Baseline diabetes as a way to predict CV outcomes in a lipid-modifying trial: a meta-analysis of 330,376 patients from 47 landmark studies

Background Diabetes is a major cardiovascular risk factor. However, its influence on the rate of occurrence of cardiovascular (CV) events during a clinical trial that included a diabetes subgroup has not yet been quantified. Aims To establish equations relating baseline diabetes prevalence and incident CV events, based on comparator arms data of major lipid-modifying trials. Methods Meta-analysis of primary outcomes (PO) rates of key prospective trials, for which the baseline proportion of diabetics was reported, including studies having specifically reported CV outcomes within their diabetic subgroups. Results 47 studies, representing 330,376 patients (among whom 124,115 diabetics), were analyzed as regards the relationship between CV outcomes rates (including CHD) and the number of diabetics enrolled. Altogether, a total of 18,445 and 16,156 events occurred in the comparator and treatment arms, respectively. There were significant linear relationships between diabetes prevalence and both PO and CHD rates (%/year): y = 0.0299*x + 3.12 [PO] (p = 0.0128); and y = 0.0531*x + 1.54 [CHD] (p = 0.0094), baseline diabetes predicting PO rates between 3.12 %/year (no diabetic included) and 6.11 %/year (all patients diabetic); and CHD rates between 1.54 %/year (no diabetic) and 6.85 %/year (all patients diabetic). The slopes of the equations did not differ according to whether they were derived from primary or secondary prevention trials. Conclusions Absolute and relative CV risk associated with diabetes at inclusion can be readily predicted using linear equations relating diabetes prevalence to primary outcomes or CHD rates.


Introduction
Key prospective trials have demonstrated the effectiveness of long-term control of conventional risk factors (RFs) to prevent cardiovascular (CV) events. Next to decreasing tobacco use and physical inactivity, indisputable gains were achieved by targeting hypertension and hypercholesterolemia. Nevertheless, there remained a high residual risk of incident CV events in control and comparator arms of these trials, even in patients receiving appropriate standard of care [1][2][3][4]. This residual risk is driven by non-modifiable RFs (age; gender; familial or genetic features; and diabetes) and by modifiable conventional or emerging RFs (eg. atherogenic dyslipidemia; remnant lipoproteins; hyperglycaemia; hyperinsulinaemia; metabolic syndrome; subclinical inflammation; and chronic kidney disease).
Based on epidemiology and prospective studies, type 2 diabetes mellitus (T2DM) significantly increases the absolute risk of developing coronary heart disease (CHD), and confers a higher residual risk of large and small vessel damage. In the microcirculation, such risk is directly related to hyperglycaemia, whereas in large vessels, this residual risk is linked to hypertension, low-density lipoproteins (LDL); non-LDL dyslipidemias; and other metabolic comorbidities [5][6][7][8][9][10]. As a result, having T2DM, either individually or at a sub-group level (within a cohort or population) increases residual CV risk to an extent that needs to be determined. Since residual risk varies considerably from one study to another, such an evaluation would require going beyond comparing CV outcomes rates in diabetic vs. nondiabetic subgroups of individual trials.
The aim of this work was to establish equations relating baseline diabetes prevalence and incident CV events, based on comparator arms data of major clinical trials having investigated the potential CV benefit of various pharmacological or dietary interventions targeting, in the vast majority, lipids and lipoproteins. We performed a systematic meta-analysis of CV outcomes rates of those key prospective studies, for which the baseline proportion of diabetics was reported and, where available, studies having reported CV outcomes of diabetic subgroups   (Table 1).

Patients and methods
To be selected for inclusion, major clinical trials with CV outcomes had to meet three requirements: (i) the main purpose of the trial was to study the effect on CHD of a pharmacological or dietary intervention targeting lipids or lipoproteins, with CHD rates as sole primary outcome (PO), or with a major adverse CV event (MACE) composite PO comprising CHD; (ii) to focus exclusively on diabetic patients, or (iii) to report data on a sufficient number of diabetic patients from pre-/posthoc analyses of DM subgroups of the main trial. Among studies conducted non-exclusively in DM patients, eligible trials had to comply with ≥1 of the following criteria: (i) the main trial had a subgroup of patients already diagnosed with DM at baseline, whose proportion was deemed sufficiently representative (>15 %); or (ii) the trial enrolled at least 100 DM patients, regardless of on-study new-onset diabetes.
For each study, the following items were analyzed: CV risk category at baseline (primary prevention [PP], secondary prevention [SP] or mixed [PP-SP]); number of patients included; number and proportion of patients with DM at baseline; number of patients in the active or comparator arms; duration of follow-up; age at inclusion; number of males; DM type and duration; HbA 1c ; total cholesterol (TC); low-density lipoprotein cholesterol (LDL-C); highdensity lipoprotein cholesterol (HDL-C); non-HDLcholesterol (non-HDL-C); apolipoprotein B 100 (apoB); triglycerides (TG); type of pharmacological or dietary intervention; primary trial outcome; CHD outcomes (see Table 2 for CV outcomes categories); and CV events number and rates for each trial.
Results are presented as means (±1 standard deviation (SD)), or as proportions (%), with between-study range [BSR] described when needed. Linear regression was computed using the least-squares method. Results were considered statistically significant or non-significant (NS) for p <0.05 or p ≥0.05, respectively.
Among the 33 predominantly non-diabetic studies, a total of 14,732 and 12,604 events occurred in the comparator and treatment arms, respectively. On an annual basis, this was equivalent to an average rate of occurrence for the primary CV outcome Among the 14 studies focusing on diabetes, a total of 3,713 and 3,552 events occurred in the comparator and treatment arms, respectively. On an annual basis, this was equivalent to an average rate of occurrence for the primary CV outcome of 3.3 (2.5) %/year [BSR 1.1-9.6] (comparator) and 2.9 (2.4) %/year [BSR 0.8-9.1] (treatment), respectively.
In addition to PO rates, which include de facto CHD, we also examined CHD rate as a separate outcome [ Table 4 and Fig. 1  The relationship between proportion of diabetic patients at inclusion and PO or CHD rates was inferred on   . 1; lower panels).
Computing occurrence rates of PO and CHD in the comparator arms showed that the proportion of diabetics at inclusion predicted PO rates ranging from 3.12 %/year (no diabetic included) to 6.11 %/year (all patients diabetic). Predicted CHD rates depending on baseline diabetes prevalence ranged from 1.54 %/year (no diabetic included) to 6.85 %/year (all patients diabetic).
This implies that a cohort exclusively composed of diabetic patients would present a PO rate already increased by an absolute 3 %/year due to the mere fact of being diabetic at baseline. Such an out-of-hand absolute increase in events rate due to the diabetic state would further increase to 5.3 %/year when it comes to the risk of incident CHD ( Fig. 1; upper panels).
By relating incidence rates of PO and CHD in the treatment arms, it appears that the proportion of diabetics at inclusion predicts PO rates ranging from 2.65 %/year (no diabetic included) to 4.31 %/year (all patients diabetic). Predicted CHD rates based on diabetes prevalence ranged from 1.64 %/year (no diabetic included) to 5.13 %/year (all patients diabetic). It follows that a cohort exclusively composed of diabetic patients would present an on-treatment PO rate increased by an absolute 1.7 %/year solely due to the presence of DM at baseline. Such an absolute increase in events rate due to diabetes would further increase to 3.5 %/year for incident CHD risk ( Fig. 1; lower panels).
The comparison of these equations linking the proportion of diabetics and outcome rates in comparator vs. treatment arms allows for determining whether being diabetic (apart from the observation that it increases the absolute rate of occurrence of CV events) is associated with an idiosyncratic on-treatment clinical response. As for PO and CHD, diabetic patients were characterized by a clinical response that was better than that calculated for a non-diabetic population that would have been subject to the same therapeutic interventions. Thus, residual CV risk

Discussion
This meta-analysis shows that the presence of diabetics in a lipid-modifying trial is a determinant of CV events rate, the impact of which can be accurately assessed once known the proportion of diabetics enrolled, regardless of the CV risk category at baseline. Thus, the linear equations derived from this meta-analysis can be used to determine the absolute and relative enhancement of CV risk related to the inclusion of diabetics in a trial. Conversely, these algorithms can be used to estimate the proportion of diabetics to be included when designing a prospective study, in order to achieve a given number of CV events. Major guidelines recognize a higher risk of CHD in DM patients, even in situations of primary prevention, as compared to non-diabetic subjects. The events rates in the comparator arms of randomized controlled trials and the meta-analyses of key statin trials show that CHD risk from hypercholesterolemia in non-diabetic   Table 1 for acronyms definition and trials' references, and Table 2 and Table 4 for primary outcomes classification and description as a subgroup, in a clinical trial not focusing on diabetes. This follows from the fact that studies focusing on diabetes had a lower CV risk at inclusion, as well as lesser PO or CHD events during the study. As a result, the impact of DM on CV events must be qualified according to whether it is evaluated from diabetic subgroups of cohorts followed in cardiology (mostly in a macrovascular setting), or whether it is obtained in patients from clinical trials focusing on nutrition or diabetes (usually dealing with glycemic control or microvascular risk reduction). In addition, variation in residual risk related to T2DM in key trials may result from inhomogeneity in inclusion criteria; varying baseline CV risk; individual differences in diabetes duration or severity; and heterogeneous RFs exposure among diabetics.
As opposed to what occurs in microvessels, and unlike a widely held view about it, residual risk targeting large vessels is related to a limited extent only by hyperglycaemia in (pre)diabetes states. Rather, the accrued macrovascular risk is associated with the common form of T2DM (that is to say the one that expresses a MetS phenotype, including insulin resistance and hyperinsulinemia). The common pathogenic factors underlying the observed association between hyperglycemia and CHD are involved either (i) at the onset of diabetes (promoting B-cell decompensation or altering one or two variable(s) of the hyperbolic product between insulin secretion and insulin sensitivity), and/or (ii) because they embody cardiometabolic comorbidities that increase the macrovascular risk regardless of glucose levels.
It should be noted that the slopes of the relationships between CV events and percentage of included diabetics were less marked when it came to comparing PO vs. CHD events rates, both in comparator and treatment arms, on one hand, or when it came to comparing PO or CHD events rates in treated arms vs. comparator arms, on the other hand. These observations suggest (i) that the presence of diabetes at baseline has less adverse effect on the occurrence of certain constituents of the PO, such as allcause deaths or coronary revascularization; and (ii) that diabetic patients derive more benefits from the different treatment approaches studied than non-diabetic patients as regards the occurrence of macrovascular events [91]. In this meta-analysis, we have not distinguished between studies on the basis of pharmacological or nutritional interventions, since we based our findings on patients from comparator arms, usually receiving a placebo or standard care. When comparing less recent (published <2005) and more contemporary studies (published ≥2005), a decrease in absolute and relative events rates was observed (-28 % and -1 % respectively), suggestive of a reduction in exposure to CV RFs over time and/or of improved overall CV management. Such changes were however not significant and further, diabetic patients benefited less from this trend, reducing the absolute and relative rates by only -14 % and -0.7 %. It seemed therefore appropriate to include all studies in this analysis regardless of publication year.
It is noteworthy that the increased risk of CV events due to the presence of a subgroup of diabetics had a pretty similar slope, whatever the CV risk category at baseline. It follows that the excess CV risk associated with the inclusion of people with diabetes in a lipidmodifying trial is relatively independent of study design, expanding the applicability of equations derived from this meta-analysis. There exists a positive relationship between biomarkers and occurrence of CV events [92]; our meta-analysis suggests that documenting the frequency of enlisted T2DM patients can also be used as surrogate biomarker predicting a non-modifiable component of residual CV risk. Considering that our analysis focused on populations enrolled in the comparator arms of mostly LMT studies, it would be interesting to determine the impact on residual risk arising from enlistment of diabetics in clinical trials testing several interventions in primary care [93].
This study has several limitations. Firstly, the risk estimates attributed to DM were not adjusted for age or other CV RFs comorbid to T2DM and, as in all systematic collection of published data, there is always a potential bias related to publications [94]. Secondly, the adequacy of these equations to predict CV outcomes has not been independently validated in a prospective context. Thirdly, for reasons related to the design and reporting of individual studies, it was not feasible to derive specific equations applicable to T1DM vs. T2DM subgroups, or to newlydiagnosed vs. long-standing T2DM patients [95]. We were not able to analyze the potential influence of glycaemic control in diabetic subgroups at baseline, due to the low reporting rate of HbA 1c values [96]. Finally, we did not examine, for reasons of brevity, the relationship between diabetes prevalence and non-CHD outcomes, such as HF, which will require dedicated meta-analyses [97].

Conclusion
This study attempted to quantify the impact of diabetes on the occurrence of CV events during a lipid-modifying trial, based on the proportion of known diabetics included. The component of absolute and relative residual CV risk associated with diabetes can be measured from linear equations relating diabetes prevalence to primary outcomes or CHD rates. Such calculations may help clinical study designers when selecting inclusion criteria; cohort size; and planned diabetics' enrollment, so as to achieve sufficient CV events over time.