In this study, we utilised prospectively collected longitudinal data on alcohol consumption from six cohorts to examine the association of 10-year drinking trajectories and risk of developing and/or dying from CHD. Through iterative modelling that accounted for heterogeneity across the datasets and potential confounders of the alcohol–CHD association, our work has shown that incident CHD risk is significantly higher amongst both non-drinkers and former drinkers compared to drinkers who always adhered to lower-risk intake guidelines. We have also demonstrated that the stability of such adherence is pertinent. Participants who mostly drank moderately, but not persistently so, had greater risk of incident CHD compared to their consistently moderate drinking counterparts. In terms of CHD mortality, former drinkers and consistent non-drinkers were again found to be at higher risk, although the effect for the persistent abstainers was somewhat attenuated after adjustment for smoking status and socioeconomic status. We found no evidence that heavy drinking was associated with risk of CHD, and reasons for this are discussed below. Overall, the findings from this study support the notion of a cardioprotective effect of moderate alcohol intake relative to non-drinking. However, crucially, stability in the level of alcohol consumption over time appears to be an important modifier of this association.

The use of repeat measurements of alcohol consumption in lieu of a one-time assessment has enabled us to measure the stability of consumption over time and to address the call for research on the role of intake trajectories in CHD onset [43]. Through this approach, we have demonstrated how intermittent adherence to lower-risk drinking guidelines, i.e. an inconsistently moderate intake, is associated with an increased risk of incident CHD. This provides some support for the proposal that variability in alcohol intake levels can offset the potential protective effects of moderate drinking [2, 20]. An association was found between inconsistently heavy drinkers and fatal CHD, although the wide confidence bounds and weakening of the association following maximal adjustment for confounding factors limits interpretation of this effect. It may be that unstable drinking patterns reflect wider lifestyle changes across the life course, and possibly even the impact of periods of ill health or life stress. The effects were further attenuated when adjustment was made for clinical characteristics, namely BMI and hypertension, suggesting that these may both act as potential pathways through which unstable drinking trajectories are associated with CHD. The impact of BMI could also reflect the role of other lifestyle choices, such as diet and exercise.

Access to prospectively recorded alcohol intake data across multiple assessment times has also allowed the current study to distinguish recent abstainers from longer-term non-drinkers in a manner that helps reduce the potential for recall bias. Such bias can occur where drinking behaviour is retrospectively measured at a single time point [44], a technique commonly used in alcohol epidemiology research. In line with the sick-quitter hypothesis [14], former drinkers were found in the present study to have an elevated risk of both incident and fatal CHD. These effects were attenuated following adjustment for the clinical covariates, suggesting that poor health may explain former drinkers’ increased likelihood of developing CHD and perhaps may even have motivated the decision to abstain itself. Consistent non-drinkers, however, did also have a significant risk of incident CHD after adjustment for potential confounders, and although the error bounds were wider, their CHD mortality estimate was equivalent to that of former drinkers, implying that short- and long-term abstinence are both associated with an increased risk of CHD.

Despite our finding of parity in CHD risk amongst non-drinkers and former drinkers in the pooled sample analyses, previous research has suggested that there may be age-dependent differences in this association. However, this observation was based on studies in which abstinence was determined retrospectively from a single baseline assessment [42], in contrast to the repeated measures design used in the current study. When we stratified our sample by age, the associations between both abstainer groups and incident CHD risk was comparable for younger (≤55 years) and older (>55 years) participants. As similar results were also observed for risk of fatal CHD, our findings challenge the argument that there are age-dependent differences between long-term and more recent abstainers, yet the wide confidence bounds around the fatal CHD risk estimates for those aged 55 or below arguably restricts such inferences. A divergence between the age groups was found for inconsistently moderate drinkers. Such drinkers in the older subsample had a significantly elevated risk of incident CHD, an effect that was not evident in the younger group. Older participants may have been more likely to experience lifestyle changes that influenced their drinking habits. Retirement, for example, is known to co-occur with increases in alcohol intake [45, 46], particularly amongst existing drinkers [47].

It has also been suggested that the J-shaped association between alcohol consumption and CHD may be more pronounced in women than men [23], a theory that our study supports in part. Whilst both male and female former drinkers had significantly increased risk of incident CHD, only female consistent non-drinkers showed such an elevated risk. Female non-drinkers (both long term and more recent abstainers) were similarly at risk of fatal CHD, even after maximal adjustment for confounding factors. Research has also suggested that alcohol intake may increase oestrogen levels in women, which in turn act as a protective factor against CHD [48]. Male former drinkers also showed significantly greater risk of CHD mortality than consistently moderate drinkers after accounting for age and other characteristics, but this difference was attenuated once the estimates were adjusted for lifestyle behaviours such as smoking. This suggests that these additional covariates may play a greater role than drinking in the occurrence of fatal CHD events for males. Previous literature has proposed that smoking can offset any alcohol-related differences in CHD mortality risk amongst men [49].

In the present study, no association with CHD risk was found for consistently heavy drinkers. Stable patterns of heavy drinking may reflect continued good health across the assessment interval [50], the converse of the sick quitter type. Statistically significant associations between high levels of alcohol intake and CHD onset risk have been observed in some previous research [21, 51], but not persistently so [52,53,54]. Although our study identified heavy drinkers across all cohorts, only a limited number were in the female sample, potentially limiting statistical power in their analysis, and by extension, in the non-stratified analysis. This issue of small counts for female heavy drinkers has similarly constrained earlier work in this area [1]. Particularly heavy drinkers may be under-represented in the datasets utilised in this study, which could have biased downwards the estimate of association between heavy intake and cardiovascular risk. If further data are available, it may be possible to explore alternative intake thresholds and validate the present study’s findings. Similarly, additional data may enable the disaggregation of CHD phenotypes, which could provide more nuanced insights into how heavy drinking is associated with different variants of the disease [55]. Consequently, the interpretation of the absence of an effect amongst heavy drinkers in the current study should be done cautiously, particularly in light of the known wider health impact of heavy alcohol intake levels [56].

There are additional limitations to our study that warrant consideration. For example, selection bias may have occurred [57], in which participants dropped out of the cohort studies before the outcome assessment period. It is possible that some heavy drinkers could have experienced adverse health outcomes at a younger age and discontinued their research participation. Particularly heavy drinkers are already known to be under-sampled in population-level surveys [32, 58], so caution is required in drawing inferences about such elevated intake levels. Similarly, information on alcohol intake prior to the exposure assessment period was not consistently available, so the long-term abstainers modelled in this current study may include some participants who ceased drinking prior to recruitment. Given that the current work included only cohort studies for which we had access to individual-level data, the concept of availability bias [59] is also pertinent. Access to additional datasets may help further validate our findings. Such increased sample sizes would also permit more detailed examination than was possible in the current study into the intake variance that occurs amongst drinkers who are inconsistent in their adherence to lower-risk drinking guidelines. Relatedly, the identification of drinking trajectories in the present study was based on drinking volume only and so we were not equipped to look at the role of episodic heavy drinking [60]. Further clarification of the alcohol–CHD association may be achieved where sufficient data are available on other characteristics of consumption, such as drinking frequency. All cohorts included in the current study used self-report for determining alcohol intake; although this is vulnerable to estimation errors, research has shown that drinking data collected through this method remains valid and reliable [44, 61]. A further design consideration in interpreting the current study’s results is the harmonisation of data across the different cohort datasets. Establishing equivalent variable definitions in the harmonisation of data constrains the level of detail and raises the possibility of residual confounding. For example, it was not possible to establish a more nuanced smoking variable due to data availability and so there is a possibility of residual confounding by smoking intensity. Relatedly, although an equal number of intake measurements was used across cohorts to establish intake trajectories, the observed time intervals varied (see Section S2 of Additional file 1). While adjustment was made through inclusion of assessment interval length in the regression modelling, it remains possible that limitations in the cohort data harmonisation may have introduced bias. Although country-specific drink conversions were used to calculate alcohol intake [31], there remains potential differences between GAZEL and the other cohorts, such as the possible influence of dietary differences for which residual confounding could also have occurred [62]. The French paradox, for example, implies that that there is an inverse relationship between saturated fat intake and CHD onset risk that is specific to France [63], a relationship in which alcohol debatably plays a role [64]. However, sensitivity analyses showed that the exclusion of GAZEL data did not modify the current study findings. Moreover, the use throughout this study of mixed-effects modelling has helped account for data clustering and thereby helped improve the validity of the results obtained.