Introduction: Insulin therapy is essential in the management of type 2 diabetes mellitus (T2DM), but is often associated with treatment burden, hypoglycemia, and potential overtreatment. Insulin deintensification is increasingly recommended for appropriately selected patients; however, there is limited real-world evidence to guide patient selection and to identify modifiable factors that influence successful insulin deintensification. This study aimed to identify clinical predictors, including modifiable factors, associated with successful insulin deintensification in a primary care setting. Methodology: A multicentre retrospective observational study was conducted across seven government primary care clinics in the Petaling District. Adult patients with T2DM undergoing insulin deintensification were included. Successful insulin deintensification was defined as maintenance or improvement of hemoglobin A1c following insulin discontinuation, dose reduction, or reduction in injection frequency. Paired outcomes were analyzed using the Wilcoxon signed-rank test. Between-group comparisons were performed using the Mann–Whitney U test and Chi-square or Fisher’s exact test. Multivariable logistic regression was used to identify independent predictors of successful insulin deintensification. Results: A total of 261 patients were included. Glycemic control remained stable following insulin deintensification (p = 0.334). Significant reductions in body weight (−0.41 kg, p = 0.012) and total daily insulin dose (23.1% reduction, p <0.001) were observed. Univariate analysis did not demonstrate significant differences between groups. However, multivariable logistic regression identified SGLT-2 inhibitor use (aOR 3.23, 95% CI 1.28–8.18, p = 0.013) and regular SMBG (aOR 2.00, 95% CI 1.05–3.81, p = 0.035) as independent predictors of successful insulin deintensification. Conclusion Insulin deintensification can be successfully implemented without compromising glycemic control. Identified predictors, including modifiable factors such as SGLT-2 inhibitor use and SMBG, provide clinically actionable insights to guide patient selection and treatment optimization. These findings challenge the traditional reluctance toward insulin deintensification and support a more evidence-based and individualized approach in routine clinical practice.
Primary Health Care ; Insulins

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact in Asia and has placed significant burden on already stretched healthcare systems. We examined the impact of COVID-19 on the safety attitudes among healthcare workers (HCWs), as well as their associated demographic and occupational factors, and measures of burnout, depression and anxiety. METHODS: A cross-sectional survey study utilising snowball sampling was performed involving doctors, nurses and allied health professions from 23 hospitals in Singapore, Malaysia, India and Indonesia between 29 May 2020 and 13 July 2020. This survey collated demographic data and workplace conditions and included three validated questionnaires: the Safety Attitudes Questionnaire (SAQ), Oldenburg Burnout Inventory and Hospital Anxiety and Depression Scale. We performed multivariate mixed-model regression to assess independent associations with the SAQ total percentage agree rate (PAR). RESULTS: We obtained 3,163 responses. The SAQ total PARs were found to be 35.7%, 15.0%, 51.0% and 3.3% among the respondents from Singapore, Malaysia, India and Indonesia, respectively. Burnout scores were highest among respondents from Indonesia and lowest among respondents from India (70.9%-85.4% vs. 56.3%-63.6%, respectively). Multivariate analyses revealed that meeting burnout and depression thresholds and shifts lasting ≥12 h were significantly associated with lower SAQ total PAR. CONCLUSION Addressing the factors contributing to high burnout and depression and placing strict limits on work hours per shift may contribute significantly towards improving safety culture among HCWs and should remain priorities during the pandemic.
Humans ; Cross-Sectional Studies ; Pandemics ; COVID-19/epidemiology* ; Burnout, Psychological ; Health Personnel

Background/Aims: Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive and personalized care to improve outcomes. ChatGPT (Generative Pre-trained Transformer), a large language model, holds the potential to provide professional yet patient-friendly support. We aimed to examine the accuracy and reproducibility of ChatGPT in answering questions regarding knowledge, management, and emotional support for cirrhosis and HCC. Methods: ChatGPT’s responses to 164 questions were independently graded by two transplant hepatologists and resolved by a third reviewer. The performance of ChatGPT was also assessed using two published questionnaires and 26 questions formulated from the quality measures of cirrhosis management. Finally, its emotional support capacity was tested. Results: We showed that ChatGPT regurgitated extensive knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The performance was better in basic knowledge, lifestyle, and treatment than in the domains of diagnosis and preventive medicine. For the quality measures, the model answered 76.9% of questions correctly but failed to specify decision-making cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines variations, such as HCC screening criteria. However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis. Conclusions We analyzed the areas of robustness and limitations of ChatGPT’s responses on the management of cirrhosis and HCC and relevant emotional support. ChatGPT may have a role as an adjunct informational tool for patients and physicians to improve outcomes.

Objectives: To 1) report prevalence of ‘osteosarcopenia’ (OS) and osteosarcopenic obesity (OSO) entities using evidence-based diagnostic techniques and definitions, 2) examine if OSO offers additional predictive value of functional decline over its components, and 3) identify associated factors in a multi-racial Southeast Asian population. Methods: We performed a cross-sectional study of a representative sample of 542 community-dwelling adults (21–90 years old), and assessed anthropometry, cognition, functional performance, and self-report sociodemographic, health and lifestyle questionnaires. Low muscle mass, and the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, were used to assess sarcopenia. Obesity was defined using percentage body fat and fat mass index. Osteopenia/osteoporosis was determined using lumbar spinal bone mineral density. Associated factors were examined using logistic regression, and OSO’s value investigated using linear regressions with functional performance. Results: OS and OSO prevalence were 1.8% and 0% (21–59 years), 12.9% and 2.8% (≥ 60 years), 17.3% and 4.1% (≥ 65 years), and 25.5% and 7.0% (≥75 years), respectively. OSO entity as defined was not a significant predictor (P > 0.05) and did not improve explanations for functional decline over sarcopenia or sarcopenic obesity. Age, sex, race and body mass index (BMI) were associated with OS, while age, sex, race and alcoholism were associated with OSO. Conclusions Our results do not support OSO as a distinct entity in relation to functional decline. Aside from biological age, sex, and race, amenable lifestyle factors such as BMI and alcohol intake are important variables that can influence the co-existence of osteopenia/osteoporosis, sarcopenia and obesity.