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: Numerous heart failure risk scores have been developed but there is none for Asians. We aimed to develop a risk calculator, the Singapore Heart Failure Risk Score, to predict 1- and 2-year survival in Southeast Asian patients hospitalised for heart failure. MATERIALS AND METHODS: Consecutive patients admitted for heart failure were identified from the Singapore Cardiac Databank Heart Failure registry. The follow-up was 2 to 4 years and mortality was obtained from national registries. RESULTS: The derivation (2008-2009) and 2 validation cohorts (2008-2009, 2013) included 1392, 729 and 804 patients, respectively. Ten variables were ultimately included in the risk model: age, prior myocardial infarction, prior stroke, atrial fibrillation, peripheral vascular disease, systolic blood pressure, QRS duration, ejection fraction and creatinine and sodium levels. In the derivation cohort, predicted 1- and 2-year survival was 79.1% and 68.1% compared to actual 1- and 2-year survival of 78.2% and 67.9%. There was good agreement between the predicted and observed mortality rates (Hosmer-Lemeshow statistic = 14.36, = 0.073). C-statistics for 2-year mortality in the derivation and validation cohorts were 0.73 (95% CI, 0.70-0.75) and 0.68 (95% CI, 0.64-0.72), respectively. CONCLUSION We provided a risk score based on readily available clinical characteristics to predict 1- and 2-year survival in Southeast Asian patients hospitalised for heart failure via a simple online risk calculator, the Singapore Heart Failure Risk Score.

The main areas of emphasis in the Advanced Cardiac Life Support (ACLS) guidelines are: early recognition of cardiac arrest and call for help; good-quality chest compressions; early defibrillation when applicable; early administration of drugs; appropriate airway management ensuring normoventilation; and delivery of appropriate post-resuscitation care to enhance survival. Of note, it is important to monitor the quality of the various care procedures. The resuscitation team needs to reduce unnecessary interruptions to chest compressions in order to maintain adequate coronary perfusion pressure during the ACLS drill. In addition, the team needs to continually look out for reversible causes of the cardiac arrest.

INTRODUCTIONRemote monitoring of cardiac implantable electronic devices (CIED) has been shown to improve patient safety and reduce in-office visits. We report our experience with remote monitoring via the Medtronic CareLink(®) network.

METHODSPatients were followed up for six months with scheduled monthly remote monitoring transmissions in addition to routine in-office checks. The efficacy of remote monitoring was evaluated by recording compliance to transmissions, number of device alerts requiring intervention and time from transmission to review. Questionnaires were administered to evaluate the experiences of patients, physicians and medical technicians.

RESULTSA total of 57 patients were enrolled; 16 (28.1%) had permanent pacemakers, 34 (59.6%) had implantable cardioverter defibrillators and 7 (12.3%) had cardiac resynchronisation therapy defibrillators. Overall, of 334 remote transmissions scheduled, 73.7% were on time, 14.5% were overdue and 11.8% were missed. 84.6% of wireless transmissions were on time, compared to 53.8% of non-wireless transmissions. Among all transmissions, 4.4% contained alerts for which physicians were informed and only 1.8% required intervention. 98.6% of remote transmissions were reviewed by the second working day. 73.2% of patients preferred remote monitoring. Physicians agreed that remote transmissions provided information equivalent to in-office checks 97.1% of the time. 77.8% of medical technicians felt that remote monitoring would help the hospital improve patient management. No adverse events were reported.

CONCLUSIONRemote monitoring of CIED is safe and feasible. It has possible benefits to patient safety through earlier detection of arrhythmias or device malfunction, permitting earlier intervention. Wireless remote monitoring, in particular, may improve compliance to device monitoring. Patients may prefer remote monitoring due to possible improvements in quality of life.

Aged ; Arrhythmias, Cardiac ; diagnosis ; Defibrillators, Implantable ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Monitoring, Physiologic ; methods ; Pacemaker, Artificial ; Patient Safety ; Pilot Projects ; Prospective Studies ; Quality of Life ; Remote Consultation ; methods ; Singapore ; Surveys and Questionnaires

INTRODUCTIONDevice therapy is efficacious in preventing sudden cardiac death (SCD) in patients with reduced ejection fraction. However, few who need the device eventually opt to undergo implantation and even fewer reconsider their decisions after deliberation. This is due to many factors, including unresolved patient barriers. This study identified the factors that influenced patients' decision to decline implantable cardioverter defibrillator (ICD) implantation, and those that influenced patients who initially declined an implant to reconsider having one.

METHODSA single-centre survey was conducted among 240 patients who had heart failure with reduced ejection fraction and met the ICD implantation criteria, but had declined ICD implantation.

RESULTSParticipants who refused ICD implantation were mostly male (84%), Chinese (71%), married (72%), currently employed (54%), and had up to primary or secondary education (78%) and monthly income of < SGD 3,000 (51%). Those who were more likely to reconsider their decision were aware that SCD was a consequence of heart failure with reduced ejection fraction, knowledgeable of the preventive role of ICDs, currently employed and aware that their doctor strongly recommended the implant. Based on multivariate analysis, knowledge of the role of ICDs for primary prophylaxis was the most important factor influencing patient decision.

CONCLUSIONThis study identified the demographic and social factors of patients who refused ICD therapy. Knowledge of the role of ICDs in preventing SCD was found to be the strongest marker for reconsidering ICD implantation. Measures to address this information gap may lead to higher rates of ICD implantation.

Cross-Sectional Studies ; Death, Sudden, Cardiac ; prevention & control ; Defibrillators, Implantable ; Female ; Heart Failure ; mortality ; physiopathology ; therapy ; Humans ; Male ; Middle Aged ; Primary Prevention ; methods ; Risk Factors ; Singapore ; epidemiology ; Stroke Volume ; physiology ; Survival Rate ; trends