Alcohol consumption and the risk of rheumatoid arthritis

doi:10.21203/rs.3.rs-4619975/v1

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Alcohol consumption and the risk of rheumatoid arthritis

https://doi.org/10.21203/rs.3.rs-4619975/v1

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Background

Though accumulated evidence has indicated an inverse relationship between alcohol intake and rheumatoid arthritis (RA) risk, it remained uncertain whether such association was causal or biased by confounding. We aimed to explore the dose-response relationship and the potential causality between alcohol consumption and RA risk by using both prospective study and Mendelian randomization (MR).

Methods

We first performed an updated meta-analysis on the association between alcohol consumption and the RA risk in PubMed and Web of Science database. Then we assessed the association of alcohol intake-related phenotypes with RA risk based on UK Biobank. The association was examined using Cox regression, while the potential non-linear relationship was modeled by restricted cubic splines (RCS). Stratification analyses based on sex, age, and ethnicity, as well as a series of sensitivity analyses were further performed. In addition, linkage disequilibrium score regression (LDSC) was used to calculate the heritability and genetic correlation between these traits, and two-sample MR was employed to assess the association of genetically predicted alcohol consumption with the risk of RA.

Results

Findings from the meta-analysis suggested an inverse association between alcohol intake and RA (relative risk (RR): 0.85; 95% confidence interval (CI): 0.78, 0.83). Similarly, in UK Biobank cohort, one standard deviation increases of alcohol intake per day was related to a 6% lower risk of RA (hazard ratio (HR): 0.94; 95% CI: 0.91, 0.97). RCS models revealed a J-shaped dose-response association between alcohol consumption and RA, with moderate intake associated with a reduced risk. However, alcohol use disorder (AUD) was associated with a 30% higher risk of RA (HR: 1.30; 95% CI: 1.08, 1.56). A sex- and age-dependent association of alcohol intake and RA was observed in stratification analysis. Findings from LDSC and MR both suggested AUD as a potential risk factor for RA, while no statistically significant association between alcohol consumption and RA was observed.

Conclusion

Our study revealed a dose- and sex- dependent pattern of alcohol consumption on RA risk. Though a slightly protective effect was observed during a specific range of alcohol consumption, it should not be recommended as a prevention strategy for RA.

alcohol consumption

dose-response relationship

linkage disequilibrium score regression

Mendelian randomization

meta-analysis

rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by persistent symmetric polyarthritis that primarily affect the joints and can cause progressive disability and socioeconomic burden [1]. Based on data from the Global Burden of Diseases, Injuries, and Risk Factors study, the age-standardized point annual incidence rates of RA were 14.9 (95% uncertainty interval (UI) 13.3, 16.4) in 2017, which increased by 8.2% (95% UI 5.9, 10.5) since 1990 [2]. Increasing incident RA cases worldwide suggested the importance of defining its potential etiology which can contribute to the prevention.

RA is a heterogeneous disease, with both genetic and environmental risk factors involved in its onsets and development [3]. To date, genetic susceptibility, tobacco smoking as well as other modifiable lifestyle behaviors has been proposed as the potential risk factors for RA [3].

Alcohol is commonly believed to have negative effects on health and associated with an elevated risk of diseases, such as cancer, liver disease, and tuberculosis [4–7]. It accounts for approximately 3 million deaths each year globally, and 13.5% of all deaths result from alcohol use in people age 20–39 years [8]. However, in recent years, some studies found that moderate intake of alcohol was associated with a reduced risk of illness, including RA. For example, a meta-analysis comprising six case–control (3,564 cases; 8,477 controls) and three cohort studies (84,421 individuals) published in 2013 showed that alcohol drinkers had a lower risk of RA, compared with non-drinkers (odds ratio (OR): 0.78; 95% CI 0.63, 0.96) [9]. Similarly, a dose-response meta-analysis published in 2014, which consisted of 195,029 participants and 1,878 RA cases, indicated that low to moderate alcohol use was inversely associated with the risk of RA (relative risk (RR): 0.86; 95% confidence interval (CI): 0.78, 0.94) [10]. On the contrary, persistent heavy drinking can lead to an alcohol use disorder (AUD), a problematic pattern of alcohol use accompanied by clinically significant impairment or distress [11]. As a prevalent mental disorder worldwide, 237 million adult males (8.6% of adult males) and 46.0 million adult females (1.7% of adult females) were reported to have an AUD in 2016, showing a large variation by sex [8]. AUD is not only associated with a higher risk of other mental health problems and significant morbidity and mortality, but also was one of the leading contributors to the burden of diseases and injury caused by alcohol, accounting for 13.9% of all alcohol-attributable disability-adjusted life years[8]. A recent US cohort study also found that hospitalizations due to AUD increased to 3.5-fold from 1998 to 2014 in individuals with RA [12]. Overall, previous studies suggested a potential relationship between alcohol use and the risk of RA, while the evidence from observational studies is not always consistent and is prone to be biased by confounders and reverse causality.

Such limitation may be obviated by using genetic epidemiological approaches such as linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR). LDSC quantifies the contribution of polygenic effects and confounding by exploring the relationship between association test statistics and linkage disequilibrium (LD) based on genome-wide association study (GWAS) summary statistics, and has been widely used to assess the genetic correlation between multiple traits [13, 14]. MR is also a genetic-based approach that utilizes genetic variants as proxies for a proposed exposure (ie, alcohol use) to assess the potential causal association with an outcome (ie, RA). Since the genetic variants are randomly assorted from parents to offspring during fertilization and conception, the association observed by MR is less susceptible to confounding and reverse causation bias [15].

To further clarify the association of alcohol use and the risk of RA, we first performed an updated meta-analysis on the association between alcohol intake and the risk of RA. Then, we assessed the associations of different drinking behaviors with incident RA by using data from UK Biobank. Furthermore, we applied LDSC and traditional two-sample MR approaches to measure the association of alcohol use and the risk of RA.

Dose-response meta-analysis of observational studies

We performed a systematic review and meta-analysis on prospective studies that explores the association between alcohol consumption and the risk of RA in PubMed and Web of Science datasets from Jan 2013 to Jan 2023. The following keywords were used to collect relevant literatures: “beer OR wine OR liquor OR alcohol OR ethanol) AND (rheumatoid arthritis)” in PubMed and “(TS = (beer OR wine OR liquor OR alcohol OR ethanol)) AND TS = (rheumatoid arthritis)” in Web of Science. Studies fulfilling the following criteria were retrieved in meta-analysis: 1) prospective cohort or nested case–control study design; 2) exploring the association of alcohol consumption and the risk of RA; 3) reporting the results with hazard ratios (HRs), RRs, or ORs and corresponding 95% CIs, or providing sufficient data to calculate them. Title and abstract were initially reviewed, and studied not related with the association of alcohol and RA were excluded. If the study population overlapped between several studies, the one with the largest sample size was selected. Two authors independently reviewed the retrieved articles and included the studies that were eligible for meta-analysis. Disagreements were resolved through discussion among researchers.

The following information was extracted and recorded onto a standardized form: first author, year, design of study, region, study outcome (ie, seropositive RA, rheumatoid factor positive RA), alcohol consumption level, gender, number of cases and total participants, adjusted variables, follow-up year (if applicable), and the effect sizes with 95% CIs of different alcohol consumption categories. We combined the studies based on different alcohol consumption levels. For studies that reported more than one consumption levels other than reference (the lowest group), each subgroup was combined into a single estimate as high category, and then the pooled estimates of each individual study were further combined. In addition, meta-analyses on the highest group vs. low; moderate groups vs. low; drinking vs. no drinking were performed to assess the effect of alcohol consumption level on RA. The quality assessment was conducted by the same authors using the modified Newcastle–Ottawa Scale[16, 17].

UK Biobank participants

UK Biobank is a population-based prospective cohort study which collected deep genetic and phenotypic data of over 500,000 individuals from across the United Kingdom, aged 40–69 at recruitment between 2006 and 2010 [18]. Socioeconomic demographic factors, lifestyle and other health-related information were collected through self-completed questionnaires and physical measures at recruitment. Biological samples in blood, urine and saliva were also collected at baseline. Participants were genotyped with UK Biobank Axiom Array, and the 1000 Genomes Project (phase 3) was used as a reference panel for imputation. More details about data acquisition process have been described previously [19]. The UK Biobank project was approved by the North West Centre for Research Ethics Committee (Ref 11/NW/0382); and all participants provided informed consent.

Outcome assessment

International Classification of Diseases (ICD)-9 codes and ICD-10 codes of RA were used to identify incident RA cases (Supplementary Table 1). The study end point was defined as the occurrence of RA (March 29th 2021), death, or loss of follow-up, for which came first.

Measurement of exposure and covariables

In the baseline survey, participants were asked to self-report the frequency of drinking alcohol by answering the touchscreen question "About how often do you drink alcohol?". The response items include: “never”, “special occasions only”, “one to three times a month”, “once or twice a week”, “3–4 times a week”, and “daily or almost daily”. Drinking status was classified as “previous”, “current”, and “never”. Then, based on the frequency data and information on alcohol type (red wine, champagne, beer plus cider, spirits, fortified wine, and other alcoholic drinks), we constructed the following alcohol variables: alcohol intake per day (g), drink group, whether over weekly limits by using the methods developed by Kiran et al [20]. Specifically, 1 standard American measures of alcohol consumption per day equivalent to 2 g of alcohol. The total alcohol intake per day (g) was transformed based on the following formula: 1 pint of beer or cider = 1.14 measures, 1 standard glass of white/red wine = 1.2 measures, 1 glass of fortified wine = 1.01 measures, 1 shot of spirits or liquor = 0.57 measures, 1 glass of other alcohol drinks = 0.86 measures. In addition, alcohol intake per day (g) was standardized as a proxy of alcohol intake. As a categorical variable, drink group was classified as abstainers (0 drinks/week), light (0-8.4 drinks/week), moderate (8.4–15.4 drinks/week), heavy (15.4–24.5 drinks/week) and abusive (> 24.5 drinks/week)[21]. Over daily limits were defined as > 2 standard drinks per day for men and > 1 drinks/week for women. Individual who below the weekly limits was classified as not over daily limits. The AUD cases were defined as alcohol abuse or dependence based on ICD-9 and ICD-10 codes (Supplementary Table 1). Cases of RA were excluded if the disease record prior to the diagnosis of AUD.

The following covariables (Supplementary Table 2) collected at baseline were included in our analysis: age, sex, body mass index (BMI), education (high, middle, other), ethnic background (White British, Asian or Asian British, Black or Black British, mixed or other ethnicity), high blood pressure, insomnia (never or rarely, sometimes, usually), physical activity (no, yes), smoking status (never, previous, current), and townsend deprivation index. Townsend deprivation index was divided into four quartiles ( ≤ − 3.64, (− 3.65; −2.14], (− 2.14; 0.55], > 0.55). Participants who reported “Prefer not to answer” or “Do not know” for the above covariables were set as missing indicators.

Summary-level genetic association data and instrumental variables selection

Summary level statistics for alcohol consumption were obtained from the largest meta-analysis of GWASs compromising 941,280 individuals of European ancestry [22]. Alcohol consumption, measured as drinks per week in this study, was defined as the average number of drinks a participant reported drinking each week, aggregated across all types of alcohol [22]. Midpoint of the range were used if a study recorded response ranges in binary. Besides, the exposure was left-anchored at 1 and log-transformed prior to analysis to obviate the bias caused by outliers. A total of 99 genome-wide significant (P < 5×10^− 8) single nucleotide polymorphisms (SNPs) associated with drinks per week were identified in the GWAS, and 93 SNPs not in LD (r² < 0.1) were included as IVs.

Drinks per week reflects individual’s general drinking behavior, while AUD represents a pathological pattern of using alcohol. Thus, we obtained 13 genetic variants associated with AUD as the exposure from a GWAS based on 202,004 participants (34,658 cases; 167,346 controls) recruited from Million Veteran Program[23]. Individuals with alcohol dependence or abuse diagnosed by either ICD-9/10 code were included as AUD cases. In addition, we excluded IVs that overlapped with drinks per week associated IVs, and referred them as AUD conditioning on drinks per week.

The summary-level data of RA was obtained from a GWAS meta-analysis consisting of 14,361 RA cases and 43,923 controls from 18 studies of Europeans [24]. All patients with RA met the 1987 criteria of the American College of Rheumatology [25] for RA diagnosis, or were diagnosed by a professional rheumatologist [24]. Besides, the GWAS summary data for drinks per week and AUD was also obtained for genetic correlation analysis.

Statistical analysis

Meta-analysis of prospective observational studies

Heterogeneity across individual studies was assessed by Cochran’s Q test and I² statistics[26]. A random-effects model (DerSimonian and Laird method) was used (I² ≥ 50%) if no substantial heterogeneity was observed; otherwise, a fixed-effects model (inverse variance method) was applied [27]. Since the incident rate of RA is relatively low, RRs are regarded as surrogate of ORs and HRs [28]. Begg’s and Egger’s test were utilized to evaluate the potential publication bias. Leave-one-out analysis were performed to assess the robustness of results.

Stratification analyses were also conducted based on study design (nest-cohort, cohort), region (Asia, Europe, America), RA subtypes (seropositive or seronegative; rheumatoid factor positive or rheumatoid factor negative), and alcohol subtypes (beer, wine, liquor).

For dose-response meta-analysis, if the total number of person-year or cases were reported but the data of each tertile or quintile were not available, the study would be excluded from dose-response analysis. If the highest category was open-ended, we assumed the dose as 1.2 times as the low bound of this category [10]. We used the method proposed by Greenland and Longnecker (GL method) to estimate the linear trend by using generalized least-squares regression [29, 30]. The lowest exposure level was served as the referent group in dose-response analysis. The nonlinear correlation between alcohol intake and RA risk was assessed by restricted cubic splines (RCS) with four knots at 5%, 35%, 65% and 95% of the distribution in a fixed model [30]. P-values of linearity and nonlinearity were obtained by testing the joint null hypothesis that regression coefficients of the second spline was equal to zero [30]. Meta analyses were performed by STATA (SE).

Observational analysis

In univariable analysis, continuous variables were presented in mean ± standard deviation (SD) and analyzed using t-test or the analysis of variance (ANOVA), while categorical variables were presented in number and percentage (%) and analyzed using χ² test.

Cox proportional hazards model was further used to assess the five alcohol-related phenotypes (alcohol intake frequency, drink per week, drinking status, over weekly limits, and AUD) in relation to the risk of RA. The HRs and corresponding 95% CIs were documented. Model 1 was the multivariable model for the association between each alcohol-drinking phenotypes and risk of RA that adjusted for age and sex. In Model 2, we further adjusted for BMI, education, physical activity, smoking status, and townsend deprivation index. For the relation of AUD and RA risk, Model 2 was additionally adjusted for alcohol intake per day.

To examine whether the effect of alcohol consumption on the risk of RA differed by age and sex, we performed stratification analysis bases on sex, age (categorized to < 50 year, 50–60 years, and >60 years), and ethnicity (White, Asian, Black, and mixed ethnicity).

Finally, to test the robustness of our findings, we performed sensitivity analysis. We repeated the primary analysis after removing the RA cases diagnosed with other autoimmune diseases, including gout, inflammatory bowel diseases, multiple sclerosis, systemic lupus erythematosus, and psoriasis. Analyses were conducted in R version 4.0.2. Two-sided P-values less than 0.05 were consider as statistically significant.

Genetic correlation analysis

We used the LDSC to measure the heritability of each alcohol-related phenotypes (drinks per week and AUD) and RA, and calculate the genetic correlation (r_g) between these phenotypes and RA [31]. Bonferroni-correction for multiple comparisons was applied, and a P-value < 0.008 (0.05/6) was considered significantly correlated. PLINK was used to calculated heritability and genetic correlation.

Two-sample MR analysis

Associations of IVs for alcohol phenotypes with RA risk were assessed by inverse variance weighted (IVW) method as primary analysis. IVW assumes that all genetic instruments are valid IVs or invalid but the overall pleiotropy is balanced to be zero, and pools the ratio estimates of the causal effect of exposure on outcome for each SNP to provide a causal estimate of exposure with outcome [32]. Heterogeneity across individual SNPs was evaluated by Cochran’s Q test, as P < 0.05 indicates a substantial heterogeneity, and the random IVW effects model was used; otherwise, a fixed-effects model was used. Sensitivity analyses were conducted using alternative MR methods, including weighted median, maximum likelihood–based methods, and MR pleiotropy residual sum and outlier (MR-PRESSO) test. Since IVW will be biased if one genetic instrument is invalid, we employed simple median method and weighted median method as supplementary methods, which provide a consistent estimate of the causal effect when at least 50% of IVs are valid. Simple median MR provides a middle ratio estimate, and weighted median MR was served as a complementary since simple median estimator may be insufficient when the precision of the individual estimates varies considerably [33]. The causal association of exposure and outcome estimated by direct maximization of the likelihood method is maximum likelihood–based method, which assumes all genetic variants to be same [34]. The MR-PRESSO test provides the corrected causal association of exposure on outcome after the removal of outlier variants with potential horizontal pleiotropy [35]. MR-Egger regression allows variants to be invalid, and were performed to detect directional pleiotropy, with a P-value of the intercept term < 0.05 suggesting a directional pleiotropic effect across the variants [36].

MR analysis was performed using “MendelianRandomization” [37] and “MR-PRESSO” [35] R packages in R v4.0.2.

Dose-response meta-analysis of observational study

A total of 896 and 1,526 relevant articles were identified in PubMed and Web of Science, respectively. After removal of 456 duplicate articles, 1,966 literatures were retrieved for further review. Among these, 1,950 studies failed to meet the inclusion criteria and were thus excluded. Full-text evaluation resulted in the exclusion of 8 studies, while 5 additional articles were retrieved from the previous meta-analysis. Thus, a total of 13 literatures were included in this meta-analysis, with a total of 2,780,630 participants and 12,307 RA cases. The detailed information about the included literatures was presented in Supplementary Table 3. Among these literatures, ten were cohort studies, and the remaining three were nested case-control studies. Nine studies were from Europe, two were from Asia and two were from the USA.

Compared with low level of alcohol consumption, participants with high alcohol intake had a 14% reduced risk of RA (RR: 0.86; 95% CI: 0.78, 0.93; P < 0.001 by random-effects model; I² = 63.4%, P heterogeneity = 0.001, Fig. 1A). Begg’s and Egger’s tests did not indicate significant publication bias (P_Begg = 0.583; P_Egger = 0.225). Compared with non-alcohol drinkers, alcohol drinkers had a lower risk of RA (RR = 0.86; 95% CI: 0.79, 0.94; P = 0.001 by random-effects model; I² = 65.3%, P heterogeneity < 0.001, Fig. 1B). When combining all drinking groups in the middle level, compared with the low drinking group, the moderate group had 18% decreased risk of RA (RR = 0.82; 95% CI: 0.78, 0.68; P < 0.001 by fixed-effects model; I² = 43.9%, P heterogeneity = 0.045, Fig. 1C). The similar negative association was also observed when comparing the highest drinking group with the low drinking group (RR = 0.75; 95% CI: 0.69, 0.82; P < 0.001 by fixed-effects model; I² = 26.0%, P heterogeneity = 0.182, Fig. 1D). In leave-one-out analysis, the association remained stable (Supplementary Fig. 1). When assessing potential non-linear relationship, we found that moderate alcohol intake (8 to 32 g per day) was associated with a lower risk of RA compared to low and high alcohol intake (Fig. 2).

In subgroup analyses, inverse associations of alcohol consumption with RA risk were observed in the subgroup of cohort study design (RR: 0.84; 95% CI: 0.76, 0.92; P < 0.001, Supplementary Table 4), Europe (RR: 0.84; 95% CI: 0.75, 0.95; P = 0.005), and seropositive RA, (RR: 0.86; 95% CI: 0.78, 0.95; P = 0.002) and beer intake (RR: 0.85; 95% CI: 0.73, 0.99; P = 0.042). No significant heterogeneities were observed between subgroups.

Basic characteristics of UK Biobank participants

The flowchart of participants selection was presented in Supplementary Fig. 2. Of the 499,943 participants, 6,721 developed RA during a median follow-up period of 12.07 years, with the incident density of 11.42 per 10,000-person year. The mean (SD) diagnosed age of RA was 59.48 (7.21). The baseline characteristics of eligible UK Biobank participants by incident RA status and HRs with corresponding 95% CIs are presented in Table 1. Briefly, participants with RA were more likely to be older, female, smokers, physically inactive, with a higher body mass index, had a lower education level and a lower Townsend depreciation index, had a shorter sleep duration and suffered more from insomnia.

The percentage of current alcohol drinkers in non-RA participants and incident RA cases were 92.04% and 86.90%, with the mean alcohol intake of 21.52 (21.49) g per day among non-RA participants and 18.83 (21.81) g among incident RA cases, respectively. For AUD status, we excluded 67 cases that were diagnosed with AUD after the diagnose of RA, retaining 6,654 incident cases in the analysis of AUD and RA. Among them, there were 9,947 (2.02%) and 154 (2.31%) AUD in non-RA participants and incident RA cases, respectively.

Observational associations between alcohol intake and RA risk

In univariate Cox regression analyses as presented in Table 1, previous drinking was associated with an increased risk of RA (HR: 1.14; 95% CI: 1.00, 1.30; P = 4.97×10^− 02), while current drinking was associated with a decreased risk of RA (HR: 0.60; 95% CI: 0.55, 0.66; P = 4.08×10^− 26). For alcohol intake frequency, we observed that higher frequency was associated a lower RA risk. In terms of AUD, it was related with a higher risk of RA (HR: 1.24; 95% CI: 1.05, 1.45; P = 9.63×10^− 3).

Table 1

Baseline characteristics of the cohort members, hazard ratios (HR) and corresponding 95% confidence intervals (CI) for the association with rheumatoid arthritis (RA).
Characteristic		Healthy individuals (N = 493222) N (%)	Incident RA cases (N = 6721) N (%)	Test statistics*	P-value	Total number (Event)	HR (95%CI) (Unadjusted model)	P-value
Sex (%)	Female	267027 (54.14)	4546 (67.64)	-33.95	< 0.001	499943 (6721)	Ref
	Male	226195 (45.86)	2175 (32.36)				0.58 (0.55, 0.61)	< 0.001
Age (Mean (SD))		56.47 (8.10)	59.48 (7.21)	486.97	< 0.001	499943 (6721)	1.05 (1.05, 1.06)	< 0.001
Body mass index (Mean (SD))		27.41 (4.78)	28.80 (5.57)	-23.38	< 0.001	499943 (6721)	1.05 (1.05, 1.06)	< 0.001
Education (%)	High	159323 (32.96)	1356 (20.75)	839.75	< 0.001	489881 (6534)	Ref
	Middle	216264 (44.74)	2822 (43.19)				1.54 (1.44, 1.64)	< 0.001
	Other	107760 (22.29)	2356 (36.06)				2.60 (2.44, 2.78)	< 0.001
Ethnicity (%)	White British	463478 (94.49)	6242 (93.61)	16.10	0.001	497180 (6668)	Ref
	Asian or Asian British	11161 (2.28)	182 (2.73)				1.23 (1.06, 1.43)	0.006
	Black or Black British	7863 (1.60)	139 (2.08)				1.35 (1.14, 1.59)	5.33×10^− 04
	Mixed or other	8010 (1.63)	105 (1.57)				0.98 (0.81, 1.19)	0.872
Townsend depreciation index quintile (%)	Q1	123512 (25.07)	1425 (21.22)	151.74	< 0.001	499321 (6721)	Ref
	Q2	123372 (25.04)	1539 (22.92)				1.09 (1.01, 1.17)	0.023
	Q3	123140 (25.00)	1666 (24.81)				1.19 (1.11, 1.27)	2.08×10^− 06
	Q4	122583 (24.88)	2084 (31.04)				1.51 (1.41, 1.61)	< 0.001
Physical activity (%)	No	139234 (28.28)	2479 (36.99)	247.02	< 0.001	499071 (6701)	Ref
	Yes	353136 (71.72)	4222 (63.01)				0.67 (0.63, 0.7)	< 0.001
Smoking status (%)	Never	269338 (54.93)	3064 (46.01)	218.07	< 0.001	497023 (6660)	Ref
	Previous	169273 (34.52)	2692 (40.42)				1.42 (1.35, 1.49)	< 0.001
	Current	51752 (10.55)	904 (13.57)				1.58 (1.47, 1.7)	< 0.001
Sleep duration (Mean (SD))		7.15 (1.11)	7.09 (1.39)	4.72	< 0.001	6623 (495780)	0.95 (0.93, 0.97)	2.35×10^− 06
Insomnia (%)	Never or rarely	119352 (24.27)	1047 (15.65)	537.16	< 0.001		Ref
	Sometimes	234791 (47.75)	2989 (44.67)			498447 (6691)	1.45 (1.35, 1.56)	< 0.001
	Usually	137613 (27.98)	2655 (39.68)			498447 (6691)	2.21 (2.05, 2.37)	< 0.001
High blood pressure status (%)	No	373737 (75.77)	4612 (68.62)	184.36	< 0.001	499943 (6721)	Ref
	Yes	119485 (24.23)	2109 (31.38)				1.44 (1.37, 1.52)	< 0.001
Alcohol drinker status (%)	Never	21712 (4.42)	462 (6.91)	243.33	< 0.001	498299 (6685)	Ref
	Previous	17438 (3.55)	414 (6.19)				1.14 (1.00, 1.30)	4.97×10^− 02
	Current	452464 (92.04)	5809 (86.90)				0.60 (0.55, 0.66)	4.08×10^− 26
Alcohol intake (g/d) (Mean (SD))		21.52 (21.49)	18.83 (21.81)	8.79	< 0.001	5440 (425504)	0.9987 (0.9985, 0.999)	1.17×10^− 30
Alcohol intake frequency (%)	Never	39297 (7.99)	880 (13.16)	531.99	< 0.001	498450 (6689)	Ref
	Special occasions only	56385 (11.47)	1132 (16.92)				0.89 (0.81, 0.97)	8.24×10^− 03
	One to three times a month	54780 (11.14)	780 (11.66)				0.62 (0.57, 0.69)	9.47×10^− 22
	Once or twice a week	127059 (25.84)	1594 (23.83)				0.55 (0.51, 0.6)	4.03×10^− 46
	Three or four times a week	113924 (23.17)	1167 (17.45)				0.45 (0.41, 0.49)	3.68×10^− 72
	Daily or almost daily	100316 (20.40)	1136 (16.98)				0.5 (0.46, 0.55)	1.77×10^− 53
Drink group (%)	Abstainer (0g)	32055 (7.96)	655 (13.03)	215.94	< 0.001	407652 (5026)	Ref
	Light (0-16.8 g/day)	184357 (45.79)	2398 (47.71)				0.63 (0.57, 0.68)	2.66×10^− 26
	Moderate (16.8–30.8 g/day)	91371 (22.69)	975 (19.40)				0.51 (0.46, 0.56)	4.45×10^− 41
	Heavy (30.8–49 g/day)	57408 (14.26)	597 (11.88)				0.5 (0.44, 0.55)	3.42×10^− 35
	Abusive (> 49 g/day)	37435 (9.30)	401 (7.98)				0.52 (0.46, 0.58)	2.13×10^− 25
Whether over weekly limits (%)	No	231299 (57.45)	3089 (61.46)			498450 (6689)	Ref
	Yes	171327 (42.55)	1937 (38.54)	32.71	< 0.001	425504 (5440)	0.83 (0.79, 0.88)	3.24×10^− 10
AUD status (%)	No	483275 (97.98)	6500 (97.69)			6654 (499876)	Ref
	Yes	9947 (2.02)	154 (2.31)	2.94	0.087		1.24 (1.05, 1.45)	4.97×10^− 02
Abbreviation: AUD, alcohol use disorder; SD, standard deviation.
* For categorical data, the test-statistics was obtained from X-squared test; for continuous data, they were obtained from student t-test.

Results obtained from multivariable Cox regression model are presented in Fig. 3. Overall, AUD was associated with a higher risk of RA (Model 2, HR: 1.30; 95% CI: 1.08, 1.56; P = 0.006). However, alcohol intake was associated with a lower risk of RA. Specifically, compared with never-drinking participants, previous drinkers had a 25% increased risk of RA in Model 1 (HR: 1.25; 95% CI: 1.09, 1.43; P = 0.001), while further adjustment of BMI, Townsend deprivation index, education level, smoking status, and physical activity attenuated such association (Model 2, HR: 1.09; 95% CI: 0.95, 1.26; P = 0.206). Per SD increment of alcohol intake was related with a 6% lower risk of RA (Model 2, HR: 0.94; 95% CI: 0.91, 0.97; P = 2.34×10^− 04). Besides, we found that higher frequency of alcohol intake was associated with a lower risk of RA (Model 2, HR special occasions only vs. never: 0.86; 95% CI: 0.79, 0.95; P = 0.002; HR one to three times a month vs. never: 0.73; 95% CI: 0.66, 0.81; P = 1.05×10^− 09; HR once or twice a week vs. never: 0.69; 95% CI: 0.63, 0.75; P = 1.97×10^− 17; HR three or four times a week vs. never: 0.62; 95% CI: 0.56, 0.68; P = 3.49×10^− 24; HR daily or almost daily: 0.65; 95% CI: 0.59, 0.71; P = 1.50×10^− 19). We also found individuals that drink over weekly limits had a lower risk of developing RA compared with under weekly limits individuals (Model 2, HR: 0.84; 95% CI: 0.80, 0.90; P = 2.24×10^− 08). Results from sensitivity analysis remained robust (Supplementary Table 5).

Similar associations of alcohol use and RA risk were also observed in stratification analyses (Table 2). For per SD increment of alcohol intake per day, a negative association was observed among female, White population, and individuals aged older than 60 years. Among different age groups, individuals aged over 60 years had the highest risk of RA, followed by individuals aged between 50 to 60 and less than 50 years (Table 2). However, for AUD and RA, a positive association was observed in male and White population, and individuals aged under 60 (Table 2).

Table 2. Stratified analysis on the association between alcohol and the risk of RA.
Subgroup	Model 1		Model 2
Subgroup	HR (95% CI)	P value	HR (95% CI)	P value
Alcohol use disorder
No	1.00 (ref)		1.00 (ref)
Yes	1.47 (1.25,1.73)	2.68×10^− 06	1.30 (1.08, 1.56)	0.006
Stratified by sex
Female	1.32 (1.01, 1.72)	0.042	1.24 (0.91, 1.68)	0.178
Male	1.57 (1.29, 1.92)	1.10×10^− 05	1.32 (1.04, 1.66)	0.022
Stratified by age
Age < 50	3.01 (2.06, 4.41)	1.34×10^− 08	2.44 (1.49, 4.00)	3.97×10^− 04
50 < = Age < = 60	1.57 (1.02, 2.43)	0.004	1.73 (1.08, 2.78)	0.022
Age > 60	1.20 (0.95, 1. 51)	0.125	1.03 (0.79, 1.35)	0.802
Stratified by ethnicity
White	1.46 (1.24, 1.72)	7.57×10^− 06	1.29 (1.07, 1.56)	0.008
Asian	0.81 (0.11, 5.79)	0.831	0.88 (0.11, 6.71)	0.899
Black	2.66 (0.84, 8.43)	0.096	1.04 (0.14, 7.68)	0.973
Mixed or other	1.52 (0.37, 6.18)	0.561	1.43 (0.18, 11.17)	0.735
Alcohol intake per day (per SD)
	0.94 (0.91, 0.98)	6.93×10^− 04	0.94 (0.91, 0.97)	2.34×10^− 04
Stratified by sex
Female	0.82 (0.78, 0.87)	1.67×10^− 12	0.86 (0.82, 0.91)	9.17×10^− 08
Male	1.03 (0.99, 1.08)	0.957	0.99 (0.95, 1.04)	0.765
Stratified by age
Age < 50	0.93 (0.84, 1.04)	0.227	0.93 (0.83, 1.03)	0.164
50 < = Age < = 60	0.94 (0.86, 1.03)	0.171	0.92 (0.84, 1.01)	0.094
Age > 60	0.93 (0.89, 0.97)	7.65×10^− 04	0.92 (0.88, 0.97)	5.11×10^− 04
Stratified by ethnicity
White	0.96 (0.93, 0.99)	0.013	0.95 (0.92, 0.98)	0.002
Asian	0.94 (0.68, 1.31)	0.735	0.92 (0.66, 1.30)	0.640
Black	1.09 (0.90, 1.31)	0.388	1.09 (0.90, 1.32)	0.396
Mixed or other	0.81 (0.55, 1.19)	0.281	0.86 (0.57, 1.28)	0.448
Abbreviation: CI, confidence interval; HR, hazard ratio.
Model 1: adjusted for age and sex; Model 2: further adjusted for alcohol intake per day, body mass index, education, physical activity, townsend index, and smoking status. For alcohol use disorder, model 2 additionally adjusted for alcohol intake per day.

Furthermore, we found a potential J-shaped relationship between alcohol intake per day and the risk of RA (Fig. 4). Alcohol intake was inversely associated with the risk of RA when alcohol intake was from 17.90 to 69.59 grams alcohol intake per day (below the dash line in Fig. 4A) was significantly associated with a reduced risk of RA. According to Kiran et al [20], 1 standard American measures of alcohol consumption per day equivalent to 2 g of alcohol. Therefore, 17.90 to 69.59 grams intake per day approximate 9 drinks to 35 drinks per day.

When stratifying the population by sex, we found that the negative association was higher in males (Fig. 4B). Drinking can reduce the risk of RA in female participants who drank more than 15.91 grams per day but thereafter RA risk was increased as alcohol intake exceeded a threshold of 67.61 grams per day (Fig. 4B). In male participants, a reverse relationship was observed from alcohol intake of 1.99 grams per day to 121.29 grams per day (Fig. 4B), After that threshold, alcohol intake was associated with increased risk of RA. Among different ages groups, the J-shape was most evident in groups with individuals over 60 years, followed by individuals aged between 50 to 60 and less than 50 years (Fig. 4C). Before reaching the threshold of 155.09 grams per day and exceeding 338.03 grams per day, alcohol intake was associated with increased risk of RA among individuals less than 50 years. In 50–60 age group, a negative association was observed between 15.91 grams alcohol intake per day and 65.62 grams per day. While among individuals over 60 years, alcohol intake was associated with increased risk of RA regardless of dose.

Genetic correlation of alcohol consumption-related traits

The heritability of drink per week, AUD, and RA are 0.047, 0.035, and 0.144. By using LDSC, we found that drink per week and AUD were highly correlated (r_g = 0.305, P = 4.52×10^− 40, Supplementary Fig. 3). We also found a positive association between AUD and RA (r_g = 0.213; P = 0.002, Supplementary Fig. 3) and a negative association between drinks per week and RA (r_g = -0.036; P = 0.305, Supplementary Fig. 3).

Genetically predicted association between alcohol intake and the risk of RA

In two-sample MR study, 89 SNPs and 11 SNPs associated with drinks per week and AUD, respectively, were harmonized with the outcome data set (Supplementary Table 6 and Supplementary Table 7). No significant association were detected between drink per week and the risk of RA under IVW method (OR: 0.76; 95% CI: 0.50, 1.14; P-value = 0.182), weighted-median (OR: 1.00; 95% CI: 0.55, 1.84; P-value = 1.000), simple median (OR: 1.00; 95% CI: 0.55, 1.83; P-value = 1.000), and maximum-likelihood methods (OR: 0.75; 95% CI: 0.50, 1.83; P-value = 0.180) (Supplementary Table 8). Potential pleiotropic effect was detected by using MR-egger method (P-value = 0.049). After the removal of nine pleiotropic SNPs identified by GWAS Catalog, the null association remained similar (Supplementary Table 8).

However, AUD was associated with a 33% (95% CI: 7%-66%) increased risk of RA by using IVW method (P = 0.01, Supplementary Table 8). Evidence from sensitivity analyses yielded similar results (OR: 1.25; 95% CI: 1.05, 1.48; P = 0.012 by maximum-likelihood; OR: 1.35, 95% CI: 1.00-1.81; P = 0.09 by MR-PRESSO). However, no statistically significant association was detected by weighted-median method (OR: 1.27; 95% CI: 0.94, 1.72; P = 0.231). Results from AUD conditioning on drink per week obtained the similar results as presented in Supplementary Table 8.

In the present study, we found the potential dose-dependent and sex-dependent negative association of alcohol intake on the risk of RA in the meta-analysis and prospective cohort study. Moderate alcohol consumption (17.90 g/day to 69.59 g/day) was inversely associated with the risk of RA, and high intake (≥ 111 g/ day) was positively associated with the risk of RA. Besides, AUD was found to be positively associated with RA risk in prospective UK Biobank cohort. Genetic correlation and two-sample MR study yielded similar results.

Recent years, accumulating evidence has supported that moderate alcohol intake may have potential health benefits including reduced risk of RA. For example, a recent prospective UKB study found low to moderate alcohol consumption was preventive to RA onset (adjusted HR: (0.69, 0.83) [38]. In our study, in addition to multiple indicators of alcohol intake, such as alcohol intake frequency, drink groups, and whether drink over limits, which have been included in previous studies, we transformed the categorical variable, alcohol intake frequency, into continuous variables, and performed a dose-response analysis, whose results suggested a U-shape relationship. A meta-analysis in 2014 that included 195,029 participants and 1,878 incident RA cases also found a dose-response relationship between alcohol consumption and the risk of RA, with the low to moderate alcohol consumption associated with a reduced risk of RA [10]. Besides, it was found that females who had low to moderate alcohol consumption had 19% decreased risk of RA [10]. In our sex-stratified analysis, we found that the protective effect was only observed in women.

We converted the alcohol consumption frequency to alcohol intake per day, and developed a RCS model. Our study detected a potential inverse association of alcohol consumption on RA in UK Biobank cohort with the narrow 95% CIs, suggesting that such protective pattern was limited for RA, even though it was found statistically significant possibly due to the large sample size. Besides, we found that the effect of alcohol consumption may differ by age, suggesting that individuals in different age groups may have different susceptibility to alcohol intake on RA risk. A recent Global Burden of Disease Study found that alcoholic drinks may be harmful for adults younger than 40 years compared with those over 40 years [39]. Since UK Biobank only included participants aged over 40 years, it was possible that the health benefit of alcohol was more likely to be detected in UKB Biobank participants. Besides, harmful use of alcohol was particularly concentrated in males aged 15–39 years, whose population was lacking in UK Biobank.

A possible mechanism of the J-shaped relationship between alcohol intake and the risk of RA was that alcohol could dose-dependently affect immune system in the aspects of innate and adaption[40]. Low to moderate alcohol might downregulate immune response and decrease the production of pro-inflammatory cytokines, such as IL-1, IL-6 and TNF[12, 40]. In a Nurses’ Health Study cohort study, a J-shaped association of daily alcohol consumption (up to 20g) with IL-6 levels was found in patients of RA prior to symptoms[41]. Besides, results from a large cross-sectional study in the UK found that moderate alcohol intake up to 40g per day was associated with lower concentrations of CRP compared to non-drinking and heavy drinking [42], suggesting that excessive drinking could increase inflammation.

While low to moderate alcohol intake might have a protective effect on disease incidence and development, persistent heavy drinking could affect immune systems, decrease white blood cells, platelets, and granulocyte mobility, as well as shorten various disease onsets [43]. AUD was considered as a brain disorder caused by long-term alcohol misuse, encompassing both alcohol dependence and alcohol abuse. A National Inpatient Sample cohort study found that AUD hospitalizations increased over the 19-year study period from 1998 to 2014 to 3.5-fold in RA [44], suggesting a potential relationship between AUD and RA.

The present evidence from genetic study was largely consistent with observational evidence. By using two-sample MR, we found that genetically predicted alcohol intake was not associated with the increased risk of RA, while AUD was positively associated with RA. Results from LDSC also found that alcohol intake was not correlated with RA, while AUD was positively associated with the risk of RA. Consistent with previous studies, Bae et al. did not found evidence of a causal association between alcohol intake and RA (beta = 0.218, SE = 0.213, P = 0.306) using IVW method [45]. Besides, Jiang et al. did not detect a potential association between alcohol intake and RA, based on two-sample MR (OR: 0.80; 95% CI: 0.54, 1.19; P = 0.27) and genetic correlation analysis (r_g = -0.07; P = 0.21)[46].

Though the underlying biological mechanism between AUD and RA is still unclear, there are some possible explanations. First, regular heavy drinking can promote pro-inflammatory immune responses and affects the normal functioning of the adaptive immune response which may increase the risk of RA [47]. In addition, binge drinking may reduce telomere length that could increase secretion of inflammatory cytokines such as IL-6, which could contribute to an increased inflammatory load, increasing the risk of RA [48, 49]. Furthermore, patients of AUD often have depression, which may increase the risk of RA through increasing immune cytokines like PGE2 [50, 51]. Although these explanations are biologically plausible, more studies are needed to explore the exact mechanism.

The present study has the following strength. First, our study was based on large prospective biomedical dataset, which increased the power and enabled our study to detect significant associations even though the effect was small. Second, our study confirmed a potential U-shaped relationship between alcohol intake and the risk of RA as suggested by previous studies. Besides, we also revealed a sex-specific pattern of alcohol intake in RA risk. However, several weaknesses worth the consideration. First, although the efforts to reduce pleiotropic effect across genetic variants, we cannot exclude the possibility of a shared genetic basis for alcohol consumption or AUD with RA. In that case, the association detected in our genetic study may not represent a direct causal relationship.

In summary, our study found that moderate alcohol consumption has potential protective effect on RA risk, while habitual consumption of alcohol, represented by AUD, was positively associated with RA risk. Genetic study revealed a causal association between AUD and the risk of RA. Despite the potential protective effect of moderate alcohol intake in RA incidence, people should not start drinking for any potential health benefits. Further studies are warranted to explore the potential biological mechanism underlying the alcohol-RA association.

ANOVA: analysis of variance

AUD: alcohol use disorder

CI: confidence interval

GWAS: genome-wide association study

HR: hazard ratio

ICD: International Classiﬁcation of Diseases

IVW: inverse variance weighted

LDSC: linkage disequilibrium score regression

MR: Mendelian randomization

MR-PRESSO: MR pleiotropy residual sum and outlier

OR: odds ratio

RA: rheumatoid arthritis

RCS: restricted cubic splines

RR: relative risk

SD: standard deviation

SNP: single nucleotide polymorphism

Ethics approval and consent to participate

All the data are publicly available. No ethics approval is required for this study.

Consent for publication

Not applicable.

Availability of data and materials

Summary-level GWAS meta-analysis statistics for RA can be accessed at: http://plaza.umin.ac.jp/~yokada/datasource/software.htm. The UK Biobank individual-level data is available through application: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.

Competing interests

The authors declare no conflict of interests.

Funding

The study was jointly supported by grants from Natural Science Foundation of China (82174208, 81973663), Natural Science Foundation of Zhejiang Province (LY22H260005).

Authors' contributions

Y.M. and C.W. contributed to study design and conceptualization; D.Y. and X.S. contributed to accessing UK Biobank data; J.G. and J.L. contributed to data collection; L.L. contributed to data analyses and manuscript writing; B.L. and J.Y. contributed to data analyses; Y.M., Z.H., and Y.Q. contributed to manuscript revision.

Acknowledgements

We thank everyone who participated in and contributed to the GWAS study and UK Biobank study.

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Alcohol consumption and the risk of rheumatoid arthritis

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Dose-response meta-analysis of observational studies

UK Biobank participants

Outcome assessment

Measurement of exposure and covariables

Summary-level genetic association data and instrumental variables selection

Statistical analysis

Meta-analysis of prospective observational studies

Observational analysis

Genetic correlation analysis

Two-sample MR analysis

Results

Dose-response meta-analysis of observational study

Basic characteristics of UK Biobank participants

Observational associations between alcohol intake and RA risk

Genetic correlation of alcohol consumption-related traits

Genetically predicted association between alcohol intake and the risk of RA

Discussion

Conclusions

Abbreviations

Declarations

References

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