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Abstract Background Patients with type 2 diabetes (T2D) are at high risk of developing multiple complications, affecting their health‐related quality of life (HRQoL). Existing studies only considered impact of complication on HRQoL in the year of occurrence but not its residual impacts in subsequent years. We investigated temporal impacts of diabetes‐related complications on HRQoL in a 12‐year prospective cohort of ambulatory Chinese patients with T2D enrolled in the clinic‐based Joint Asia Diabetes Evaluation (JADE) Register. Methods HRQoL utility measures were derived from EuroQol five‐dimensional three‐level questionnaire (EQ‐5D‐3L) questionnaires completed by 19 322 patients with T2D in Hong Kong (2007–2018). Temporal EQ‐5D utility decrements associated with subtypes of cardiovascular‐renal events were estimated using generalized linear regression model after stepwise selection of covariates with p

Abstract Objective High-density lipoproteins (HDL) comprise particles of different size, density and composition and their vasoprotective functions may differ. Diabetes modifies the composition and function of HDL. We assessed associations of HDL size-based subclasses with incident cardiovascular disease (CVD) and mortality and their prognostic utility. Research design and methods HDL subclasses by nuclear magnetic resonance spectroscopy were determined in sera from 1991 fasted adults with type 2 diabetes (T2D) consecutively recruited from March 2014 to February 2015 in Hong Kong. HDL was divided into small, medium, large and very large subclasses. Associations (per SD increment) with outcomes were evaluated using multivariate Cox proportional hazards models. C-statistic, integrated discrimination index (IDI), and categorial and continuous net reclassification improvement (NRI) were used to assess predictive value. Results Over median (IQR) 5.2 (5.0–5.4) years, 125 participants developed incident CVD and 90 participants died. Small HDL particles (HDL-P) were inversely associated with incident CVD [hazard ratio (HR) 0.65 (95% CI 0.52, 0.81)] and all-cause mortality [0.47 (0.38, 0.59)] (false discovery rate

Abstract Background The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. Methods We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. Results Our PRSs aggregating 84–549 genetic variants (0.251

Abstract Background Nationwide studies on contemporary trends in incidence of diabetes-related complications in Asia are lacking. We describe trends in incident coronary heart disease (CHD), stroke, heart failure, hyperglycaemic crisis, and lower-extremity amputation (LEA) in people with diabetes in Hong Kong between 2001 and 2016. Methods The Hong Kong Diabetes Surveillance Database (HKDSD) is a territory-wide diabetes cohort identified from Hong Kong Hospital Authority electronic medical record system. We identified events of CHD, stroke, heart failure and hyperglycaemic crisis using hospital principal diagnosis codes at discharge and that of LEA using inpatient procedure codes. We used Joinpoint regression analysis to describe incidence trends by age and sex. Results Between 2001 and 2016, a total of 390,071 men and 380,007 women aged 20 years or older with diabetes were included in the HKDSD. Event rates of CHD, stroke, heart failure, hyperglycaemic crisis and LEA declined by 69.4% (average annual percent change: − 7.6, 95% CI − 10.2, − 5.0), 70.3% (− 8.7, 95% CI − 9.8, − 7.5), 63.6% (− 6.4, 95% CI − 8.0, − 4.7), 59.1% (− 6.6, 95% CI − 12.4, − 0.44), and 67.5% (− 5.8, 95% CI − 7.2, − 4.4), in men and by 77.5% (− 9.9, 95% CI − 11.8, − 7.9), 74.5% (− 9.0, 95% CI − 9.6, − 8.4), 65.8% (− 7.0, 95% CI − 8.0, − 6.0), 81.7% (− 8.5, 95% CI − 10.5, − 6.5), and 72.7% (− 9.1. 95% CI − 12.2, − 5.8) in women, respectively, over a 16-year period in people with diabetes in Hong Kong. Joinpoint analysis identified greater declines in event rates of the five diabetes-related complications in the earlier one-third of study period and slowed down but remained significant until 2016. Event rates decreased for all age groups above 45 years for both sexes. There was no significant change in event rates in the group aged 20–44 years except for decline in hyperglycaemic crisis. Conclusions The event rates of diabetes-related complications have declined substantially with no evidence of stabilization or increase in Hong Kong up to 2016. Improvements in outcome were observed for all age subgroups but not in young people with diabetes, calling for urgent action to improve quality of care to prevent complications in young people at risk.

Type 2 diabetes (T2D)-associated end-stage kidney disease (ESKD) is a global burden, while the renoprotective effects of metformin remain controversial. In a population-based cohort (2002–2018) including 96,643 patients with T2D observed for 0.7 million person-years, we estimated the risk association of metformin and its dose-relationship with ESKD in a propensity-score overlap-weighting (PS-OW) cohort by eGFR categories. Amongst 96,643, 83,881 (86.8%) had eGFR-G1/G2 (≥60 mL/min/1.73 m2), 8762 (9.1%) had eGFR-G3a (≥45–60 mL/min/1.73 m2), 3051 (3.2%) had eGFR-G3b (≥30–45 mL/min/1.73 m2), and 949 (1.0%) had eGFR-G4 (≥15–30 mL/min/1.73 m2). The respective proportions of metformin users in these eGFR categories were 95.1%, 81.9%, 53.8%, and 20.8%. In the PS-OW cohort with 88,771 new-metformin and 7872 other oral glucose-lowering-drugs (OGLDs) users, the respective incidence rates of ESKD were 2.8 versus 22.4/1000 person-years. Metformin use associated with reduced risk of ESKD (hazard ratio (HR) = 0.43 [95% CI: 0.35–0.52] in eGFR-G1/G2, 0.64 [0.52–0.79] in eGFR-G3a, 0.67 [0.56–0.80] in eGFR-G3b, and 0.63 [0.48–0.83] in eGFR-G4). Metformin use was associated with reduced or neutral risk of major adverse cardiovascular events (MACE) (7.2 versus 16.0/1000 person-years) and all-cause mortality (14.6 versus 65.1/1000 person-years). Time-weighted mean daily metformin dose was 1000 mg in eGFR-G1/G2, 850 mg in eGFR-G3a, 650 mg in eGFR-G3b, and 500 mg in eGFR-G4. In a subcohort of 14,766 patients observed for 0.1 million person-years, the respective incidence rates of lactic acidosis and HR in metformin users and non-users were 42.5 versus 226.4 events/100,000 person-years (p = 0.03) for eGFR-G1/G2 (HR = 0.57, 0.25–1.30) and 54.5 versus 300.6 events/100,000 person-years (p = 0.01) for eGFR-G3/G4 (HR = 0.49, 0.19–1.30). These real-world data underscore the major benefits and low risk of lactic acidosis with metformin use down to an eGFR of 30 mL/min/1.73 m2 and possibly even 15 mL/min/1.73 m2, while reinforcing the importance of dose adjustment and frequent monitoring of eGFR.

Background Diabetes outcomes are influenced by host factors, settings, and care processes. We examined the association of data-driven integrated care assisted by information and communications technology (ICT) with clinical outcomes in type 2 diabetes in public and private healthcare settings. Methods and findings The web-based Joint Asia Diabetes Evaluation (JADE) platform provides a protocol to guide data collection for issuing a personalized JADE report including risk categories (1–4, low–high), 5-year probabilities of cardiovascular-renal events, and trends and targets of 4 risk factors with tailored decision support. The JADE program is a prospective cohort study implemented in a naturalistic environment where patients underwent nurse-led structured evaluation (blood/urine/eye/feet) in public and private outpatient clinics and diabetes centers in Hong Kong. We retrospectively analyzed the data of 16,624 Han Chinese patients with type 2 diabetes who were enrolled in 2007–2015. In the public setting, the non-JADE group (n = 3,587) underwent structured evaluation for risk factors and complications only, while the JADE (n = 9,601) group received a JADE report with group empowerment by nurses. In a community-based, nurse-led, university-affiliated diabetes center (UDC), the JADE-Personalized (JADE-P) group (n = 3,436) received a JADE report, personalized empowerment, and annual telephone reminder for reevaluation and engagement. The primary composite outcome was time to the first occurrence of cardiovascular-renal diseases, all-site cancer, and/or death, based on hospitalization data censored on 30 June 2017. During 94,311 person-years of follow-up in 2007–2017, 7,779 primary events occurred. Compared with the JADE group (136.22 cases per 1,000 patient-years [95% CI 132.35–140.18]), the non-JADE group had higher (145.32 [95% CI 138.68–152.20]; P = 0.020) while the JADE-P group had lower event rates (70.94 [95% CI 67.12–74.91]; P < 0.001). The adjusted hazard ratios (aHRs) for the primary composite outcome were 1.22 (95% CI 1.15–1.30) and 0.70 (95% CI 0.66–0.75), respectively, independent of risk profiles, education levels, drug usage, self-care, and comorbidities at baseline. We reported consistent results in propensity-score–matched analyses and after accounting for loss to follow-up. Potential limitations include its nonrandomized design that precludes causal inference, residual confounding, and participation bias. Conclusions ICT-assisted integrated care was associated with a reduction in clinical events, including death in type 2 diabetes in public and private healthcare settings. Lee-Ling Lim and colleagues reveal how personalised medical guidance can reduce clinical outcomes for diabetes patients in Hong Kong. Author summary Why was this study done? The public healthcare systems are overburdened with manpower shortage, long waiting time, infrequent structured evaluation, and insufficient patient engagement, while structured patient assessment and education is often not provided in the private sector due to high costs and/or lack of capacity. In 2007, we developed the web-based Joint Asia Diabetes Evaluation (JADE) Program, a multicomponent data-driven integrated care program, to close care gaps in both private and public sectors in Hong Kong. It combines the use of nonphysician personnel (e.g., nurses), information and communications technology (ICT), and structured evaluation with issue of personalized JADE reports to empower patients and promote shared decision-making. In 2007, we also established a community-based, nurse-led diabetes center to complement public and private care in Hong Kong by increasing community access to JADE-assisted evaluation for personalized empowerment and engagement. What did the researchers do and find? This retrospective analysis involved 16,624 patients with type 2 diabetes enrolled from the public and private sector in Hong Kong between 2007 and 2015. We categorized these patients into 3 groups, namely non-JADE, JADE, and JADE-Personalized (JADE-P), and compared their risk for clinical events after 6 years of follow-up. Compared with the JADE group (publicly funded evaluation with JADE reports and group education), the non-JADE group (publicly funded evaluation only) had 19%–34% higher risk of clinical events, including hospitalization. Compared with the JADE group, the JADE-P group (self-paid evaluation with JADE reports, personalized empowerment, and annual telephone reminder for engagement) had 23%–36% lower risk of clinical events, including hospitalization and death. What do these findings mean? Multicomponent, data-driven integrated care, assisted by nonphysician personnel and ICT, is associated with a reduction in clinical events and death in patients with type 2 diabetes. Its implementation in the private sector is an affordable option for patients who opt for a more user-friendly and personalized care and may reduce the burden of hospitalization in the public sector.

Abstract Background Diet quality has been linked to obesity, but this relationship remains unclear in individuals with type 2 diabetes (T2D). The aim of this study is to examine the association between diet quality and obesity in Chinese adults with T2D. Methods Between April and November 2016, a total of 211 Chinese T2D adults who underwent assessment of diabetes-related treatment goals and metabolic control were recruited into two groups based on their body mass index (BMI): obese group (BMI ≥30 kg/m2) and non-obese group (BMI = 18.5–24.9 kg/m2). Diet quality indices including Alternate Healthy Eating Index-2010 (AHEI-2010), Diet Quality Index-International (DQI-I), and Dietary Approach to Stop Hypertension (DASH) score, were derived from a validated food frequency questionnaire. Results Obese T2D patients had significantly lower AHEI-2010 (P

Background and objectivesUndiagnosed depression is an important comorbidity in type 2 diabetes (T2D) which can be detected using the Geriatric Depression Scale (GDS-15) questionnaire. In this cross-sectional study, we examined the associations of depression using GDS score with control of cardiometabolic risk factors and health status in elderly patients with T2D.Setting and participantsBetween February and December 2013, patients aged ≥65 years who underwent structured comprehensive assessment as a quality improvement program at the Diabetes Center of a teaching hospital were invited to complete the GDS-15 questionnaire.Main outcome measuresDepression was defined as a GDS score ≥7. Demographic data, prior history of co-morbidities, frequency of self-reported hypoglycemia, and attainment of treatment targets defined as HbA1c,

Introduction We designed and implemented a patient-centered, data-driven, holistic care model with evaluation of its impacts on clinical outcomes in patients with young-onset type 2 diabetes (T2D) for which there is a lack of evidence-based practice guidelines.Research design and methods In this 3-year Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes-Randomized Controlled Trial, we evaluate the effects of a multicomponent care model integrating use of information and communication technology (Joint Asia Diabetes Evaluation (JADE) platform), biogenetic markers and patient-reported outcome measures in patients with T2D diagnosed at ≤40 years of age and aged ≤50 years. The JADE-PRISM group received 1 year of specialist-led team-based management using treatment algorithms guided by biogenetic markers (genome-wide single-nucleotide polymorphism arrays, exome-sequencing of 34 monogenic diabetes genes, C-peptide, autoantibodies) to achieve multiple treatment goals (glycated hemoglobin (HbA1c)

BackgroundOlder adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes.Methods and findingsWe adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation.ConclusionsOur novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Introduction Type 2 diabetes is preventable in subjects with impaired glucose tolerance based on 2-hour plasma glucose (2hPG) during 75 g oral glucose tolerance test (OGTT). We incorporated routine biochemistry to improve the performance of a non-invasive diabetes risk score to identify individuals with abnormal glucose tolerance (AGT) defined by 2hPG≥7.8 mmol/L during OGTT.Research design and methods We used baseline data of 1938 individuals from the community-based “Better Health for Better Hong Kong - Hong Kong Family Diabetes Study (BHBHK-HKFDS) Cohort” recruited in 1998–2003. We incorporated routine biochemistry in a validated non-invasive diabetes risk score, and evaluated its performance using area under receiver operating characteristics (AUROC) with internal and external validation.Results The AUROC of the original non-invasive risk score to predict AGT was 0.698 (95% CI, 0.662 to 0.733). Following additional inclusion of fasting plasma glucose, serum potassium, creatinine, and urea, the AUROC increased to 0.778 (95% CI, 0.744 to 0.809, p

BackgroundLittle is known about the lifetime risk of progression to diabetes in the Asian population. We determined remaining lifetime risk of diabetes and life years spent with diabetes in Chinese people with normoglycemia and prediabetes.Methods and findingsUsing territory-wide diabetes surveillance data curated from electronic medical records of Hong Kong Hospital Authority (HA), we conducted a population-based cohort study in 2,608,973 individuals followed from 2001 to 2019. Prediabetes and diabetes were identified based on laboratory measurements, diagnostic codes, and medication records. Remaining lifetime risk and life years spent with diabetes were estimated using Monte Carlo simulations with state transition probabilities based on a Markov chain model. Validations were performed using several sensitivity analyses and modified survival analysis. External replication was performed using the China Health and Retirement Longitudinal Survey (CHARLS) cohort (2010 to 2015). The expected remaining lifetime risk of developing diabetes was 88.0 (95% confidence intervals: 87.2, 88.7)% for people with prediabetes and 65.9 (65.8, 65.9)% for people with normoglycemia at age 20 years. A 20-year-old person with prediabetes would live with diabetes for 32.5 (32.0, 33.1) years or 51.6 (50.8, 52.3)% of remaining life years, whereas a person with normoglycemia at 20 years would live 12.7 (12.7, 12.7) years with diabetes or 18.4 (18.4, 18.5)% of remaining life years. Women had a higher expected remaining lifetime risk and longer life years with diabetes compared to men. Results are subjected to possible selection bias as only people who undertook routine or opportunistic screening were included.ConclusionsThese findings suggest that Hong Kong, an economically developed city in Asia, is confronted with huge challenge of high lifetime risk of diabetes and long life years spent with diabetes, especially in people with prediabetes. Effective public health policies and targeted interventions for preventing progression to diabetes are urgently needed.

BACKGROUND:There is very limited data on the time trend of diabetes incidence in Asia. Using population-level data, we report the secular trend of the incidence of type 1 and type 2 diabetes in Hong Kong between 2002 and 2015. METHODS AND FINDINGS:The Hong Kong Diabetes Surveillance Database hosts clinical information on people with diabetes receiving care under the Hong Kong Hospital Authority, a statutory body that governs all public hospitals and clinics. Sex-specific incidence rates were standardised to the age structure of the World Health Organization population. Joinpoint regression analysis was used to describe incidence trends. A total of 562,022 cases of incident diabetes (type 1 diabetes [n = 2,426]: mean age at diagnosis is 32.5 years, 48.4% men; type 2 diabetes [n = 559,596]: mean age at diagnosis is 61.8 years, 51.9% men) were included. Among people aged

BackgroundRecent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.MethodologyWe selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).ResultsWe observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18)ConclusionIn a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).