AIMS: We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.
METHODS: Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).
RESULTS: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).
CONCLUSIONS: ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.
Methodology: We conducted a cross-sectional study using 6-days CGMS to detect the prevalence of hypoglycaemia in 31 insulin-treated pregnant women with diabetes who achieved HbA1c <6.0%. Patients were required to log-keep their self-monitoring blood glucose (SMBG) readings and hypoglycaemia events.
Results: Eight women experienced confirmed hypoglycaemia with additional seven experienced relative hypoglycaemia, giving rise to prevalence rate of 45.2% (one had both confirmed and relative hypoglycaemia). Nine relative hypoglycaemia and 17 confirmed hypoglycaemic events were recorded. Sixteen (94%) out of 17 confirmed hypoglycaemia events recorded by CGMS were asymptomatic and were missed despite performing regular SMBG. Nocturnal hypoglycaemia events were recorded in seven women. Univariable analysis did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.
Conclusion: Insulin-treated pregnant women with diabetes who achieved HbA1c <6.0% were associated with high prevalence of hypoglycaemia. Asymptomatic hypoglycaemia is common in our cohort and frequently missed despite regular SMBG. Present study did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.
STUDY DESIGN: A wide range of socio-demographic characteristics of Chinese, Malay and Indian women attending routine gynecologic care in Singapore were prospectively collected. Physical performance was objectively measured by hand grip strength and the Short Physical Performance Battery. Percent VAT was determined by dual-energy X-ray absorptiometry. Fasting serum concentrations of glucose, insulin, IL-6, TNF- α, and hs-CRP were measured.
MAIN OUTCOME MEASURE: was insulin resistance, expressed as the homeostatic model assessment of insulin resistance (HOMA-IR).
RESULTS: 1159 women were analyzed, mean age 56.3 (range 45-69) years, comprising women of Chinese (84.0%), Indian (10.2%), and Malay (5.7%) ethnic origins. The adjusted mean differences for obesity (0.66, 95% CI 0.32-1.00), VAT area in the highest vs lowest tertile (1.03, 95% CI 0.73-1.34), low physical performance (0.63, 95% CI 0.05-1.24), and highest vs lowest tertile of TNF- α (0.35, 95% CI 0.13-0.57) were independently associated with HOMA-IR. Women of Malay and Indian ethnicity had higher crude HOMA-IR than Chinese women. However, after adjustment for obesity, VAT, physical performance, and TNF- α, no differences in mean HOMA-IR remained, when comparing Chinese women with those of Malay ethnicity (0.27, 95% CI -0.12 to 0.66) and with those of Indian ethnicity (0.30, 95% CI -0.01 to 0.66).
CONCLUSIONS: Insulin resistance was independently associated with obesity, high VAT, low physical performance, and high levels of TNF- α in midlife Singaporean women. These variables entirely explained the significant differences in insulin resistance between women of Chinese, Malay and Indian ethnicity.
MATERIALS AND METHODS: This longitudinal study included 2198 participants with mean age 43.4 ± 7.7 years, who underwent dental examinations in Yokohama, Japan, at two time points, 2003-2004 and 2008-2009, at an interval of 5 years. Periodontal condition was assessed by the mean value of probing pocket depth (PPD) and clinical attachment level (CAL). Glycaemic status was assessed by fasting glucose and glycated haemoglobin (HbA1c).
RESULTS: The cross-lagged panel models showed the effect of HbA1c at baseline on mean PPD at follow-up (β = 0.044, p = .039). There was a marginal effect of fasting glucose on the mean PPD (β = 0.037, p = .059). It was similar to the effect of fasting glucose or HbAlc on mean CAL. However, in the opposite direction, no effect of mean PPD or CAL at baseline on fasting glucose or HbAlc at follow-up was identified.
CONCLUSIONS: This study demonstrated a unidirectional relationship between glycaemic status and periodontal condition. The study population, however, had mostly mild periodontitis. Future studies are needed to investigate the effect of periodontal condition on glycaemic status in patients with severe periodontitis.
METHODS: Participants were consented to answer a physician-administered questionnaire following Ramadan 2020. Impact of COVID-19 on the decision of fasting, intentions to fast and duration of Ramadan and Shawal fasting, hypoglycaemia and hyperglycaemia events were assessed. Specific analysis comparing age categories of <65 years and ≥65 years were performed.
RESULTS: Among the 5865 participants, 22.5% were ≥65 years old. Concern for COVID-19 affected fasting decision for 7.6% (≥65 years) vs 5.4% (<65 years). More participants ≥65 years old did not fast (28.8% vs 12.7%, <65 years). Of the 83.6%, participants fulfilling Ramadan-fasting, 94.8% fasted ≥15 days and 12.6% had to break fast due to diabetes-related illness. The average number of days fasting within and post-Ramadan were 27 and 6 days respectively, regardless of age. Hypoglycaemia and hyperglycaemia occurred in 15.7% and 16.3% of participants respectively, with 6.5% and 7.4% requiring hospital care respectively. SMBG was performed in 73.8% of participants and 43.5% received Ramadan-focused education.
CONCLUSION: During the COVID-19 pandemic, universally high rates of Ramadan-fasting were observed regardless of fasting risk level. Glycemic complications occurred frequently with older adults requiring higher rates of acute hospital care. Risk stratification is essential followed by pre-Ramadan interventions, Ramadan-focused diabetes education and self-monitoring to reduce and prevent complications, with particular emphasis in older adults.
OBJECTIVES: In this study, we aimed to investigate the effects of KH on the brain of MetS-induced rats.
METHODS: Forty male Wistar rats were divided into 5 groups; 8 weeks (C8) and 16 weeks control groups (C16), groups that received High-Carbohydrate High Fructose (HCHF) diet for 8 weeks (MS8) and 16 weeks (MS16), and a group that received HCHF for 16 weeks with KH supplemented for the last 35 days (KH).
RESULTS: Serum fasting blood glucose decreased in the KH group compared to the MS16 group. HDL levels were significantly decreased in MetS groups compared to control groups. Open field experiments showed that KH group exhibits less anxious behavior compared to the MetS group. Probe trial of Morris water maze demonstrated significant memory retention of KH group compared to the MS16 group. Nissl staining showed a significant decrease in the pyramidal hippocampal cells in the MS16 compared to the KH group.
CONCLUSION: KH has the ability to normalise blood glucose and reduce serum triglyceride and LDL levels in MetS rats, while behavior studies complement its effect on anxiety and memory. This shows a promising role of KH in attenuating neurodegenerative diseases through the antioxidant activity of its polyphenolic content.
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.
Methods: A cross-sectional study was conducted at the Universiti Kebangsaan Malaysia Medical Centre (UKMMC) using outpatient population diabetic patients. Demographic data on social and clinical characteristics were collected from participants. Several questionnaires were administered, including the Beck Depression Inventory-II (BDI-II) to measure depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) to assess anxiety symptoms, the Big Five Inventory (BFI) to evaluate personality traits, and the WHO Quality of Life-BREF (WHOQOL-BREF) to assess QOL. Multivariate binary logistic regression was performed to determine the predictors of poor glycaemic control.
Results: 300 patients with diabetes mellitus were recruited, with the majority (90%) having type 2 diabetes. In this population, the prevalence of poor glycaemic control (HbA1C ≥ 7.0%) was 69%, with a median HbA1C of 7.6% (IQR = 2.7). Longer duration of diabetes mellitus and a greater number of days of missed medications predicted poor glycaemic control, while older age and overall self-perception of QOL protected against poor glycaemic control. No psychological factors were associated with poor glycaemic control.
Conclusion: This study emphasizes the importance of considering the various factors that contribute to poor glycaemic control, such as duration of diabetes, medication adherence, age, and QOL. These findings should be used by clinicians, particularly when planning a multidisciplinary approach to the management of diabetes.