Abstract
Background
The relationship between cystatin C and cardiovascular and kidney outcomes is not well explored in individuals with type 2 diabetes (T2D) and normal or mild kidney dysfunction.
Methods
This nested prospective biomarker study included patients enrolled in DECLARE-TIMI 58. Participants were categorized into quartiles of CysC/creatinine and eGFRCysC/eGFRCr and classified according to Kidney Disease Improving Global Outcomes (KDIGO) risk categories. Hazard ratios were estimated for a composite of cardiovascular death and hospitalization for heart failure (CVD/HHF), major adverse cardiovascular events (MACE), and a kidney-specific composite outcome (persistent ≥40% decrease in eGFR, end-stage renal disease, or renal death).
Results
Among 1899 placebo-treated patients, higher baseline CysC was associated with higher risk for CVD/HHF, MACE, and the kidney-specific composite outcome. A gradient of risk across quartiles was observed for CVD/HHF [Q4 v Q1: 1.33 (0.98–1.80), p-trend = 0.016]; MACE [1.34 (1.06–1.69), p-trend = 0.013]; and kidney-specific composite outcome [1.95 (0.95–3.99), p-trend = 0.036]. When compared with creatinine, CysC showed more pronounced and statistically significant risk gradients (CVD/HHF: KDIGO-CysC p-trend < 0.001 vs. p-trend Cr 0.34; MACE: CysC p-trend < 0.001 vs. Cr p-trend 0.52). No heterogeneity was observed in the effect of dapagliflozin across CysC concentrations (p-interactions >0.05).
Conclusion
In patients with T2D and predominantly preserved Cr-based eGFR, CysC was associated with increased cardiovascular and kidney risk. Although discrimination assessed by c-statistic was similar for CysC and Cr, risk gradients seen with CysC became attenuated and no longer statistically significant when using Cr-based analogs. The effect of dapagliflozin was consistent irrespective of baseline CysC.
Trial registration
ClinicalTrials.gov Identifier: NCT01730534
Keywords: Cystatin C, Creatinine, KDIGO classification, Estimated glomerular filtration rate, Sodium-glucose transporter 2 inhibitors, Cardiovascular diseases, Kidney diseases
1. Introduction
Cystatin C (CysC), a low-molecular-weight protein secreted by all human nucleated cells at a constant rate, is freely filtered by the renal glomerulus and metabolized by proximal tubular cells [1]. CysC has been suggested as an alternative to serum creatinine (Cr) for estimated glomerular filtration rate (eGFR) because its production is less influenced by non-GFR factors, including age, sex, muscle mass, physical activity, nutritional status, and protein intake [2], [3], [4], [5]. Prior studies have suggested that in addition to more precisely calculating eGFR, circulating CysC levels are independently associated with cardiovascular risk [6], [7], [8]. While CysC has been shown to be an accurate and reliable marker of kidney function and a predictive biomarker for cardiovascular and kidney risk, there remains a need to understand its utility across various patient populations.
Diabetes is a leading cause of chronic kidney disease (CKD), a significant risk factor for cardiovascular morbidity and death [9]. The ability of CysC, compared to Cr, to predict risk of cardiovascular and kidney outcomes in patients with type 2 diabetes (T2D) and predominantly preserved kidney function is less certain. Examining the predictive role of CysC in a cohort of T2D patients with predominantly preserved Cr-based eGFR may inform the use of CysC-based alternatives to Cr-based measures in clinical scenarios where Cr measurements may be less reliable. Albuminuria, a component of diabetic kidney disease, is associated with an increased risk for all-cause and cardiovascular mortality in patients with diabetes [10]. Improved cardiovascular risk prediction has been demonstrated in patients with CKD through the use of Cr-based eGFR and albuminuria utilizing risk categories established by the Kidney Disease Improving Global Outcomes (KDIGO) guidelines incorporating both measures [11], [12]. Less is known about the predictive ability of the combined assessment of CysC-based estimated GFR (eGFRCysC) and albuminuria for both cardiovascular and kidney outcomes.
Dapagliflozin is a sodium-glucose cotransporter 2 inhibitor (SGLT2i), a class of medications that lower blood sugar and have consistently shown favorable cardiovascular and kidney effects in patients with and without T2D [13]. The Dapagliflozin Effect on Cardiovascular Events (DECLARE)–TIMI 58 trial evaluated dapagliflozin vs. placebo among patients with T2D and either multiple risk factors for atherosclerotic cardiovascular disease or established cardiovascular disease [14]. This trial demonstrated a significant 17% reduction in cardiovascular death or hospitalization for heart failure with dapagliflozin and a reduction in the progression of kidney disease [15]. In the current study, we assessed the association between CysC and Cr and risk of cardiovascular and kidney outcomes in a population with T2D and predominantly preserved Cr-based eGFR. In addition, we investigated the treatment effect of dapagliflozin compared to placebo according to baseline CysC concentrations.
2. Methods
2.1. Study population
This was a nested biomarker substudy from DECLARE-TIMI 58. The trial design, baseline characteristics, and main results have previously been published [14], [15], [16]. In brief, DECLARE-TIMI 58 was a randomized, double-blind, placebo-controlled, multinational trial of dapagliflozin among 17,160 patients with T2D and either established atherosclerotic cardiovascular disease (ASCVD) [age ≥40 years and either ischemic heart disease, cerebrovascular disease, or peripheral arterial disease] or multiple risk factors for ASCVD (age ≥55 years for men or ≥60 years for women plus at least one of dyslipidemia, hypertension, or current tobacco use) and a creatinine clearance (CrCl) of ≥60 mL/min. Eligibility for DECLARE-TIMI 58 required CrCl ≥60 mL/min at screening, estimated using the Cockcroft-Gault equation. Because kidney function was reassessed at randomization using CKD-EPI eGFR, some participants had baseline eGFR <60 mL/min/1.73 m2 despite meeting the screening eligibility criterion [17]. The trial protocol, including the biomarker substudy, was approved by the institutional review board at each participating site and all participants provided written informed consent.
2.2. Outcomes of interest
The trial had dual primary efficacy endpoints: a composite of cardiovascular death (CVD) and hospitalization for heart failure (HHF) and major adverse cardiovascular events (MACE, a composite of CVD, myocardial infarction, or ischemic stroke). A kidney-specific composite outcome (persistent ≥40% decrease in eGFR, end-stage renal disease, or renal death) was also evaluated [14], [15]. All components of the primary outcomes were adjudicated in a blinded manner by an independent clinical events committee using previously published definitions [14]. The change in eGFR for the kidney composite outcome was calculated based on creatinine measured in a central laboratory.
2.3. Study design
We designed a nested prospective biomarker study among patients enrolled in the biomarker substudy of DECLARE-TIMI 58. The analysis of the association between CysC and cardiovascular and kidney outcomes included 1899 placebo-treated subjects. The full cohort of 3698 patients in whom CysC was measured was used for the analysis of any effect modification with dapagliflozin. We utilized a case-cohort design. Cases were determined by the presence of the respective outcomes of interest, and controls were selected from a random sample of the entire cohort with no restrictions. Hazard ratios were determined according to baseline CysC and CysC-based measures of kidney function with multivariable adjustment.
2.4. Biomarkers
Blood samples were collected on the day of randomization and isolated plasma was stored at -20°C or colder until shipped on dry ice to the TIMI Clinical Trials Laboratory (Boston, Massachusetts). Serum cystatin C (CysC) was measured using a commercially available immunoturbidimetric assay (Roche Diagnostics, Indianapolis, IN).
2.5. Measures of kidney function
The Cr-based eGFR was determined by using the 2012 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Creatinine Equation [18]. CysC-based eGFR was calculated using the 2012 CKD-EPI Cystatin C Equation [19].
We categorized participants by quartiles of baseline CysC/Cr and eGFRCysC/eGFRCr. In addition, we classified participants according to KDIGO risk categories defined by eGFRCysC/eGFRCr and albuminuria at baseline [20]. When classified by eGFR alone, categories were condensed as ≥90, 60 to <90, and <60 mL/min/1.73 m2. For KDIGO risk classification, participants were categorized into the established KDIGO risk groups (low, moderate, high, and very high risk) using the KDIGO combined eGFR and albuminuria categories. Albuminuria was categorized as A1 (normal to mildly increased; ACR <30 mg/g), A2 (moderately increased; ACR 30–300 mg/g), or A3 (severely increased; ACR >300 mg/g).
2.6. Statistical analyses
Baseline characteristics are presented according to quartiles of baseline CysC. Continuous variables are summarized as medians (interquartile ranges [IQRs]) and categorical variables as counts and percentages. The p-trend for each outcome represents a test of trend across quartiles using the Cochran Armitage trend test for categorical covariates and the Jonckheere-Terpstra test for continuous covariates. The associations between biomarker measures and outcomes were examined within the placebo arm using Cox proportional hazard regression models adjusted for age, sex, race, smoking, history of heart failure, body mass index, T2D duration, insulin use, baseline albuminuria, coronary artery disease, myocardial infarction, ischemic stroke, peripheral artery disease, dyslipidemia, and hypertension. P-values for trend across quartiles were derived from the corresponding adjusted Cox proportional hazards models using the same covariates. Harrell's C-statistic with corresponding 95% confidence intervals was used to assess model discrimination [21]. In sensitivity analyses, Cr-based eGFR and corresponding KDIGO risk categories were recalculated using the 2021 CKD-EPI creatinine equation. As an exploratory analysis, category-free net reclassification improvement (NRI) was calculated to assess whether addition of CysC improved risk classification beyond a model incorporating creatinine-based eGFR. To explore the non-linear association between biomarker concentrations and the treatment effect of dapagliflozin for each outcome in the full cohort of 3698 patients, we further fit Cox models with CysC concentrations using restricted cubic splines. Two-sided p-values <0.05 were considered to indicate nominal statistical significance. SAS v9.4 (SAS Institute, Cary, NC) was used for the analysis.
3. Results
3.1. Study population
Among the 3698 participants in the full cohort, 2429 patients were male, the median age was 64.0 (60.0, 69.0), and the median (25th, 75th percentile) duration of type 2 diabetes was 11.0 (6.0, 17.0) years (Table 1). No significant difference was found in baseline characteristics between the 1899 placebo-treated subjects included in the primary analysis population and the full cohort of 3698 patients with a CysC measurement (Supplemental Table S1). At baseline, 192 of 1899 participants (10.1%) in the placebo-treated primary analysis cohort had a Cr-based eGFR <60 mL/min/1.73 m2. CysC levels ranged from 0.2 to 3.7 mg/L with a median of 0.93 mg/L. Subjects with higher baseline CysC levels tended to be older and were more likely to have a history of heart failure, peripheral artery disease, coronary artery disease, and cerebrovascular disease. HbA1c levels were similar across quartiles. Baseline UACR was available in 3644 of 3698 participants, with a median UACR of 15.5 mg/g (IQR 6.5–53.4); 66% had UACR <30 mg/g.
Table 1.
Baseline characteristics of subjects.
| A. Baseline Characteristics of All Subjects with CysC Measurements by Quartiles | |||||
|---|---|---|---|---|---|
| Characteristic | Overall (N = 3698) | Q1 (N = 977) | Q2 (N = 882) | Q3 (N = 937) | Q4 (N = 902) |
| CysC Quartiles | – | <0.81 | 0.81–0.93 | 0.93–1.1 | >1.1 |
| Age, Median (IQR) | 64.0 (60.0, 69.0) | 61.0 (57.0, 65.0) | 64.0 (60.0, 68.0) | 66.0 (61.0, 70.0) | 66.0 (61.0, 71.0) |
| Male Sex (%) | 65.7 | 68.7 | 65.9 | 63.3 | 64.7 |
| White (%) | 82.8 | 80.5 | 82.2 | 83.6 | 85.1 |
| Black (%) | 2.9 | 2.3 | 2.2 | 3.1 | 4.0 |
| Asian (%) | 11.1 | 12.4 | 12.2 | 10.6 | 9.2 |
| Other Race (%) | 3.2 | 4.9 | 3.4 | 2.8 | 1.7 |
| BMI, Median (IQR) | 31.7 (28.3, 36.1) | 30.5 (27.5, 34.4) | 31.2 (27.8, 34.9) | 32.0 (28.3, 36.2) | 34.2 (29.8, 39.0) |
| History of HF (%) | 14.8 | 10.0 | 10.7 | 16.4 | 22.3 |
| History of PAD (%) | 7.6 | 6.9 | 5.7 | 7.9 | 10.0 |
| History of CAD (%) | 42.2 | 39.4 | 37.9 | 44.0 | 47.7 |
| History of CVD (%) | 10.2 | 8.2 | 8.0 | 11.3 | 13.2 |
| eGFR (CKD-EPI), Median (IQR) | 87.0 (73.0, 96.0) | 98.0 (92.0, 103.0) | 91.0 (83.0, 96.0) | 83.0 (73.0, 91.0) | 66.0 (56.0, 78.0) |
| Duration of DM (years), Median (IQR) | 11.0 (6.0, 17.0) | 10.0 (6.0, 15.0) | 10.0 (6.0, 16.0) | 11.0 (6.0, 18.0) | 13.0 (8.0, 19.0) |
| HbA1c, Median (IQR) | 8.1 (7.4, 9.1) | 8.3 (7.5, 9.4) | 8.0 (7.3, 9.0) | 7.9 (7.3, 8.9) | 8.0 (7.4, 9.1) |
| Baseline insulin use (%) | 45.9 | 38.7 | 42.0 | 46.0 | 57.3 |
| Baseline Albuminuria (ACR ≥30), % (n/N) | 34.1 (1242/3644) | 28.2 (272/965) | 25.3 (220/870) | 34.9 (321/919) | 48.2 (429/890) |
| B. Baseline Characteristics of Subjects by CysC-Based KDIGO Risk Categories | |||||
|---|---|---|---|---|---|
| Characteristic | Overall (N = 3644) | Low Risk (N = 2112) | Moderate Risk (N = 958) | High Risk (N = 407) | Very High Risk (N = 167) |
| Age, Median (IQR) | 64.0 (60.0, 69.0) | 63.0 (59.0, 68.0) | 64.0 (60.0, 69.0) | 66.0 (61.0, 71.0) | 67.0 (62.0, 73.0) |
| Male Sex (%) | 65.7 | 65.1 | 67.5 | 64.9 | 65.9 |
| White (%) | 82.7 | 83.6 | 81.8 | 81.1 | 79.0 |
| Black (%) | 2.9 | 2.6 | 2.5 | 4.2 | 6.6 |
| Asian (%) | 11.2 | 10.5 | 12.9 | 11.1 | 10.2 |
| Other Race (%) | 3.2 | 3.3 | 2.7 | 3.7 | 4.2 |
| BMI, Median (IQR) | 31.7 (28.3, 36.1) | 31.2 (27.9, 35.2) | 32.1 (28.3, 36.9) | 32.8 (29.1, 37.2) | 35.0 (30.5, 39.6) |
| History of HF (%) | 14.8 | 12.4 | 15.3 | 19.7 | 31.1 |
| History of PAD (%) | 7.5 | 5.5 | 10.0 | 10.1 | 12.6 |
| History of CAD (%) | 42.2 | 40.3 | 41.8 | 49.4 | 50.9 |
| History of CVD (%) | 10.0 | 8.4 | 11.9 | 10.6 | 18.0 |
| eGFR (CKD-EPI), Median (IQR) | 87.0 (73.0, 96.0) | 90.0 (80.0, 97.0) | 85.0 (70.0, 96.0) | 72.0 (59.0, 89.0) | 56.0 (46.0, 67.0) |
| Duration of DM (years), Median (IQR) | 11.0 (6.0, 17.0) | 10.0 (6.0, 15.0) | 11.5 (7.0, 18.0) | 13.0 (8.0, 20.0) | 16.0 (10.0, 22.0) |
| HbA1c, Median (IQR) | 8.1 (7.4, 9.1) | 7.9 (7.3, 8.9) | 8.3 (7.5, 9.3) | 8.2 (7.4, 9.2) | 8.5 (7.6, 9.5) |
| Baseline insulin use (%) | 45.7 | 39.3 | 51.0 | 59.7 | 62.3 |
| Baseline Albuminuria (ACR ≥30), % | 34.1 | 0 | 77.2 | 86.0 | 91.0 |
A. Baseline characteristics of subjects by cystatin C quartiles. B, Baseline characteristics of subjects by cystatin C-based KDIGO risk categories. KDIGO risk categories (Low, Moderate, High, and Very High Risk) were defined by the combination of eGFR (≥90, 60 to <90, or <60 mL/min/1.73 m2) and albuminuria (A1, ACR <30 mg/g; A2, ACR 30–300 mg/g; or A3, ACR >300 mg/g). Abbreviations: HF, heart failure; PAD, peripheral artery disease; CAD, coronary artery disease; CVD, cerebrovascular disease; DM, diabetes mellitus.
3.2. Relationship between cystatin C and cardiovascular and kidney outcomes
Among those allocated to placebo, patients with higher baseline CysC levels had higher rates of CVD/HHF, MACE, and the kidney-specific composite outcome after multivariable adjustment (Fig. 1). Application of CysC-based KDIGO risk categories, which also incorporate albuminuria, identified a very high-risk group (n = 167) (Fig. 1). C-statistics were not significantly different across the measures.
Fig. 1.
Adjusted hazard ratios for CVD/HHF, MACE, and kidney-specific composite outcome in the DECLARE-TIMI 58 placebo group (N = 1899) according to CysC and Cr measures.
A, Adjusted hazard ratios according to CysC-based eGFR quartiles and CysC-based KDIGO risk categories. B, Adjusted hazard ratios according to Cr-based eGFR quartiles and Cr-based KDIGO risk categories. All groups adjusted for age, sex, race, smoking, history of HF, BMI, T2D duration, insulin use, CAD, MI, ischemic stroke, PAD, dyslipidemia, HTN, and baseline albuminuria. KDIGO risk categories incorporate both eGFR and albuminuria and are designated as low, moderate, high, and very high risk. KDIGO risk categories (Low, Moderate, High, and Very High Risk) were defined by the combination of eGFR (≥90, 60 to <90, or <60 mL/min/1.73 m2) and albuminuria (A1, ACR <30 mg/g; A2, ACR 30–300 mg/g; or A3, ACR >300 mg/g). Abbreviations: CysC, cystatin C; Cr, creatinine; CVD, cardiovascular death; HHF, hospitalization for heart failure; MACE, major adverse cardiovascular event; KDIGO, Kidney Disease: Improving Global Outcomes.
3.3. Comparing cystatin C and creatinine measures
Though overall discrimination assessed by c-statistic was similar with CysC compared to Cr across biomarker quartiles, eGFR, and KDIGO risk categories, the risk gradients with Cr-based measures were generally attenuated and did not meet statistical significance (Fig. 1). Sensitivity analyses using the 2021 CKD-EPI creatinine equation resulted in limited reclassification and did not materially alter the overall associations observed with Cr-based eGFR quartiles or KDIGO risk categories (Supplemental Table S2).
3.4. Treatment effect of dapagliflozin according to baseline cystatin C levels
The treatment effect of dapagliflozin was consistent irrespective of baseline CysC levels for CVD/HHF, MACE, and kidney-specific composite outcome. There was no evidence for heterogeneity in the effect of dapagliflozin across the continuous biomarker concentration (p-interaction >0.05) for all measured outcomes (Fig. 2).
Fig. 2.
Treatment effect of dapagliflozin vs. placebo according to baseline serum CysC levels for CVD/HHF, MACE, and kidney composite.
Solid line indicates HR; shading indicates the 95% confidence interval; top and bottom 5% excluded from plot (HRs reflect entire cohort).
3.5. Risk reclassification with cystatin C
In exploratory analyses, addition of CysC to a model incorporating Cr-based eGFR resulted in category-free NRIs of 0.11 (95% CI, 0.02 to 0.20) for MACE, 0.07 (−0.04 to 0.18) for CVD/HHF, and 0.19 (−0.03 to 0.40) for the kidney-specific composite outcome (Supplemental Table S3). Results were directionally consistent in sensitivity analyses using the 2021 CKD-EPI equation.
4. Discussion
In this analysis from the DECLARE-TIMI 58 trial, among patients with T2D who were eligible based on initially preserved kidney function, higher CysC concentrations were associated with increased risk for CVD/HHF, MACE, and the kidney-specific composite outcome after multivariable adjustment. Overall discrimination assessed by c-statistic was similar with CysC compared with Cr across biomarker quartiles, eGFR, and KDIGO risk categories. However, the significant risk gradients seen with the CysC-based measurements generally became attenuated when using Cr-based analogs and no longer met statistical significance. We found no evidence of heterogeneity in the effect of dapagliflozin across CysC concentration for any of these endpoints. Nevertheless, CysC may identify higher-risk patients who warrant closer monitoring and optimization of cardiovascular and kidney risk-reduction strategies because of their potential for greater absolute benefits with established therapies.
Traditionally, eGFRCr has been the standard for clinical assessment of kidney function and is used in the classification of patients with CKD [22]. Limitations inherent to creatinine as a biomarker, particularly variability introduced by non-GFR determinants, can obscure underlying risk signals [19], [23], [24]. CysC-based measures demonstrated independent risk gradients for cardiovascular and kidney outcomes after adjustment for multiple covariates. GFR estimation is an especially important clinical tool to screen for CKD in high-risk groups, such as those with T2D. Despite these findings, CysC is not currently widely used in clinical practice to assess kidney function in part due to having not been validated in different patient groups with varying levels of kidney function and due to limitations in cost-effectiveness [25]. Distinct non-GFR determinants of CysC have been documented, including use of steroids, thyroid dysfunction, adiposity, and inflammation [26]. Compared to Cr, CysC appears to be affected by fewer factors, exhibits a lesser degree of susceptibility, and is more uniformly produced across diverse populations [1].
Taken together with our findings, the advantages of CysC, particularly its susceptibility to fewer non-GFR determinants, support a role for CysC alongside creatinine in risk assessment for patients with T2D and preserved baseline Cr-based eGFR. Moreover, the numerically greater risk gradient observed with the combined assessment of eGFRCysC and albuminuria suggests enhanced risk stratification compared to CysC or eGFRCysC alone. Our findings highlight the clinical relevance of CysC as a complementary biomarker to creatinine to enhance risk stratification among patients with T2D who have predominantly preserved baseline Cr-based eGFR.
4.1. Limitations
Our findings reflect the baseline characteristics of this clinical trial study population. This population has a narrower range of kidney function than the general T2D population due to the enrollment criteria. However, it is possible that these relationships could be stronger when studied in a population with a broader range of kidney function and may help to identify a population with worse CysC-based kidney function that has greater benefit from SGLT2i. DECLARE-TIMI 58 eligibility required CrCl ≥60 mL/min at screening. Kidney function was subsequently reassessed at the randomization visit using CKD-EPI eGFR. Therefore, some participants had eGFR <60 mL/min/1.73 m2 at randomization despite meeting the screening eligibility criterion. Classification of kidney function based on a single measurement is a limitation of this analysis. Although enrollment required preserved Cr-based eGFR at screening, albuminuria was not an enrollment criterion, and approximately one-third of participants had baseline UACR ≥30 mg/g. We had no direct measures of muscle mass or clinical conditions that may influence CysC levels, though our data included surrogates for these characteristics, including age and BMI. We therefore cannot specifically comment on these relationships. Racial and ethnic minority groups were underrepresented in the trial population compared with the general population with T2D, limiting our ability to assess the potential added utility of CysC in these populations.
5. Conclusions
In this population with T2D and predominantly preserved Cr-based eGFR, CysC-based measures of kidney function were independently associated with CVD/HHF, MACE, and a kidney-specific composite outcome with significant risk gradients across quartiles, eGFR, and KDIGO risk categories. Dapagliflozin had a consistent effect across the entire spectrum of CysC with respect to CVD/HHF, MACE, and kidney-specific composite outcome.
CRediT authorship contribution statement
Andre M. Small: Writing – review & editing, Writing – original draft, Investigation, Data curation, Conceptualization. David D. Berg: Writing – review & editing. Itamar Raz: Writing – review & editing. Petr Jarolim: Writing – review & editing. Erica L. Goodrich: Writing – review & editing, Validation, Software, Methodology, Formal analysis, Data curation. Filipe A. Moura: Writing – review & editing. Ofri Mosenzon: Writing – review & editing. Avivit Cahn: Writing – review & editing. Deepak L. Bhatt: Writing – review & editing. Jamie P. Dwyer: Writing – review & editing. Lawrence A. Leiter: Writing – review & editing. John P.H. Wilding: Writing – review & editing. Ingrid Gause-Nilsson: Writing – review & editing, Funding acquisition. Marc S. Sabatine: Writing – review & editing, Conceptualization. David A. Morrow: Writing – review & editing, Writing – original draft, Investigation, Conceptualization. Stephen D. Wiviott: Writing – review & editing, Writing – original draft, Investigation, Conceptualization.
Declaration of competing interest
MSS reports research grant support through Brigham and Women's Hospital from: Abbott; Amgen; Anthos Therapeutics; AstraZeneca; Boehringer Ingelheim; Daiichi-Sankyo; Ionis; Merck; Novartis; Pfizer; Saghmos Therapeutics; Verve Therapeutics, and consulting for: Amgen; Anthos Therapeutics; AstraZeneca; Beren Therapeutics; Boehringer Ingelheim; Dr. Reddy's Laboratories; Merck; Moderna; Novo Nordisk; Precision BioSciences; Silence Therapeutics.
DLB discloses the following relationships - Advisory Board: Angiowave, Antlia Bioscience, Bayer, Boehringer Ingelheim, CellProthera, Cereno Scientific, E-Star Biotech, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, NirvaMed, Novo Nordisk, Repair Biotechnologies, Stasys, SandboxAQ (stock options), Tourmaline Bio, Viatris; Board of Directors: American Heart Association New York City, Angiowave (stock options), Bristol Myers Squibb (stock), DRS.LINQ (stock options), High Enroll (stock); Consultant: Alnylam, Altimmune, Broadview Ventures, Corcept Therapeutics, Corsera, GlaxoSmithKline, Hims, Sanofi, SERB, SFJ, Summa Therapeutics, Worldwide Clinical Trials; Data Monitoring Committees: Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research, Boston Scientific (Chair, PEITHO trial), Cleveland Clinic, Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ABILITY-DM trial, funded by Concept Medical; for ALLAY-HF, funded by Alleviant Medical), Novartis, Population Health Research Institute; Rutgers University (for the NIH-funded MINT Trial); Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Chair, ACC Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol-Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), CSL Behring (AHA lecture), Duke Clinical Research Institute, Engage Health Media, HMP Global (Editor in Chief, Journal of Invasive Cardiology), Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Oakstone CME (Course Director, Comprehensive Review of Interventional Cardiology), Philips (Becker's Webinar on AI), Population Health Research Institute, WebMD (CME steering committees), Wiley (steering committee); Other: Clinical Cardiology (Deputy Editor, unpaid); Progress in Cardiovascular Diseases (Deputy Editor, unpaid); Added Health (Editorial Board; stock options); Patent: Sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon; neither I nor Brigham and Women's Hospital receive any income from this patent); Research Funding: Abbott, Acesion Pharma, Afimmune, Alnylam, Amarin, Amgen, AstraZeneca, Atricure, Bayer, Boehringer Ingelheim, Boston Scientific, CellProthera, Cereno Scientific, Chiesi, Cleerly, CSL Behring, Faraday Pharmaceuticals, Fractyl, Idorsia, Janssen, Javelin, Lexicon, Lilly, Medtronic, Merck, MiRUS, Moderna, Novartis, Novo Nordisk, Pfizer, PhaseBio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, 89Bio; Royalties: Elsevier (Editor, Braunwald's Heart Disease); Site Co-Investigator: Cleerly.
AC reports personal fees from AstraZeneca and Novo Nordisk, Eli Lilly, Sanofi, Boehringer Ingelheim, Pfizer and Bayer.
JW reports consultancy for Altimmune, AstraZeneca, Boehringer Ingelheim, Cytoki, Lilly, Napp, Novo Nordisk, Menarini, Pfizer, Rhythm Pharmaceuticals, Sanofi, Saniona, Tern, Shionogi & Ysopia; research grants for clinical trials from AstraZeneca and Novo Nordisk contracted via the University of Liverpool (no personal payment), personal honoraria/lecture fees from AstraZeneca, Boehringer Ingelheim, Medscape, Napp, Novo Nordisk and Rhythm. He is past president of the World Obesity Federation, a member of the Association for the Study of Obesity, Diabetes UK, EASD, ADA, Society for Endocrinology and the Rank Prize Funds Nutrition Committee. He is national lead for the Metabolic and Endocrine Speciality Group of the UK NIHR Clinical Research Network.
LAL has received research funding from, has provided CME on behalf of, and/or has acted as an adviser to AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Merck, Novartis, NovoNordisk.
ELG is a member of the TIMI Study Group which has received institutional research grant support through Brigham and Women's Hospital from Abbott, Amgen, Anthos Therapeutics, ARCA Biopharma, Inc., AstraZeneca, Daiichi-Sankyo, Ionis Pharmaceuticals, Inc., Janssen Research and Development, LLC, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Roche, Softcell Medical Limited, The Medicines Company, Zora Biosciences.
FAM. reports consulting fees from Janssen and was supported by a T32 postdoctoral training grant from the National Institutes of Health, National Heart, Lung, and Blood Institute (T32HL007604).
PJ reports research support through BWH from Abbott Laboratories, Amgen, Inc., AstraZeneca, LP, Eisai, Inc., Roche Diagnostic Corporation, Siemens Healthineers.
DAM is a member of the TIMI Study Group which has received institutional research grant support through Brigham and Women's Hospital from Abbott, Amgen, Anthos Therapeutics, ARCA Biopharma, Inc., AstraZeneca, Daiichi-Sankyo, Ionis Pharmaceuticals, Inc., Janssen Research and Development, LLC, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Roche, Softcell Medical Limited, The Medicines Company, Zora Biosciences. He has received consulting fees from Abbott, Merck, Regeneron and Roche.
DDB is a member of the TIMI Study Group, which has received institutional research grant support through Brigham and Women's Hospital from Abbott, Abiomed, Inc., Amgen, Anthos Therapeutics, ARCA Biopharma, Inc., AstraZeneca, Boehringer Ingelheim, Daiichi-Sankyo, Ionis Pharmaceuticals, Inc., Janssen Research and Development, LLC, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Roche, Saghmos Therapeutics, Inc., Siemens Healthcare Diagnostics, Inc., Softcell Medical Limited, The Medicines Company, Verve Therapeutics, Inc., Zora Biosciences; he has received consulting fees from AstraZeneca, Pfizer, Mobility Bio, Inc., and Youngene Therapeutics, honoraria from the Medical Education Speakers Network (MESN) and USV Private Limited, and participates on clinical endpoint committees for studies sponsored by Beckman Coulter, Kowa Pharmaceuticals, and Tosoh Biosciences.
SDW has received research grants from Amgen, AstraZeneca, Janssen, Merck, and Pfizer; has received consulting fees from Icon, Novo Nordisk, and Verian; and has received speaking honoraria from Harvard Medical School. As of 16 June 2025, Dr. Wiviott is employed by AstraZeneca. Dr. Wiviott's wife, Dr. Caroline Fox has been employed by Vertex Pharmaceuticals and Pfizer.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ahjo.2026.100929.
Appendix A. Supplementary data
Supplementary tables
References
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