A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes

Prediction of new-onset heart failure in patients with type 2 diabetes derived from ALTITUDE and CANVAS

AIM

To create and validate a prediction model to identify patients with type 2 diabetes (T2D) at high risk of new-onset heart failure (HF), including those treated with a sodium-glucose cotransporter-2 (SGLT2) inhibitor.

METHODS

A prediction model was developed from the Aliskiren Trial in Type 2 Diabetes Using Cardiorenal Endpoints (ALTITUDE), a trial in T2D patients with albuminuria or cardiovascular disease. We included 5081 patients with baseline N-terminal pro B-type natriuretic peptide (NT-proBNP) measurement and no history of HF. The model was developed using Cox regression and validated externally in the placebo arm of the Canagliflozin Cardiovascular Assessment Study (CANVAS), which included 996 participants with T2D and established cardiovascular disease or high cardiovascular risk, and in patients treated with canagliflozin.

RESULTS

ALTITUDE participants (mean age 64 ± 9.8 years) had a median serum NT-proBNP level of 157 (25th-75th percentile 70-359) pg/mL. Higher NT-proBNP level, troponin T (TnT) level and body mass index (BMI) emerged as significant and independent predictors of new-onset HF in both cohorts. The model further contained urinary albumin-to-creatinine ratio, glycated haemoglobin, age, haematocrit, and use of calcium channel blockers. A prediction model including these variables had a C-statistic of 0.828 (95% confidence interval [CI] 0.801-0.855) in ALTITUDE and 0.800 (95% CI 0.720-0.880) in CANVAS. The C-statistic of this model increased to 0.847 (95% CI 0.792-0.902) in patients after 1 year of canagliflozin treatment.

CONCLUSION

In patients with T2D, higher NT-proBNP level, TnT level and BMI are independent and externally validated predictors of new-onset HF, including patients using an SGLT2 inhibitor. This newly developed model may identify patients at high risk of new-onset HF, contributing to early recognition and possibly prevention.

Methods to predict heart failure in diabetes patients

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is one of the leading causes of cardiovascular disease and powerful predictor for new-onset heart failure (HF).

AREAS COVERED

We focus on the relevant literature covering evidence of risk stratification based on imaging predictors and circulating biomarkers to optimize approaches to preventing HF in DM patients.

EXPERT OPINION

Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.

Optimal Screening for Predicting and Preventing the Risk of Heart Failure Among Adults With Diabetes Without Atherosclerotic Cardiovascular Disease: A Pooled Cohort Analysis

BACKGROUND

The optimal approach to identify individuals with diabetes who are at a high risk for developing heart failure (HF) to inform implementation of preventive therapies is unknown, especially in those without atherosclerotic cardiovascular disease (ASCVD).

METHODS

Adults with diabetes and no HF at baseline from 7 community-based cohorts were included. Participants without ASCVD who were at high risk for developing HF were identified using 1-step screening strategies: risk score (WATCH-DM [Weight, Age, Hypertension, Creatinine, HDL-C, Diabetes Control, QRS Duration, MI, and CABG] ≥12), NT-proBNP (N-terminal pro-B-type natriuretic peptide ≥125 pg/mL), hs-cTn (high-sensitivity cardiac troponin T ≥14 ng/L; hs-cTnI ≥31 ng/L), and echocardiography-based diabetic cardiomyopathy (echo-DbCM; left atrial enlargement, left ventricular hypertrophy, or diastolic dysfunction). High-risk participants were also identified using 2-step screening strategies with a second test to identify residual risk among those deemed low risk by the first test: WATCH-DM/NT-proBNP, NT-proBNP/hs-cTn, NT-proBNP/echo-DbCM. Across screening strategies, the proportion of HF events identified, 5-year number needed to treat and number needed to screen to prevent 1 HF event with an SGLT2i (sodium-glucose cotransporter 2 inhibitor) among high-risk participants, and cost of screening were estimated.

RESULTS

The initial study cohort included 6293 participants (48.2% women), of whom 77.7% without prevalent ASCVD were evaluated with different HF screening strategies. At 5-year follow-up, 6.2% of participants without ASCVD developed incident HF. The 5-year number needed to treat to prevent 1 HF event with an SGLT2i among participants without ASCVD was 43 (95% CI, 29-72). In the cohort without ASCVD, high-risk participants identified using 1-step screening strategies had a low 5-year number needed to treat (22 for NT-proBNP to 37 for echo-DbCM). However, a substantial proportion of HF events occurred among participants identified as low risk using 1-step screening approaches (29% for echo-DbCM to 47% for hs-cTn). Two-step screening strategies captured most HF events (75-89%) in the high-risk subgroup with a comparable 5-year number needed to treat as the 1-step screening approaches (30-32). The 5-year number needed to screen to prevent 1 HF event was similar across 2-step screening strategies (45-61). However, the number of tests and associated costs were lowest for WATCH-DM/NT-proBNP ($1061) compared with other 2-step screening strategies (NT-proBNP/hs-cTn: $2894; NT-proBNP/echo-DbCM: $16 358).

CONCLUSIONS

Selective NT-proBNP testing based on the WATCH-DM score efficiently identified a high-risk primary prevention population with diabetes expected to derive marked absolute benefits from SGLT2i to prevent HF.

Analysis and prediction of readmissions for heart failure in the first year after discharge with INCA score

To determine the readmissions trends and the comorbidities of patients with heart failure that most influence hospital readmission rates. Heart failure (HF) is one of the most prevalent health problems as it causes loss of quality of life and increased health-care costs. Its prevalence increases with age and is a major cause of re-hospitalisation within 30 days after discharge. INCA study had observational and ambispective design, including 4,959 patients from 2000 to 2019, with main diagnosis of HF in Extremadura (Spain). The variables examined were collected from discharge reports. To develop the readmission index, capable of discriminating the population with higher probability of re-hospitalisation, a Competing-risk model was generated. Readmission rate have increased over the period under investigation. The main predictors of readmission were: age, diabetes mellitus, presence of neoplasia, HF without previous hospitalisation, atrial fibrillation, anaemia, previous myocardial infarction, obstructive pulmonary disease (COPD) and chronic kidney disease (CKD). These variables were assigned values with balanced weights, our INCA index showed that the population with values greater than 2 for men and women were more likely to be re-admitted. Previous HF without hospital admission, CKD, and COPD appear to have the greatest effect on readmission. Our index allowed us to identify patients with different risks of readmission.

Genome-wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population

OBJECTIVE

Type 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown.

METHODS

This study included 93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D.

RESULTS

An ideally fitting PRS model (variance explained, R2  = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20-0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17.

CONCLUSIONS

This study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.

Prognostic models for heart failure in patients with type 2 diabetes: a systematic review and meta-analysis

OBJECTIVE

To provide a systematic review, critical appraisal, assessment of performance and generalisability of all the reported prognostic models for heart failure (HF) in patients with type 2 diabetes (T2D).

METHODS

We performed a literature search in Medline, Embase, Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus (from inception to July 2022) and grey literature to identify any study developing and/or validating models predicting HF applicable to patients with T2D. We extracted data on study characteristics, modelling methods and measures of performance, and we performed a random-effects meta-analysis to pool discrimination in models with multiple validation studies. We also performed a descriptive synthesis of calibration and we assessed the risk of bias and certainty of evidence (high, moderate, low).

RESULTS

Fifty-five studies reporting on 58 models were identified: (1) models developed in patients with T2D for HF prediction (n=43), (2) models predicting HF developed in non-diabetic cohorts and externally validated in patients with T2D (n=3), and (3) models originally predicting a different outcome and externally validated for HF (n=12). RECODe (C-statistic=0.75 95% CI (0.72, 0.78), 95% prediction interval (PI) (0.68, 0.81); high certainty), TRS-HFDM (C-statistic=0.75 95% CI (0.69, 0.81), 95% PI (0.58, 0.87); low certainty) and WATCH-DM (C-statistic=0.70 95% CI (0.67, 0.73), 95% PI (0.63, 0.76); moderate certainty) showed the best performance. QDiabetes-HF demonstrated also good discrimination but was externally validated only once and not meta-analysed.

CONCLUSIONS

Among the prognostic models identified, four models showed promising performance and, thus, could be implemented in current clinical practice.

A gender specific risk assessment of coronary heart disease based on physical examination data

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .

Biomarkers for the Diagnosis of Heart Failure in People with Diabetes: A Consensus Report from Diabetes Technology Society

Diabetes Technology Society assembled a panel of clinician experts in diabetology, cardiology, clinical chemistry, nephrology, and primary care to review the current evidence on biomarker screening of people with diabetes (PWD) for heart failure (HF), who are, by definition, at risk for HF (Stage A HF). This consensus report reviews features of HF in PWD from the perspectives of 1) epidemiology, 2) classification of stages, 3) pathophysiology, 4) biomarkers for diagnosing, 5) biomarker assays, 6) diagnostic accuracy of biomarkers, 7) benefits of biomarker screening, 8) consensus recommendations for biomarker screening, 9) stratification of Stage B HF, 10) echocardiographic screening, 11) management of Stage A and Stage B HF, and 12) future directions. The Diabetes Technology Society panel recommends 1) biomarker screening with one of two circulating natriuretic peptides (B-type natriuretic peptide or N-terminal prohormone of B-type natriuretic peptide), 2) beginning screening five years following diagnosis of type 1 diabetes (T1D) and at the diagnosis of type 2 diabetes (T2D), 3) beginning routine screening no earlier than at age 30 years for T1D (irrespective of age of diagnosis) and at any age for T2D, 4) screening annually, and 5) testing any time of day. The panel also recommends that an abnormal biomarker test defines asymptomatic preclinical HF (Stage B HF). This diagnosis requires follow-up using transthoracic echocardiography for classification into one of four subcategories of Stage B HF, corresponding to risk of progression to symptomatic clinical HF (Stage C HF). These recommendations will allow identification and management of Stage A and Stage B HF in PWD to prevent progression to Stage C HF or advanced HF (Stage D HF).

Predicting and preventing heart failure in type 2 diabetes

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

Effect of Canagliflozin on Heart Failure Hospitalization in Diabetes According to Baseline Heart Failure Risk

BACKGROUND

In the CANVAS (Canagliflozin Cardiovascular Assessment Study) program, canagliflozin reduced the risk of heart failure (HF) hospitalization among individuals with type 2 diabetes mellitus (T2DM).

OBJECTIVES

The purpose of this study was to evaluate heterogeneity in absolute and relative treatment effects of canagliflozin on HF hospitalization according to baseline HF risk as assessed by diabetes-specific HF risk scores (WATCH-DM [Weight (body mass index), Age, hyperTension, Creatinine, HDL-C, Diabetes control (fasting plasma glucose) and QRS Duration, MI and CABG] and TRS-HF_DM [TIMI Risk Score for HF in Diabetes]).

METHODS

Participants in the CANVAS trial were categorized into low, medium, and high risk for HF using the WATCH-DM score (for participants without prevalent HF) and the TRS-HF_DM score (for all participants). The outcome of interest was time to first HF hospitalization. The treatment effect of canagliflozin vs placebo for HF hospitalization was compared across risk strata.

RESULTS

Among 10,137 participants with available HF data, 1,446 (14.3%) had HF at baseline. Among participants without baseline HF, WATCH-DM risk category did not modify the treatment effect of canagliflozin (vs placebo) on HF hospitalization (P interaction = 0.56). However, the absolute and relative risk reduction with canagliflozin was numerically greater in the high-risk group (cumulative incidence, canagliflozin vs placebo: 8.1% vs 12.7%; HR: 0.62 [95% CI: 0.37-0.93]; P = 0.03; number needed to treat: 22) than in the low- and intermediate-risk groups. When overall study participants were categorized according to the TRS-HF_DM score, a statistically significant difference in the treatment effect of canagliflozin across risk strata was observed (P interaction = 0.04). Canagliflozin significantly reduced the risk of HF hospitalization by 39% in the high-risk group (HR: 0.61 [95% CI: 0.48-0.78]; P < 0.001; number needed to treat: 20) but not in the intermediate- or low-risk groups.

CONCLUSIONS

Among participants with T2DM, the WATCH-DM and TRS-HF_DM can reliably identify those at high risk for HF hospitalization and most likely to benefit from canagliflozin.

Epidemiology of the diabetes-cardio-renal spectrum: a cross-sectional report of 1.4 million adults

Background

Type-2 diabetes (T2D), chronic kidney disease, and heart failure (HF) share epidemiological and pathophysiological features. Although their prevalence was described, there is limited contemporary, high-resolution, epidemiological data regarding the overlap among them. We aimed to describe the epidemiological intersections between T2D, HF, and kidney dysfunction in an entire database, overall and by age and sex.

Methods

This is a cross-sectional analysis of adults ≥ 25 years, registered in 2019 at Maccabi Healthcare Services, a large healthcare maintenance organization in Israel. Collected data included sex, age, presence of T2D or HF, and last estimated glomerular filtration rate (eGFR) in the past two years. Subjects with T2D, HF, or eGFR < 60 mL/min/1.73 m² were defined as within the diabetes-cardio-renal (DCR) spectrum.

Results

Overall, 1,389,604 subjects (52.2% females) were included; 445,477 (32.1%) were 25– < 40 years, 468,273 (33.7%) were 40– < 55 years, and 475,854 (34.2%) were ≥ 55 years old. eGFR measurements were available in 74.7% of the participants and in over 97% of those with T2D or HF. eGFR availability increased in older age groups. There were 140,636 (10.1%) patients with T2D, 54,187 (3.9%) with eGFR < 60 mL/min/1.73m², and 11,605 (0.84%) with HF. Overall, 12.6% had at least one condition within the DCR spectrum, 2.0% had at least two, and 0.23% had all three. Cardiorenal syndrome (both HF and eGFR < 60 mL/min/1.73m²) was prevalent in 0.40% of the entire population and in 2.3% of those with T2D. In patients with both HF and T2D, 55.2% had eGFR < 60 mL/min/1.73m² and 15.8% had eGFR < 30 mL/min/1.73m². Amongst those within the DCR spectrum, T2D was prominent in younger participants, but was gradually replaced by HF and eGFR < 60 mL/min/1.73m² with increasing age. The congruence between all three conditions increased with age.

Conclusions

This large, broad-based study provides a contemporary, high-resolution prevalence of the DCR spectrum and its components. The results highlight differences in dominance and degree of congruence between T2D, HF, and kidney dysfunction across ages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01521-9.

Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HF_DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HF_DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HF_DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HF_DM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P=0.96). TRS-HF_DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HF_DM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.