Methods: A total of 413 individuals (163 men and 250 women) aged 30-60 years were selected by stratified random sampling. The participants had safe alcohol consumption habits (<2 drinks/day) and no symptoms of hepatitis B and C. NAFLD was diagnosed through ultrasound. Blood pressure, anthropometric, and body composition measurements were made and liver function tests were conducted. Biochemical assessments, including the measurement of fasting blood sugar (FBS) and ferritin levels, as well as lipid profile tests were also performed. Metabolic syndrome was evaluated according to the International Diabetes Federation (IDF) criteria.
Results: The overall prevalence of ultrasound-diagnosed NAFLD was 39.3%. The results indicated a significantly higher prevalence of NAFLD in men than in women (42.3% vs 30.4%; P < 0.05). Binary logistic regression analysis was performed to determine the significant variables as NAFLD predictors. Overall, male gender, high body mass index (BMI), high alanine aminotransferase (ALT), high FBS, and high ferritin were identified as the predictors of NAFLD. The only significant predictors of NAFLD among men were high BMI and high FBS. These predictors were high BMI, high FBS, and high ferritin in women (P < 0.05 for all variables).
Conclusions: The metabolic profile can be used for predicting NAFLD among men and women. BMI, FBS, ALT, and ferritin are the efficient predictors of NAFLD and can be used for NAFLD screening before liver biopsy.
Methods: We searched seven databases up to July 2020 for randomized controlled trials investigating the effectiveness of telemedicine in the delivery of diabetes care in low- and middle-income countries. We extracted data on the study characteristics, primary end-points and effect sizes of outcomes. Using random effects analyses, we ran a series of meta-analyses for both biochemical outcomes and related patient properties.
Findings: We included 31 interventions in our meta-analysis. We observed significant standardized mean differences of -0.38 for glycated haemoglobin (95% confidence interval, CI: -0.52 to -0.23; I2 = 86.70%), -0.20 for fasting blood sugar (95% CI: -0.32 to -0.08; I2 = 64.28%), 0.81 for adherence to treatment (95% CI: 0.19 to 1.42; I2 = 93.75%), 0.55 for diabetes knowledge (95% CI: -0.10 to 1.20; I2 = 92.65%) and 1.68 for self-efficacy (95% CI: 1.06 to 2.30; I2 = 97.15%). We observed no significant treatment effects for other outcomes, with standardized mean differences of -0.04 for body mass index (95% CI: -0.13 to 0.05; I2 = 35.94%), -0.06 for total cholesterol (95% CI: -0.16 to 0.04; I2 = 59.93%) and -0.02 for triglycerides (95% CI: -0.12 to 0.09; I2 = 0%). Interventions via telephone and short message service yielded the highest treatment effects compared with services based on telemetry and smartphone applications.
Conclusion: Although we determined that telemedicine is effective in improving several diabetes-related outcomes, the certainty of evidence was very low due to substantial heterogeneity and risk of bias.
METHOD: A multicenter cross-sectional observational study was conducted in 388 diabetes patients attending daily diabetes clinics and teaching hospitals in Pakistan's twin city between August 2019 and February 2020. The chi-square test and linear regression were used to detect RLS-related factors in type 2 diabetes mellitus.
RESULTS: The prevalence of RLS found was; 3.1% patients with diabetes were suffering from very severe RLS, 23.5% from severe RLS, 34% from moderate RLS, 21.1% from mild RLS and 18.3% from non-RLS. Gender, age, education, blood glucose fasting (BSF), blood glucose random (BSR) and HBA1c were found to be significant predictors of RLS in patients with diabetes.
CONCLUSION: Policy makers can develop local interventions to curb the growing RLS prevalence by keeping in control the risk factors of RLS in people living with type 2 diabetes.
METHOD: This cross-sectional study involved eighty-three (n=83) adults attending a health screening program at Universiti Putra Malaysia (UPM). Demographic data, anthropometric measurements and blood samples for fasting blood glucose (FBG), fasting lipid profile (FSL), glycated haemoglobin (HbA1c) and hsCRP were taken. Respondents were grouped according to FRS and the Joint Interim Statement into 10-year CVD risk categories (low, intermediate and high) and MetS, respectively.
RESULTS: hsCRP was significantly increased in patients with high body mass index (BMI) (p=0.001), at-risk waist circumference (WC) (p=0.001) and MetS (p=0.009). Spearman's correlation coefficient showed a significant positive correlation between hsCRP level and total FRS score (r=0.26, p<0.05) and HDL-C score (r=0.22, p<0.05).
CONCLUSION: The significant difference of hsCRP levels across obesity levels and MetS with its modest correlation with FRS scores supported the adjunctive role of hsCRP in CVD risk prediction, most likely capturing the inflammatory pathological aspect and thus partly accounting for the residual CVD risk.
Materials and Methods: A cross-sectional study was conducted at Hospital Universiti Sains Malaysia (Hospital USM), Health Campus, Kubang Kerian, Kelantan, Malaysia. Thirty newly diagnosed patients with PCOS attending gynecology clinic between July 2016 and April 2017 were recruited. Fasting venous blood samples were collected from the subjects. Serum AMH, insulin, adiponectin, triglycerides, high-density lipoprotein cholesterol (HDL-C), and plasma glucose levels were measured, and insulin resistance was calculated based on homeostasis model of assessment-insulin resistance (HOMA-IR). The serum AMH level was estimated, and the correlation of serum AMH level with the metabolic parameters was analyzed.
Results: The median of serum AMH levels in women with PCOS was 6.8 ng/mL (interquartile range: 7.38 ng/mL). There was a significant negative correlation between serum AMH and HOMA-IR or triglyceride levels (r = -0.49, P = 0.006 and r = -0.55, P = 0.002, respectively). A significant positive correlation was observed between serum AMH and serum HDL-C or serum adiponectin levels (r = 0.56, P = 0.001 and r = 0.44, P = 0.014, respectively) in all study subjects.
Conclusion: The serum AMH level is associated with HOMA-IR, triglycerides, HDL-C, and adiponectin levels, and hence it may be used as a potential cardiometabolic risk marker in women with PCOS.