Predicting macro- and microvascular complications in type 2 diabetes: the Japan Diabetes Complications Study/the Japanese Elderly Diabetes Intervention Trial risk engine

The Long-Term Cost-Effectiveness of Tirzepatide 5 mg versus Dulaglutide 0.75 mg for the Treatment of People with Type 2 Diabetes in Japan

INTRODUCTION

This analysis aimed to evaluate the long-term cost-effectiveness of tirzepatide 5 mg versus dulaglutide 0.75 mg (both administered once weekly) in people not achieving glycemic control on metformin, based on the results of the head-to-head SURPASS J-mono trial from a Japanese healthcare payer perspective.

METHODS

A cost-utility analysis was performed over a 50-year time horizon using an implementation of the UKPDS Outcomes Model 2 developed in Microsoft Excel. Baseline cohort characteristics, treatment effects and adverse event rates were sourced from the SURPASS J-mono trial. Simulated patients were assumed to receive either tirzepatide 5 mg or dulaglutide 0.75 mg until HbA1c exceeded 8.0%, at which point treatment was discontinued and basal insulin was initiated. Direct costs were derived from the Japan Medical Data Center claims database. Future costs and clinical benefits were discounted at 2% annually.

RESULTS

In this cost-utility modeling analysis, tirzepatide 5 mg was associated with lower diabetes-related complication rates, improved life expectancy, improved quality-adjusted life expectancy and higher direct costs versus dulaglutide 0.75 mg. This resulted in an incremental cost-effectiveness ratio (ICER) of JPY (Japanese yen) 1,302,240 per quality-adjusted life year (QALY) gained for tirzepatide 5 mg versus dulaglutide 0.75 mg (JPY 140 = USD 1). Tirzepatide remained cost-effective versus dulaglutide over a range of sensitivity analyses.

CONCLUSIONS

In this analysis, tirzepatide 5 mg was associated with an ICER below the commonly quoted willingness-to-pay threshold of JPY 5,000,000 per QALY gained, suggesting that tirzepatide is a cost-effective treatment option for adult patients with type 2 diabetes mellitus, compared with dulaglutide 0.75 mg.

Japan Diabetes Complications Study: Revisiting one of the first large-scale clinical studies in East Asians with diabetes

This review highlights the significance of the Japan Diabetes Complications Study (JDCS), one of the earliest large-scale studies of people with type 2 diabetes outside Europe and the United States, in understanding type 2 diabetes mellitus among East Asian populations, particularly in Japan. Historically, large-scale clinical studies on type 2 diabetes mellitus have predominantly focused on Western populations, despite East Asians comprising the largest proportion of diabetic patients globally. The JDCS, which was initiated in 1996, enrolled 2,033 Japanese type 2 diabetes mellitus patients. It aimed to evaluate the effects of intensive lifestyle interventions on diabetic complications. The study demonstrated that lifestyle-focused interventions significantly reduced the risk of stroke and other complications compared to conventional treatment. Key findings of its sub-analyses include the unique characteristics of Japanese patients with type 2 diabetes mellitus, such as their lower body mass index (BMI) compared to Western counterparts and a stronger association between even modest BMI increases and beta cell dysfunction. Additionally, the JDCS provided insights into the risk factors for nephropathy, retinopathy, and macrovascular complications, emphasizing the importance of controlling blood pressure, glycemia, and lifestyle factors. The study also explored the impact of diet, exercise, and mental health on diabetic outcomes, revealing the protective effects of physical activity and a balanced diet, while highlighting the risks associated with high salt intake and depression. A risk prediction model tailored to Japanese patients was also developed. Overall, this study made a significant contribution to the evidence-based management of type 2 diabetes mellitus in East Asia.

Development and validation of prediction models for stroke and myocardial infarction in type 2 diabetes based on health insurance claims: does machine learning outperform traditional regression approaches?

BACKGROUND

Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning methods.

METHODS

We used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and calibration.

RESULTS

Among n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well calibrated.

CONCLUSIONS

Discrimination performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive Predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature richness and complexity were exploited before the choice of algorithm could become critical to maximize performance. Future research might focus on the impact of different feature derivation approaches on performance ceilings. In the absence of other more powerful screening alternatives, applying transparent regression-based models in routine claims, though certainly imperfect, remains a promising scalable low-cost approach for population-based cardiovascular risk prediction and stratification.

Initial Retinal Nerve Fiber Layer Loss and Risk of Diabetic Retinopathy Over a Four-Year Period

Purpose

The purpose of this study was to investigate whether the rapid rate of peripapillary retinal nerve fiber layer (pRNFL) thinning in short-term is associated with the future risk of developing diabetic retinopathy (DR).

Methods

This prospective cohort study utilized 4-year follow-up data from the Guangzhou Diabetic Eye Study. The pRNFL thickness was measured by optical coherence tomography (OCT). DR was graded by seven-field fundus photography after dilation of the pupil. Correlations between pRNFL thinning rate and DR were analyzed using logistic regression. The additive predictive value of the prediction model was assessed using the C-index, net reclassification index (NRI), and integrated discriminant improvement index (IDI).

Results

A total of 1012 patients with diabetes (1012 eyes) without DR at both baseline and 1-year follow-up were included in this study. Over the 4-year follow-up, 132 eyes (13%) developed DR. After adjusting for confounding factors, a faster rate of initial pRNFL thinning was significantly associated with the risk of DR (odds ratio per standard deviation [SD] decrease = 1.15, 95% confidence interval [CI] = 1.08 to 1.23, P < 0.001). Incorporating either the baseline pRNFL thickness or its thinning rate into conventional prediction models significantly improved the discriminatory power. Adding the rate of pRNFL thinning further enhanced the discriminative power compared with models with only baseline pRNFL thickness (C-index increased from 0.685 to 0.731, P = 0.040). The IDI and NRI were 0.114 and 0.463, respectively (P < 0.001).

Conclusions

The rate of initial pRNFL thinning was associated with DR occurrence and improved discriminatory power of traditional predictive models. This provides new insights into the management and screening of DR.

Prediction of diabetic retinopathy among type 2 diabetic patients in University of Gondar Comprehensive Specialized Hospital, 2006-2021: A prognostic model

BACKGROUND

There has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient's demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients.

METHODS

A retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10-fold cross-validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR.

RESULTS

Logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients.

CONCLUSIONS

The model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR.

Revolutionizing type 1 diabetes management: Exploring oral insulin and adjunctive treatments

Type 1 diabetes (T1D) is a chronic autoimmune condition that affects millions of people worldwide. Insulin pumps or injections are the standard treatment options for this condition. This article provides a comprehensive overview of the several type 1 diabetes treatment options, focusing on oral insulin. The article is divided into parts that include immune-focused treatments, antigen vaccination, cell-directed interventions, cytokine-directed interventions, and non-immunomodulatory adjuvant therapy. Under the section on non-immunomodulatory adjunctive treatment, the benefits and drawbacks of medications such as metformin, amylin, sodium-glucose cotransporter inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 Ras), and verapamil are discussed. The article also discusses the advantages of oral insulin, including increased patient compliance and more dependable and regular blood sugar control. However, several variables, including the enzymatic and physical barriers of the digestive system, impair the administration of insulin via the mouth. Researchers have looked at a few ways to get over these challenges, such as changing the structure of the insulin molecule, improving absorption with the use of absorption enhancers or nanoparticles, and taking oral insulin together with other medications. Even with great advancements in the use of these treatment strategies, T1D still needs improvement in the therapeutic difficulties. Future studies in these areas should focus on creating tailored immunological treatments, looking into combination medications, and refining oral insulin formulations in an attempt to better control Type 1 Diabetes. The ultimate objective is to create accurate, customized strategies that will enhance glycemic management and the quality of life for individuals with the condition.

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Diabetes-Related Macrovascular Complications Are Associated With an Increased Risk of Diabetic Microvascular Complications: A Prospective Study of 1518 Patients With Type 1 Diabetes and 20 802 Patients With Type 2 Diabetes in the UK Biobank

BACKGROUND

Diabetic vascular complications share common pathophysiological mechanisms, but the relationship between diabetes-related macrovascular complications (MacroVCs) and incident diabetic microvascular complications remains unclear. We aimed to investigate the impact of MacroVCs on the risk of microvascular complications.

METHODS AND RESULTS

There were 1518 participants with type 1 diabetes (T1D) and 20 802 participants with type 2 diabetes from the UK Biobank included in this longitudinal cohort study. MacroVCs were defined by the presence of macrovascular diseases diagnosed after diabetes at recruitment, including coronary heart disease, peripheral artery disease, stroke, and ≥2 MacroVCs. The primary outcome was incident microvascular complications, a composite of diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy. During a median (interquartile range) follow-up of 11.61 (5.84-13.12) years and 12.2 (9.50-13.18) years, 596 (39.3%) and 4113 (19.8%) participants developed a primary outcome in T1D and type 2 diabetes, respectively. After full adjustment for conventional risk factors, Cox regression models showed significant associations between individual as well as cumulative MacroVCs and the primary outcome, except for coronary heart disease in T1D (T1D: diabetes coronary heart disease: 1.25 [0.98-1.60]; diabetes peripheral artery disease: 3.00 [1.86-4.84]; diabetes stroke: 1.71 [1.08-2.72]; ≥2: 2.57 [1.66-3.99]; type 2 diabetes: diabetes coronary heart disease: 1.59 [1.38-1.82]; diabetes peripheral artery disease: 1.60 [1.01-2.54]; diabetes stroke: 1.50 [1.13-1.99]; ≥2: 2.66 [1.92-3.68]). Subgroup analysis showed that strict glycemic (glycated hemoglobin <6.5%) and blood pressure (<140/90 mm Hg) control attenuated the association.

CONCLUSIONS

Individual and cumulative MacroVCs confer significant risk of incident microvascular complications in patients with T1D and type 2 diabetes. Our results may facilitate cost-effective high-risk population identification and development of precise prevention strategies.

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.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis

Background

The role of certain biomarkers in the development of single cardiometabolic disease (CMD) has been intensively investigated. Less is known about the association of biomarkers with multiple CMDs (cardiometabolic multimorbidity, CMM), which is essential for the exploration of molecular targets for the prevention and treatment of CMM. We aimed to systematically synthesize the current evidence on CMM-related biomarkers.

Methods

We searched PubMed, Embase, Web of Science, and Ebsco for relevant studies from inception until August 31st, 2022. Studies reported the association of serum/plasma biomarkers with CMM, and relevant effect sizes were included. The outcomes were five progression patterns of CMM: (1) no CMD to CMM; (2) type 2 diabetes mellitus (T2DM) followed by stroke; (3) T2DM followed by coronary heart disease (CHD); (4) T2DM followed by stroke or CHD; and (5) CHD followed by T2DM. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies. A meta-analysis was conducted to quantify the association of biomarkers and CMM.

Results

A total of 68 biomarkers were identified from 42 studies, which could be categorized into five groups: lipid metabolism, glycometabolism, liver function, immunity, and others. Lipid metabolism biomarkers were most reported to associate with CMM, including TC, TGs, HDL-C, LDL-C, and Lp(a). Fasting plasma glucose was also reported by several studies, and it was particularly associated with coexisting T2DM with vascular diseases. According to the quantitative meta-analysis, HDL-C was negatively associated with CHD risk among patients with T2DM (pooled OR for per 1 mmol/L increase = 0.79, 95% CI = 0.77-0.82), whereas a higher TGs level (pooled OR for higher than 150 mg/dL = 1.39, 95% CI = 1.10-1.75) was positively associated with CHD risk among female patients with T2DM.

Conclusion

Certain serum/plasma biomarkers were associated with the progression of CMM, in particular for those related to lipid metabolism, but heterogeneity and inconsistent findings still existed among included studies. There is a need for future research to explore more relevant biomarkers associated with the occurrence and progression of CMM, targeted at which is important for the early identification and prevention of CMM.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

[Impact of smoking on the presence of diabetic cardiomyopathy]

INTRODUCTION

Type 2 diabetes is associated with an increased risk of coronary disease and is the leading cause of morbidity and mortality in this population. The main objective of our work is to study the correlation of left atrial volume index with coronary disease in type 2 diabetics.

MATERIAL AND METHODS

Cross-sectional, analytical, single-center study with prospective recruitment of 330 type 2 diabetic patients carried out at the Constantine Regional Military University Hospital over a period of 03 years (2016-2018) among which 18.8% (62 patients) are smokers. Early cardiac involvement represented by diastolic dysfunction was assessed by two-dimensional transthoracic echocardiography. Data were analyzed using Epi info 7.2.1.0 software to study the impact of smoking on the presence of left ventricular diastolic dysfunction.

RESULTS

The average age of our cohort is 52.7 ± 8.4 years, an average of 7.1 ± 1.3% of glycated hemoglobin, an average of 5.3 ± 4.3 years of diabetes duration, a sex ratio to 1.01. 34.8% of patients had left atrial volume index ≥ 34 ml/m2. The prevalence of coronary disease is 27.0%. In multivariate analysis; left atrial volume index is significantly correlated with coronary stenosis (OR = 1.75, 95% CI [1.60 - 2.05], p = 0.02).

CONCLUSION

The prevalence of cardiomyopathy is high in type 2 diabetes and smoking is significantly correlated with the presence of this diabetic cardiomyopathy.

Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change (FEEDBACK): study protocol for a cluster randomised controlled trial

BACKGROUND

The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings.

METHODS

A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a 'heart age' tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002).

DISCUSSION

This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour.

TRIAL REGISTRATION

The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.

Cardiovascular disease prevalence in adults with type 2 diabetes in Japan: results from the Japanese centers in the CAPTURE study

Introduction

CAPTURE was a cross-sectional, non-interventional study (NCT03786406, NCT03811288) investigating the prevalence and characteristics of cardiovascular disease (CVD) in adults with type 2 diabetes (T2D) across 13 countries worldwide. Here we present the findings for Japan.

Materials and methods

Data were collected from adults aged ≥ 20 years (aged ≥ 18 years in countries outside Japan) with T2D who were managed in clinics or hospitals in 2019. Standardized methodology was used for all countries. The prevalence of CVD and its subtypes was estimated, weighted by care setting (clinics versus hospitals).

Results

Among participants from Japan (total: 800; clinics: 440; hospitals: 360), mean (standard deviation) age was 65.6 (11.2) years and glycated hemoglobin 7.2% (0.9). Sixty-seven percent of participants were male, 57.8% had diabetes duration > 10 years, 49.8% had body mass index ≥ 25 kg/m2 and 63.1% had hypertension. The weighted prevalences (95% confidence interval [CI]) of CVD and atherosclerotic CVD were 37.3% (34.2;40.3) and 33.5% (30.6;36.4), respectively. The prevalence (95% CI) of the most common subtypes of CVD was: carotid artery disease 20.5% (18.2;22.8), coronary heart disease 11.9% (9.7;14.1) and cerebrovascular disease 10.4% (8.3;12.5).

Conclusions

These contemporary data from the CAPTURE study on CVD prevalence in adults with T2D in Japan show that approximately one in three adults with T2D had established CVD, which is comparable to the prevalence in the global study cohort.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13340-022-00612-y.

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

BACKGROUND

Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential.

OBJECTIVE

This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes.

METHODS

In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values.

RESULTS

Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications.

CONCLUSIONS

Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

Development and validation of risk prediction models for stroke and mortality among patients with type 2 diabetes in northern China

BACKGROUND

Stroke is one of the leading causes of disability and mortality in patients with type 2 diabetes mellitus (T2DM). Risk models have been developed for predicting stroke and stroke-associated mortality among patients with T2DM. Here, we evaluated risk factors of stroke for individualized prevention measures in patients with T2DM in northern China.

METHODS

In the community-based Tianjin Chronic Disease Cohort study, 58,042 patients were enrolled between January 2014 and December 2019. We used multiple imputation (MI) to impute missing variables and univariate and multivariate Cox's proportional hazard regression to screen risk factors of stroke. Furthermore, we established and validated first-ever prediction models for stroke (Model 1 and Model 2) and death from stroke (Model 3) and evaluated their performance.

RESULTS

In the derivation and validation groups, the area under the curves (AUCs) of Models 1-3 was better at 5 years than at 8 years. The Harrell's C-index for all models was above 0.7. All models had good calibration, discrimination, and clinical net benefit. Sensitivity analysis using the MI dataset indicated that all models had good and stable prediction performance.

CONCLUSION

In this study, we developed and validated first-ever risk prediction models for stroke and death from stroke in patients with T2DM, with good discrimination and calibration observed in all models. Based on lifestyle, demographic characteristics, and laboratory examination, these models could provide multidimensional management and individualized risk assessment. However, the models developed here may only be applicable to Han Chinese.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative

OBJECTIVE

To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline.

RESEARCH DESIGN AND METHODS

A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score.

RESULTS

A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8.

CONCLUSIONS

In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.

Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings

Background

Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening.

Methods

Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India.

Findings

A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival.

Interpretation

We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation.

Funding

This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

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/.

Differential Effect of Generalized and Abdominal Obesity on the Development and Progression of Diabetic Retinopathy in Chinese Adults With Type 2 Diabetes

Background

The relationship between obesity and diabetic retinopathy (DR) remains controversial. The aim of this study was to assess the association of generalized obesity [assessed by body mass index (BMI)] and abdominal obesity [assessed by waist to hip ratio (WHR)] with incident DR, and vision-threatening DR (VTDR), and DR progression among Chinese adults with type 2 diabetic mellitus (T2DM).

Method

This prospective cohort study was conducted at the Zhongshan Ophthalmic Center, from November 2017 to December 2020. DR was assessed based on the 7-filed fundus photographs using the modified Airlie House Classification. Multivariable logistic regression models were used to evaluate the associations of BMI and WHR with the development and progression of DR after adjusting for age, sex, traditional risk factors, and mutually for BMI and WHR.

Results

Among the 1,370 eligible participants, 1,195 (87.2%) had no sign of any DR and 175 (12.8%) had DR at baseline examination. During the 2 years follow-up visit, 342 (28.6%) participants had incident DR, 11 (0.8%) participants developed VTDR, 15 (8.6%) demonstrated DR progression. After adjusting for confounders, the BMI was negatively associated with incident DR [relative risk (RR) =0.31; 95% confidence interval (CI), 0.26-0.38; P < 0.001] and incident VTDR (RR = 0.22; 95%CI, 0.11-0.43; P < 0.001), while WHR was positively associated with incident DR (RR = 1.47; 95% CI, 1.27-1.71; P < 0.001). BMI and WHR level were not significantly associated with 2-year DR progression in multivariate models (all P > 0.05).

Conclusions

This study provides longitudinal evidence that generalized obesity confer a protective effect on DR, while abdominal obesity increased the risk of DR onset in Chinese patients, indicating that abdominal obesity is a more clinically relevant risk marker of DR than generalized obesity.

Establishment and validation of the cut-off values of estimated glomerular filtration rate and urinary albumin-to-creatinine ratio for diabetic kidney disease: A multi-center, prospective cohort study

OBJECTIVE

We aimed to study the cut-off values of estimated glomerular filtration rate (eGFR) and the urinary albumin creatinine ratio (UACR) in the normal range for diabetic kidney disease (DKD).

METHODS

In this study, we conducted a retrospective, observational cohort study included 374 type 2 diabetic patients who had baseline eGFR ≥60 mL/min/1.73 m² and UACR <30 mg/g with up to 6 years of follow-up. The results were further validated in a multi-center, prospective cohort study.

RESULTS

In the development cohort, baseline eGFR (AUC: 0.90, cut-off value: 84.8 mL/min/1.73 m², sensitivity: 0.80, specificity: 0.85) or UACR (AUC: 0.74, cut-off value: 15.5mg/g, sensitivity: 0.69, specificity: 0.63) was the most effective single predictor for DKD. Moreover, compared with eGFR or UACR alone, the prediction model consisted of all of the independent risk factors did not improve the predictive performance (P >0.05). The discrimination of eGFR at the cut-off value of 84.80 mL/min/1.73 m2 or UACR at 15.5mg/g with the largest Youden's index was further confirmed in the validation cohort. The decrease rate of eGFR was faster in patients with UACR ≥15.5mg/g (P <0.05). Furthermore, the decrease rate of eGFR or increase rate of UACR and the incidence and severity of cardiovascular disease (CVD) were higher in patients with eGFR ≤84.8 mL/min/1.73 m² or UACR ≥15.5mg/g (P <0.05).

CONCLUSIONS

In conclusion, eGFR ≤84.8mL/min/1.73 m² or UACR ≥15.5mg/g in the normal range may be an effective cut-off value for DKD and may increase the incidence and severity for CVD in type 2 diabetic patients.

Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses

OBJECTIVE

To inform a clinical practice guideline (BMJ Rapid Recommendations) considering sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists for treatment of adults with type 2 diabetes, we summarised the available evidence regarding the performance of validated risk models on cardiovascular and kidney outcomes in these patients.

METHODS

We systematically searched bibliographic databases in January 2020 to identify observational studies evaluating risk models for all-cause and cardiovascular mortality, heart failure (HF) hospitalisations, end-stage kidney disease (ESKD), myocardial infarction (MI) and ischaemic stroke in ambulatory adults with type 2 diabetes. Using a random effects model, we pooled discrimination measures for each model and outcome, separately, and descriptively summarised calibration plots, when available. We used the Prediction Model Risk of Bias Assessment Tool to assess risk of bias of each included study and the Grading of Recommendations, Assessment, Development, and Evaluation approach to evaluate our certainty in the evidence.

RESULTS

Of 22 589 publications identified, 15 observational studies reporting on seven risk models proved eligible. Among the seven models with >1 validation cohort, the Risk Equations for Complications of Type 2 Diabetes (RECODe) had the best calibration in primary studies and the highest pooled discrimination measures for the following outcomes: all-cause mortality (C-statistics 0.75, 95% CI 0.70 to 0.80; high certainty), cardiovascular mortality (0.79, 95% CI 0.75 to 0.84; low certainty), ESKD (0.73, 95% CI 0.52 to 0.94; low certainty), MI (0.72, 95% CI 0.69 to 0.74; moderate certainty) and stroke (0.71, 95% CI 0.68 to 0.74; moderate certainty). This model does not, however, predict risk of HF hospitalisations.

CONCLUSION

Of available risk models, RECODe proved to have satisfactory calibration in primary validation studies and acceptable discrimination superior to other models, though with high risk of bias in most primary studies.

TRIAL REGISTRATION NUMBER

CRD42020168351.

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan

Objective

In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospital B). Outlier tests were performed on the patient data from Hospital A to exclude the outliers. Prediction equation was developed using the patient data excluding the outliers and was subjected to external validation.

Results

By excluding outlier data, we could develop a new prediction equation for the incidence of coronary heart disease (CHD) as a complication of type 2 diabetes, incorporating the use of antidiabetic drugs with a high risk of hypoglycemia. This is the first prediction equation in Japan that incorporates the use of antidiabetic drugs. We believe that it will be useful in preventive medicine for treatment for people at high risk of CHD as a complication of diabetes or other diseases. In the future, we would like to confirm the accuracy of this equation at other facilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05844-w.

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.

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.

Developing a health economic model for Asians with type 2 diabetes based on the Japan Diabetes Complications Study and the Japanese Elderly Diabetes Intervention Trial

INTRODUCTION

Cost-effectiveness analyses are becoming increasingly important in Japan following the introduction of a health technology assessment scheme. The study objective was to develop an economic model to evaluate the cost-effectiveness of two interventions for type 2 diabetes in a Japanese population.

RESEARCH DESIGN AND METHODS

The Japan Diabetes Complications Study/Japanese Elderly Diabetes Intervention Trial risk engine (JJRE) Cost-Effectiveness Model (JJCEM) was developed, incorporating validated risk equations in Japanese patients with type 2 diabetes from the JJRE. Weibull regression models were developed for progression of the model outcomes, and a targeted literature review was performed to inform default values for utilities and costs. To illustrate outcomes, two simulated analyses were performed in younger (aged 40 years) and older (aged 80 years) Japanese populations, comparing a hypothetical treatment with placebo.

RESULTS

The model considers a population based on user-defined values for 11 baseline characteristic parameters and simulates rates of diabetic complications over a defined time horizon. Costs, quality-adjusted life years, and an incremental cost-effectiveness ratio are estimated. The model provides disaggregated results for two competing interventions, allowing visualization of the key drivers of cost and utility. A scatterplot of simulations and cost-effectiveness acceptability curve are generated for each analysis.

CONCLUSIONS

This is the first cost-effectiveness model for East Asian patients with type 2 diabetes, developed using Japan-specific risk equations. This population constitutes the largest share of the global population with diabetes, making this model highly relevant. The model can be used to evaluate the cost-effectiveness of anti-diabetic interventions in patients with type 2 diabetes in Japan and other East Asian populations.

A retrospective study on the usefulness of the JJ risk engine for predicting the incidence rate of coronary heart disease in type 2 diabetes patients

Objective

In 2018, we conducted a retrospective survey using the medical records of 484 patients with type 2 diabetes. The observed value of coronary heart disease (CHD) incidence after 5 years and the predicted value by the JJ risk engine as of 2013 were compared and verified using the discrimination and calibration values.

Results

Among the total cases analyzed, the C-statistic was 0.588, and the calibration was p < 0.05; thus, the JJ risk engine could not correctly predict the risk of CHD. However, in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646; the predictability of the JJ risk engine was relatively accurate. Therefore, it is difficult to accurately predict the complication rate of patients using the JJ risk engine based on the diabetes treatment policy after the Kumamoto Declaration 2013. The JJ risk engine has several input items (variables), and it is difficult to satisfy them all unless the environment is well-equipped with testing facilities, such as a university hospital. Therefore, it is necessary to create a new risk engine that requires fewer input items than the JJ risk engine and is applicable to several patients.

A Japanese Study Assessing Glycemic Control with Use of IDegAsp Co-formulation in Patients with Type 2 Diabetes in Clinical Practice: The JAGUAR Study

Introduction

The aim of this study was to evaluate the glycemic control and safety of insulin degludec/insulin aspart (IDegAsp) co-formulation in Japanese patients with type 2 diabetes (T2D) in a real-world clinical setting, including elderly patients (aged > 75 years).

Methods

Patients (≥ 18 years) diagnosed with T2D, previously treated with insulin were included from the Japanese Medical Data Vision database. Baseline data were taken at the index date, defined as the first IDegAsp prescription claim. Change in glycated hemoglobin (HbA_1c) at 12 months was estimated using a mixed model repeated measures analysis. The proportion of patients achieving target HbA_1c < 8.0% without experiencing hypoglycemia (identified by International Classification of Disease codes) was calculated at 12 months (365 ± 90 days) after baseline.

Results

Overall, 10,798 patients were included, 3940 were aged > 75 years, and 913 had baseline HbA_1c values available. Switching to IDegAsp was associated with significantly improved HbA_1c values at 12 months (− 1.23% [− 1.43, − 1.02]_95%CI, p < 0.001) versus baseline. Moreover, relative to baseline, a significantly greater proportion of patients achieved HbA_1c < 8.0% without hypoglycemia at 12 months, relative rate (RR) 1.30 [1.15, 1.45]_95%CI, p < 0.001. Results were similar for patients aged ≤ 75 years and aged > 75 years; 66% and 64% of patients, respectively, achieved HbA_1c < 8.0% without hypoglycemia at 12 months.

Conclusion

Switching from insulin to IDegAsp co-formulation was associated with significantly improved glycemic control and a reduction in hypoglycemia rate during 12 months of follow-up in Japanese patients with T2D, including those aged > 75 years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-021-01623-y.

Clinical inertia in patients with type 2 diabetes treated with oral antidiabetic drugs: Results from a Japanese cohort study (JDDM53)

AIMS/INTRODUCTION

Treatment intensification is commonly delayed in people with type 2 diabetes, resulting in poor glycemic control for an unacceptable length of time and increased risk of complications.

MATERIALS AND METHODS

This retrospective study investigated clinical inertia in 33,320 Japanese adults with type 2 diabetes treated with oral antidiabetic drugs (OADs) between 2009 and 2018, using data from the Computerized Diabetes Care (CoDiC® ) database.

RESULTS

The median time from first reported glycated hemoglobin (HbA1c) ≥7.0% (≥53 mmol/mol) to treatment intensification was considerably longer and HbA1c levels were higher the more OADs the patient was exposed to. For patients receiving three OADs, the median times from HbA1c ≥7.0% (53 mmol/mol) to intensification with OAD, glucagon-like peptide-1 receptor agonist or insulin were 8.1, 9.1 and 6.7 months, with a mean HbA1c level at the time of intensification of 8.4%, 8.9% and 9.3%, respectively. The cumulative incidence for time since the first reported HbA1c ≥7.0% (≥53 mmol/mol) to intensification confirmed the existence of clinical inertia, identifying patients whose treatment was not intensified despite poor glycemic control. HbA1c levels ≥7.0% (≥53 mmol/mol) after ≥6 months on one, two or three OADs were observed in 42%, 51% and 58% of patients, respectively, showing that approximately 50% of patients are above HbA1c target regardless of how many OADs they take.

CONCLUSIONS

Real-world data here show clinical inertia in Japanese adults with type 2 diabetes from early diabetes stages when they are receiving OADs, and illustrate a need for earlier, more effective OADs or injectable treatment intensification and better communication around the existence of clinical inertia.

Kaempferol attenuates diabetic nephropathy in streptozotocin-induced diabetic rats by a hypoglycaemic effect and concomitant activation of the Nrf-2/Ho-1/antioxidants axis

This study examined the protective effect of Kaempferol against streptozotocin-induced diabetic nephropathy (DN) in rats and studies the underlying mechanisms. Rats were divided into 4 groups as control, control + Kaempferol, STZ, and STZ + Kaempferol. All treatments were conducted for 8 weeks daily after the induction of diabetes. Kaempferol prevented STZ-induced weight and food loss and attenuated renal damage and the alterations in all biochemical related parameters. Concomitantly, Kaempferol reduced renal levels of TNF-α and IL-6, cleaved caspase-3, p38, and Bax, suppressing JNK phosphorylation and NF-κB p65 transactivation, and upregulation of Bcl-2. In both control and STZ-diabetic rats, Kaempferol reduced fasting glucose levels, increased fasting insulin levels and HOMA-β, reduced the levels of ROS and MDA, stimulated SOD and GSH levels, and increased the expression of Nrf2 and HO-1. In conclusion, Kaempferol prevents STZ-induced diabetic nephropathy, mainly, by antioxidant potential, mediated by the upregulation of the Nrf-2/HO-1 axis.

Sodium Intake and Incidence of Diabetes Complications in Elderly Patients with Type 2 Diabetes-Analysis of Data from the Japanese Elderly Diabetes Intervention Study (J-EDIT)

This study investigates the associations between sodium intake and diabetes complications in a nationwide cohort of elderly Japanese patients with type 2 diabetes aged 65–85. Data from 912 individuals regarding their dietary intake at baseline is analyzed and assessed by the Food Frequency Questionnaire based on food groups. Primary outcomes are times to diabetic retinopathy, overt nephropathy, cardiovascular disease (CVD), and all-cause mortality during six years. We find that mean sodium intake in quartiles ranges from 2.5 g to 5.9 g/day. After adjustment for confounders, no significant associations are observed between sodium intake quartiles and incidence of diabetes complications and mortality, except for a significant trend for an increased risk of diabetic retinopathy (p = 0.039). Among patients whose vegetable intake was less than the average of 268.7 g, hazard ratios (HRs) for diabetic retinopathy in patients in the second, third, and fourth quartiles of sodium intake compared with the first quartile were 0.87 (95% CI, 0.31–2.41), 2.61 (1.00–6.83), and 3.70 (1.37–10.02), respectively. Findings indicate that high sodium intake under conditions of low vegetable intake is associated with an elevated incidence of diabetic retinopathy in elderly patients with type 2 diabetes.

The Effects of Non-Nutritive Sweetener Consumption in the Pediatric Populations: What We Know, What We Don't, and What We Need to Learn

Childhood obesity is increasing at an alarming rate in the United States. This trend carries serious risk of children developing obesity-related diseases including Type 2 diabetes and cardiovascular disease. Non-nutritive sweeteners (NNS) are used as substitution for table sugar as a way to prevent weight gain. Their consumption is ubiquitous in adults and children; however the long-term health outcomes of chronic NNS consumption in children are unclear. Conflicting observational studies suggest that children consuming NNS are at risk of obesity and development of type 2 diabetes, while others concluded some benefits in weight reduction. Here, we review the physiological mechanisms that can contribute to the negative metabolic effects of NNS. We will focus on how NNS alters the sweet perception leading to increase caloric consumption, how NNs alters the gut microbiota, and how NNS may disrupt glucose homeostasis and initiate a vicious cycle of pancreatic endocrine dysfunction. Studies focused on the pediatric population are limited but necessary to determine whether the potential weight loss benefits outweigh the potential negative metabolic outcomes during this critical development period.

Predictors for diabetic retinopathy progression-findings from nominal group technique and Evidence review

Objectives

Risk stratification is needed for patients referred to hospital eye services by Diabetic Eye Screening Programme UK. This requires a set of candidate predictors. The literature contains a large number of predictors. The objective of this research was to arrive at a small set of clinically important predictors for the outcome of the progression of diabetic retinopathy (DR). They need to be evidence based and readily available during the clinical consultation.

Methods and analysis

Initial list of predictors was obtained from a systematic review of prediction models. We sought the clinical expert opinion using a formal qualitative study design. A series of nominal group technique meetings to shorten the list and to rank the predictors for importance by voting were held with National Health Service hospital-based clinicians involved in caring for patients with DR in the UK. We then evaluated the evidence base for the selected predictors by critically appraising the evidence.

Results

The source list was presented at nominal group meetings (n=4), attended by 44 clinicians. Twenty-five predictors from the original list were ranked as important predictors and eight new predictors were proposed. Two additional predictors were retained after evidence check. Of these 35, 21 had robust supporting evidence in the literature condensed into a set of 19 predictors by categorising DR.

Conclusion

We identified a set of 19 clinically meaningful predictors of DR progression that can help stratify higher-risk patients referred to hospital eye services and should be considered in the development of an individual risk stratification model.

Study design

A qualitative study and evidence review.

Setting

Secondary eye care centres in North East, Midlands and South of England.

Lower Drug Cost of Successfully Treating Patients with Type 2 Diabetes to Targets with Once-Weekly Semaglutide versus Once-weekly Dulaglutide in Japan: A Short-Term Cost-Effectiveness Analysis

INTRODUCTION

In the head-to-head trial (SUSTAIN 7), the novel, injectable, once-weekly GLP-1 analogue semaglutide showed superiority in both glycemic outcomes and body weight reduction, compared with once-weekly dulaglutide in the treatment of type 2 diabetes (T2D). However, no economic evaluation using these data has yet been conducted in the Japanese setting. The objective of this analysis was to assess the short-term cost-effectiveness in Japan of once-weekly semaglutide 0.5 mg (the approved maintenance dose in Japan) compared with once-weekly dulaglutide 0.75 mg (the only licensed dose in Japan) over a 1-year period using Japanese cost data.

METHODS

Responder endpoints were obtained from the SUSTAIN 7 trial to assess the cost of successfully treating patients to these targets ("cost of control"). Responder endpoint definitions consisted of single, dual, and triple composite endpoints related to glycemic control, body weight, and hypoglycemia outcomes. The cost of treatment was accounted from a healthcare payer perspective, capturing drug costs only.

RESULTS

Treatment with once-weekly semaglutide 0.5 mg was associated with a lower cost and a lower cost per patient treated to target for all endpoints, compared with once-weekly dulaglutide 0.75 mg. For each JPY 1 spent on bringing patients to target with once-weekly semaglutide 0.5 mg, JPY 1.58, JPY 1.44, JPY 1.60, JPY 2.10, and JPY 2.33 would need to be spent on once-weekly dulaglutide 0.75 mg to achieve an equivalent outcome for endpoints of HbA1c ≤ 6.5%, HbA1c < 7.0%, HbA1c < 7.0% without hypoglycemia, and no weight gain, weight loss ≥ 5%, and ≥ 1.0% HbA1c reduction and ≥ 3.0% weight loss, respectively.

CONCLUSIONS

These findings suggest that once-weekly semaglutide is a cost-effective treatment option compared with once-weekly dulaglutide for patients with T2D in Japan. In the future, this finding should be extrapolated to traditional long-term cost-effectiveness analysis, using common outcomes such as quality-adjusted life years.

Predicting the Development of Myocardial Infarction in Middle-Aged Adults with Type 2 Diabetes: A Risk Model Generated from a Nationwide Population-Based Cohort Study in Korea

BACKGROUND

Most of the widely used prediction models for cardiovascular disease are known to overestimate the risk of this disease in Asians. We aimed to generate a risk model for predicting myocardial infarction (MI) in middle-aged Korean subjects with type 2 diabetes.

METHODS

A total of 1,272,992 subjects with type 2 diabetes aged 40 to 64 who received health examinations from 2009 to 2012 were recruited from the Korean National Health Insurance database. Seventy percent of the subjects (n=891,095) were sampled to develop the risk prediction model, and the remaining 30% (n=381,897) were used for internal validation. A Cox proportional hazards regression model and Cox coefficients were used to derive a risk scoring system. Twelve risk variables were selected, and a risk nomogram was created to estimate the 5-year risk of MI.

RESULTS

During 7.1 years of follow-up, 24,809 cases of MI (1.9%) were observed. Age, sex, smoking status, regular exercise, body mass index, chronic kidney disease, duration of diabetes, number of anti-diabetic medications, fasting blood glucose, systolic blood pressure, total cholesterol, and atrial fibrillation were significant risk factors for the development of MI and were incorporated into the risk model. The concordance index for MI prediction was 0.682 (95% confidence interval [CI], 0.678 to 0.686) in the development cohort and 0.669 (95% CI, 0.663 to 0.675) in the validation cohort.

CONCLUSION

A novel risk engine was generated for predicting the development of MI among middle-aged Korean adults with type 2 diabetes. This model may provide useful information for identifying high-risk patients and improving quality of care.

Are quality-adjusted medical prices declining for chronic disease? Evidence from diabetes care in four health systems

Improvements in medical treatment have contributed to rising health spending. Yet there is relatively little evidence on whether the spending increase is "worth it" in the sense of producing better health outcomes of commensurate value-a critical question for understanding productivity in the health sector and, as that sector grows, for deriving an accurate quality-adjusted price index for an entire economy. We analyze individual-level panel data on medical spending and health outcomes for 123,548 patients with type 2 diabetes in four health systems: Japan, The Netherlands, Hong Kong and Taiwan. Using a "cost-of-living" method that measures value based on improved survival, we find a positive net value of diabetes care: the value of improved survival outweighs the added costs of care in each of the four health systems. This finding is robust to accounting for selective survival, end-of-life spending, and a range of values for a life-year or fraction of benefits attributable to medical care. Since the estimates do not include the value from improved quality of life, they are conservative. We, therefore, conclude that the increase in medical spending for management of diabetes is offset by an increase in quality.

Association between Low Protein Intake and Mortality in Patients with Type 2 Diabetes

The aim of this study was to investigate the association between protein intake and mortality risk in patients with type 2 diabetes. We analyzed a pooled data of 2494 diabetic patients from two prospective longitudinal studies. Nutritional intake was assessed using a Food Frequency Questionnaire at baseline. Protein intake per body weight (kg) per day was categorized into quartile groups. Adjusted hazard ratios (HRs) and 95% confidence interval (CI) were calculated using Cox regression analysis. During the six-year follow-up, there were 152 incidents of all-cause mortality. The HR for mortality in the lowest quartile of protein intake per body weight compared with the highest quartile was 2.26 (95% CI: 1.34–3.82, p = 0.002) after adjustment for covariates. Subgroup analyses revealed significant associations between low protein intake and mortality in patients aged over 75 years or under 65 years. After further adjustment of the total energy intake, a significant association between protein intake and mortality remained in patients aged ≥ 75 years, whereas the association was attenuated in those aged < 65 years. Our results suggest that adequate protein intake is necessary in older diabetic patients over 75 years, whereas with diabetes, whereas whole optimal total energy intake is required in younger patients with type 2 diabetes.

Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting

AIMS/HYPOTHESIS

The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort.

METHODS

A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed.

RESULTS

Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91).

CONCLUSIONS/INTERPRETATION

Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care.

REGISTRATION

PROSPERO registration ID CRD42018089122.

A model to predict risk of stroke in middle-aged adults with type 2 diabetes generated from a nationwide population-based cohort study in Korea

AIMS

The incidence of stroke differs between Asians and Caucasians, and between people with or without diabetes mellitus (DM). This study aimed to develop a model to predict the risk of stroke in middle-aged patients with type 2 DM.

METHODS

Using the National Health Insurance Database in Korea, data from patients aged 40-64 years with type 2 DM who received a health examination from 2009 to 2012 (n = 1,297,131) were analyzed as development (n = 907,992) and validation (n = 389,139) cohorts. Cox proportional-hazards regression model was used to derive a risk-scoring system, and 13 predictive variables were selected. A risk score nomogram based on the risk prediction model was created to estimate the 5-year risk of stroke.

RESULTS

In patients with type 2 DM, significant predictors for the development of stroke were older age, being male or a current smoker, lack of exercise, low body mass index, low estimated glomerular filtration rate, presence of coronary heart disease, longer duration of DM, insulin or multiple oral hypoglycemic agents use, low (<100 mg/dL) or high (≥140 mg/dL) fasting blood glucose, high systolic blood pressure, high total cholesterol, and presence of atrial fibrillation. The concordance indexes for stroke prediction were 0.703 (95% confidence interval [CI] 0.700-0.707) in the development cohort and 0.703 (95% CI 0.698-0.708) in the validation cohort.

CONCLUSIONS

We developed a risk model using various clinical parameters to predict stroke in patients with type 2 DM. This model may provide helpful information for identifying high-risk patients and guide prevention of stroke in this specific population.

Dose-response, efficacy, and safety of oral semaglutide monotherapy in Japanese patients with type 2 diabetes (PIONEER 9): a 52-week, phase 2/3a, randomised, controlled trial

BACKGROUND

Given the unique phenotype of type 2 diabetes in Japanese patients, novel therapies such as oral semaglutide require evaluation in this population. PIONEER 9 aimed to assess the dose-response of oral semaglutide and to compare the efficacy and safety of oral semaglutide with placebo and a subcutaneous GLP-1 receptor agonist in a Japanese population.

METHODS

PIONEER 9 was a 52-week, phase 2/3a, randomised, controlled trial done at 16 sites (clinics and university hospitals) in Japan. Japanese patients aged 20 years or older with uncontrolled type 2 diabetes managed by diet or exercise or with oral glucose-lowering drug monotherapy (washed out) were randomly assigned (1:1:1:1:1) to receive double-blind once-daily oral semaglutide (3 mg, 7 mg, or 14 mg) or placebo, or open-label subcutaneous once-daily liraglutide 0·9 mg. The primary endpoint was change in HbA_1c from baseline to week 26 with the trial product (primary) estimand (which assumes all patients remained on trial product without rescue medication use) in all randomly assigned patients. This trial is registered with ClinicalTrials.gov, NCT03018028.

FINDINGS

Between Jan 10, and July 11, 2017, 243 patients were randomly assigned to oral semaglutide 3 mg (n=49), 7 mg (n=49), or 14 mg (n=48), or placebo (n=49), or to liraglutide 0·9 mg (n=48). Changes in HbA_1c from baseline (mean 8·2%) to week 26 were dose-dependent with oral semaglutide (mean change -1·1% [SE 0·1] for oral semaglutide 3 mg, -1·5% [0·1] for 7 mg, and -1·7% [0·1] for 14 mg), -0·1% (0·1) with placebo, and -1·4% (0·1) with liraglutide 0·9 mg. Estimated treatment differences for change in HbA_1c compared with placebo were -1·1 percentage points (95% CI -1·4 to -0·8; p<0·0001) for oral semaglutide 3 mg, -1·5 percentage points (-1·7 to -1·2; p<0·0001) for oral semaglutide 7 mg, and -1·7 percentage points (-2·0 to -1·4; p<0·0001) for oral semaglutide 14 mg. Estimated treatment differences for change in HbA_1c compared with liraglutide 0·9 mg were 0·3 percentage points (95% CI -0·0 to 0·6; p=0·0799) for oral semaglutide 3 mg, -0·1 percentage points (-0·4 to 0·2; p=0·3942) for oral semaglutide 7 mg, and -0·3 percentage points (-0·6 to -0·0; p=0·0272) for oral semaglutide 14 mg. Gastrointestinal events, predominantly of mild or moderate severity, were the most frequently reported class of adverse event with oral semaglutide: constipation was most common, occurring in five to six (10-13%) patients with oral semaglutide, three (6%) with placebo, and nine (19%) with liraglutide 0·9 mg.

INTERPRETATION

This study showed that oral semaglutide provides significant reductions in HbA_1c compared with placebo in a dose-dependent manner in Japanese patients with type 2 diabetes, and has a safety profile consistent with that of GLP-1 receptor agonists.

FUNDING

Novo Nordisk.

Establishment and Validation of a Risk Prediction Model for Early Diabetic Kidney Disease Based on a Systematic Review and Meta-Analysis of 20 Cohorts

BACKGROUND

Identifying patients at high risk of diabetic kidney disease (DKD) helps improve clinical outcome.

PURPOSE

To establish a model for predicting DKD.

DATA SOURCES

The derivation cohort was from a meta-analysis. The validation cohort was from a Chinese cohort.

STUDY SELECTION

Cohort studies that reported risk factors of DKD with their corresponding risk ratios (RRs) in patients with type 2 diabetes were selected. All patients had estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m² and urinary albumin-to-creatinine ratio (UACR) <30 mg/g at baseline.

DATA EXTRACTION

Risk factors and their corresponding RRs were extracted. Only risk factors with statistical significance were included in our DKD risk prediction model.

DATA SYNTHESIS

Twenty cohorts including 41,271 patients with type 2 diabetes were included in our meta-analysis. Age, BMI, smoking, diabetic retinopathy, hemoglobin A_1c, systolic blood pressure, HDL cholesterol, triglycerides, UACR, and eGFR were statistically significant. All these risk factors were included in the model except eGFR because of the significant heterogeneity among studies. All risk factors were scored according to their weightings, and the highest score was 37.0. The model was validated in an external cohort with a median follow-up of 2.9 years. A cutoff value of 16 was selected with a sensitivity of 0.847 and a specificity of 0.677.

LIMITATIONS

There was huge heterogeneity among studies involving eGFR. More evidence is needed to power it as a risk factor of DKD.

CONCLUSIONS

The DKD risk prediction model consisting of nine risk factors established in this study is a simple tool for detecting patients at high risk of DKD.

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach

OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

RESEARCH DESIGN AND METHODS

A total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m2 and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use.

RESULTS

The prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery.

CONCLUSIONS

The IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.

Health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients: A cluster randomized controlled trial

BACKGROUND

The diabetes patients in China have low health literacy and low levels of physical activities which may result in the poor glycemic control and other clinical outcomes. This study is designed to evaluate the effectiveness of health literacy and exercise-focused interventions on clinical outcomes among Chinese patients with type 2 diabetes (T2DM).

METHODS

In this cluster randomized controlled trial, 799 T2DM patients with the most recent A1c ≥ 7·5% (58 mmol/mol, or fasting glucose level ≥10 mmol/L) were recruited from 35 clinics in 8 communities in Shanghai, China, and randomized into one standard care (control) arm and three intervention arms receiving interventions focused on health literacy, exercise or both. A1c (primary outcome), blood pressure and lipids (secondary outcomes) were measured at baseline, 3-, 6-, 12-months of intervention period and 12-months after completion of the interventions. This trial is registered with the International Standard RCT Number Register, number ISRCTN76130594.

FINDINGS

The three intervention groups had more reductions in A1c than the control group, with 0·90% reduction in the health literacy, 0·83% in the exercise and 0·54% in the comprehensive group at 12-months (p<0·001) and these improvements remained even after a 1-year follow-up period post intervention. The risk of suboptimal A1c (≥7·0% or 53 mmol/mol) was also significantly lower in three intervention groups than control group at each follow-up visit, with adjusted risk ratios (RR) ranging from 0.06 to 0.16. However, the control group has greater reductions in low-density lipoprotein (LDL) than the health literacy group from baseline to 12-months (β=0·55, p<0·0001) and from baseline to 24-months (β=0·62, p<0·0001). A higher risk of abnormal LDL was also observed for the health literacy group at 12-months [adjusted risk ratio (RR): 2·22, 95%CI: 1·11-4·44] and 24-months [adjusted risk ratio (RR): 2·37, 95%CI: 1·16-4·87] compared to the control group. No significant benefits in systolic blood pressure (SBP), diastolic blood pressure (DBP) and low-density lipoprotein (HDL) were observed from the interventions compared to the usual care.

INTERPRETATION

The health literacy and exercise interventions result in significant improvements in A1c. However, no significant benefits in blood pressure and lipids control were observed. These effective interventions may have potential of scaling up in China and other countries to help diabetes patients manage their blood glucose levels.

FUNDING

This Study was supported by the China Medical Board (CMB) Open Competition Project (No.13-159) and the Social Science Fund of China National Ministry of Education (No.14YJAZH092).

Beyond pros and cons - developing a patient decision aid to cultivate dialog to build relationships: insights from a qualitative study and decision aid development

BACKGROUND

An individualized approach using shared decision-making (SDM) and goal setting is a person-centred strategy that may facilitate prioritization of treatment options. SDM has not been adopted extensively in clinical practice. An interprofessional approach to SDM with tools to facilitate patient participation may overcome barriers to SDM use. The aim was to explore decision-making experiences of health professionals and people with diabetes (PwD), then develop an intervention to facilitate interprofessional shared decision-making (IP-SDM) and goal-setting.

METHODS

This was a multi-phased study. 1) Feasibility: Using a descriptive qualitative study, individual interviews with primary care physicians, nurses, dietitians, pharmacists, and PwD were conducted. The interviews explored their experiences with SDM and priority-setting, including facilitators and barriers, relevance of a decision aid for priority-setting, and integration of SDM and a decision aid into practice. 2) Development: An evidence-based SDM toolkit was developed, consisting of an online decision aid, MyDiabetesPlan, and implementation tools. MyDiabetesPlan was reviewed by content experts for accuracy and comprehensiveness. Usability assessment was done with 3) heuristic evaluation and 4) user testing, followed by 5) refinement.

RESULTS

Seven PwD and 10 clinicians participated in the interviews. From interviews with PwD, we identified that: (1) approaches to decision-making were diverse and dynamic; (2) a trusting relationship with the clinician and dialog were critical precursors to SDM; and, (3) goal-setting was a dynamic process. From clinicians, we found: (1) complementary (holistic and disease specific) approaches to the complex patient were used; (2) patient-provider agendas for goal-setting were often conflicting; (3) a flexible approach to decision-making was needed; and, (4) conflict could be resolved through SDM. Following usability assessment, we redesigned MyDiabetesPlan to consist of data collection and recommendation stages. Findings were used to finalize a multi-component toolkit and implementation strategy, consisting of MyDiabetesPlan, instructional card and videos, and orientation meetings with participating patients and clinicians.

CONCLUSIONS

A decision aid can provide information, facilitate clinician-patient dialog and strengthen the therapeutic relationship. Implementation of the decision aid can fit into a model of team care that respects and exemplifies professional identity, and can facilitate intra-team communication.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier: NCT02379078. Date of Registration: 11 February 2015.