End-stage renal disease risk equations for Hong Kong Chinese patients with type 2 diabetes: Hong Kong Diabetes Registry

Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study

OBJECTIVE

We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years.

METHOD

Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model.

RESULT

At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728-0.756] in the development cohort and 0.881(95%CI, 0.867-0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model.

CONCLUSION

Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD.

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank

AIMS/HYPOTHESIS

The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers.

METHODS

From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts.

RESULTS

At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts.

CONCLUSIONS/INTERPRETATION

Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

IMPORTANCE AND AIMS

Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

METHODS

A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

RESULTS

Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

CONCLUSIONS AND RELEVANCE

There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Consensus Statements from the Diabetologists & Endocrinologists Alliance for the Management of People with Hypertension and Type 2 Diabetes Mellitus

Hypertension and type 2 diabetes mellitus (T2DM) are important, intertwined public health issues. People with both conditions face significantly elevated risks of cardiovascular (CV) and renal complications. To optimize patient care, a multidisciplinary expert panel met to review recent evidence on optimal blood pressure (BP) targets, implications of albuminuria, and treatment regimens for hypertensive patients with T2DM, with the aim of providing recommendations for physicians in Hong Kong. The panel reviewed the relevant literature, obtained by searching PubMed for the publication period from January 2015 to June 2021, to address five discussion areas: (i) BP targets based on CV/renal benefits; (ii) management of isolated systolic or diastolic hypertension; (iii) roles of angiotensin II receptor blockers; (iv) implications of albuminuria for CV/renal events and treatment choices; and (v) roles and tools of screening for microalbuminuria. The panel held three virtual meetings using a modified Delphi method to address the discussion areas. After each meeting, consensus statements were derived and anonymously voted on by every panelist. A total of 17 consensus statements were formulated based on recent evidence and expert insights regarding cardioprotection and renoprotection for hypertensive patients with T2DM.

Nonalbuminuric Diabetic Kidney Disease and Risk of All-Cause Mortality and Cardiovascular and Kidney Outcomes in Type 2 Diabetes: Findings From the Hong Kong Diabetes Biobank

RATIONALE & OBJECTIVE

Nonalbuminuric diabetic kidney disease (DKD) has become the prevailing DKD phenotype. We compared the risks of adverse outcomes among patients with this phenotype compared with other DKD phenotypes.

STUDY DESIGN

Multicenter prospective cohort study.

SETTINGS & PARTICIPANTS

19,025 Chinese adults with type 2 diabetes enrolled in the Hong Kong Diabetes Biobank.

EXPOSURES

DKD phenotypes defined by baseline estimated glomerular filtration rate (eGFR) and albuminuria: no DKD (no decreased eGFR or albuminuria), albuminuria without decreased eGFR, decreased eGFR without albuminuria, and albuminuria with decreased eGFR.

OUTCOMES

All-cause mortality, cardiovascular disease (CVD) events, hospitalization for heart failure (HF), and chronic kidney disease (CKD) progression (incident kidney failure or sustained eGFR reduction ≥40%).

ANALYTICAL APPROACH

Multivariable Cox proportional or cause-specific hazards models to estimate the relative risks of death, CVD, hospitalization for HF, and CKD progression. Multiple imputation was used for missing covariates.

RESULTS

Mean participant age was 61.1 years, 58.3% were male, and mean diabetes duration was 11.1 years. During 54,260 person-years of follow-up, 438 deaths, 1,076 CVD events, 298 hospitalizations for HF, and 1,161 episodes of CKD progression occurred. Compared with the no-DKD subgroup, the subgroup with decreased eGFR without albuminuria had higher risks of all-cause mortality (hazard ratio [HR], 1.59 [95% CI, 1.04-2.44]), hospitalization for HF (HR, 3.08 [95% CI, 1.82-5.21]), and CKD progression (HR, 2.37 [95% CI, 1.63-3.43]), but the risk of CVD was not significantly greater (HR, 1.14 [95% CI, 0.88-1.48]). The risks of death, CVD, hospitalization for HF, and CKD progression were higher in the setting of albuminuria with or without decreased eGFR. A sensitivity analysis that excluded participants with baseline eGFR <30 mL/min/1.73 m² yielded similar findings.

LIMITATIONS

Potential misclassification because of drug use.

CONCLUSIONS

Nonalbuminuric DKD was associated with higher risks of hospitalization for HF and of CKD progression than no DKD, regardless of baseline eGFR.

External validation of prognostic models for chronic kidney disease among type 2 diabetes

BACKGROUND

Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort.

METHODS

A nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting.

RESULTS

Six relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565-0.605) to 0.786 (0.765-0.806) for CKD and 0.657 (0.610-0.703) to 0.760 (0.705-0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975-1.024) to 1.009 (0.929-1.090). Hosmer-Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low's of 0.114 for CKD and Elley's of 0.025 for ESRD.

CONCLUSIONS

All models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low's (developed in Singapore) and Elley's model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Clinical Predictors and Long-term Impact of Acute Kidney Injury on Progression of Diabetic Kidney Disease in Chinese Patients With Type 2 Diabetes

We aim to assess the long-term impact of acute kidney injury (AKI) on progression of diabetic kidney disease (DKD) and all-cause mortality and investigate determinants of AKI in Chinese patients with type 2 diabetes (T2D). A consecutive cohort of 9,096 Chinese patients with T2D from the Hong Kong Diabetes Register was followed for 12 years (mean ± SD age 57 ± 13.2 years; 46.9% men; median duration of diabetes 5 years). AKI was defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria using serum creatinine. Estimated glomerular filtration rate measurements were used to identify the first episode with chronic kidney disease (CKD) and end-stage renal disease (ESRD). Polygenic risk score (PRS) composed of 27 single nucleotide polymorphisms (SNPs) known to be associated with serum uric acid (SUA) in European populations was used to examine the role of SUA in pathogenesis of AKI, CKD, and ESRD. Validation was sought in an independent cohort including 6,007 patients (age 61.2 ± 10.9 years; 59.5% men; median duration of diabetes 10 years). Patients with AKI had a higher risk for developing incident CKD (hazard ratio 14.3 [95% CI 12.69-16.11]), for developing ESRD (12.1 [10.74-13.62]), and for all-cause death (7.99 [7.31-8.74]) compared with those without AKI. Incidence rate for ESRD among patients with no episodes of AKI and one, two, and three or more episodes of AKI was 7.1, 24.4, 32.4, and 37.3 per 1,000 person-years, respectively. Baseline SUA was a strong independent predictor for AKI. A PRS composed of 27 SUA-related SNPs was associated with AKI and CKD in both discovery and replication cohorts but not ESRD. Elevated SUA may increase the risk of DKD through increasing AKI. The identification of SUA as a modifiable risk factor and PRS as a nonmodifiable risk factor may facilitate the identification of individuals at high risk to prevent AKI and its long-term impact in T2D.

Skin autofluorescence is associated with progression of kidney disease in type 2 diabetes: A prospective cohort study from the Hong Kong diabetes biobank

BACKGROUND AND AIMS

Skin autofluorescence (SAF) can non-invasively assess the accumulation of tissue AGEs. We investigated the association between SAF and kidney dysfunction in participants with T2D.

METHODS

Of 4030 participants consecutively measured SAF at baseline, 3725 participants free of end-stage kidney disease (ESKD) were included in the analyses. The association of SAF with incident ESKD or ≥30% reduction in estimated glomerular filtration rate (eGFR) was examined with Cox regression, linear mixed-effects model for the association with annual eGFR decline, and mediation analyses for the mediating roles of renal markers.

RESULTS

During a median (IQR) 1.8 (1.1-3.1) years of follow-up, 411 participants developed the outcome. SAF was associated with progression of kidney disease (hazard ratio 1.15 per SD, 95% confidence interval [CI] [1.04, 1.28]) and annual decline in eGFR (β -0.39 per SD, 95% CI [-0.71, -0.07]) after adjustment for risk factors, including baseline eGFR and urinary albumin-creatinine ratio (UACR). Decreased eGFR (12.9%) and increased UACR (25.8%) accounted for 38.7% of the effect of SAF on renal outcome.

CONCLUSIONS

SAF is independently associated with progression of kidney disease. More than half of its effect is independent of renal markers. SAF is of potential to be a prognostic marker for kidney dysfunction.

Derivation and Validation of a Prediction Model of End-Stage Renal Disease in Patients With Type 2 Diabetes Based on a Systematic Review and Meta-analysis

OBJECTIVES

To develop and validate a model for predicting the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes.

METHODS

The derivation cohort was from a meta-analysis. Statistically significant risk factors were extracted and combined to the corresponding risk ratio (RR) to establish a risk assessment model for ESRD in type 2 diabetes. All risk factors were scored according to their weightings to establish the prediction model. Model performance is evaluated using external validation cohorts. The outcome was the occurrence of ESRD defined as eGFR<15 ml min^-1 1.73 m^-2 or received kidney replacement therapy (dialysis or transplantation).

RESULTS

A total of 1,167,317 patients with type 2 diabetes were included in our meta-analysis, with a cumulative incidence of approximately 1.1%. The final risk factors of the prediction model included age, sex, smoking, diabetes mellitus (DM) duration, systolic blood pressure (SBP), hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and triglyceride (TG). All risk factors were scored according to their weightings, with the highest score being 36.5. External verification showed that the model has good discrimination, AUC=0.807(95%CI 0.753-0.861). The best cutoff value is 16 points, with the sensitivity and specificity given by 85.33% and 60.45%, respectively.

CONCLUSION

The study established a simple risk assessment model including 8 routinely available clinical parameters for predicting the risk of ESRD in type 2 diabetes.

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

BACKGROUND

Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models.

METHODS

Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D).

RESULTS

In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively.

CONCLUSIONS

Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018105287.

Skin autofluorescence is associated with higher risk of cardiovascular events in Chinese adults with type 2 diabetes: A prospective cohort study from the Hong Kong Diabetes Biobank

AIMS

To investigate association between skin autofluorescence (SAF) and cardiovascular events (CVE) and assess its predictive value in Chinese adults with type 2 diabetes (T2D).

MATERIALS AND METHODS

SAF was measured non-invasively in 3806 Chinese adults with T2D between 2016 and 2019 with CVE as primary endpoint and individual components as secondary endpoints. Cox proportional hazard models were used to examine associations between SAF and endpoints with adjustment for conventional risk factors. C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were performed to evaluate SAF's predictive value.

RESULTS

During a median 1.8 (interquartile range, 1.2-3.1) years of follow-up, 172 individuals experienced CVE. Multivariate Cox model showed that SAF was independently associated with CVE (HR 1.18 per SD, 95% CI [1.02, 1.37]), coronary heart disease (HR 1.29 per SD, 95% CI [1.02, 1.63]), and congestive heart failure (HR 1.53 per SD, 95% CI [1.14, 2.05]). SAF yielded additional value on CVE risk stratification with enhanced IDI (95% CI) (0.023 [0.001, 0.057]) and continuous NRI (0.377 [0.002, 0.558]) over traditional risk factors.

CONCLUSIONS

Higher SAF was independently associated with CVE in Chinese adults with T2D and yielded incremental predictive information for CVE. SAF has potential as a prognostic maker for CVE.

Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases

(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged.

Prediction models and nomograms for 10-year risk of end-stage renal disease in Chinese type 2 diabetes mellitus patients in primary care

AIMS

To develop and validate 10-year risk prediction models, nomograms and charts for end-stage renal disease (ESRD) in Chinese patients with type 2 diabetes mellitus (T2DM) in primary care, in order to guide individualized treatment.

MATERIALS AND METHODS

This was a 10-year population-based observational cohort study. A total of 141 516 Chinese T2DM patients without history of cardiovascular disease or ESRD who were managed in public primary care clinics in 2008 were included and followed up until December 2017. Two-thirds of these patients were randomly selected to develop sex-specific ESRD risk prediction models using Cox regressions. The validity and accuracy of the models were tested on the remaining third of patients using Harrell's C-index. We selected variables based on their clinical and statistical importance to construct the nomograms and charts.

RESULTS

The median follow-up period was 9.75 years. The cumulative incidence of ESRD was 6.0% (men: 6.1%, women: 5.9%). Age, diabetes duration, systolic blood pressure (SBP), SBP variability, diastolic blood pressure, triglycerides, glycated haemoglobin (HbA1c), HbA1c variability, urine albumin to creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) were significant predictors for both sexes. Smoking and total cholesterol to HDL cholesterol ratio were additional significant predictors for men and women, respectively. The models showed Harrell's C-statistics of 0.889/0.889 (women/men). Age, eGFR, UACR, SBP and HbA1c were selected for both sexes to develop nomograms and charts.

CONCLUSIONS

Using routinely available variables, the 10-year ESRD risk of Chinese T2DM patients in primary care can be predicted with approximately 90% accuracy. We have developed different tools to facilitate routine ESRD risk prediction in primary care, so that individualized care can be provided to prevent or delay ESRD in T2DM patients.

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

OBJECTIVES

The objective of this study was to investigate predictors and develop risk equations for stage-3 chronic kidney disease (CKD) in Thai patients with type 2 diabetes mellitus (DM).

METHODS

A retrospective cohort study was conducted in patients with type 2 DM. The outcome was the development of stage-3 CKD. The data set was randomly split into training and validation data sets. Cox proportional hazard regression was used for model development. Discrimination (Harrell's C statistic) and calibration (the Hosmer-Lemeshow chi-square test and survival probability curve) were applied to evaluate model performance.

RESULTS

In total, 2178 type 2 DM patients without stage-3 CKD, visiting the hospital from January 1, 2008, to December 31, 2017, were recruited, with median follow-up time of 1.29 years (interquartile range, 0.5-2.5 years); 385 (17.68%) subjects had developed stage-3 CKD. The final predictors included age, male sex, urinary albumin to creatinine ratio, estimated glomerular filtration rate, and hemoglobin A1c. Two 3-year stage-3 CKD risk models, model 1 (laboratory model) and model 2 (simplified model), had the C statistic in validation data sets of 0.890 and 0.812, respectively.

CONCLUSIONS

Two 3-year stage-3 CKD risk models were developed for Thai patients with type 2 DM. Both models have good discrimination and calibration. These stage-3 CKD prediction models could equip health providers with tools for clinical management and supporting patient education.

Use of sodium-glucose co-transporter-2 inhibitors in Asian patients with type 2 diabetes and kidney disease: An Asian perspective and expert recommendations

Early onset of type 2 diabetes and a high prevalence of co-morbidities predispose the Asian population to a high risk for, and rapid progression of, diabetic kidney disease (DKD). Apart from renin-angiotensin system inhibitors, sodium-glucose co-transporter-2 (SGLT-2) inhibitors have been shown to delay renal disease progression in patients with DKD. In this review article, we consolidate the existing literature on SGLT-2 inhibitor use in Asian patients with DKD to establish contemporary guidance for clinicians. We extensively reviewed recommendations from international and regional guidelines, data from studies on Asian patients with DKD, global trials (DAPA-CKD, CREDENCE and DELIGHT) and cardiovascular outcomes trials. In patients with DKD, SGLT-2 inhibitor therapy significantly reduced albuminuria and the risk of hard renal outcomes (defined as the onset of end-stage kidney disease, substantial decline in renal function from baseline and renal death), cardiovascular outcomes and hospitalization for heart failure. In all the cardiovascular and renal outcomes trials, there was an initial decline in the estimated glomerular filtration rate (eGFR), which was followed by a slowing in the decline of renal function compared with that seen with placebo. Despite an attenuation in glucose-lowering efficacy in patients with low eGFR, there were sustained reductions in body weight and blood pressure, and an increase in haematocrit. Based on the available evidence, we conclude that SGLT-2 inhibitors represent an evidence-based therapeutic option for delaying the progression of renal disease in Asian patients with DKD and preserving renal function in patients at high risk of kidney disease.

Individualised risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong

OBJECTIVES

This study is aimed to develop and validate a prediction model for multistate transitions across different stages of chronic kidney disease (CKD) in patients with type 2 diabetes mellitus under primary care.

SETTING

We retrieved the anonymised electronic health records of a population-based retrospective cohort in Hong Kong.

PARTICIPANTS

A total of 26 197 patients were included in the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

The new-onset, progression and regression of CKD were defined by the transitions of four stages that were classified by combining glomerular filtration rate and urine albumin-to-creatinine ratio. We applied a multiscale multistate Poisson regression model to estimate the rates of the stage transitions by integrating the baseline demographic characteristics, routine laboratory test results and clinical data from electronic health records.

RESULTS

During the mean follow-up time of 1.8 years, there were 2632 patients newly diagnosed with CKD, 1746 progressed to the next stage and 1971 regressed into an earlier stage. The models achieved the best performance in predicting the new-onset and progression with the predictors of sex, age, body mass index, systolic blood pressure, diastolic blood pressure, serum creatinine, haemoglobin A1c, total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides and drug prescriptions.

CONCLUSIONS

This study demonstrated that individual risks of new-onset and progression of CKD can be predicted from the routine physical and laboratory test results. The individualised prediction curves developed from this study could potentially be applied to routine clinical practices, to facilitate clinical decision making, risk communications with patients and early interventions.

Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank

BACKGROUND

Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D.

METHODS AND FINDINGS

In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort.

CONCLUSIONS

Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.

Development and validation of a predictive model for end-stage renal disease risk in patients with diabetic nephropathy confirmed by renal biopsy

This study was performed to develop and validate a predictive model for the risk of end-stage renal disease (ESRD) inpatients with diabetic nephropathy (DN) confirmed by renal biopsy. We conducted a retrospective study with 968 patients with T2DM who underwentrenal biopsy for the pathological confirmation of DNat the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015; the patients were followed until December 2018. The outcome was defined as a fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death due to chronic renal failure or ESRD). The dataset was randomly split into development (75%) and validation (25%) cohorts. We used stepwise multivariablelogistic regression to identify baseline predictors for model development. The model’s performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic and the P value of the Hosmer-Lemeshow test. During the 3-year follow-up period, there were 225 outcome events (47.1%) during follow-up. Outcomes occurred in 187 patients (52.2%) in the derivation cohort and 38 patients (31.7%) in the validation cohort. The variables selected in the final multivariable logistic regression after backward selection were pathological grade, Log Urinary Albumin-to-creatinine ratio (Log ACR), cystatin C, estimated glomerular filtration rate (eGFR) and B-type natriuretic peptide (BNP). 4 prediction models were created in a derivation cohort of 478 patients: a clinical model that included cystatin C, eGFR, BNP, Log ACR; a clinical-pathological model and a clinical-medication model, respectively, also contained pathological grade and renin-angiotensin system blocker (RASB) use; and a full model that also contained the pathological grade, RASB use and age. Compared with the clinical model, the clinical-pathological model and the full model had better C statistics (0.865 and 0.866, respectively, vs. 0.864) in the derivation cohort and better C statistics (0.876 and 0.875, respectively, vs. 0.870) in the validation cohort. Among the four models, the clinical-pathological model had the lowest AIC of 332.53 and the best P value of 0.909 of the Hosmer-Lemeshow test. We constructed a nomogram which was a simple calculator to predict the risk ratio of progression to ESRD for patients with DN within 3 years. The clinical-pathological model using routinely available clinical measurements was shown to be accurate and validated method for predicting disease progression in patients with DN. The risk model can be used in clinical practice to improve the quality of risk management and early intervention.

Clinicopathological features and outcomes of diabetic kidney disease with extracapillary hypercellularity: a Japanese single-center experience

BACKGROUND

The prognostic significance of glomerular extracapillary hypercellularity (EXHC) in diabetic kidney disease (DKD) is unclear. The aim of this study was to investigate the clinicopathological features and outcomes of DKD patients with EXHC.

METHODS

We studied 70 cases of renal biopsy-confirmed type 2 DKD that were diagnosed between 2004 and 2014 and compared the clinicopathological features and outcomes of 22 patients with EXHC (EXHC group) with those of 48 patients without EXHC (control group). All of the patients were Japanese. We assessed the renal biopsy specimens based on the Renal Pathology Society classification system. Clinical and laboratory data were collected at the time of the renal biopsy, and renal outcomes were assessed based on progression to end-stage renal disease (ESRD) requiring renal replacement therapy. The median duration of the observation period was 3 years.

RESULTS

In pathological features, nodular sclerosis (Kimmelstiel-Wilson lesions) was observed more frequently in the EXHC group than in the control group (63.6% vs. 35.4%, P = 0.027). There were no significant intergroup differences in clinical features or renal outcomes. Univariate and multivariate Cox regression analyses of all patients showed that a high level of proteinuria, a low initial eGFR, and severe interstitial inflammation were poor prognostic factors.

CONCLUSIONS

EXHC is related to nodular sclerosis, which is a known risk factor for ESRD. Careful observation is needed during the follow-up of DKD patients with EXHC, although there were no significant differences in renal outcomes between the EXHC and control groups.

From Hong Kong Diabetes Register to JADE Program to RAMP-DM for Data-Driven Actions

In 1995, the Hong Kong Diabetes Register (HKDR) was established by a doctor-nurse team at a university-affiliated, publicly funded, hospital-based diabetes center using a structured protocol for gathering data to stratify risk, triage care, empower patients, and individualize treatment. This research-driven quality improvement program has motivated the introduction of a territory-wide diabetes risk assessment and management program provided by 18 hospital-based diabetes centers since 2000. By linking the data-rich HKDR to the territory-wide electronic medical record, risk equations were developed and validated to predict clinical outcomes. In 2007, the HKDR protocol was digitalized to establish the web-based Joint Asia Diabetes Evaluation (JADE) Program complete with risk levels and algorithms for issuance of personalized reports to reduce clinical inertia and empower self-management. Through this technologically assisted, integrated diabetes care program, we have generated big data to track secular trends, identify unmet needs, and verify interventions in a naturalistic environment. In 2009, the JADE Program was adapted to form the Risk Assessment and Management Program for Diabetes Mellitus (RAMP-DM) in the publicly funded primary care clinics, which reduced all major events by 30-60% in patients without complications. Meanwhile, a JADE-assisted assessment and empowerment program provided by a university-affiliated, self-funded, nurse-coordinated diabetes center, aimed at complementing medical care in the community, also reduced all major events by 30-50% in patients with different risk levels. By combining universal health coverage, public-private partnerships, and data-driven integrated care, the Hong Kong experience provides a possible solution than can be adapted elsewhere to make quality diabetes care accessible, affordable, and sustainable.

The association between serum uric acid to creatinine ratio and renal disease progression in type 2 diabetic patients in Chinese communities

AIMS

Serum uric acid (UA) increases in patients with kidney disease due to the impaired UA clearance. The present study sought to evaluate the association between UA/creatinine ratio (UA/Cr) and renal disease progression in patients with type 2 diabetes mellitus (T2DM) in Chinese communities.

METHODS

In the present retrospective longitudinal study, 3432 Chinese T2DM patients recruited from 11 community healthcare centers in Nanjing, China were included. Renal disease progression was defined as the occurrence of estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m² or doubling of baseline serum creatinine level. Cox regression analysis was used to estimate the association between UA/Cr and renal disease progression.

RESULTS

During a median follow-up of 30 months, 58 (1.70%) patients experienced progression of renal disease, which was more common among those with older ages, longer diabetes duration, and higher baseline eGFR. Multivariate analysis revealed that UA/Cr was an independent risk factor for renal disease progress (hazard ratio 1.364 [95% CI 1.131-1.646], P = 0.001) independently of age, sex, and other potential confounders.

CONCLUSIONS

UA/Cr might be a novel predictor of chronic kidney disease progression in T2DM patients.

Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of diabetic kidney disease is not well understood. We aimed to identify patterns of estimated GFR (eGFR) trajectory and to determine the clinical and genetic factors and their associations of these different patterns with all-cause mortality in patients with type 2 diabetes. Among 6330 patients with baseline eGFR >60 ml/min per 1.73 m² in the Hong Kong Diabetes Register, a total of 456 patients (7.2%) developed Stage 5 chronic kidney disease or ESRD over a median follow-up of 13 years (incidence rate 5.6 per 1000 person-years). Joint latent class modeling was used to identify different patterns of eGFR trajectory. Four distinct and non-linear trajectories of eGFR were identified: slow decline (84.3% of patients), curvilinear decline (6.5%), progressive decline (6.1%) and accelerated decline (3.1%). Microalbuminuria and retinopathy were associated with accelerated eGFR decline, which was itself associated with all-cause mortality (odds ratio [OR] 6.9; 95% confidence interval [CI]: 5.6-8.4 for comparison with slow eGFR decline). Of 68 candidate genetic loci evaluated, the inclusion of five loci (rs11803049, rs911119, rs1933182, rs11123170, and rs889472) improved the prediction of eGFR trajectories (net reclassification improvement 0.232; 95% CI: 0.057--0.406). Our study highlights substantial heterogeneity in the patterns of eGFR decline among patients with diabetic kidney disease, and identifies associated clinical and genetic factors that may help to identify those who are more likely to experience an accelerated decline in kidney function.

Risk factors of chronic kidney diseases in Chinese adults with type 2 diabetes

In this study we conducted a cross sectional study to comprehensively evaluated the risk factors of chronic kidney disease (CKD) in a large sample of Chinese adults under primary care for type 2 diabetes mellitus (T2DM). We investigated the risk factors associated with the prevalence of CKD in adults with T2DM, who were enrolled in the Risk Factor Assessment and Management Programme for Patients with Diabetes Mellitus (RAMP-DM) of Hong Kong from July 2014 to June 2017. We collected the individual data of 31,574 subjects, with mean age of 63.0 (±10.8) years and mean DM duration of 7.4 (±6.4) years. Of them 9,386 (29.7%) had CKD and 7,452 (23.6%) had micro- or macro-albuminuria. After adjustment for multiple demographic and lifestyle confounders, we identified several modifiable risk factors associated with higher rate of CKD: obesity (OR = 1.54), current smoking (OR = 1.33), higher systolic blood pressure (OR = 1.01), dyslipidemia (OR = 1.32 and 0.61 for triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C)), hyperglycemia (OR = 1.11 for HbA1c), diabetic retinopathy (OR = 1.36 and 2.60 for non-sight and sight threatening retinopathy), and stroke (OR = 1.43). The risk factors of lower dialytic blood pressure and coronary heart disease were identified only in men, whereas peripheral arterial disease only in women. In conclusion, several modifiable and gender specific risk factors were significantly associated with higher prevalence of CKD in Chinese adults with T2DM. The high-risk populations identified in this study shall receive regular screening for renal functions to achieve better patient management in primary care settings.

Evolution of Diabetes Care in Hong Kong: From the Hong Kong Diabetes Register to JADE-PEARL Program to RAMP and PEP Program

The rapid increase in diabetes prevalence globally has contributed to large increases in health care expenditure on diabetic complications, posing a major health burden to countries worldwide. Asians are commonly observed to have poorer β-cell function and greater insulin resistance compared to the Caucasian population, which is attributed by their lower lean body mass and central obesity. This "double phenotype" as well as the rising prevalence of young onset diabetes in Asia has placed Asians with diabetes at high risk of cardiovascular and renal complications, with cancer emerging as an important cause of morbidity and mortality. The experience from Hong Kong had demonstrated that a multifaceted approach, involving team-based integrated care, information technological advances, and patient empowerment programs were able to reduce the incidence of diabetic complications, hospitalizations, and mortality. System change and public policies to enhance implementation of such programs may provide solutions to combat the burgeoning health problem of diabetes at a societal level.

Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: a systematic literature review

BACKGROUND

Albuminuria, elevated serum creatinine and low estimated glomerular filtration rate (eGFR) are pivotal indicators of kidney decline. Yet, it is uncertain if these and emerging biomarkers such as uric acid represent independent predictors of kidney disease progression or subsequent outcomes among individuals with type 2 diabetes mellitus (T2DM). This study systematically examined the available literature documenting the role of albuminuria, serum creatinine, eGFR, and uric acid in predicting kidney disease progression and cardio-renal outcomes in persons with T2DM.

METHODS

Embase, MEDLINE, and Cochrane Central Trials Register and Database of Systematic Reviews were searched for relevant studies from January 2000 through May 2016. PubMed was searched from 2013 until May 2016 to retrieve studies not yet indexed in the other databases. Observational cohort or non-randomized longitudinal studies relevant to albuminuria, serum creatinine, eGFR, uric acid and their association with kidney disease progression, non-fatal cardiovascular events, and all-cause mortality as outcomes in persons with T2DM, were eligible for inclusion. Two reviewers screened citations to ensure studies met inclusion criteria.

RESULTS

From 2249 citations screened, 81 studies were retained, of which 39 were omitted during the extraction phase (cross-sectional [n = 16]; no outcome/measure of interest [n = 13]; not T2DM specific [n = 7]; review article [n = 1]; editorial [n = 1]; not in English language [n = 1]). Of the remaining 42 longitudinal study publications, biomarker measurements were diverse, with seven different measures for eGFR and five different measures for albuminuria documented. Kidney disease progression differed substantially across 31 publications, with GFR loss (n = 9 [29.0%]) and doubling of serum creatinine (n = 5 [16.1%]) the most frequently reported outcome measures. Numerous publications presented risk estimates for albuminuria (n = 18), serum creatinine/eGFR (n = 13), or both combined (n = 6), with only one study reporting for uric acid. Most often, these biomarkers were associated with a greater risk of experiencing clinical outcomes.

CONCLUSIONS

Despite the utility of albuminuria, serum creatinine, and eGFR as predictors of kidney disease progression, further efforts to harmonize biomarker measurements are needed given the disparate methodologies observed in this review. Such efforts would help better establish the clinical significance of these and other biomarkers of renal function and cardio-renal outcomes in persons with T2DM.

A Point-based Mortality Prediction System for Older Adults with Diabetes

The mortality prediction models for the general diabetic population have been well established, but the corresponding elderly-specific model is still lacking. This study aims to develop a mortality prediction model for the elderly with diabetes. The data used for model establishment were derived from the nationwide adult health screening program in Taiwan in 2007-2010, from which we applied a 10-fold cross-validation method for model construction and internal validation. The external validation was tested on the MJ health screening database collected in 2004-2007. Multivariable Cox proportional hazards models were used to predict five-year mortality for diabetic patients ≥65 years. A total of 220,832 older subjects with diabetes were selected for model construction, of whom 23,241 (10.5%) died by the end of follow-up (December 31, 2011). The significant predictors retained in the final model included age, gender, smoking status, body mass index (BMI), fasting glucose, systolic and diastolic blood pressure, leukocyte count, liver and renal function, total cholesterol, hemoglobin, albumin, and uric acid. The Harrell's C in the development, internal-, and external-validation datasets were 0.737, 0.746, and 0.685, respectively. We established an easy-to-use point-based model that could accurately predict five-year mortality risk in older adults with diabetes.

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study

BACKGROUND

Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care.

METHODS

A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell's C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models.

RESULTS

Prediction models showed better discrimination with Harrell's C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females.

CONCLUSIONS

Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients.

Clinical characteristics of Chinese patients with duration of type 2 diabetes >40 years

BACKGROUND

Although type 2 diabetes mellitus (T2DM) shortens life expectancy by 10-12 years, some patients survive extremely long durations of diabetes. The clinical characteristics of Chinese patients with >40 years T2DM remain unknown. Thus, the aim of the present study was to document the clinical profile of patients with T2DM for ≥40 years.

METHODS

The present study evaluated 157 survivors with >40 years T2DM from a total of 582 773 patients with T2DM enrolled in a Chinese national survey of HbA1c. Two matched case-control studies were performed on long T2DM survivors (cases) separately matched according to: (1) survey day, sex, and survey hospital; and (2) age, sex, and survey hospital. Conditional logistic regression analysis was performed to obtain odds ratios (ORs) for long survival.

RESULTS

Patients with a long duration of T2DM had a mean (± SD) age of 75 ± 10 years. Their T2DM had been diagnosed at a mean age of 32 years and the median duration of diabetes was 41 years. In both case-control studies, long-duration T2DM was associated with an increased risk of hyperglycemia (OR 6.31; 95% confidence interval [CI] 1.89-21.09) and coronary heart disease (CHD; OR 2.18 95% CI 1.01-4.70). However, long-duration T2DM was not associated with a higher likelihood of abnormal lipids, diabetic nephropathy (DN), or stroke compared with patients with a shorter duration of T2DM.

CONCLUSIONS

The present study suggests that Chinese patients with long-term T2DM also had increased risks of hyperglycemia and non-fatal CHD. Further studies are needed to investigate whether survival of these patients was associated with non-increased risk of DN.

Development and validation of a predictive model for Chronic Kidney Disease progression in Type 2 Diabetes Mellitus based on a 13-year study in Singapore

AIMS

This study aims to develop and validate a predictive model for Chronic Kidney Disease (CKD) progression in Type 2 Diabetes Mellitus (T2DM).

METHODS

We conducted a prospective study on 1582 patients with T2DM from a Diabetes Centre in regional hospital in 2002-2014. CKD progression was defined as deterioration across eGFR categories with ⩾25% drop from baseline. The dataset was randomly split into development (70%) and validation (30%) datasets. Stepwise multivariable logistic regression was used to identify baseline predictors for model development. Model performance in the two datasets was assessed.

RESULTS

During median follow-up of 5.5years, 679 (42.9%) had CKD progression. Progression occurred in 467 (42.2%) and 212 patients (44.6%) in development and validation datasets respectively. Systolic blood pressure, HbA1c, estimated glomerular filtration rate and urinary albumin-to-creatinine ratio were associated with progression. Areas under receiving-operating-characteristics curve for the training and test datasets were 0.80 (95%CI, 0.77-0.83) and 0.83 (95%CI, 0.79-0.87). Observed and predicted probabilities by quintiles were not statistically different with Hosmer-Lemeshow χ² 0.65 (p=0.986) and 1.36 (p=0.928) in the two datasets. Sensitivity and specificity were 71.4% and 72.2% in development dataset, and 75.6% and 72.3% in the validation dataset.

CONCLUSIONS

A model using routinely available clinical measurements can accurately predict CKD progression in T2DM.

Predictors of Renal Function Decline in Chinese Patients with Type 2 Diabetes Mellitus and in a Subgroup of Normoalbuminuria: A Retrospective Cohort Study

BACKGROUND

Risk factors related to renal function decline in type 2 diabetes mellitus (T2DM) remain uncertain. This study aimed to investigate risk factors in relation to renal function decline in patients with T2DM and in a subgroup of patients with normoalbuminuria.

METHODS

This study was a retrospective cohort study, which included 451 patients with T2DM aged 63 ± 14 years admitted to a tertiary hospital in Beijing, China, between April and December 2010 and followed up for 6-60 months. Endpoint was renal function decline, defined as estimated glomerular filtration rate less than 60 mL/min 1.73 m² or at least twofold increase of serum creatinine. Cox proportional hazards analysis was used to estimate hazard ratios (HRs) for candidate risk factors of renal function decline.

RESULTS

After a median follow-up of 3.3 years, 94 (20.8%) patients developed renal function decline. Increased age (HR, 1.045; 95% CI, 1.020-1.070), albuminuria (HR, 1.956; 95%CI, 1.271-3.011), mild renal dysfunction (HR, 4.521; 95%CI, 2.734-7.476), hyperfiltration (HR, 3.897; 95%CI, 1.572-9.663), and increased hemoglobin A1c (HR, 1.128; 95%CI, 1.020-1.249) were identified as major risk factors. Among a subgroup of 344 patients with normoalbuminuria at baseline, 53 (15.4%) patients developed renal function decline. Increased age (HR, 1.089; 95%CI, 1.050-1.129), mild renal dysfunction (HR, 4.667; 95%CI, 2.391-9.107), hyperfiltration (HR, 5.677; 95%CI, 1.544-20.872), smoking (HR, 2.886; 95%CI, 1.370-6.082), higher pulse pressure (HR, 1.022; 95%CI, 1.004-1.040), and increased fasting glucose (HR, 1.104; 95%CI, 1.020-1.194) were major risk factors.

CONCLUSIONS

Risk factors of diabetic renal impairment in T2DM should be screened and evaluated at an early stage of diabetes. Albuminuria, mild renal dysfunction, hyperfiltration, increased blood glucose, increased pulse pressure, and smoking were all predictors for diabetic renal impairment and interventions that focus on these risk factors may reduce further decline in renal function.

Progression to treatment failure among Chinese patients with type 2 diabetes initiated on metformin versus sulphonylurea monotherapy--The Hong Kong Diabetes Registry

AIMS

To assess the development of treatment failure in Chinese patients with type 2 diabetes mellitus (T2DM) initiated on metformin or sulphonylurea (SU) monotherapy, with consideration of various potential sources of biases.

METHODS

A 1:1-matched new metformin and SU user cohort on immortal time and mean propensity score after multiple imputation was selected from a cohort of 5889 Chinese patients with T2DM. Treatment failure was defined as progression to (i) combination oral anti-hyperglycemia drug therapy, (ii) insulin use, or (iii) a treatment haemoglobin A1c (HbA1c) >7.5% (58 mmol/mol). Stratified Cox regression analysis on the matched pairs was employed to examine the associations between initial monotherapy and onset of treatment failure.

RESULTS

Of 554 new metformin and 840 new SU users, 380 were matched. During a median follow-up duration of 3 years, 173 (45.6%) metformin users and 220 (57.9%) SU users experienced treatment failure (annual failure rates of 15% and 19%, respectively). The median time from monotherapy starting to treatment failure was 3.0 [inter-quartile range (IQR): 1.8-5.4] years for metformin users, versus 1.8 (IQR: 0.9-4.1) years for SU users (p<0.001). Stratified Cox regression analysis showed significantly lower risk of treatment failure for metformin users (HR [95% CI], 0.62[0.47-0.81]; p<0.001). Consistent results were found in analyses based on traditional adjustment schemes with or without imputation.

CONCLUSIONS

By systematically incorporating new-user design, multiple imputation and matching methods, we found that Chinese patients with T2DM initiated on metformin monotherapy were associated with a significant delay in the onset of treatment failure compared to SU monotherapy.

Hypoglycaemia, Abnormal Lipids, and Cardiovascular Disease among Chinese with Type 2 Diabetes

We recruited a group of 6713 consecutive Chinese patients with T2D but normal renal and liver function who were admitted to one of 81 top tertiary care hospitals in China. Mild hypoglycaemia was defined as having symptomatic hypoglycaemia in one month before hospitalization. Severe hypoglycaemia was defined as having hypoglycaemia that needed assistance from other people in three months before hospitalization. Prior cardiovascular disease (CVD) was defined as having coronary heart disease, stroke, or peripheral arterial disease. Of 6713 patients, 80 and 304 had severe and mild hypoglycaemia episodes, respectively, and 561 had CVD. Patients with severe and mild hypoglycaemia episodes were more likely to have prior CVD (32.5% versus 16.5% versus 7.7%, P < 0.0001). Both mild and severe hypoglycaemia were associated with increased risk of CVD (adjusted odds ratios (ORs): 2.64, 95% CI: 1.85–3.76 for mild hypoglycaemia; 6.59, 95% CI: 3.79–11.45 for sever hypoglycaemia) than those patients free of hypoglycaemia. Further adjustment for lipid profile did not change these two ORs. In the same way, the ORs of lipid profile for CVD were similar before and after adjustment for hypoglycaemia. We concluded that hypoglycaemia and lipid profile were independently associated with increased risk of CVD.

Lack of association between anemia and renal disease progression in Chinese patients with type 2 diabetes

AIMS/INTRODUCTION

Anemia has a close interaction with renal dysfunction in diabetes patients. More proof is still awaited on the relationship between anemia and the progression of renal disease in this population.

MATERIALS AND METHODS

In the present longitudinal study, 1,645 Chinese type 2 diabetes patients without end-stage renal disease were included in the analysis in Nanjing, China, during January 2006 and December 2012. All patients were managed by staged diabetes management protocol, and clinical parameters were collected at each visit. The end-point of progression of renal disease was evaluated during the follow up. Cox regression analysis was used to estimate the risk of anemia on renal disease progression.

RESULTS

On recruitment, 350 (21.3%) patients had anemia, which was more common among those with older ages, longer diabetes duration, lower estimated glomerular filtration rate or more albuminura. On median follow up of 49 months (range 28-62 months), 37 patients (2.2%) developed the defined renal end-point. Compared with those without anemia, patients with anemia had a higher risk of renal disease progression. However, multivariate analysis showed that anemia lost its statistical significance once estimated glomerular filtration rate was added into the model. Although the incidence of renal disease progression markedly increased by anemia status in patients of estimated glomerular filtration rate <60 mL/min/1.73 m(2), anemia was still not an independent risk factor for renal disease progression in this subgroup.

CONCLUSIONS

Anemia was a common finding in Chinese type 2 diabetes patients. Anemia was a risk factor for renal disease progression, but lost its significance once baseline renal function was adjusted.

Delivery of integrated diabetes care using logistics and information technology--the Joint Asia Diabetes Evaluation (JADE) program

Diabetes is a global epidemic, and many affected individuals are undiagnosed, untreated, or uncontrolled. The silent and multi-system nature of diabetes and its complications, with complex care protocols, are often associated with omission of periodic assessments, clinical inertia, poor treatment compliance, and care fragmentation. These barriers at the system, patient, and care-provider levels have resulted in poor control of risk factors and under-usage of potentially life-saving medications such as statins and renin-angiotensin system inhibitors. However, in the clinical trial setting, use of nurses and protocol with frequent contact and regular monitoring have resulted in marked differences in event rates compared to epidemiological data collected in the real-world setting. The phenotypic heterogeneity and cognitive-psychological-behavioral needs of people with diabetes call for regular risk stratification to personalize care. Quality improvement initiatives targeted at patient education, task delegation, case management, and self-care promotion had the largest effect size in improving cardio-metabolic risk factors. The Joint Asia Diabetes Evaluation (JADE) program is an innovative care prototype that advocates a change in clinic setting and workflow, coordinated by a doctor-nurse team and augmented by a web-based portal, which incorporates care protocols and a validated risk engine to provide decision support and regular feedback. By using logistics and information technology, supported by a network of health-care professionals to provide integrated, holistic, and evidence-based care, the JADE Program aims to establish a high-quality regional diabetes database to reflect the status of diabetes care in real-world practice, confirm efficacy data, and identify unmet needs. Through collaborative efforts, we shall evaluate the feasibility, acceptability, and cost-effectiveness of this "high tech, soft touch" model to make diabetes and chronic disease care more accessible, affordable, and sustainable.

Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis

Background

Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD).

Methods

In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset.

Results

Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models.

Conclusions

Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD.

Evaluation of the quality of care of a multi-disciplinary risk factor assessment and management programme (RAMP) for diabetic patients

BACKGROUND

Type 2 Diabetes Mellitus (DM) is a common chronic disease associated with multiple clinical complications. Management guidelines have been established which recommend a risk-stratified approach to managing these patients in primary care. This study aims to evaluate the quality of care (QOC) and effectiveness of a multi-disciplinary risk assessment and management programme (RAMP) for type 2 diabetic patients attending government-funded primary care clinics in Hong Kong. The evaluation will be conducted using a structured and comprehensive evidence-based evaluation framework.

METHOD/DESIGN

For evaluation of the quality of care, a longitudinal study will be conducted using the Action Learning and Audit Spiral methodologies to measure whether the pre-set target standards for criteria related to the structure and process of care are achieved. Each participating clinic will be invited to complete a Structure of Care Questionnaire evaluating pre-defined indicators which reflect the setting in which care is delivered, while process of care will be evaluated against the pre-defined indicators in the evaluation framework.Effectiveness of the programme will be evaluated in terms of clinical outcomes, service utilization outcomes, and patient-reported outcomes. A cohort study will be conducted on all eligible diabetic patients who have enrolled into RAMP for more than one year to compare their clinical and public service utilization outcomes of RAMP participants and non-participants. Clinical outcome measures will include HbA1c, blood pressure (both systolic and diastolic), lipids (low-density lipoprotein cholesterol) and future cardiovascular diseases risk prediction; and public health service utilization rate will include general and specialist outpatient, emergency department attendances, and hospital admissions annually within 5 years. For patient-reported outcomes, a total of 550 participants and another 550 non-participants will be followed by telephone to monitor quality of life, patient enablement, global rating of change in health and private health service utilization at baseline, 6, 12, 36 and 60 months.

DISCUSSION

The quality of care and effectiveness of the RAMP in enhancing the health for patients with type 2 diabetes will be determined. Possible areas for quality enhancement will be identified and standards of good practice can be established. The information will be useful in guiding service planning and policy decision making.

Effects of proactive population-based nephrologist oversight on progression of chronic kidney disease: a retrospective control analysis

Background

Benefits of early nephrology care are well-established, but as many as 40% of U.S. patients with end-stage renal disease (ESRD) do not see a nephrologist before its onset. Our objective was to evaluate the effect of proactive, population-based nephrologist oversight (PPNO) on chronic kidney disease (CKD) progression.

Methods

Retrospective control analysis of Kaiser Permanente Hawaii members with CKD using propensity score matching methods. We matched 2,938 control and case pairs of individuals with stage 3a CKD for the pre-PPNO period (2001–2004) and post-PPNO period (2005–2008) that were similar in other characteristics: age, gender, and the presence of diabetes and hypertension. After three years, we classified the stage outcomes for all individuals. We assessed the PPNO effect across all stages of progression with a χ²- test. We used the z-score test to assess the proportional differences in progression within a stage.

Results

The progression within the post-PPNO period was less severe and significantly different from the pre-PPNO period (p = 0.027). Within the stages, there were 2.6% more individuals remaining in 3a in the post-period (95% confidence interval [CI], 1.5% to 3.8%; P value < 0.00001). Progression from 3a to 3b was 2.2% less in the post-period (95% [CI], 0.7% to 3.6%; P value = 0.0017), 3a to 4/5 was 0.2% less (95% CI, 0.0% to 0.87%; P value = 0.26), and 3a to ESRD was 0.24% less (95% CI, 0.0% to 0.66%, P value = 0.10).

Conclusions

Proactive, population-based nephrologist oversight was associated with a statistically significant decrease in progression. With enabling health information technology, risk stratification and targeted intervention by collaborative primary and specialty care achieves population-level care improvements. This model may be applicable to other chronic conditions.

Diabetes, insulin and cancer risk

There is a consensus that both type 1 and type 2 diabetes are associated with a spectrum of cancers but the underlying mechanisms are largely unknown. On the other hand, there are ongoing debates about the risk association of insulin use with cancer. We have briefly reviewed recent related research on exploration of risk factors for cancer and pharmacoepidemiological investigations into drug use in diabetes on the risk of cancer, as well as the current understanding of metabolic pathways implicated in intermediary metabolism and cellular growth. Based on the novel findings from the Hong Kong Diabetes Registry and consistent experimental evidence, we argue that use of insulin to control hyperglycemia is unlikely to contribute to increased cancer risk and that dysregulations in the AMP-activated protein kinase pathway due to reduced insulin action and insulin resistance, the insulin-like growth factor-1 (IGF-1)-cholesterol synthesis pathway and renin-angiotensin system, presumably due to reduced insulin secretion and hyperglycemia, may play causal roles in the increased risk of cancer in diabetes. Further exploration into the possible causal relationships between abnormalities of these pathways and the risk of cancer in diabetes is warranted.

The Complexity of Vascular and Non-Vascular Complications of Diabetes: The Hong Kong Diabetes Registry

Diabetes is a complex disease characterized by chronic hyperglycemia and multiple phenotypes. In 1995, we used a doctor-nurse-clerk team and structured protocol to establish the Hong Kong Diabetes Registry in a quality improvement program. By 2009, we had accrued 2616 clinical events in 9588 Chinese type 2 diabetic patients with a follow-up duration of 6 years. The detailed phenotypes at enrollment and follow-up medications have allowed us to develop a series of risk equations to predict multiple endpoints with high sensitivity and specificity. In this prospective database, we were able to validate findings from clinical trials in real practice, confirm close links between cardiovascular and renal disease, and demonstrate the emerging importance of cancer as a leading cause of death. In addition to serving as a tool for risk stratification and quality assurance, ongoing data analysis of the registry also reveals secular changes in disease patterns and identifies unmet needs.

Evaluation of erectile dysfunction and associated cardiovascular risk using structured questionnaires in Chinese type 2 diabetic men

Erectile dysfunction (ED) is not uncommon, but frequently underdiagnosed in type 2 diabetic men. In this study, we aimed to explore the frequency and severity of ED in Chinese type 2 diabetic men using a structured questionnaire. We furthermore sought to investigate the associations of ED with diabetes-related complications and metabolic indices. A consecutive cohort of 313 Chinese type 2 diabetic men aged between 25 and 76 years attending a diabetic centre were recruited between October 2006 and June 2007. Of the study population, the frequency of ED was 39.3% according to the National Institutes of Health (NIH) Consensus Conference criteria, compared with 84.3% (41.7% of them having moderate to severe ED) as diagnosed by International Index of Erectile Function (IIEF-5) questionnaire. After adjusting for potential confounding factors by multivariable logistic regression, ED defined by NIH criterion was associated with advanced age [OR = 1.05 (95% CI 1.01-1.09), p = 0.012], the presence of diabetic retinopathy [OR = 2.43 (95% CI 1.27-4.66), p = 0.008] and coronary heart disease [OR = 2.63 (95% CI 1.21-5.70), p = 0.015]. ED defined by IIEF-5 was associated with advanced age [OR = 1.12 (95% CI 1.06-1.17), p < 0.0001], use of insulin therapy [OR = 2.94 (95% CI 1.12-7.73), p = 0.029] and urinary albumin-creatinine ratio [OR = 2.29 (95% CI 1.05-5.01), p = 0.037]. In conclusion, ED was highly prevalent in Chinese type 2 diabetic men and was associated with multiple cardiovascular risk factors and complications. Advanced age, use of insulin therapy, the existence of microvascular complications such as retinopathy, albuminuria and coronary heart disease were associated with ED. NIH criteria diagnosed a much lower rate of ED compared with IIEF-5. Overall, structured questionnaires are useful and objective tools to detect ED, which should prompt a comprehensive risk assessment in these subjects.

From design to implementation--the Joint Asia Diabetes Evaluation (JADE) program: a descriptive report of an electronic web-based diabetes management program

BACKGROUND

The Joint Asia Diabetes Evaluation (JADE) Program is a web-based program incorporating a comprehensive risk engine, care protocols, and clinical decision support to improve ambulatory diabetes care.

METHODS

The JADE Program uses information technology to facilitate healthcare professionals to create a diabetes registry and to deliver an evidence-based care and education protocol tailored to patients' risk profiles. With written informed consent from participating patients and care providers, all data are anonymized and stored in a databank to establish an Asian Diabetes Database for research and publication purpose.

RESULTS

The JADE electronic portal (e-portal: http://www.jade-adf.org) is implemented as a Java application using the Apache web server, the mySQL database and the Cocoon framework. The JADE e-portal comprises a risk engine which predicts 5-year probability of major clinical events based on parameters collected during an annual comprehensive assessment. Based on this risk stratification, the JADE e-portal recommends a care protocol tailored to these risk levels with decision support triggered by various risk factors. Apart from establishing a registry for quality assurance and data tracking, the JADE e-portal also displays trends of risk factor control at each visit to promote doctor-patient dialogues and to empower both parties to make informed decisions.

CONCLUSIONS

The JADE Program is a prototype using information technology to facilitate implementation of a comprehensive care model, as recommended by the International Diabetes Federation. It also enables health care teams to record, manage, track and analyze the clinical course and outcomes of people with diabetes.

White blood cell count and renin-angiotensin system inhibitors for the risk of cancer in type 2 diabetes

BACKGROUND

High white blood cell (WBC) predicted cancer-associated mortality and renin-angiotensin system (RAS) inhibitors have immunomodulating effects. We hypothesize that RAS inhibitors may reduce cancer risk associated with high WBC in type 2 diabetes mellitus (T2DM).

METHODS

A prospective cohort of 4570 Chinese T2DM patients, free of cancer at enrolment, were analyzed. Biological interaction between WBC groups and use of RAS inhibitors was estimated using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (S). RERI>0, AP>0 or S>1 indicates biological interaction.

RESULTS

During 4.89 years of follow-up, 205 (4.49%) patients developed cancer. WBC > or = 8.2 x 10(9) counts/L plus non-use of RAS inhibitors was associated with elevated cancer risks in multivariable models. The RERI and AP for interaction between WBC > or = 8.2 x 10(9) counts/L and non-use of RAS inhibitors were, respectively, 1.26 (95% CI: 0.22-2.31) and 0.50 (0.23-0.78). In patients with WBC > or = 8.2 x 10(9) counts/L, use of RAS inhibitors was associated with 64% (31-81%) cancer risk reduction in multivariable analysis.

CONCLUSIONS

In T2DM, increased WBC predicts cancer while use of RAS inhibitors may reduce cancer risks associated with high WBC count.

Effects of systolic and diastolic blood pressures on incident coronary heart disease and all-cause death in Chinese women with Type 2 diabetes: the Hong Kong Diabetes Registry

BACKGROUND

Elevated blood pressure (BP) is an important risk factor for the development of coronary heart disease (CHD), although the threshold above which the risk increases has not been clearly defined. The aim of the present study was to examine the full-range association between BP and CHD.

METHODS

A prospective cohort of 3861 Chinese women with Type 2 diabetes mellitus (T2DM) was followed for a median of 5.61 years. Restricted cubic spline analysis was used to examine the relationship between BP and CHD.

RESULTS

Subjects who developed CHD were older, more likely to be smokers, had a significantly longer duration of diabetes, higher systolic BP (SBP), glycated hemoglobin, albuminuria, low-density lipoprotein-cholesterol, and triglycerides, and lower estimated glomerular filtration rate and high-density lipoprotein-cholesterol. Mortality was higher in those who developed CHD compared with those who did not, with all-cause death in 30.2% and 7.8% of patients, respectively. Over 21,641 and 22 049 person-years follow up, 4.4% of patients (n = 169) developed CHD and 8.8% (n = 340) died, respectively. The relative risk of SBP for CHD was constant up to 120 mmHg, after which it started to rise: from 130 mmHg, each 10-mmHg increase in SBP was associated with a 1.13-fold increased risk of CHD.

CONCLUSIONS

We identified 130 mmHg as the threshold of SBP for increased risk of CHD in Chinese female patients with T2DM. It appears that 67-77 mmHg is the optimal range for diastolic BP, within which the risk of CHD is lowest.

Use of anti-diabetic drugs and glycaemic control in type 2 diabetes-tThe Hong Kong Diabetes Registry

In this report, we examined the usage of anti-diabetic treatments including oral anti-diabetic drug (OAD) and/or insulin and their combination from baseline data of a consecutive cohort of 7549 Chinese type 2 diabetic subjects in the Hong Kong Diabetes Registry. Pattern of usage of anti-diabetic treatment and corresponding glycemic control was analyzed. OAD failure was defined as the need to add insulin to maintain glycemic target (glycated hemoglobin, HbA(1c) level<7%) with or without continuation of OAD. There were 4109 [54.4%] women and 3440 [45.6%] men (age: median 57.0 years; range 13-92 years). The mean HbA(1c) level was 7.7+/-1.8% with 39.7% attaining glycemic target. Long disease duration was associated with more complex regimens and the respective rates of OAD failure requiring insulin use were 23.7%, 39.3%, 57.1% and 75.9% in those with disease duration <5 years, 5-9.9 years, 10-19.9 years and > or =20 years (p<0.001). In conclusion, in a clinic-based type 2 diabetic population, 39.7% attained glycemic target with HbA(1c)<7%. Long disease duration and complexity of treatment regimens were associated with suboptimal glycemic control. Early intensification of therapy and system improvement are needed to enhance the effectiveness of these drugs in clinical practice.

Independent associations between low-density lipoprotein cholesterol and cancer among patients with type 2 diabetes mellitus

BACKGROUND

The risk association between low-density lipoprotein (LDL) cholesterol and cancer remains controversial and largely unexplored for people not receiving statin therapy.

METHODS

We examined the risk association between LDL cholesterol and cancer among patients with type 2 diabetes mellitus who were free of cancer at enrolment and whose statin use was known. We considered a variety of nonlinear relationships in our analysis.

RESULTS

During a median follow-up period of 4.90 years, cancer developed in 270 (4.4%) of 6107 patients. Among the 3800 patients who did not receive statin therapy, the risk association between LDL cholesterol and cancer was represented by a V-shaped curve. Compared with patients whose LDL cholesterol was at least 2.80 mmol/L but less than 3.80 mmol/L, the risk of cancer, death from any cause or the composite outcome of cancer or death was greater among those with an LDL cholesterol level of less than 2.80 mmol/L (hazard ratio for cancer 1.74, 95% confidence interval [CI] 1.20-2.52) and those with an LDL cholesterol level of 3.80 mmol/L or greater (hazard ratio for cancer 1.87, 95% CI 1.29-2.71). Using 3.8 mmol/L as a reference point, we found that the hazard ratio for cancer for every millimole per litre absolute change in LDL cholesterol was 1.54 (95% CI 1.19-1.99) among patients not using statins; the hazard ratio was reduced to 1.24 (1.01-1.53) for the entire sample (statin users and those not using statins). These associations persisted after adjustment for covariates and exclusion of patients with less than 2.5 years of follow-up.

INTERPRETATION

Among patients with type 2 diabetes, the association between LDL cholesterol and cancer was V-shaped, whereby both low and high levels of LDL cholesterol were associated with elevated risk of cancer.

Type 2 diabetes mellitus and its renal complications in relation to apolipoprotein E gene polymorphism

The apolipoprotein E (APOE) epsilon2 allele is reported to be associated with greater risk of renal impairment in type 2 diabetes. Relationships among APOE polymorphisms, renal impairment, and biochemical parameters were explored. A prospective study of 405 consenting Chinese type 2 diabetic patients [mean age +/- standard deviation (SD): 59.2 +/- 10.3 years] without advanced complications at entry was conducted. APOE genotyping and measurement of plasma biomarkers of oxidative stress and antioxidants were performed at entry. HbA1C, plasma glucose, lipids, creatinine, urine albumin/creatinine, and blood pressure were measured at entry and at up to 4 years of follow-up. APOE allelic frequencies were in Hardy-Weinberg equilibrium. Odds ratios of albuminuria at entry and/or during follow-up for different APOE groups were not significantly different. The non-epsilon2 (epsilon3/3, epsilon3/4, epsilon4/4) group had significantly greater plasma ascorbate (51.6 +/- 20.1 mumol/L) than the epsilon2 (epsilon2/2, epsilon2/3) group (44.5 +/- 16.2 mumol/L, P = 0.021), but higher plasma ascorbate levels did not seem to decrease the risk of renal impairment in the non-epsilon2 group. Baseline plasma lipid-standardized alpha-tocopherol levels were least in epsilon2 subjects with persistent albuminuria (3.6 +/- 1.1 mumol/mmol of total cholesterol plus triglycerides, P = 0.008) compared with epsilon2 subjects who had no albuminuria at entry or during follow-up (4.5 +/- 0.8 mumol/mmol of total cholesterol plus triglycerides). The APOE epsilon2 allele does not seem to be associated with increased risk of renal impairment in Chinese type 2 diabetic patients. Plasma lipid-standardized alpha-tocopherol may play a role in determining risk of renal dysfunction in type 2 diabetes.

Thresholds of risk factors for ischemic stroke in type 2 diabetic patients with and without albuminuria: a non-linear approach

OBJECTIVES

Multiple risk factors in type 2 diabetes may explain their high risk for ischemic stroke (IS). However, it remains unknown whether these risk factors exhibit threshold characteristics and whether these relationships are influenced by albuminuria. The study aims to investigate whether risk factors exhibit any albuminuria specific threshold for IS.

PATIENTS AND METHODS

This is a prospective cohort study with 6969 Chinese type 2 diabetic patients without history of stroke after a median follow-up of 5.36 years. We identified thresholds of risk factors for IS using hazard ratio plots followed by confirmation using traditional Cox regression analysis.

RESULTS

In the non-albuminuric group (n=4008), IS risk started to increase rapidly at a body mass index threshold of 24 kg/m(2). The risk of IS declined with increasing blood hemoglobin reaching a threshold value of 14 g/dl. Using these threshold values as cutoff point, body mass index > or =24 kg/m(2) and hemoglobin <14 g/dl were associated with 2-fold increased risk of IS in these subjects. In the albuminuric group (n=2961). IS risk started to increase rapidly from a systolic blood pressure threshold of 135 mmHg and declined with increasing estimated glomerular filtration rate (eGFR) reaching a trough of 115 ml/min per 1.73 m(2). Using these values as cutoff points, patients with systolic blood pressure > or =135 mmHg and eGFR <115 ml/min per 1.73 m(2) had 2-fold increased risk of IS.

CONCLUSION

In type 2 diabetic patients, body mass index, hemoglobin, systolic blood pressure and eGFR exhibit different risk relationships and thresholds for IS contingent upon presence or absence of albuminuria.

Development and validation of a risk score for hospitalization for heart failure in patients with Type 2 diabetes mellitus

Background

There are no risk scores available for predicting heart failure in Type 2 diabetes mellitus (T2DM). Based on the Hong Kong Diabetes Registry, this study aimed to develop and validate a risk score for predicting heart failure that needs hospitalisation in T2DM.

Methods

7067 Hong Kong Chinese diabetes patients without history of heart failure, and without history and clinical evidence of coronary heart disease at baseline were analyzed. The subjects have been followed up for a median period of 5.5 years. Data were randomly and evenly assigned to a training dataset and a test dataset. Sex-stratified Cox proportional hazard regression was used to obtain predictors of HF-related hospitalization in the training dataset. Calibration was assessed using Hosmer-Lemeshow test and discrimination was examined using the area under receiver's operating characteristic curve (aROC) in the test dataset.

Results

During the follow-up, 274 patients developed heart failure event/s that needed hospitalisation. Age, body mass index (BMI), spot urinary albumin to creatinine ratio (ACR), HbA_1c, blood haemoglobin (Hb) at baseline and coronary heart disease during follow-up were predictors of HF-related hospitalization in the training dataset. HF-related hospitalization risk score = 0.0709 × age (year) + 0.0627 × BMI (kg/m²) + 0.1363 × HbA_1c(%) + 0.9915 × Log₁₀(1+ACR) (mg/mmol) - 0.3606 × Blood Hb(g/dL) + 0.8161 × CHD during follow-up (1 if yes). The 5-year probability of heart failure = 1-S₀(5)^{EXP{0.9744 × (Risk Score - 2.3961)}}. Where S₀(5) = 0.9888 if male and 0.9809 if female. The predicted and observed 5-year probabilities of HF-related hospitalization were similar (p > 0.20) and the adjusted aROC was 0.920 for 5 years of follow-up.

Conclusion

The risk score had adequate performance. Further validations in other cohorts of patients with T2DM are needed before clinical use.

Effects of albuminuria and renal dysfunction on development of dyslipidaemia in type 2 diabetes--the Hong Kong Diabetes Registry

BACKGROUND

It is uncertain whether albuminuria precedes the future development of high total cholesterol (TC > 6.2 mmol/l) and high LDL-C (>4.1 mmol/l) while renal dysfunction precedes the future development of low HDL-C (<0.9 mmol/l) in type 2 diabetes.

METHODS

A prospective cohort of 2761 type 2 diabetic patients without significant dyslipidaemia and having at least one measurement of TC, LDL-C and HDL-C during 2.8 years of follow-up was analysed. The spline Cox regression model was used to derive hazard ratio (HR) curves of the spot urinary albumin:creatinine ratio (ACR) and the estimated glomerular filtration rate (eGFR) for dyslipidaemia, followed by standard Cox models to confirm the findings from the HR curves.

RESULTS

Seven percent of the cohort developed high TC, 4.6% developed high LDL-C and 5.7% developed low HDL-C during follow-up. In multivariate analysis, the HR of ACR for high TC and high LDL-C increased rapidly and linearly from zero with no apparent threshold. Patients with macroalbuminuria (ACR >/=25 mg/mmol) were, respectively, 1.6- and 2.4 folds more likely to develop high TC and high LDL-C than those with normoalbuminuria at baseline. The HR of eGFR for low HDL-C increased rapidly with declining eGFR at <110 ml/min/ 1.73 m(2). Subjects with eGFR <60 ml/min/1.73 m(2) and >/=60-<110 ml/min/1.73 m(2), respectively, had 3.0-fold and 1.8-fold risks of low HDL-C compared to those with eGFR >/=110-<140 ml/min/1.73 m(2).

CONCLUSIONS

In type 2 diabetes, macroalbumninuria predicts high TC and high LDL-C, while reduced renal function, even within normal range, predicts low HDL-C.