Associations of Alcohol Consumption with Cardiovascular Disease-Related Proteomic Biomarkers: The Framingham Heart Study

Enhancing selection of alcohol consumption-associated genes by random forest

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Circulating metabolites may illustrate relationship of alcohol consumption with cardiovascular disease

BACKGROUND

Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD.

METHODS

Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine, and liquor on average of 19 years in 2428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure).

RESULTS

We identified 60 metabolites associated with cumulative average alcohol consumption (p < 0.05/211 ≈ 0.00024). For example, 1 g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta = 0.023 ± 0.002, p = 6.3e - 45) and phosphatidylcholine (e.g., PC 32:1, beta = 0.021 ± 0.002, p = 3.1e - 38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11 (95% CI = [1.02, 1.21], p = 0.02) vs 0.88 (95% CI = [0.78, 0.98], p = 0.02).

CONCLUSIONS

We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

Alcohol consumption and epigenetic age acceleration across human adulthood

The alcohol-associated biological aging remains to be studied across adulthood. We conducted linear regression analyses to investigate the associations between alcohol consumption and two DNA methylation-based biological age acceleration metrics in 3823 Framingham Heart Study participants (24-92 years and 53.8% women) adjusting for covariates. We also investigated whether the two epigenetic aging metrics mediated the association of alcohol consumption with hypertension. We found that higher long-term average alcohol consumption was significantly associated with biological age acceleration assessed by GrimAge acceleration (GAA) and PhenoAge acceleration (PAA) in middle-aged (45-64 years, n = 1866) and older (65-92 years, n = 1267) participants while not in young participants (24-44 years, n = 690). For example, one additional standard drink of alcohol (~14 grams of ethanol per day) was associated with a 0.71 ± 0.15-year (p = 2.1e-6) and 0.60 ± 0.18-year (p = 7.5e-4) increase in PAA in middle-aged and older participants, respectively, but the association was not significant in young participants (p = 0.23). One additional standard serving of liquor (~14 grams of ethanol) was associated with a greater increase in GAA (0.82-year, p = 4.8e-4) and PAA (1.45-year, p = 7.4e-5) than beer (GAA: 0.45-year, p = 5.2e-4; PAA: 0.48-year, p = 0.02) and wine (GAA: 0.51-year, p = 0.02; PAA: 0.91-year, p = 0.008) in middle-aged participant group. We observed that up to 28% of the association between alcohol consumption and hypertension was mediated by GAA or PAA in the pooled sample. Our findings suggest that alcohol consumption is associated with greater biological aging quantified by epigenetic aging metrics, which may mediate the association of alcohol consumption with quantitative traits, such as hypertension.

Circulating Metabolites May Illustrate Relationship of Alcohol Consumption with Cardiovascular Disease

Background

Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD.

Methods

Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine and liquor on average of 19 years in 2,428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure).

Results

We identified 60 metabolites associated with cumulative average alcohol consumption (p<0.05/211≈0.00024). For example, one g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta=0.023±0.002, p=6.3e-45) and phosphatidylcholine (e.g., PC 32:1, beta=0.021±0.002, p=3.1e-38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11(95% CI=[1.02, 1.21],p=0.02) vs 0.88 (95% CI=[0.78, 0.98], p=0.02).

Summary

We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study

BACKGROUND

The relations of alcohol consumption and gene expression remain to be elucidated.

MATERIALS AND METHODS

We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants.

RESULTS

We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (β = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (β = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (β = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes.

CONCLUSIONS

We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.