002-0.014), p=0.013] with adjustment for age, BMI, sex, work, hyperlipidemia, complication and medication, and in model 2 [NAH: 1.177 (1.013-1.367), p=0.033; TBR: 0.008 (0.002-0.014), p=0.012] after adjusting for diabetes duration based on model 1, as well as in model 3 [NAH: 1.244 (1.057-1.464), p=0.009; TBR: 0.009 (0.002-0.016), p=0.007] with further adjustment for HbA1c based on model 2. In addition, no significant interactions were found between LAGE and sex, age, HbA1c, duration of diabetes, BMI and insulin therapy on the risk of NAH. The receiver operator characteristic (ROC) curve shows the ideal cutoff value of LAGE for the prediction of NAH was 3.48 mmol/L with 66.7% sensitivity, 50% specificity and 0.587 (95% CI: 0.509-0.665) of area under the ROC curve. Conclusions: High glycemic variability is strongly associated with the risk of NAH. The LAGE based on SMBG could be an independent predictor of NAH for outpatients with type 2 diabetes, and LAGE greater than 3.48 mmol/L could act as a warning alarm for high risk of NAH in daily life. [ABSTRACT FROM AUTHOR]

Background: It has been reported that earlier age at menarche is associated with a higher risk for type 2 diabetes mellitus. However, the relationship between age at menarche and gestational diabetes mellitus is inconsistent across studies. We hypothesized that an earlier age at menarche would predict the gestational diabetes mellitus risk.Methods: This was a population-based, retrospective cohort study of 70,041 women aged 18 to 53 years old, conducted between 2011 and 2018. The subjects were recruited from the Medical Birth Registry in Xiamen, China. Age at menarche was categorized as 8-12, 13, 14, 15, 16-19 years old. Logistic regression analysis and spline analysis was used to assess the risk of the menarche age group for gestational diabetes mellitus. Linear regression analysis was performed to evaluate independent associations between age at menarche and fasting plasma glucose and blood glucose 1 hour and 2 hours after a 75-g of glucose load between 24 and 28 weeks' gestation.Results: The overall prevalence of GDM was 17.6%. After adjustment for family history of diabetes, earlier age at menarche (8-12, and 13 years old) was associated with increased odds for GDM (OR, 1.08; 95% CI, 1.02-1.15, and OR, 1.07; 95% CI, 1.03-1.14, respectively) compared with average age at menarche (14 years old). With further adjustment for pre-pregnancy body mass index, blood pressure, educational level, age at delivery, and hepatitis B surface antigen status, this association was attenuated (OR, 0.93, and OR, 1.02, respectively). Multivariable-adjusted spline regression models showed a linear dose-response association between age at menarche and GDM (P for nonlinearity, 0.203; P for linearity, 0.006). On linear regression analysis, earlier age at menarche was significantly associated with increased blood glucose one and 2 hours after a glucose load but not with the fasting plasma glucose.Conclusions: As expected, early age at menarche was found to be associated with an increased risk of gestational diabetes mellitus. However, this association may be mediated by potential confounding factors other than age. An additional finding was that earlier menarche was significantly related with elevated pregnancy glucose concentrations after a glucose load. [ABSTRACT FROM AUTHOR]

Objective Laboratory models suggested that Fetuin-B impaired insulin action in myotubes and hepatocytes and caused glucose intolerance in mice. We aimed to explore the independent associations and pathways among serum Fetuin-B, nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D). Methods A cross-sectional study of 1318 obese adults who underwent serum Fetuin-B test and hepatic ultrasonography scanning was conducted in Xiamen, China. Multivariable logistic regression was used to calculate adjusted odds ratio (OR) and 95% confidence intervals (CI) of serum Fetuin-B level and NAFLD for T2D in different models with adjustment for potential confounders. Structural equation modeling (SEM) was used to examine the paths among NAFLD, serum Fetuin-B, metabolic/insulin resistance syndrome and T2D. Results Subjects with T2D or NAFLD showed significantly increased serum Fetuin-B levels compared to their controls (4.25 ± 1.35 vs. 4.08 ± 1.38 µg/ml for diabetes; and 4.26 ± 1.41 vs. 4.07 ± 1.33 µg/ml for NAFLD; both p-values < 0.05). NAFLD and higher serum Fetuin-B were significantly associated with higher risk of T2D with adjustment for sociodemographic and lifestyle habits; and the adjusted ORs (95%CIs) were 2.90 (2.17–3.87, p < 0.001) and 1.16 (1.01–1.32, p = 0.032), respectively. With further adjustment for metabolic/insulin resistance syndrome (BMI, systolic and diastolic BP, triglyceride, total cholesterol, HDL- and LDL-cholesterol, HOMA-IR and serum uric acid), NAFLD but not serum Fetuin-B was significantly associated with increased risk of T2D (ORs (95%CIs): 1.58 (1.12–2.21, p = 0.009) and 1.07 (0.92–1.23, p = 0.384), respectively). A one pathway model by using SEM fitted well (χ 2  = 497.92, p < 0.001; CFI = 0.965; TLI = 0.926; and RMSEA = 0.097) and showed that NAFLD increased serum Fetuin-B and elevated Fetuin-B increased fasting insulin level, which in turn induced insulin resistance and T2D. Besides, NAFLD increased the risk of T2D directly in addition to its indirect effects of inducing metabolic/insulin resistance syndrome which in turn increased the risk of T2D. Conclusions Fetuin-B links NAFLD to T2D via inducing insulin resistance, and NAFLD contributes to the pathogenesis of T2D via multiple mechanisms. [ABSTRACT FROM AUTHOR]