Heterogeneity in cardiovascular death or hospitalization for heart failure benefits with flozins is linked to weight

Abstract

Aims

Cardiovascular outcome trials with sodium–glucose cotransporter 2 inhibitors (SGLT‐2is) have documented a positive impact on micro‐ and macrovascular complications of type 2 diabetes (T2D). Most analyses suggest that these benefits are independent of achieving metabolic control. This meta‐regression analysis was undertaken to explore the relationship between metabolic components positively influenced by SGLT‐2is and a reduction in cardiovascular death (CV death) or hospitalization due to heart failure (hHF).

Methods and results

A database search was conducted using the Cochrane Library to identify relevant studies. Analysis was conducted using CMA and RStudio (2022.07.1) software. The hazard ratios of the individual studies were used to compute the random effects model mean effect size for CV death or hHF, and the prediction interval was used to identify the uncertainty in the summary treatment effect. Heterogeneity was quantified using Q statistics. A pooled population of 46 969 patients from five studies was included for analysis. The Cochrane risk of bias tool was used to assess the quality of the studies. There was a significant 23% reduction in CV deaths or hHFs in the SGLT‐2i arm compared with the placebo arm [hazard ratio (HR): 0.77, 95% confidence interval (CI) 0.70–0.85]. However, the prediction interval (0.57–1.05) and the Q statistics [8.06 > degrees of freedom (df) of 4] were indicative of uncertainty in the true effect or heterogeneity. Nearly 50% of the variance of the observed effects were related to the true effects (I ² = 50%). Among the moderators selected, a significant correlation of the outcomes was found with the weight variable (P < 0.01). Weight differential could explain the entire variance in true effect size (R ² = 1.00) ruling out any sampling error.

Conclusions

The results of this meta‐regression analysis suggest that the beneficial effects of SGLT‐2is in reducing CV deaths and hHFs are related to the weight variable.

Introduction

Traditionally, the management of type 2 diabetes mellitus (T2D) has been glucocentric, with most experts advocating that achieving near normal glycaemia would prevent most if not all T2D‐related outcomes, as evidenced by the seminal UK Prospective Diabetes Study (UKPDS) documenting a significant 21% reduction in retinopathy, 33% reduction in albuminuria, and a 12% reduction in any diabetes‐related endpoints with a 0.9% reduction in HbA1c from baseline. ¹ Although there was no significant impact on the macrovascular outcomes in the initial UKPDS data, the 10‐year follow‐up data from the UKPDS demonstrated a 15% reduction in myocardial infarction (MI) and a 13% reduction in all‐cause mortality. ² This was not replicated in any subsequent randomized controlled trials. In fact, the results from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial raised a red flag as far as attempting to achieve near normal glycaemia, especially in T2D patients with a long duration of diabetes and advanced age. ³ Almost concurrently, studies with rosiglitazone revealed seriously adverse cardiac outcomes resulting in the drug regulatory authorities laying down rules and regulations related to the methods of conducting clinical trials. ⁴ , ⁵ Although the rationale for such stringent regulations was being debated, the results of the EMPA‐REG OUTCOME trial were published, illustrating a marked reduction in major adverse cardiovascular events (MACEs) driven by a reduction in cardiovascular (CV) death and all‐cause mortality. ⁶ Thereafter, multiple cardiovascular outcome trials (CVOTs) were published that showed a substantial improvement in cardiorenal outcomes with the usage of sodium–glucose cotransporter 2 inhibitors (SGLT‐2is) or flozins. ⁷ Thus, T2D management underwent a paradigm shift wherein the focus shifted dramatically from glycaemic control to the usage of agents that could alter both macrovascular and microvascular outcomes, arguably without the need for metabolic control or control of any other risk factors like blood pressure or weight.

Thus, the following dilemma. Is achieving metabolic control of no importance in comparison with choosing the appropriate agents? Additionally, were the benefits seen in the CVOTs exclusively due to the SGLT‐2is independent of metabolic control, to the extent that some authorities have suggested that these agents replace metformin as the first‐line therapy? ⁸

Another group of molecules that have shown significant and robust improvement in cardiovascular outcomes are glucagon‐like peptide‐1 receptor agonists (GLP1‐RAs), which seemingly seem to exert their benefit independent of metabolic control, according to multiple studies. Recent meta‐regression analysis from two different sources, however, pointed at a small chink in the armour of these seemingly robust suggestions. ⁹ , ¹⁰ A significant heterogeneity in the effect size of MACEs was encountered while analysing the nine CVOTs with GLP1‐RAs. In all CVOTs with GLP1‐RAs, systolic blood pressure (SBP) and lipids were optimized. The reduction in weight between the GLP1‐RA and placebo groups was not large enough to influence outcomes. However, the HbA1c difference between the two groups differed significantly across the trials. Both meta‐regression analyses found that a considerable proportion of this variability between the observed MACE effect size and the true MACE effect size could be explained by a reduction in HbA1c. As a result, one could propose that the positive impact of GLP1‐RAs was not independent of metabolic control but probably complementary to it.

In view of the above, it could also be hypothesized that the positive impact of SGLT‐2is on CV death or hospitalization due to heart failure (hHF) endpoints in T2D are not exclusively because of these drugs on the heart but because of their known salutary effects on blood glucose, weight, and blood pressure control. This analysis was therefore conducted to identify any heterogeneity in the CV death or hHF effect size and if any moderators could explain this variability.

This meta‐analysis was designed following the PICO question format (shown below):

• P (patient population) = patients diagnosed with T2D;

• I (intervention) = received drugs belonging to the SGLT‐2i group;

• C (control group) = compared with a control group that received a placebo; and

• O (outcome) = the primary aim was to analyse whether the outcome benefits seen in the included studies were dependent or independent of metabolic benefits.

Methods

This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement. ¹¹ Our review protocol was prospectively registered [PROSPERO 2022 CRD42022321558].

Search strategy and eligibility criteria

An electronic database search was conducted using the Cochrane Library. Keywords included (a) in the molecule‐specific search were as follows: ‘Sodium Glucose Transporter 2 Inhibitors’ [Medical Subject Headings, MeSH], ‘SGLT‐2 inhibitors’, ‘dapagliflozin’, ‘empagliflozin’, ‘canagliflozin’, ‘ertugliflozin’, and ‘ipragliflozin’; and (b) in the outcome‐specific search were as follows: ‘cardiovascular death or hospitalization due to heart failure’ and ‘CV death or hHF’. The drug‐related and outcome‐related searches were combined using the Boolean ‘OR’, and both these entities were combined using the Boolean ‘AND’. Additional filters used were randomized controlled trial (RCT) and date of selection after August 2008 [the publication of Food and Drug Administration (FDA) guidance for the industry] without any restriction on the language of reporting. As an additional screening method, studies with a placebo in the comparator arm and those with standardized primary outcomes as the outcome of interest were included (Fig. 1 ).

Figure 1.

The study selection process.

Data extraction and quality assessment of the included studies

Having identified the screened studies, further eligibility was assessed based on prespecified inclusion criteria. The inclusion criteria were as follows:

• randomized controlled trials;

• type 2 diabetes patients aged 18–75 years;

• placebo as the comparator arm;

• outcomes included renal composite and CV death or hHF; and

• reporting of the metabolic components influenced by SGLT‐2is, including HbA1c, weight, and SBP.

Having identified the five studies eligible for the meta‐regression analysis, the raw data were entered by SG into a blank sheet provided by comprehensive meta‐analysis (CMA) software. The baseline meta‐analysis with determination of the prediction interval was conducted using the RStudio (2022.07.1) software. The meta‐regression was performed using the comprehensive meta‐analysis (CMA) software. The accuracy of data entry was cross‐checked by B. S., R. M., and R. M. on another day. Any discrepancy was solved based on mutual consensus. The quality of the individual studies was assessed using the Cochrane risk of bias algorithm (Figure  S1 ).

Patient approval and ethical committee clearance

This study was a systematic review and meta‐regression analysis, so there was no direct patient handling. Because all the data used are in the public domain in the form of published articles and their associated supplementary materials, no ethical committee approval was sought.

Statistical analysis

The planned analysis was divided into two stages:

• Step 1:

  The first stage involved documenting the effect size (hazard ratio, HR) related to the use of SGLT‐2is and the cardiac outcomes. We planned to perform a meta‐analysis on CV death or hHF using a random effects model (because the baseline characteristics of these studies varied considerably and did not represent a single population of interest). Although the effect sizes of these five studies had already been reported, our main aim was to identify the variation in the effect size from one study to another and the degree of heterogeneity present in the mean effect size. The prediction interval was added to the forest plot to assess the width of the distribution of the effect size. With the help of Q statistics, we aimed to identify the sum of squared deviations (of observed effects from the mean) on a standardized scale. A rejection of the null hypothesis (P < 0.1) would indicate that the true effect size varied across studies. ¹² In addition, we aimed to look at I ² statistics, which indicated the relationship between the variance in the observed effect and the variance in the true effect.

• Step 2:

  Having identified significant variance (if any), a meta‐regression analysis was conducted using the metabolic parameters of interest (HbA1c, weight, and SBP) as the probable covariates explaining the variance in the true effect size across the studies. We planned to use the three moderators independently and identify the model that best fit the outcomes of interest. Having identified the model, we looked at the proportion of the variance explained by the model using R ² statistics.

The issue of multiplicity (inflation of type 1 error) was addressed by three approaches. First, by identifying a single outcome (CV death or hHF) as primary. Second, to include a composite outcome thereby circumventing the issue of competing risks and increasing the statistical power of the analysis. Finally, we kept the P‐value of significance to assess heterogeneity at a very conservative estimate of 0.1 instead of 0.05.

Results

Baseline characteristics of the included studies

The meta‐analysis was conducted with a pooled population of 46 969 patients, with 26 765 patients in the SGLT‐2i arm and 20 204 participants in the placebo arm. ⁶ , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ The mean age of participants across these studies ranged from 62.9 to 64.4 years. The duration of follow‐up ranged from 2.62 to 4.2 years, with the DECLARE TIMI‐58 trial having the longest follow‐up duration. All the studies had a placebo as a comparator. The mean baseline HbA1c ranged from 8.07% to 8.3%, with the EMPA‐REG trial representing the former and the DECLARE TIMI‐58 and CREDENCE trials representing the latter. The mean baseline body mass index (BMI) and SBP ranged from 30.6 to 32.1 kg/m² and 133 to 139 mmHg, respectively. There was no significant risk of bias identified in any of the nine studies according to the Cochrane risk of bias algorithm. There was no significant publication bias for either of the outcomes of interest as assessed visually with funnel plots and quantitatively with Egger's regression intercept (for CV death or hHF 2‐tailed P‐value = 0.16, for renal composite two‐tailed P‐value = 0.80) (Figure  S2 ). The baseline characteristics of the studies are detailed in Table 1 .

Table 1.

Baseline characteristics of the studies included for analysis

Study [year]	SGLT‐2is	Age (years)	SGLT‐2i group/placebo group (n)	Duration of follow‐up	Baseline HbA1c (%)	Baseline weight (BMI‐ kg/m2)	Baseline SBP (mmHg)
EMPA‐REG [2015]9	Empagliflozin	63.0 ± 8.6)	4687/2333	3.1	8.07 ± 0.86	30.6 ± 5.2	134.9 ± 16.8
CANVAS [2017]13	Canagliflozin	63.2 ± 8.3	5795/4347	3.6	8.2 ± 0.9	31.9 ± 5.9	136.4 ± 15.8
DECLARE TIMI‐58 [2018]14	Dapagliflozin	63.9 ± 6.8	8582/8578	4.2	8.3 ± 1.2	32.1 ± 6.0	135.1 ± 15.3
CREDENCE [2019]15	Canagliflozin	62.9 ± 9.2	2202/2199	2.62	8.3 ± 1.3	31.4 ± 6.2	139.8 ± 15.6
VERTIS CV [2020]16	Ertugliflozin	64.4 ± 8.1	5499/2747	3.5	8.2 ± 1.0	31.9 ± 5.4	133.5 ± 13.7

Results of the meta‐analysis related to within‐study variance

CV death or hHF

The Q statistics (8.06) and associated P‐value (0.09) indicated that the effect size of CV death or hHF varied significantly across the studies. A mean effect size of 0.77 (HR) with a confidence interval (CI) ranging from 0.70 to 0.85 indicated that in a universe of comparable populations, the effect size mean fell within this interval. However, it did not indicate the true effect size variations expected across the various populations with similar inclusion criteria. The prediction interval (indicative of the distribution of the effect size) ranging between 0.57 and 1.05 was indicative of populations responding extremely well to the interventions at one end of the spectrum to others not responding at all (Fig. 2 ). Hence, it was extremely important to identify covariates or moderators that could be predictive of the positive outcomes and address them accordingly.

Figure 2.

Meta‐analysis of SGLT‐2is versus placebo and CV death or hHF highlighting the mean effect size and prediction interval.

Influence of moderators on effect size variance

Covariates and their probable association with CV death or hHF

We used three covariates for the meta‐regression analysis (HbA1c, weight, and SBP). Because the number of included studies did not support the use of a combined model, we assessed each of the moderators separately. The model using the difference in HbA1c (P = 0.17) between the two groups or difference in SBP (P = 0.86) did not explain the variance in the observed effect size. However, the difference in weight between the SGLT‐2i group and placebo group correlated significantly with the variance in CV death or hHF effect size (95% CI 0.05–0.32, P < 0.01) (Table  S1 ). An R ² of 1.00 strongly supported the weight differential explaining the variance between the observed and true effect size, ruling out sampling error. The coefficient of the weight moderator was 0.18, indicating a positive correlation between the weight differential and the HR for CV death or hHF (Fig. 3 ).

Figure 3.

Positive and significant impact of the weight differential on the primary outcomes. A regression of the log hazard ratio on weight.

Main results for Model 1. Random effects (MM), Z‐distribution, log hazard ratio: CV death or hHF
Covariate	Coefficient	Standard error	95% CI (lower)	95% CI (upper)	Z value	Two‐sided P‐value
Moderator 1: HbA1c
Intercept	−0.51	0.19	−0.89	−0.13	−2.65	>0.01
HbA1c difference	0.59	0.44	−0.26	1.44	1.37	0.17
Moderator 2: SBP
Intercept	−0.22	0.22	−0.66	0.22	−0.99	0.32
SBP difference	−0.01	0.08	−0.16	0.14	−0.17	0.86
Moderator 3: Weight
Intercept	−0.54	0.11	−0.76	−0.32	−4.74	<0.01
Weight difference	0.18	0.06	0.05	0.32	2.71	<0.01
Statistics for Moderator 3
Test of the model: Simultaneous test that all coefficients (excluding the intercept) are zero Test of the model: Simultaneous test that all coefficients (excluding the intercept) are zero Q = 7.36, df = 1, P = 0.0067 Goodness of fit: Test that unexplained variance is zero Tau2 = 0.0000, Tau = 0.0000, I 2 = 0.00%, Q = 0.70, df = 3, P = 0.8734 Comparison of Model 1 with the null model Total between‐study variance (intercept only) Tau2 = 0.0065, Tau = 0.0807, I 2 = 50.34%, Q = 8.06, df = 4, P = 0.0896 Proportion of total between‐study variance explained by Model 1 R 2 analog = 1.00

Discussion

Background

Management of T2D has undergone a paradigm shift since the introduction of GLP1‐RAs and SGLT‐2is. The significant cardiorenal benefits associated with these groups of agents shifted the focus to molecules themselves rather than their metabolic benefits to the extent that it was presumed that the cardiorenal benefits associated with these groups of antihyperglycaemic agents were independent of metabolic control. ¹⁷

It is important to note that only a meagre 30% of patients randomized in the TECOS trial achieved their ABC targets (A1C, BP, and cholesterol). ¹⁸ It is worth speculating that, if a larger population of patients achieved their ABC target, the results would be different. Incidentally, there are no available data from any of the CVOTs conducted with GLP1‐RAs or SGLT‐2is on what percentage of the recruited patients achieved their ABC targets in the active as well as the placebo arm, making it difficult to rule out any contribution from metabolic benefits to the positive outcomes.

The only way to resolve this question is to perform either a subgroup analysis or a meta‐regression analysis to try and identify significant heterogeneity in the outcome benefits.

This meta‐regression analysis was undertaken to identify significant heterogeneity (if any) in the cardiorenal outcomes from SGLT‐2is and to identify whether metabolic benefits could explain it.

Findings from our study

We found significant heterogeneity in the CV death or hHF (Q = 8.06, df = 4, P = 0.09) effect size in the pooled meta‐analysis of five studies using the random effects model, indicating that there were covariates apart from the use of SGLT‐2is that could explain the outcome benefit. There was no significant effect size variance seen with the individual components of the primary outcomes. We took the three metabolic covariates influenced by SGLT‐2is as covariates, which could possibly explain the heterogeneity in the effect size. Reductions in HbA1c and SBP did not explain the heterogeneity of the effect size. There was a significant association of the weight variable with heterogeneity in the CV death or hHF (Q = 7.36, df = 1, P < 0.01) effect size. An R ² of 1.00 indicated that 100% of the variance in the true effect could be explained by our model.

Limitations and strengths

The major limitation of this meta‐regression analysis is that the covariate was not reported uniformly across the selected studies. Another important limitation is the exploratory nature of the analysis. Thus, no causality could be inferred from the results. Due to the paucity of studies available for the meta‐regression, our analysis could not be performed including multiple covariates. This could result in confounding because some of the covariates were correlated with each other. There is also the issue of inflation of the type 1 error (multiplicity) due to analysis of multiple outcomes. To minimize the impact of multiplicity, we included a single composite outcome for analysis and used a very conservative alpha for testing heterogeneity.

The main strengths of this analysis were a large sample size along with data from randomized controlled trials, which all had prejudicated and prespecified analytical designs. In addition, there was no significant bias associated with the included studies. The use of a random effects model was used throughout the analysis because we were interested in the heterogeneity of effect size. In addition, an R ² of 1.00 for the weight covariate was indicative of its robust predictive property.

Literature review

The link between obesity and heart failure (HF) has been an emerging field of research, with existing work by Pandey et al. highlighting the mechanism that may predispose people with higher BMI to clinical heart failure with preserved ejection fraction (HFpEF) via increased leptin levels, lowered chest wall compliance and decreased B‐type natriuretic peptide (BNP) levels. ¹⁹

The problem with establishing this link is that there seems to be an inverse relationship between weight loss and a better prognosis for heart failure with reduced ejection fraction (HFrEF). This was explored by Adamson et al., who studied the correlation between dapagliflozin and BMI. ²⁰ This study extrapolated data from 4742 adults with HFrEF from the DAPA‐HF trial and found that the lowest risk of adverse outcomes was found among patients with a BMI ranging from 30.0 to 34.9 kg/m², with an increased risk for worse outcomes for both extremes. ²¹ This confirmed what they referred to as the ‘obesity survival paradox’.

Regarding HFpEF patients, Tadic and Cuspidi discussed the available data, with trials such as the I‐PRESERVE trial confirming a ‘U‐shaped relationship’ between BMI and mortality among HFpEF patients. ²² , ²³ Ather et al. showed that obesity decreases mortality risk more among HFpEF patients than among HFrEF patients, but this finding did not reach statistical significance. ²⁴

In effect, BMI changes can mask the risk of mortality for obese patients, who may have worse prognoses post HF and remain to be further studied. In addition, a post hoc data from the CHARM trial documented a 50% increase in the hazard for mortality at 6 months with a 5% weight loss from baseline in patients with HFpEF. ²⁵ Body weight and BMI are integrally related to HF and drugs like SGLT‐2is that influence body weight and probably produce their salutary effect through their effects on this covariate.

Conclusions

This meta‐regression analysis clearly demonstrates that in CVOTs with SGLT‐2is for individuals with T2D, body weight is a covariate that is integrally linked to cardiovascular death and heart failure. It appears that the salutary effects of SGLT‐2is in reducing CV death and HF are related to their effect on body weight. This hypothesis, however, needs further study.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

No funds were received in the preparation of this manuscript.

Supporting information

Figure S1. Assessment of the risk of bias associated with individual studies using the Cochrane risk of bias algorithm.

Figure S2. Publication bias assessed with a funnel plot and Egger's regression intercept.

Table S1. Meta‐regression analysis using moderators (HbA1c, weight, and SBP) to explain the variance in the effect size related to CV death or hHF.

Ghosal, S. , Sinha, B. , and Mukherjee, R. (2023) Heterogeneity in cardiovascular death or hospitalization for heart failure benefits with flozins is linked to weight. ESC Heart Failure, 10: 1242–1249. 10.1002/ehf2.14296.