At the heart of the problem - A person-centred, developmental perspective on the link between alcohol consumption and cardio-vascular events

A longitudinal evaluation of alcohol intake throughout adulthood and colorectal cancer risk

BACKGROUND

Alcohol intake is an established risk factor for colorectal cancer (CRC); however, there is limited knowledge on whether changing alcohol drinking habits during adulthood modifies CRC risk.

OBJECTIVE

Leveraging longitudinal exposure assessments on alcohol intake at different ages, we examined the relationship between change in alcohol intake and subsequent CRC risk.

METHODS

Within the European Prospective Investigation into Cancer and Nutrition, changes in alcohol intake comparing follow-up with baseline assessments were investigated in relation to CRC risk. The analysis included 191,180, participants and 1530 incident CRC cases, with exclusion of the first three years of follow-up to minimize reverse causation. Trajectory profiles of alcohol intake, assessed at ages 20, 30, 40, 50 years, at baseline and during follow-up, were estimated using latent class mixed models and related to CRC risk, including 407,605 participants and 5,008 incident CRC cases.

RESULTS

Mean age at baseline was 50.2 years and the follow-up assessment occurred on average 7.1 years later. Compared to stable intake, a 12 g/day increase in alcohol intake during follow-up was positively associated with CRC risk (HR = 1.15, 95%CI 1.04, 1.25), while a 12 g/day reduction was inversely associated with CRC risk (HR = 0.86, 95%CI 0.78, 0.95). Trajectory analysis showed that compared to low alcohol intake, men who increased their alcohol intake from early- to mid- and late-adulthood by up to 30 g/day on average had significantly increased CRC risk (HR = 1.24; 95%CI 1.08, 1.42), while no associations were observed in women. Results were consistent by anatomical subsite.

CONCLUSIONS

Increasing alcohol intake during mid-to-late adulthood raised CRC risk, while reduction lowered risk.

The J-shaped curve-conceptual and methodological challenges

Purpose

The purpose of this paper is to review the conceptual and methodological challenges of a J-shaped association between alcohol consumption (AC), coronary heart disease (CHD) and all-cause mortality. In associated papers in this journal, Skovenborg et al., 2021 reviews the evidence for the J-shaped curve, and Ellison et al., 2021 examines the advantages and drawbacks of Mendelian randomization studies of the J-shaped curve.

Design/methodology/approach

A number of methodological problems are common in observational research in general, and some of the methodological problems suggested for the J-shaped alcohol-CHD-associations are discussed. The extent of the methodological problems in studies of the J-shaped curve is reviewed, and the possibility that the J-shaped curve is an artifact created by reverse causality and residual confounding is discussed. Further, the issue of interaction with drinking pattern and type of alcohol is discussed.

Findings

Imprecise categorization of alcohol intake information seems to have had little effect on the J-shaped alcohol-CHD-associations, nor has it affected the ability of these studies to show increasing mortality from a range of causes with increasing AC. The problem of “sick quitters” has been resolved by large studies using lifelong abstainers or infrequent drinkers as reference group. Many studies lack information on drinking patterns with regard to regular, moderate consumption versus binge drinking. Stratified analyses by important risk factors for CHD have not significantly changed the J-shaped association observed in most epidemiologic studies.

Originality/value

Potential biases and residual confounding probably do not overcome the J-shaped alcohol-CDH-association observed in most epidemiologic studies; however, the existence of a J-shaped curve is challenged by some degree of uncertainty. The actual review together with the associated papers by Skovenborg et al., 2021 and Ellison et al., 2021 offers a possibility to “update your priors” and achieve greater certainty when giving your patients information on the pros and cons of alcohol intake.

An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software

The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.