AHR rs4410790 genotype and IgG levels: Effect modification by lifestyle factors

Jaewon Khil; Soyoun Kim; Minhyeong Lee; Hyeonmin Gil; Seok-Seong Kang; Dong Hoon Lee; Youngeun Kwon; NaNa Keum

doi:10.1371/journal.pone.0290700

Abstract

Inflammation is a multifaceted marker resulting from complex interactions between genetic and lifestyle factors. Emerging evidence suggests Aryl hydrocarbon receptor (AHR) protein may be implicated in the regulation of immune system and inflammatory responses. To investigate whether rs4410790 genotype (TT, TC, CC) near AHR gene is related to serum IgG levels, a marker of chronic inflammation, and whether lifestyle factors modifies the relationship, we conducted a cross-sectional study by recruiting 168 Korean adults. Participants responded to a lifestyle questionnaire and provided oral epithelial cells and blood samples for biomarker assessment. Among these participants, C allele was the minor allele, with the minor allele frequency of 40%. The rs4410790 TT genotype was significantly associated with elevated IgG levels compared with TC/CC genotypes, after adjusting for potential confounders (p = 0.04). The relationship varied significantly by levels of alcohol consumption (P interaction = 0.046) and overweight/obese status (P interaction = 0.02), but not by smoking status (P interaction = 0.64) and coffee consumption (P interaction = 0.55). Specifically, higher IgG levels associated with the TT genotype were evident in frequent drinkers and individuals with BMI≥23kg/m², but not in their counterparts. Thus, rs4410790 genotype may be associated with IgG levels and the genetic predisposition to higher IgG levels may be mitigated by healthy lifestyle factors like infrequent drinking and healthy weight.

Citation: Khil J, Kim S, Lee M, Gil H, Kang S-S, Lee DH, et al. (2023) AHR rs4410790 genotype and IgG levels: Effect modification by lifestyle factors. PLoS ONE 18(10): e0290700. https://doi.org/10.1371/journal.pone.0290700

Editor: Cheorl-Ho Kim, Sungkyunkwan University and Samsung Advanced Institute of Health Science and Technology (SAIHST), REPUBLIC OF KOREA

Received: April 25, 2022; Accepted: August 15, 2023; Published: October 2, 2023

Copyright: © 2023 Khil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because the data include sensitive forms of personal information. Data are available upon evaluation by Institutional Review Board of the Dongguk University Ilsan Hospital (contact via irb@dumc.or.kr) for researchers who meet the criteria for access to confidential data.

Funding: JK was supported by the BK21-plus education program from the National Research Foundation of Korea. NK was supported by funding from the National Research Foundation of Korea (NRF-2018R1C1B6008822). YK was supported by funding from MSIT and MOHW (2021M3E5E5096464), MOE (2022R1A6A1A03053343), and MOTIE (S2021A043800034) of Korea. DHL was supported by the Yonsei University Research Fund of 2023-22-0159. SK was supported by Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01669005) from Rural Development Administration.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Aryl hydrocarbon receptor (AHR) gene on chromosome 7 encodes AHR protein [1], a ligand-activated transcription factor that modulates responses to xenobiotics (e.g., dioxins) [2]. In recent years, a new role of AHR has been increasingly recognized [3]. Expressed in diverse immune cells including macrophages, NK cells, B lymphocytes, AHR has been suggested to play a key role in regulating immune system and inflammatory responses [3, 4]. Thus, genetic variants of the AHR gene may have consequences on immune and inflammatory responses. One candidate is rs4410790, a single-nucleotide polymorphism (SNP) near the AHR gene (more specifically, located at 7p21, 54 kb upstream of AHR on chromosome 7). The rs4410790 variants were shown to be associated with cerebellum AHR gene methylation, thereby affecting the level of its genetic expression [1]. In genome-wide association studies of European descendants, rs4410790 C allele compared to T allele was associated with higher consumption of caffeine [5–7], which has a myriad physiological effect including immunomodulatory actions and anti-inflammatory effect [8]. Yet, a direct association between rs4410790 and inflammatory markers has been rarely explored.

Immunoglobin G (IgG), the most abundant (about 70–80%) antibody in the blood, plays a central role in the immune system [9]. After initial bacterial or viral infection or other antigen exposure, IgG is produced in a delayed timescale by plasma B cell [9]. The IgG activates Fcγ-receptors in the surface of immune cells, which produce and release proinflammatory cytokines [10, 11]. Thus, an elevated IgG level is a marker of chronic active infection or inflammation. Interestingly, emerging evidence suggests that AHR may affect the production of IgG by mediating the effect of AHR’s ligands on immunoglobin productions [12]. In various animal models, 2,3,7,8-tetracholorodibenzo-p-dioxin, a ligand of AHR, was shown to inhibit the differentiation of B-lymphocyte into antibody-forming cells and consequently to suppress immunoglobin expression including IgG [12]. A role of AHR in modulating IgG expression is also suggested in the observation that decreased AHR levels induced lower IgG expression [13]. Thus, rs4410790 near the AHR gene may be linked to circulating IgG levels.

Of note, circulating levels of IgG are influenced not only by antigen exposures, but also by lifestyle factors that are associated with immune and inflammatory responses such as alcohol drinking [14], smoking [15], and obesity [16]. In a study of 460 adults in Spain, serum IgG levels were lower in moderate drinkers than in light or heavy drinkers, in smokers than non-smokers, and in normal weight individuals than in obese people[17]. Considering that not only AHR but also drinking, smoking, and obesity are associated with circulating IgG levels, rs4410790 and the lifestyle factors might interact in modulating the IgG levels. Thus, in addition to investigating the relationship between rs4410790 genotype (TT, TC, CC) and IgG levels, we examined whether the aforementioned lifestyle factors such as alcohol consumption, overweight/obese and smoking modified the relationship. In addition, the variants of rs4410790 have been most widely investigated in relation to caffeine intake [5–7] and coffee is known for its anti-inflammatory effect [18]. Thus, we also explored the role of coffee consumption in mediating and modifying the relationship between rs4410790 genotype (TT, TC, CC) and IgG levels.

Materials and methods

Study participants

From March to May in 2019, a cross-sectional study was conducted in Dongguk University, Korea by recruiting healthy individuals aged 18 years or older. All participants graduated from high school and were of legal age to drink and smoke [19]. At first, 200 subjects applied for the study participation, but 20 individuals were excluded due to one of the following reasons: with a history of major diseases or surgery, on regular treatment or prescription medicine, with mental illness or anemia, unable to exercise, or pregnant or planning for it. 180 individuals (119 men, and 61 women) visited the research center for the collection of oral epithelial cells, blood draw, questionnaire response, and anthropometric measurements. We further excluded 12 individuals whose serum were contaminated by hemolysis. The analytic cohort for this study consisted of a total of 168 individuals (110 men, 58 women).

Before participation in our study, participants were given full explanation about the study and granted the right to opt out of the study at any time during the study. Participants provided written informed consents before enrollment to the study and received a gift voucher as a compensation for the study participation. The study protocol was reviewed and approved by the Institutional Review Board of the Dongguk University Ilsan Hospital (IRB number: 2018-12-006-010). We conducted this study in accordance with the Helsinki Declaration principles.

Identification of rs4410790 genotype

ExgeneTM Tissue SV (GeneAll, Seoul, Korea) was used to extract DNA from buccal swabs. A SNP array (Theragen PMRA, Seoul, Korea) was performed to identify genotype of rs4410790. The procedures of genotype analysis and quality control are described in detail in a previous study [20]. Accuracy of genotype based on the SNP array was demonstrated to be 0.94 in an Asian population for SNPs with minor allele frequencies > 5%. The call rate was ≥97% and the distribution of TT, TC, CC was in Hardy-Weinberg equilibrium (χ² test, P>0.05).

Assessment of serum IgG levels

Serum IgG levels were measured by arrays of protein G and A at different concentrations [21, 22]. Immunoassays were performed using the microarrays as follows; each chamber was treated with 2% BSA in PBST for 1 h, washed with PBST, and incubated with 200 nL of diluted human serum (1/400) in PBST for 1 h. The microarray was then washed with PBST and treated sequentially with a solution of detection antibody and a solution of Cy3-conjugated goat anti-human IgG in PBST for 1 h. The fluorescence intensity was measured using a GenePix 4000B (Axon Instruments, Union city, CA) using an excitation wavelength of 532 nm and an emission wavelength of 570 nm. The IgG signal intensity, which was denoted in arbitrary unit (A.U.), was determined by calculating the average of triplicates.

Assessment of covariates

Information on factors that could influence the level of serum IgG levels were collected in various methods. For demographic and lifestyle information, online frequency questionnaire was administered. The list includes age, sex, alcohol consumption (hardly ever, 1–2 times/week, 3–4 times/week, 5–6 times/week, every day), coffee consumption (hardly ever, 1–2 times/week, 3–4 times/week, 5–6 times/week, every day), current smoking status (yes, no), daily aerobic exercise (<30 minutes, ≥30 minutes–<1 hour, ≥1hour).

In light of the evidence that obesity in childhood and adolescence initiates metabolic inflammation that has a lasting influence on the risk of adult disease [23], we also collected information on body shape during high school.

To reflect total vitamin D reserves in the body, serum levels of 25-hydroxyvitamin D (25(OH)D) were measured using the blood drawn for IgG measurement.

To calculate current body mass index (BMI), weight in kilograms divided by the square of the height in meters (kg/m²), height and weight were measured using bioelectrical impedance analyzer (Inbody720, Seoul, Korea) in light clothing and bare feet. By the World Health Organization, overweight and obese for Asians are defined as 23kg/m²≤BMI<25 kg/m² and BMI≥25kg/m², respectively [24]. These cut-offs account for the fact that Asians have a higher fat mass for a given BMI compared to other racial groups [25].

Statistical analysis

The χ2 test for categorical variables and t-test for continuous variables were used to compare baseline characteristics of the participants according to rs4410790 genotype.

Linear regression was performed to examine the relationship between rs4410790 genotype and serum IgG level, with multivariable regression adjusting for potential confounders described in the covariate section. To examine the degree to which the association is mediated through the effect of rs4410790 genotype on caffeine, we ran another multivariable linear regression by further adjusting for coffee intake, a major source of caffeine intake.

Subgroup analyses by levels of drinking, overweight/obese, smoking, and coffee consumption were performed to explore whether the relationship between rs4410790 genotype and serum IgG level differs by these stratifying variables. Potential interactions between rs4410790 genotype and the stratifying variables were tested by adding their cross-product terms to the multivariable linear regression and running the Wald test on the terms.

P-value of < 0.05 was considered statistically significant. All statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC, USA).

Result

Baseline characteristics of participants by rs4410790 genotype

Characteristics of the 168 participants are presented according to rs4410790 genotype (T>C) in Table 1. For each genotype of TT, TC, and CC, there were 58, 85, 25 subjects, respectively. In this population, C allele was the minor allele, with the minor allele frequency of 40%. There was no trend of IgG levels with increasing number of C allele.

Download:

Table 1. Characteristic of the participants according to rs4410790 genotype.

https://doi.org/10.1371/journal.pone.0290700.t001

The mean age of the participants was 21 years (range: 18–24 years). Compared to participants with TT genotype, those with TC/CC genotype were more likely to be women, to drink alcohol, to drink coffee, and to have higher levels of circulating 25(OH)D. However, they were less likely to be overweight/obese currently and in high school, to smoke, and to exercise. Yet, the differences in baseline characteristics were not statistically significant (Table 1).

Association between rs4410790 genotype and serum IgG levels

The rs4410790 TT genotype was significantly associated with elevated IgG levels compared with TC/CC genotypes in univariable analysis (p = 0.047) (Table 2). After adjusting for potential confounders, a significant association of TT genotype with a higher IgG level persisted (p = 0.04). After further adjusting for coffee consumption, the results did not change materially, with IgG levels higher for TT genotype compared to TC/CC genotype (p = 0.02).

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Table 2. Adjusted mean levels of serum IgG by rs4410790 genotype.

https://doi.org/10.1371/journal.pone.0290700.t002

Association between rs4410790 genotype and serum IgG levels by lifestyle factors

By frequency of alcohol consumption, after adjusting for potential confounders, TT genotype had significantly higher serum IgG levels compared to TC/CC genotype among individuals drinking ≥ 1 time/week (p = 0.03), but not among individuals drinking<1 time/week (p = 0.23) (Fig 1). The interaction between rs4410790 genotype and frequency of alcohol consumption on serum IgG levels was statistically significant (P interaction = 0.046). Further adjustment of coffee consumption did not change the aforementioned results (S1 Table and S1 Fig).

Download:

Fig 1. Multivariable association between rs4410790 genotype and serum IgG levels according to frequency of alcohol consumption.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.g001

By overweight/obese status, after adjusting for potential confounders, TT genotype had significantly higher serum IgG levels compared to TC/CC genotype among individuals with BMI≥23kg/m² (p<0.01), but not among individuals with BMI <23kg/m² (p = 0.97) (Fig 2). The interaction between rs4410790 genotype and overweight/obese on serum IgG levels was statistically significant (P interaction = 0.02). Additional adjustments for coffee intake did not lead to material changes in the previous results (S2 Table and S2 Fig).

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Fig 2. Multivariable association between rs4410790 genotype and serum IgG levels according to overweight/obese status.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.g002

By current smoking status, after adjusting for potential confounders, TT genotype had a significantly higher serum IgG levels compared to TC/CC genotype among no current smokers (p = 0.03), but not among current smokers (p = 0.34) (Fig 3). The interaction between rs4410790 genotype and smoking on the serum IgG level was not statistically significant (P interaction = 0.64). Additional adjustment of coffee consumption did not change the aforementioned results (S3 Table and S3 Fig).

Download:

Fig 3. Multivariable association between rs4410790 genotype and serum IgG levels according to current smoking status.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.g003

By frequency of coffee consumption, after adjusting for potential confounders, TT genotype had a not significantly higher serum IgG levels compared to TC/CC genotype among individuals coffee consumption≥1 time/week (p = 0.11), and also among individuals consumption <1 time/week (p = 0.11) (Fig 4). The interaction between rs4410790 genotype and coffee consumption on the serum IgG level was not statistically significant (P interaction = 0.63).

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Fig 4. Multivariable association between rs4410790 genotype and serum IgG levels according to coffee consumption.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval.* Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.g004

Discussion

In this study, we observed an association between rs4410790 genotype and serum IgG levels, with the levels higher in the TT genotype than in TC/CC genotype. The relationship varied significantly by levels of alcohol consumption and overweight/obese status, but not by smoking status and coffee consumption. Specifically, higher IgG levels associated with the TT genotype were evident in frequent drinkers and individuals with BMI≥23kg/m,² but not in their counterparts.

The variants of rs4410790 have been most widely investigated in relation to caffeine intake [5]. Caffeine is primarily metabolized by CYP1A2, which accounts for approximately 95% of caffeine clearance in the liver [6]. The AHR protein regulates CYP1A2, by inducing CYP1A2 transcription [6]. Thus, the AHR gene and protein are implicated in caffeine metabolism. In a genome-wide meta-analysis of 47,341 European descendants from five U.S. studies, rs4410790 has been identified to be the most strongly associated SNP for habitual caffeine consumption [5]. In a Costa Rican population of men and women with mean age of 57 years, non-carriers of C allele were associated with lower caffeine consumption (< 100 mg/day) compared to carriers of C allele (> 400 mg/day) [6]. Individuals who metabolize caffeine slowly are likely to consume less caffeine and thus, less coffee, a major dietary source of caffeine. Indeed, in a genome-wide meta-analysis of European and African American adults, non-carriers of C allele were associated with lower coffee consumption compared to carriers of C allele [5].

While no studies have investigated the relationship between coffee intake and IgG levels, coffee is known for its anti-inflammatory effect [18]. Considering that IgG level is a marker of chronic inflammation, individuals with rs4410790 TT genotype, via lower consumption of caffeine or coffee, are likely to have higher levels of inflammation and thus, higher IgG levels. However, the potential indirect effect of rs4410790 variants on IgG levels through caffeine/coffee intake is not supported by our findings. In our multivariable analyses to examine the relationship between rs4410790 genotype and IgG levels, additional adjustment of coffee consumption did not change the results, which provides evidence against the potential mediating role of caffeine/coffee consumption. Furthermore, the relationship between rs4410790 genotype and IgG levels did not vary significantly by the level of caffeine/coffee consumption. The observation that caffeine/coffee consumption is neither mediator nor modifier for the relationship suggests that a direct influence of AHR rs4410790 genotype on serum IgG levels may exist. Indeed, previous studies showed that AHR rs4410790 variant is associated with methylation of AHR gene [1], which is likely to be implicated in the regulation of immune system and inflammatory responses, as marked by IgG levels [12].

In our study, elevated IgG levels associated with the TT genotype were pronounced among frequent drinkers and individuals with BMI≥23kg/m². When ethanol is metabolized, acetaldehyde and reactive oxygen species are produced, which activate inflammatory signaling pathways [26]. Chronic alcohol consumption also contributes to systematic inflammation by enhancing gut permeability to microflora-derived lipopolysaccharide, a cell all component of gram-negative bacteria that elicit inflammatory response [26]. Also, adipocytes are enriched in macrophages, which secret pro-inflammatory cytokines [16, 27]. Considering the established pro-inflammatory effects of alcohol and excessive fat [28–33], our results suggest that serum IgG levels may be influenced by synergistic effects of genetic and lifestyle factors. Individuals with TT genotype are genetically more prone to inflammation and such susceptibility may be readily manifest as an elevated IgG level when exposed to higher alcohol and adiposity, but be countered by avoiding alcohol intake and maintaining a healthy weight.

On the other hand, smoking status of individuals did not modify the relationship between rs4410790 genotype and serum IgG levels. While cigarette smoke contains multiple reactive oxygen species that stimulate the release of pro-inflammatory markers [15], the effects of smoking on immunity are complicated, inducing both pro-inflammatory and immune-suppressive effects [15]. Thus, relationships between smoking and multiple markers reflecting inflammatory processes do not always point to the same direction. For example, while C-reactive proteins, neutrophil extracellular trap, TNF-α and IL-6 increased significantly when individuals were exposed to smoking [34–36], serum IgG levels were higher among non-smokers compared with smokers [37]. Of note, 2,3,7,8-tetracholorodibenzo-p-dioxin, a major toxic component in cigarettes [38], functions as a ligand of AHR and is shown to suppress immunoglobin expression including IgG [12]. Yet, in our study consisting of young adults in early 20s, serum IgG levels were higher among smokers than non-smokers. The net effect of smoking on immunity depends on multiple factors (e.g., dose, type, and duration of smoking, host immunity) [15] and this complexity might have weakened potential interactions between smoking and genetic predisposition to higher IgG levels.

Our study has several strengths. First, in examining the relationship between rs4410790 variants and serum IgG levels, we controlled for the effects of age, sex, drinking, coffee consumption, smoking, overweight/obese, overweight/obese in high school, exercise, and 25(OH)D levels that have been suggested to influence serum IgG levels [17, 39–44]. Second, causal inference from a cross-sectional study is inherently limited due to reverse causality. Nonetheless, genetic variations are innate and thus, it is guaranteed in our study that rs4410790 variants precede serum IgG levels. Third, by providing evidence for the gene and environment interaction, our study suggests that genetic predisposition to higher IgG levels can be mitigated through lifestyle modifications such as drinking <1 time/week and maintaining healthy weight.

There are several limitations to acknowledge. First, as the first study that examined and identified an association between AHR rs4410790 variant and serum IgG levels, we cannot rule out a chance finding. Therefore, despite previous evidence for an association between rs4410790 variant and cerebellum AHR gene expression[1], the association between rs4410790 variant and serum IgG levels observed in our study could have been mediated by the effect of rs4410790 variant on nearby genes other than AHR gene that have influence on serum IgG concentrations. Third, considering that modest genetic predisposition tends to manifest at an early stage in life when major underlying diseases are unlikely, findings from our study population consisting of young adults may not be generalizable to older populations. Finally, our findings might be of less public health importance to other populations where the frequency of T allele for rs4410790 is relatively low (e.g., European, 38%) compared to 60% in our population [45].

Conclusion

In summary, our study suggests that rs4410790 variant near the AHR gene was associated with serum IgG levels, a likely marker of chronic inflammation, in early adulthood. An elevation in serum IgG levels associated with rs4410790 TT genotype might be mitigated by healthy lifestyle factors such as infrequent drinking and healthy weight. As this is the first study that provided evidence for a relationship between rs4410790 genotype and serum IgG levels and for its effect modification by lifestyle factors that have been shown to be associated with IgG levels, further studies are warranted to validate our findings.

Supporting information

S1 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to alcohol consumption.

*Data are presented as means (standard error). ¹Multivariable model 1 was adjusted for age, sex, alcohol consumption, coffee consumption, current overweight/obese, overweight/obese in high school, smoking status, aerobic exercise, 25(OH)D level. ²Multivariable model 2 was additionally adjusted for coffee consumption.

https://doi.org/10.1371/journal.pone.0290700.s001

(DOCX)

S2 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to obesity status.

*Data are presented as means (standard error) ¹Multivariable model 1 was adjusted for age, sex, alcohol consumption, coffee consumption, current overweight/obese, overweight/obese in high school, smoking status, aerobic exercise, 25(OH)D level. ²Multivariable model 2 was additionally adjusted for coffee consumption.

https://doi.org/10.1371/journal.pone.0290700.s002

(DOCX)

S3 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to current smoking status.

*Data are presented as means (standard error) ¹Multivariable model 1 was adjusted for age, sex, alcohol consumption, coffee consumption, current overweight/obese, overweight/obese in high school, smoking status, aerobic exercise, 25(OH)D level. ²Multivariable model 2 was additionally adjusted for coffee consumption.

https://doi.org/10.1371/journal.pone.0290700.s003

(DOCX)

S4 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to coffee consumption.

*Data are presented as means (standard error) ¹Multivariable model 1 was adjusted for age, sex, alcohol consumption, coffee consumption, current overweight/obese, overweight/obese in high school, smoking status, aerobic exercise, 25(OH)D level.

https://doi.org/10.1371/journal.pone.0290700.s004

(DOCX)

S1 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to alcohol consumption, without adjusting for overweight/obese in high school.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.s005

(TIF)

S2 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to overweight/obese status exclude adjusting overweight/obese in high school.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.s006

(TIF)

S3 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to current smoking status exclude adjusting overweight/obese in high school.

* The height of bar represents adjusted mean level of serum IgG and error bar represents its 95% confidence interval. * Difference in the adjusted mean levels of serum IgG levels between rs4410790 genotype represents the coefficient for the association between rs4410790 genotype and IgG levels.

https://doi.org/10.1371/journal.pone.0290700.s007

(TIF)

References

1. Cornelis MC, Byrne EM, Esko T, Nalls MA, Ganna A, Paynter N, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Molecular psychiatry. 2015;20(5):647–56. pmid:25288136
- View Article
- PubMed/NCBI
- Google Scholar
2. Barouki R, Aggerbeck M, Aggerbeck L, Coumoul X. The aryl hydrocarbon receptor system. 2012.
- View Article
- Google Scholar
3. Neavin DR, Liu D, Ray B, Weinshilboum RM. The role of the aryl hydrocarbon receptor (AHR) in immune and inflammatory diseases. International journal of molecular sciences. 2018;19(12):3851. pmid:30513921
- View Article
- PubMed/NCBI
- Google Scholar
4. Gutiérrez-Vázquez C, Quintana FJ. Regulation of the immune response by the aryl hydrocarbon receptor. Immunity. 2018;48(1):19–33. pmid:29343438
- View Article
- PubMed/NCBI
- Google Scholar
5. Cornelis MC, Monda KL, Yu K, Paynter N, Azzato EM, Bennett SN, et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS genetics. 2011;7(4):e1002033. pmid:21490707
- View Article
- PubMed/NCBI
- Google Scholar
6. Josse AR, Da Costa LA, Campos H, El-Sohemy A. Associations between polymorphisms in the AHR and CYP1A1-CYP1A2 gene regions and habitual caffeine consumption. The American journal of clinical nutrition. 2012;96(3):665–71. pmid:22854411
- View Article
- PubMed/NCBI
- Google Scholar
7. Nordestgaard AT, Nordestgaard BG. Coffee intake, cardiovascular disease and all-cause mortality: observational and Mendelian randomization analyses in 95 000–223 000 individuals. International journal of epidemiology. 2016;45(6):1938–52.
- View Article
- Google Scholar
8. Horrigan LA, Kelly JP, Connor TJ. Immunomodulatory effects of caffeine: friend or foe? Pharmacology & therapeutics. 2006;111(3):877–92. pmid:16540173
- View Article
- PubMed/NCBI
- Google Scholar
9. Aschermann S, Lux A, Baerenwaldt A, Biburger M, Nimmerjahn F. The other side of immunoglobulin G: suppressor of inflammation. Clinical & Experimental Immunology. 2010;160(2):161–7.
- View Article
- Google Scholar
10. Hoepel W, Allahverdiyeva S, Harbiye H, de Taeye SW, van der Ham AJ, de Boer L, et al. IgG subclasses shape cytokine responses by human myeloid immune cells through differential metabolic reprogramming. The Journal of Immunology. 2020;205(12):3400–7. pmid:33188071
- View Article
- PubMed/NCBI
- Google Scholar
11. Hoepel W, Golebski K, van Drunen CM, den Dunnen J. Active control of mucosal tolerance and inflammation by human IgA and IgG antibodies. Journal of Allergy and Clinical Immunology. 2020;146(2):273–5. pmid:32387110
- View Article
- PubMed/NCBI
- Google Scholar
12. Wourms MJ, Sulentic CE. The aryl hydrocarbon receptor regulates an essential transcriptional element in the immunoglobulin heavy chain gene. Cellular immunology. 2015;295(1):60–6. pmid:25749007
- View Article
- PubMed/NCBI
- Google Scholar
13. Kashgari BF. Determining the role of the AhR in immunoglobulin expression and class switch recombination. 2015.
- View Article
- Google Scholar
14. Barr T, Helms C, Grant K, Messaoudi I. Opposing effects of alcohol on the immune system. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2016;65:242–51. pmid:26375241
- View Article
- PubMed/NCBI
- Google Scholar
15. Lee J, Taneja V, Vassallo R. Cigarette smoking and inflammation: cellular and molecular mechanisms. Journal of dental research. 2012;91(2):142–9. pmid:21876032
- View Article
- PubMed/NCBI
- Google Scholar
16. Wellen KE, Hotamisligil GS. Obesity-induced inflammatory changes in adipose tissue. The Journal of clinical investigation. 2003;112(12):1785–8. pmid:14679172
- View Article
- PubMed/NCBI
- Google Scholar
17. Gonzalez-Quintela A, Alende R, Gude Fa, Campos J, Rey J, Meijide L, et al. Serum levels of immunoglobulins (IgG, IgA, IgM) in a general adult population and their relationship with alcohol consumption, smoking and common metabolic abnormalities. Clinical & Experimental Immunology. 2008;151(1):42–50.
- View Article
- Google Scholar
18. Paiva C, Beserra B, Reis C, Dorea J, Da Costa T, Amato A. Consumption of coffee or caffeine and serum concentration of inflammatory markers: A systematic review. Critical reviews in food science and nutrition. 2019;59(4):652–63. pmid:28967799
- View Article
- PubMed/NCBI
- Google Scholar
19. Lee SY, Kim S, Kim W-H, Heo J. Employment, economic, and sociodemographic factors associated with changes in smoking and drinking behaviors during the COVID-19 pandemic in South Korea. International journal of environmental research and public health. 2022;19(5):2802. pmid:35270495
- View Article
- PubMed/NCBI
- Google Scholar
20. Kwon Y-J, Kim JO, Park J-M, Choi J-E, Park D-H, Song Y, et al. Identification of genetic factors underlying the association between sodium intake habits and hypertension risk. Nutrients. 2020;12(9):2580. pmid:32854392
- View Article
- PubMed/NCBI
- Google Scholar
21. Ryu J, Kim S, Song J, Kim D, Keum N, Jang W, et al. Fabrication of microarrays for the analysis of serological antibody isotypes against food antigens. Sensors. 2019;19(18):3893. pmid:31509969
- View Article
- PubMed/NCBI
- Google Scholar
22. Jeon H, Jung JH, Kim Y, Kwon Y, Kim ST. Allergen microarrays for in vitro diagnostics of allergies: comparison with ImmunoCAP and AdvanSure. Annals of Laboratory Medicine. 2018;38(4):338. pmid:29611384
- View Article
- PubMed/NCBI
- Google Scholar
23. Singer K, Lumeng CN. The initiation of metabolic inflammation in childhood obesity. The Journal of clinical investigation. 2017;127(1):65–73. pmid:28045405
- View Article
- PubMed/NCBI
- Google Scholar
24. Lim JU, Lee JH, Kim JS, Hwang YI, Kim T-H, Lim SY, et al. Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients. International journal of chronic obstructive pulmonary disease. 2017:2465–75. pmid:28860741
- View Article
- PubMed/NCBI
- Google Scholar
25. Organization WH. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63. pmid:14726171
- View Article
- PubMed/NCBI
- Google Scholar
26. Wang HJ, Zakhari S, Jung MK. Alcohol, inflammation, and gut-liver-brain interactions in tissue damage and disease development. World journal of gastroenterology: WJG. 2010;16(11):1304. pmid:20238396
- View Article
- PubMed/NCBI
- Google Scholar
27. Surmi B, Hasty A. Macrophage infiltration into adipose tissue: initiation, propagation and remodeling. Future lipidology. 2008;3(5):545–56. pmid:18978945
- View Article
- PubMed/NCBI
- Google Scholar
28. Winer S, Chan Y, Paltser G, Truong D, Tsui H, Bahrami J, et al. Normalization of obesity-associated insulin resistance through immunotherapy. Nature medicine. 2009;15(8):921–9. pmid:19633657
- View Article
- PubMed/NCBI
- Google Scholar
29. Ilavská S, Horváthová M, Szabová M, Nemessányi T, Jahnová E, Tulinská J, et al. Association between the human immune response and body mass index. Human immunology. 2012;73(5):480–5. pmid:22426256
- View Article
- PubMed/NCBI
- Google Scholar
30. Wilders-Truschnig M, Mangge H, Lieners C, Gruber H-J, Mayer C, März W. IgG antibodies against food antigens are correlated with inflammation and intima media thickness in obese juveniles. Experimental and clinical endocrinology & diabetes. 2008;116(04):241–5. pmid:18072008
- View Article
- PubMed/NCBI
- Google Scholar
31. Fan W, Xu Y, Liu Y, Zhang Z, Lu L, Ding Z. Obesity or overweight, a chronic inflammatory status in male reproductive system, leads to mice and human subfertility. Frontiers in physiology. 2018;8:1117. pmid:29354072
- View Article
- PubMed/NCBI
- Google Scholar
32. Gluud C, Tage‐Jensen U. Autoantibodies and immunoglobulins in alcoholic steatosis and cirrhosis. Acta Medica Scandinavica. 1983;214(1):61–6. pmid:6605027
- View Article
- PubMed/NCBI
- Google Scholar
33. Kumar Y, Lakshmi P, Minz R, Chhabra S, Saikia B. Evaluation of serum immunoglobulins IgG, IgA, IgM and total IgE in chronic alcoholics: A community-based study. Immunochem Immunopathol: Open Access. 2015;1(102):2.
- View Article
- Google Scholar
34. Yücel G, Zhao Z, El-Battrawy I, Lan H, Lang S, Li X, et al. Lipopolysaccharides induced inflammatory responses and electrophysiological dysfunctions in human-induced pluripotent stem cell derived cardiomyocytes. Scientific reports. 2017;7(1):2935. pmid:28592841
- View Article
- PubMed/NCBI
- Google Scholar
35. Hosseinzadeh A, Thompson PR, Segal BH, Urban CF. Nicotine induces neutrophil extracellular traps. Journal of Leucocyte Biology. 2016;100(5):1105–12. pmid:27312847
- View Article
- PubMed/NCBI
- Google Scholar
36. Bakhru A, Erlinger TP. Smoking cessation and cardiovascular disease risk factors: results from the Third National Health and Nutrition Examination Survey. PLoS medicine. 2005;2(6):e160. pmid:15974805
- View Article
- PubMed/NCBI
- Google Scholar
37. Tarbiah N, Todd I, Tighe PJ, Fairclough LC. Cigarette smoking differentially affects immunoglobulin class levels in serum and saliva: An investigation and review. Basic & Clinical Pharmacology & Toxicology. 2019;125(5):474–83. pmid:31219219
- View Article
- PubMed/NCBI
- Google Scholar
38. Kobayashi S, Okamoto H, Iwamoto T, Toyama Y, Tomatsu T, Yamanaka H, et al. A role for the aryl hydrocarbon receptor and the dioxin TCDD in rheumatoid arthritis. Rheumatology. 2008;47(9):1317–22. pmid:18617548
- View Article
- PubMed/NCBI
- Google Scholar
39. Agarwal S, Cunningham-Rundles C. Assessment and clinical interpretation of reduced IgG values. Annals of Allergy, Asthma & Immunology. 2007;99(3):281–3. pmid:17910333
- View Article
- PubMed/NCBI
- Google Scholar
40. Sedrani S. Correlation between concentrations of humoral antibodies and vitamin D nutritional status: a survey study. European Journal of Clinical Nutrition. 1988;42(3):243–8. pmid:3260176
- View Article
- PubMed/NCBI
- Google Scholar
41. Khan SR, van der Burgh AC, Peeters RP, van Hagen PM, Dalm VA, Chaker L. Determinants of serum immunoglobulin levels: a systematic review and meta-analysis. Frontiers in immunology. 2021;12:1103. pmid:33897714
- View Article
- PubMed/NCBI
- Google Scholar
42. van Dam AD, van Beek L, Pronk AC, van den Berg SM, Van den Bossche J, de Winther M, et al. IgG is elevated in obese white adipose tissue but does not induce glucose intolerance via Fcγ-receptor or complement. International journal of obesity. 2018;42(2):260–9.
- View Article
- Google Scholar
43. Bachi AL, Suguri VM, Ramos LR, Mariano M, Vaisberg M, Lopes JD. Increased production of autoantibodies and specific antibodies in response to influenza virus vaccination in physically active older individuals. Results in immunology. 2013;3:10–6. pmid:24600554
- View Article
- PubMed/NCBI
- Google Scholar
44. Mossong J, O’callaghan C, Ratnam S. Modelling antibody response to measles vaccine and subsequent waning of immunity in a low exposure population. Vaccine. 2000;19(4–5):523–9. pmid:11027817
- View Article
- PubMed/NCBI
- Google Scholar
45. Medicine NLo. rs4410790 [cited 2022 September 24]. Available from: https://www.ncbi.nlm.nih.gov/snp/rs4410790.

[ref1] 1. Cornelis MC, Byrne EM, Esko T, Nalls MA, Ganna A, Paynter N, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Molecular psychiatry. 2015;20(5):647–56. pmid:25288136
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Barouki R, Aggerbeck M, Aggerbeck L, Coumoul X. The aryl hydrocarbon receptor system. 2012.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Neavin DR, Liu D, Ray B, Weinshilboum RM. The role of the aryl hydrocarbon receptor (AHR) in immune and inflammatory diseases. International journal of molecular sciences. 2018;19(12):3851. pmid:30513921
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. Gutiérrez-Vázquez C, Quintana FJ. Regulation of the immune response by the aryl hydrocarbon receptor. Immunity. 2018;48(1):19–33. pmid:29343438
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Cornelis MC, Monda KL, Yu K, Paynter N, Azzato EM, Bennett SN, et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS genetics. 2011;7(4):e1002033. pmid:21490707
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Josse AR, Da Costa LA, Campos H, El-Sohemy A. Associations between polymorphisms in the AHR and CYP1A1-CYP1A2 gene regions and habitual caffeine consumption. The American journal of clinical nutrition. 2012;96(3):665–71. pmid:22854411
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Nordestgaard AT, Nordestgaard BG. Coffee intake, cardiovascular disease and all-cause mortality: observational and Mendelian randomization analyses in 95 000–223 000 individuals. International journal of epidemiology. 2016;45(6):1938–52.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref8] 8. Horrigan LA, Kelly JP, Connor TJ. Immunomodulatory effects of caffeine: friend or foe? Pharmacology & therapeutics. 2006;111(3):877–92. pmid:16540173
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref9] 9. Aschermann S, Lux A, Baerenwaldt A, Biburger M, Nimmerjahn F. The other side of immunoglobulin G: suppressor of inflammation. Clinical & Experimental Immunology. 2010;160(2):161–7.
View Article
Google Scholar

[32] View Article

[33] Google Scholar

[ref10] 10. Hoepel W, Allahverdiyeva S, Harbiye H, de Taeye SW, van der Ham AJ, de Boer L, et al. IgG subclasses shape cytokine responses by human myeloid immune cells through differential metabolic reprogramming. The Journal of Immunology. 2020;205(12):3400–7. pmid:33188071
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref11] 11. Hoepel W, Golebski K, van Drunen CM, den Dunnen J. Active control of mucosal tolerance and inflammation by human IgA and IgG antibodies. Journal of Allergy and Clinical Immunology. 2020;146(2):273–5. pmid:32387110
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref12] 12. Wourms MJ, Sulentic CE. The aryl hydrocarbon receptor regulates an essential transcriptional element in the immunoglobulin heavy chain gene. Cellular immunology. 2015;295(1):60–6. pmid:25749007
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref13] 13. Kashgari BF. Determining the role of the AhR in immunoglobulin expression and class switch recombination. 2015.
View Article
Google Scholar

[47] View Article

[48] Google Scholar

[ref14] 14. Barr T, Helms C, Grant K, Messaoudi I. Opposing effects of alcohol on the immune system. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2016;65:242–51. pmid:26375241
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Lee J, Taneja V, Vassallo R. Cigarette smoking and inflammation: cellular and molecular mechanisms. Journal of dental research. 2012;91(2):142–9. pmid:21876032
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref16] 16. Wellen KE, Hotamisligil GS. Obesity-induced inflammatory changes in adipose tissue. The Journal of clinical investigation. 2003;112(12):1785–8. pmid:14679172
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref17] 17. Gonzalez-Quintela A, Alende R, Gude Fa, Campos J, Rey J, Meijide L, et al. Serum levels of immunoglobulins (IgG, IgA, IgM) in a general adult population and their relationship with alcohol consumption, smoking and common metabolic abnormalities. Clinical & Experimental Immunology. 2008;151(1):42–50.
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref18] 18. Paiva C, Beserra B, Reis C, Dorea J, Da Costa T, Amato A. Consumption of coffee or caffeine and serum concentration of inflammatory markers: A systematic review. Critical reviews in food science and nutrition. 2019;59(4):652–63. pmid:28967799
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref19] 19. Lee SY, Kim S, Kim W-H, Heo J. Employment, economic, and sociodemographic factors associated with changes in smoking and drinking behaviors during the COVID-19 pandemic in South Korea. International journal of environmental research and public health. 2022;19(5):2802. pmid:35270495
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref20] 20. Kwon Y-J, Kim JO, Park J-M, Choi J-E, Park D-H, Song Y, et al. Identification of genetic factors underlying the association between sodium intake habits and hypertension risk. Nutrients. 2020;12(9):2580. pmid:32854392
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Ryu J, Kim S, Song J, Kim D, Keum N, Jang W, et al. Fabrication of microarrays for the analysis of serological antibody isotypes against food antigens. Sensors. 2019;19(18):3893. pmid:31509969
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref22] 22. Jeon H, Jung JH, Kim Y, Kwon Y, Kim ST. Allergen microarrays for in vitro diagnostics of allergies: comparison with ImmunoCAP and AdvanSure. Annals of Laboratory Medicine. 2018;38(4):338. pmid:29611384
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref23] 23. Singer K, Lumeng CN. The initiation of metabolic inflammation in childhood obesity. The Journal of clinical investigation. 2017;127(1):65–73. pmid:28045405
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref24] 24. Lim JU, Lee JH, Kim JS, Hwang YI, Kim T-H, Lim SY, et al. Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients. International journal of chronic obstructive pulmonary disease. 2017:2465–75. pmid:28860741
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref25] 25. Organization WH. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63. pmid:14726171
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref26] 26. Wang HJ, Zakhari S, Jung MK. Alcohol, inflammation, and gut-liver-brain interactions in tissue damage and disease development. World journal of gastroenterology: WJG. 2010;16(11):1304. pmid:20238396
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref27] 27. Surmi B, Hasty A. Macrophage infiltration into adipose tissue: initiation, propagation and remodeling. Future lipidology. 2008;3(5):545–56. pmid:18978945
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref28] 28. Winer S, Chan Y, Paltser G, Truong D, Tsui H, Bahrami J, et al. Normalization of obesity-associated insulin resistance through immunotherapy. Nature medicine. 2009;15(8):921–9. pmid:19633657
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref29] 29. Ilavská S, Horváthová M, Szabová M, Nemessányi T, Jahnová E, Tulinská J, et al. Association between the human immune response and body mass index. Human immunology. 2012;73(5):480–5. pmid:22426256
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref30] 30. Wilders-Truschnig M, Mangge H, Lieners C, Gruber H-J, Mayer C, März W. IgG antibodies against food antigens are correlated with inflammation and intima media thickness in obese juveniles. Experimental and clinical endocrinology & diabetes. 2008;116(04):241–5. pmid:18072008
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref31] 31. Fan W, Xu Y, Liu Y, Zhang Z, Lu L, Ding Z. Obesity or overweight, a chronic inflammatory status in male reproductive system, leads to mice and human subfertility. Frontiers in physiology. 2018;8:1117. pmid:29354072
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref32] 32. Gluud C, Tage‐Jensen U. Autoantibodies and immunoglobulins in alcoholic steatosis and cirrhosis. Acta Medica Scandinavica. 1983;214(1):61–6. pmid:6605027
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref33] 33. Kumar Y, Lakshmi P, Minz R, Chhabra S, Saikia B. Evaluation of serum immunoglobulins IgG, IgA, IgM and total IgE in chronic alcoholics: A community-based study. Immunochem Immunopathol: Open Access. 2015;1(102):2.
View Article
Google Scholar

[125] View Article

[126] Google Scholar

[ref34] 34. Yücel G, Zhao Z, El-Battrawy I, Lan H, Lang S, Li X, et al. Lipopolysaccharides induced inflammatory responses and electrophysiological dysfunctions in human-induced pluripotent stem cell derived cardiomyocytes. Scientific reports. 2017;7(1):2935. pmid:28592841
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref35] 35. Hosseinzadeh A, Thompson PR, Segal BH, Urban CF. Nicotine induces neutrophil extracellular traps. Journal of Leucocyte Biology. 2016;100(5):1105–12. pmid:27312847
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref36] 36. Bakhru A, Erlinger TP. Smoking cessation and cardiovascular disease risk factors: results from the Third National Health and Nutrition Examination Survey. PLoS medicine. 2005;2(6):e160. pmid:15974805
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref37] 37. Tarbiah N, Todd I, Tighe PJ, Fairclough LC. Cigarette smoking differentially affects immunoglobulin class levels in serum and saliva: An investigation and review. Basic & Clinical Pharmacology & Toxicology. 2019;125(5):474–83. pmid:31219219
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref38] 38. Kobayashi S, Okamoto H, Iwamoto T, Toyama Y, Tomatsu T, Yamanaka H, et al. A role for the aryl hydrocarbon receptor and the dioxin TCDD in rheumatoid arthritis. Rheumatology. 2008;47(9):1317–22. pmid:18617548
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref39] 39. Agarwal S, Cunningham-Rundles C. Assessment and clinical interpretation of reduced IgG values. Annals of Allergy, Asthma & Immunology. 2007;99(3):281–3. pmid:17910333
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref40] 40. Sedrani S. Correlation between concentrations of humoral antibodies and vitamin D nutritional status: a survey study. European Journal of Clinical Nutrition. 1988;42(3):243–8. pmid:3260176
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref41] 41. Khan SR, van der Burgh AC, Peeters RP, van Hagen PM, Dalm VA, Chaker L. Determinants of serum immunoglobulin levels: a systematic review and meta-analysis. Frontiers in immunology. 2021;12:1103. pmid:33897714
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref42] 42. van Dam AD, van Beek L, Pronk AC, van den Berg SM, Van den Bossche J, de Winther M, et al. IgG is elevated in obese white adipose tissue but does not induce glucose intolerance via Fcγ-receptor or complement. International journal of obesity. 2018;42(2):260–9.
View Article
Google Scholar

[160] View Article

[161] Google Scholar

[ref43] 43. Bachi AL, Suguri VM, Ramos LR, Mariano M, Vaisberg M, Lopes JD. Increased production of autoantibodies and specific antibodies in response to influenza virus vaccination in physically active older individuals. Results in immunology. 2013;3:10–6. pmid:24600554
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref44] 44. Mossong J, O’callaghan C, Ratnam S. Modelling antibody response to measles vaccine and subsequent waning of immunity in a low exposure population. Vaccine. 2000;19(4–5):523–9. pmid:11027817
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref45] 45. Medicine NLo. rs4410790 [cited 2022 September 24]. Available from: https://www.ncbi.nlm.nih.gov/snp/rs4410790.

Figures

Abstract

Introduction

Materials and methods

Study participants

Identification of rs4410790 genotype

Assessment of serum IgG levels

Assessment of covariates

Statistical analysis

Result

Baseline characteristics of participants by rs4410790 genotype

Association between rs4410790 genotype and serum IgG levels

Association between rs4410790 genotype and serum IgG levels by lifestyle factors

Discussion

Conclusion

Supporting information

S1 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to alcohol consumption.

S2 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to obesity status.

S3 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to current smoking status.

S4 Table. Multivariable association between rs4410790 genotype and serum IgG levels according to coffee consumption.

S1 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to alcohol consumption, without adjusting for overweight/obese in high school.

S2 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to overweight/obese status exclude adjusting overweight/obese in high school.

S3 Fig. Multivariable association between rs4410790 genotype and serum IgG levels according to current smoking status exclude adjusting overweight/obese in high school.

References