Potential Roles of Cigarette Smoking on Gut Microbiota Profile among Chinese Men

doi:10.21203/rs.3.rs-4274545/v1

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Potential Roles of Cigarette Smoking on Gut Microbiota Profile among Chinese Men

https://doi.org/10.21203/rs.3.rs-4274545/v1

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Cigarette smoking is posited as a potential factor in disrupting the balance of the human gut microbiota. However, existing studies with limited sample size have yielded inconclusive results. Here, we assessed the association between cigarette smoking and gut microbial profile among Chinese males from four independent studies (N total = 3,308). Both 16S rRNA and shotgun metagenomic sequencing methods were employed, covering 206 genera and 237 species. Microbial diversity was compared among non-smokers, current smokers, and former smokers. Actinomyces[g], Atopobium[g], Haemophilus[g], Turicibacter[g], and Lachnospira[g] were found to be associated with smoking status (current smokers v.s. non-smokers). Metagenomic data provided a higher resolution at the species level, particularly for the Actinomyces[g] branch. Additionally, serum Trans-3-Hydroxycotinine was found to have a potential role in connecting smoking and Actinomyces[g]. Furthermore, we revealed putative mediation roles of gut microbiome in the associations between smoking and common diseases including cholecystitis and type 2 diabetes. In conclusion, we characterized the gut microbiota profile in male smokers and further revealed their potential involvement in mediating the impact of smoking on health outcomes. These findings advance our understanding of the intricate associations between cigarette smoking and the gut microbiome.

Cigarette smoking

Gut microbiota

Chinese

Male population

The human gut harbors a complex and dynamic community of microorganisms, known as the gut microbiota, estimated to exceed 100 trillion [1]. The gut microbiota has co-evolved with its hosts, forming an intricate and mutually beneficial relationship that intimately influence the overall health of the host [2, 3]. In turn, the host conditions (like genetic background [4] and lifestyle habits [5]) also shape the composition and functionality of the gut microbiota.

Cigarette smoking is considered a major behavioral risk factor for various health outcomes [6]. Recent evidence has suggested that this might involve alterations in the gut microbiota [7–9]. For instance, smoking-induced dysbiosis of the gut microbiota played a protumourigenic role in colorectal cancer [9]. Over the past decade, there has appeared a growing focus on understanding the connection between smoking and the gut microbiota [10, 11]. Two recent reviews have diligently compiled existing knowledge from rodent experiments, shedding light on the mechanistic effects of cigarette smoke on intestinal microbiota dysbiosis [12, 13]. This intricate procedure may involve a cascade of events, including elevated lipid peroxidation, pro-inflammatory responses, as well as regulation of the intestinal microenvironment. Emerging population-level evidence has prompted another comprehensive review of 13 existing studies which mostly conducted among European Americans [14]. In most of the studies, a consistent trend of decreased microbial diversity was observed among smokers [15–20]. However, there existed substantial interstudy heterogeneity for specific microbes, especially for analyses at genus level. For example, three studies [17, 21, 22] reported a higher level of Bacteroides[g] in smokers, whereas two other studies [15, 16] found its enrichment in non-smokers. In addition to above 13 studies on healthy subjects, there were also a number of studies conducted in specific populations, including patients with coronary artery disease and hypertension [23–27]. These existing studies encountered common challenges, including: (1) small sample sizes (only three studies included more than 300 subjects), (2) lacked consideration for confounding factors, particularly sex (only four had statistical adjustments), and (3) poor reproducibility across studies.

In our prior endeavor, we sought to establish a causal connection between tobacco smoking and gut microbiota using two-sample Mendelian randomization (MR) based on publicly available Genome-Wide Association Studies (GWASs) [28]. This analysis identified the impact of smoking on 16 taxa, exemplified by a reduction in the abundance of Haemophilus[g]. However, it is important to note that these findings are somewhat constrained to the European population. Promisingly, advancements in sequencing technology have facilitated the ongoing accumulation of microbiome data within the Chinese population. The integration of these data with large-scale cohort studies, exemplified by the Westlake Gut Project [29], has provided a unique opportunity to explore the potential role of gut microbiota in mediating associations between various exposures (e.g., cigarette smoking) and human health in China.

In the present study, our primary aim was to conduct a multi-study investigation into the gut microbial profile associated with cigarette smoking among only Chinese males, duo to the low proportion of Chinese female smokers (~ 5%). The study integrated microbial data of a total of 3,308 participants from four independent studies: Zhejiang Metabolic Syndrome Cohort (ZMSC), Guangzhou Nutrition and Health Study (GNHS), Guangdong Gut Microbiome Project (GGMP), and Sir Run Run Shaw Hospital Study (SRRSHS). Additionally, our attempts also extended into the correlations of the identified smoking-associated genera with health-related outcomes and smoking-related metabolites. This study seeks to improve our comprehension of the intricate interplay among cigarette smoking, the gut microbiome, metabolites, and health outcomes.

Description of studies and participants

The current analysis was based on the four separate studies, with ZMSC (n = 1,781, 2020 survey) and SRRSHS (n = 215) conducted in Zhejiang province, as well as GNHS (n = 4,048, 2008–2013 baseline) and GGMP (n = 7,008) in Guangdong province (Fig. 1). A detailed description regarding study design, recruitment and selection criteria of participants, stool sample collection, and sequencing procedures for ZMSC [30], GNHS [31], GGMP [32], and SRRSHS [21] has been previously documented and was also collated in the Supplementary Methods. Briefly, ZMSC and GNHS are two community-cluster prospective cohorts, while GGMP and SRRSHS follow the cross-sectional design. A subset of participant pool, including 1,382 from ZMSC, 1,933 from GNHS, 7,003 from GGMP, and 116 from SRRSHS, underwent 16S rRNA sequencing on stool samples. Shotgun metagenomic data were also available in ZMSC and GNHS. Given the substantial gender imbalance between smokers and non-smokers, female participants were excluded in the final analysis. In addition, we also excluded recent antibiotic and aspirin users, former smokers or non-smokers with secondhand smoke exposure, and those with missing data on age and BMI. A portion of ZMSC participants from the 2015 survey underwent widely targeted metabolomic measurements, involving 1,912 blood metabolites. Health-related outcomes for the majority of ZMSC participants were followed up through the linkage with regional medical health records since 2010.

Metadata collection and definition of smoking status

Demographic information for participants in all four studies was collected via the questionnaire. Individuals were categorized into different groups based on their smoking status: non-smokers, current smokers, and former smokers. In both ZMSC and GNHS, individuals classified as current smokers were those who have smoked at least 100 cigarettes throughout their lifetime and were still smoking, whereas former smokers were defined as those who have refrained from smoking for a minimum of six months. Within GGMP, respondents indicating ‘currently smokes, every day’ and ‘currently smokes, but not every day’ were categorized as current smokers, while those who stated ‘never smoked’ and ‘has quit smoking’ fell into the non-smoker and former smoker categories, respectively. In SRRSHS, current smokers were individuals with a history of continuous smoking for six months or longer, and information on smoking cessation was not available. ZMSC and GNHS also investigated the number of cigarettes smoked per day by the subjects.

Stool sample collection, DNA extraction, and 16S rRNA/shotgun metagenomic sequencing

The collection of stool samples occurred either on-site or at participants' homes, with subsequent swift transportation to the research laboratory utilizing a cold-chain vehicle and stored in -80°C freezers until further processing. Stool samples not reaching the designated collection point within the specified time frame were discarded. The extraction of total bacterial DNA was performed using suitable fecal DNA isolation kits. For 16S rRNA gene analysis, the V3 and/or V4 variable regions were amplified using corresponding barcoded primers and subsequently subjected to sequencing. For shotgun metagenomic analysis, performed in ZMSC and GNHS, sequencing was conducted on the Illumina HiSeq platform (Illumina Inc., CA, USA) using the 2×150-bp paired-end read protocol. Detailed information on stool sample collection, processing, and 16S rRNA/shotgun metagenomic sequencing of each study is provided in Supplementary Table 1.

Taxonomic profiling

Detailed information on bioinformatics analysis of the microbial sequencing data could be found in the previous publications [21, 33, 34]. For 16S rRNA data, QIIME2 software (Quantitative Insights into Microbial Ecology 2) [35] was employed for downstream analysis, including merging paired reads, filtering reads, de-noising to obtain representative sequences in the form of Amplicon Sequence Variants (ASVs, in ZMSC and GNHS) or Operational Taxonomic Units (OTUs, in GGMP and SRRSHS), and subsequently taxonomic assignment. In the taxonomic assignment, the Silva database [36] was used as a reference for annotation in ZMSC, GNHS and SRRSHS, while GreenGenes database [37] was used in GGMP. For metagenomic data, consistent across ZMSC and GNHS, we conducted data quality control using PRINSEQ (version 0.20.4). Afterward, the metagenomic taxonomy was employed by MetaPhlAn2 (version 2.6.02) with default settings [38]. The functional profiling was carried out using HUMAnN2 (version 2.8.1) [39], which reconstructed metabolic pathway abundance according to the subset of metabolic reaction-related gene families from MetaCyc [40, 41]. Genera (16S rRNA) and species (metagenome) with a prevalence of less than 10% in participants and an average relative abundance below 0.01% were excluded. Microbial pathways (metagenome) were excluded solely based on their prevalence of less than 10%. This led to the inclusion of 133 genera in ZMSC, 102 genera in GNHS, 81 genera in GGMP, and 99 genera in SRRSHS for subsequent analyses (Supplementary Fig. 1 and Supplementary Table 2–5). It is important to note that applying the same criterion to species (metagenome) with lower relative abundance might be excessively stringent. Therefore, we conducted a parallel analysis involving species that were excluded only because of a prevalence of less than 10%.

Statistical analysis

An outline of the study's analytical strategy and methodology is depicted in Fig. 1. The analyses were structured into three steps.

The comparison of the gut microbial diversity (step1)

The alpha diversity at genus/species level was estimated to reflect the richness and evenness of the gut microbiota within a sample. Indicators included Richness (number of genera), Shannon index, Simpson index, and Pielou index. The differences of four alpha diversity parameters between groups with different smoking status were evaluated using a Wilcoxon test in each study. Beta diversity, quantified by the Bray-Curtis distance between samples, was estimated based on the relative abundance matrices of genera/species and visualized using principal coordinate analysis (PCoA) plots. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was applied to discern whether and to what extent dissimilarity in the beta diversity could be attributed to cigarette smoking. This was achieved by the ‘adonis’ function of the vegan package (version 2.6-4), with adjustments for age and body mass index (BMI).

The identification of smoking-associated microbes and pathways (step2)

To identify the gut microbe at the genus level, we employed Microbiome Multivariable Association with Linear Models (MaAsLin2) in each study, a method demonstrated to be considerably effective in analyzing microbial data [42]. The relative abundance of genera was modeled as the independent variable in log-transformed form, while three smoking statuses as the dependent variable in comparison with each other. Before log-transformation, zeros in microbial data were substituted with a small value (one-tenth of the minimum abundance of the corresponding genera), which had a negligible impact on the data distribution. Following this, a meta-analysis with the fixed-effects model was conducted to integrate the MaAsLin2’s results of the four studies. In our main analyses, our models were adjusted for age and BMI in all four studies. To rule out possible impacts of alcohol consumption, sensitivity analyses were conducted with additional adjustments for drinking status. Given the execution of high-dimensional tests, we applied the Bonferroni correction (totally 206 genera), deeming associations with a Bonferroni-corrected p less than 0.05 as statistically significant. The methods mentioned above were repeated for metagenomic data at the species level and for microbial pathway analysis.

The exploration of the associations between identified genera and blood metabolites as well as health outcomes (step3)

In this regard, we conducted regression analysis based on ZMSC observational data and two-sample MR analysis based on GWAS summary data to offer epidemiological and genetic-based evidence. In regression analyses with smoking-associated microbes, logistic regression was applied for binary diseases (dependent variables), while linear regression was applied for continuous biochemical indicators (dependent variables) and blood metabolites (independent variables). We examined 14 common chronic diseases, 22 biochemical markers, and 1,912 blood metabolites available in ZMSC. The models were adjusted for age and BMI. In MR analysis, the causal links between gut microbiota (exposures) and health-related phenotypes (outcomes) were mainly estimated by inverse-variance weighted (IVW) method. The summary data on gut microbiota was derived from a GWAS containing over 7,000 individuals of Asian descent (unpublished, the GWAS summary statistics will be available at https://omics.lab.westlake.edu.cn/data.html). The summary data for the outcomes were obtained from BioBank Japan (BBJ), where we used continuous variables and disease phenotypes (excluding those specific to female) with a case count greater than 1,800 (approximately equivalent to a prevalence of 0.01%) [43]. Here, false discovery rate (FDR) using Benjamini-Hochberg method was calculated. More details about disease data, metabolic data, and MR analyses are available in the Supplementary Methods.

Characteristics of study participants

This study enrolled a total of 3,308 male participants from the four studies (Table 1). ZMSC participants were relatively older (65.46 ± 10.00 years) and SRRSHS participants were the youngest (49.90 ± 11.83 years). Generally, age varied among the three different smoking statuses (p < 0.05), while BMI showed no significant differences.

Table 1

Basic characteristics of the participants from the four independent studies
Characteristics	Total (N = 3,308)	Smoking status			P
Characteristics	Total (N = 3,308)	Non-smokes (n = 889)	Current smokers (n = 2,109)	Former smokers (n = 310)	P
ZMSC
No.subjects	383	131	161	91
Age (years, mean ± SD)	65.46 ± 10.00	66.66 ± 11.04	63.52 ± 9.47	67.18 ± 8.77	0.005
BMI (kg/m², mean ± SD)	24.28 ± 3.27	24.10 ± 3.20	24.30 ± 3.32	24.49 ± 3.32	0.669
Cigarettes per day	-	-	20.45 ± 11.60	-
Drinking status (n, %)					0.013
Never drinker	185 (48.9)	74 (56.9)	71 (45.2)	40 (44.0)
Current drinker	171 (45.2)	52 (40.0)	79 (50.3)	40 (44.0)
Ever drinker	22 (5.8)	4 (3.1)	7 (4.5)	11 (12.1)
GNHS
No.subjects	554	308	178	68
Age (years, mean ± SD)	60.88 ± 6.75	61.46 ± 6.80	60.86 ± 7.05	58.31 ± 4.94	0.002
BMI (kg/m², mean ± SD)	23.70 ± 2.70	23.66 ± 2.69	23.85 ± 2.49	23.50 ± 3.25	0.603
Cigarettes per day	-	-	15.78 ± 7.97	17.19 ± 13.16	0.474
Drinking status (n, %)					< 0.001
Never drinker	456 (83.2)	271 (89.7)	129 (72.5)	56 (82.4)
Current drinker	57 (10.4)	17 (5.6)	38 (21.3)	2 (2.9)
Ever drinker	35 (6.4)	14 (4.6)	11 (6.2)	10 (14.7)
GGMP
No.subjects	2,255	408	1,696	151
Age (years, mean ± SD)	54.42 ± 14.37	53.37 ± 16.46	53.79 ± 13.61	64.34 ± 13.00	< 0.001
BMI (kg/m², mean ± SD)	23.09 ± 3.35	23.42 ± 3.34	23.02 ± 3.32	22.92 ± 3.64	0.074
Drinking status (n, %)					< 0.001
Never drinker	1,008 (44.7)	242 (59.3)	677 (39.9)	89 (58.9)
Current drinker	1247 (55.3)	166 (40.7)	1,019 (60.1)	62 (41.1)
SRRSHS
No.subjects	116	42	74	-
Age (years, mean ± SD)	49.90 ± 11.83	52.51 ± 13.62	48.45 ± 10.53	-	0.077
BMI (kg/m², mean ± SD)	24.28 ± 2.65	23.48 ± 2.53	24.74 ± 2.63	-	0.013
Drinking status (n, %)					0.477
Never drinker	45 (38.8)	14 (33.3)	31 (41.9)	-
Current drinker	71 (61.2)	28 (66.7)	43 (58.1)	-
ZMSC, Zhejiang Metabolic Syndrome Cohort; GNHS, Guangzhou Nutrition and Health Study; GGMP, Guangdong Gut Microbiome Project; SRRSHS, Sir Run Run Shaw Hospital Study; No. or n, numbers of subjects; BMI, body mass index; SD, standard deviation.

Cigarette smoking and microbial diversity

Upon evaluating the results of alpha and beta diversity, no robust evidence supports a significant difference in microbial community structure between smokers and non-smokers. Specifically, the reduction in Pielou index in current smokers was noticed only in two studies from Zhejiang (Fig. 2a). Other alpha diversity metrics did not exhibit significant differences among the groups (Supplementary Fig. 2). As shown in the PCoA plots of beta diversity, the distribution of samples from the three groups did not show a significant separation, except for GGMP, which has the largest sample size (Fig. 2b). It is worth mentioning that the contribution of smoking status to the microbial structure variation was limited.

Figure 2. Variations in the gut microbial diversity across groups with different smoking status. (a) The difference of alpha diversity parameter Pielou between three/two groups was evaluated using a Wilcoxon test in each study. Box plots depict its median and interquartile range, while gray dots represent individual samples, and violin plots illustrate the distribution of these samples. Additional results for Richness, Shannon index, and Simpson index can be found in Supplementary Fig. 2. (b) The difference of beta diversity was visualized using principal coordinate analysis (PCoA) plot based on Bray-Cutis distance at the genus level. PERMANOVA with 999 permutations was employed to identify the variation of beta diversity among the three/two groups, adjusting for age and BMI.

Cigarette smoking and gut microbes at the genus level

A total of 206 eligible genera were analyzed in this study, of which 113 (54.9%) were shared across two or more studies (Supplementary Fig. 1). All results of MaAsLin2 on the association between smoking status and eligible genera are provided in Supplementary Table 6–9. When comparing current smokers to non-smokers, we identified five (ZMSC), six (GNHS), 18 (GGMP), and nine (SRRSHS) genera with p less than 0.05 among 133, 102, 81, and 99 genera, respectively. After meta-analysis, four genera were able to withstand the Bonferroni correction, i.e., Actinomyces[g] (β = 0.748, se = 0.108, p = 3.66E-12), Atopobium[g] (β = 0.833, se = 0.146, p = 1.23E-08), Haemophilus[g] (β = -0.923, se = 0.174, p = 1.10E-07), Turicibacter[g] (β = 0.674, se = 0.156, p = 1.52E-05) (Fig. 3 and Supplementary Table 10). We also identified Lachnospira[g] (β = -0.492, se = 0.144, p = 6.59E-04) whose association was identified in ZMSC and replicated in GGMP (Fig. 3), although the meta-analyzed p did not reach the threshold for Bonferroni correction. We identified a total of five genera that were associated with smoking. Follow-up analyses were mainly based on these five genera. In ZMSC, we also observed a nominally significant association between cigarettes smoked per day and the degree of decline in Haemophilus[g] abundance (β = -0.789, se = 0.392, p = 0.046).

When former smokers were compared to current smokers, we identified seven (ZMSC), six (GNHS), and 14 (GGMP) genera with p less than 0.05 (Supplementary Table 6–9). Through meta-analysis, Actinomyces[g] (β = -0.542, se = 0.155, p = 4.68E-04) exhibited consistency across three studies, and Turicibacter[g] (β = -0.903, se = 0.236, p = 1.27E-04) withstood Bonferroni correction (Supplementary Fig. 3 and Table 11). Taking the previous results together, these findings showed that the elevated abundance of these two microbes in current smokers had a decrease in former smokers. The intuitive comparison of their abundance between groups in each study is shown in Supplementary Fig. 4.

In addition, the results of sensitivity analyses were not substantially different, including further adjustment for drinking status (Supplementary Table 12–15) and alternative use of smoking information from the ZMSC 2015 survey (Supplementary Table 16).

Cigarette smoking and gut microbes at species level

Based on conventional exclusion criteria, our study finally included 238 species from ZMSC and GNHS (Supplementary Fig. 5 and Table 17–18). The results for alpha and beta diversity were consistent with the pattern from 16S data, with no significant differences among the three groups (Supplementary Fig. 6). All results of MaAsLin2 and meta-analysis on the association between smoking status and eligible species are provided in Supplementary Table 19–22 and Supplementary Fig. 7. Considering the lower abundance observed at the species level, we performed a sensitivity analysis that no longer excluded species with an average relative abundance of less than 0.01% (Supplementary Table 23–24). Based on this data, we identified overlapping species, including Actinomyces sp. HMSC035G02[s], Actinomyces sp. oral taxon 181[s], Actinomyces graevenitzii[s], Actinomyces sp. S6 Spd3[s], between the two studies with p less than 0.05 (Fig. 4a). Together with the four above, the meta-analysis suggested ten significant species (Bonferroni-corrected p < 0.05). Notably, eight of them fall under the branch of Actinomyces[g] (Fig. 4b and Supplementary Table 25–26). Five of the eight species demonstrated their effect directions consistent with Actinomyces[g] and were prominent in a large proportion, indicating that smoking-induced elevation in Actinomyces[g] abundance may be driven by these species (Figure 4c).

Cigarette smoking and microbial pathway

461 and 466 microbial pathways, respectively, were included for analysis in ZMSC and GNHS (Supplementary Fig. 8 and Table 27–28). Within ZMSC, lysine synthesis-related pathways (PWY-5097, PWY-2942, PWY-724) were found to be reduced in current smokers (p < 0.05) (Supplementary Table 29). Notably, by analyzing the ZMSC blood metabolomic data, there were also several serum peptides including lysine (Lys-Glu-Glu, Lys-Ile-Val-Lys, Ser-Val-Lys-Arg, and Thr-Lys-Gln-Lys) whose concentrations showed a diminished signal in current smokers compared to non-smokers (p < 0.05, Supplementary Table 32). Two lines of evidence indicated a possible link between smoking and lysine. All results of MaAsLin2 on the association between smoking status and eligible pathways are provided in Supplementary Table 29–30.

Smoking-associated genera and health-related outcomes

To understand the phenotypic consequence of smoking-associated genera, we carried out regression analysis based on ZMSC observational data and two-sample MR analysis based on GWAS summary data. In ZMSC, three associations between specific microbes and traits remained statistically significant after applying FDR correction, with one notable pair being Haemophilus[g] and chronic enteritis (β = -0.092, se = 0.031, p = 2.84E-03) (Fig. 5a). Regarding the MR analysis estimated by the IVW method, only the positive association between Atopobium[g] and chronic hepatitis C passed the significance threshold after FDR correction (β = 0.098, se = 0.028, p = 5.15E-04) (Fig. 5b). The sensitivity analyses showed similar evidence and no horizontal pleiotropy nor heterogeneity (among IVs). Here, results with corrected p less than 0.05 were considered as a high evidence level, while those with p less than 0.05 were deemed suggestive evidence. Remarkably, both the genetics-based MR study and association study pointed to an inverse correlation between Haemophilus[g] and T2D as well as its related traits (Fig. 5c). Meanwhile, this examination revealed potential smoking-genera-disease links wherein these genera may serve as mediators for the effects of smoking on these outcomes, like T2D (possibly mediated by Haemophilus[g]) and cholecystitis (possibly by Actinomyces[g]) (Fig. 5c). The comprehensive results concerning the association between the identified genera and health-related outcomes can be found in Supplementary Table 31–32.

Smoking-associated genera and smoking-associated metabolites

A total of 1,912 serum metabolites were detected in ZMSC during the 2015 survey. We estimated the association between smoking and metabolites under a cross-sectional design. The results revealed that five metabolites (i.e., 4-Hydroxytryptamine, Trans-3-Hydroxycotinine, N-1-Naphthylbenzamide, γ-Glu-Cys, and cyclo(glu-glu)) were significantly associated with cigarette smoking, with FDR corrected p less than 0.05 (Fig. 6a and Supplementary Table 33). Subsequent correlation analyses demonstrated the significant connections between these five metabolites and Actinomyces[g] (Fig. 6b and Supplementary Table 34). For example, the concentration of trans-3-Hydroxycotinine showed a positive correlation with Actinomyces[g] abundance (β = 0.186, se = 0.063, p = 3.46E-03), a relationship corroborated by evidence from the MetOrigin platform [44]. These results bring up a reasonable speculation that these five metabolites, influenced by smoking, potentially further modulated Actinomyces[g] as mediators (Fig. 6c).

In the present population-based smoking-gut microbiota association study, we integrated four independent studies and compared gut microbiota profiles among Chinese men with different smoking status. We identified five genera (Actinomyces[g], Atopobium[g], Haemophilus[g], Turicibacter[g], and Lachnospira[g]) associated with cigarette smoking using the 16S rRNA data, while metagenomic data offered a higher resolution at the species levels and additional information from the microbial pathway perspective. These identified genera were subsequently linked to several prevalent chronic diseases (such as T2D), along with the smoking-associated metabolites (such as trans-3-hydroxycotinine). This study contributes to our comprehension of the intricate interplay between cigarette smoking, the gut microbiome, metabolites, and health outcomes.

In our study, no clear evidence supported the significant changes in microbial diversity attributed to smoking. From our study and existing evidences, an intriguing observation surfaced: smaller-scale studies consistently reported a significant reduction in microbial diversity in smokers. Examples included studies by Stewart et al. (30 participants) [15], Curtis et al. (30 participants) [16], Zhang et al. (131 participants) [17], and Yan et al. (154 participants) [27]. However, in studies with larger sample size, this significance diminished, revealing merely a decreased trend. Notable instances were investigations by Prakash et al. (809 participants) [18], Nolan-Kenney et al. (249 participants) [19], and Chen et al. (118 participants) [20]. In our study, only SRRSHS showing a significant decline in alpha diversity among smokers. The aforementioned finding prompts the consideration of whether insufficient sample sizes could lead to false-positive results. The gut microbial structure is not only affected by sex and age [45, 46], but also largely by geographical location [47] and type of staple food [48]. The contribution of these factors on microbiome variation appeared more substantial compared to smoking status, which was also demonstrated in our study. In small-scale studies, balancing these factors between groups becomes more challenging, and unbalanced factors may confound the estimation. In addition, several specific explanations are also considered for our results. First, the genera analyzed in this study were those with relatively high prevalence (> 10%), which introduced a degree of artificial control for variability in genus richness between comparison groups. A typic fact was that the genera we analyzed were detected in all groups. Second, the smoking-associated microbes we identified exhibited relatively low abundance (0.01%~0.2%). Thus, the alterations in their abundance induced by smoking may not have a remarkable impact on the global composition of the gut microbiota.

In addition to diversity, variations in specific microbe may be more informative and provide important insights [49]. Among five genera identified in the present study, Haemophilus[g] has been reported in our prior MR analysis [28].The reduction abundance of Lachnospira[g] was also supported by findings from another multi-ethnic cohort [18]. Lachnospira[g] functions as a probiotic, offering diverse benefits including immune stimulation and intestinal acidification through short-chain fatty acid production (SCFA), as well as colonization resistance through lantibiotic production [50]. As smoke directly traverses the oral cavity and upper aero-digestive tract, it induces alterations in the microbiota composition within these regions. Numerous studies have reported the enrichment of Actinomyces[g] [51, 52], Atopobium[g] [53–55], and the reduction of Haemophilus[g] [51, 56] in the oral cavity of smokers. However, the impacts on distal organs remain unclear. Our study suggested that this association may extend to the gut. Even the dose-response relationship for cumulative smoking exposure on Haemophilus[g], observed in oral [56], was evident in our gut microbiota study. Experimental evidence provided a possible reason that toxic substances from cigarette smoke could be detected in the gut, which triggered similar antibiotic effects [13]. As for Turicibacter[g], the association appeared to be controversial. A mouse study revealed a potentially time-dependent effect of smoking exposure on the increase in Turicibacter[g] abundance [57]. Nevertheless, two other mice studies have reported conflicting findings on this matter [58, 59]. In any case, our study contributes new evidence to this ongoing debate from a population perspective. As for Bacteroides[g] (a typical example mentioned in the introduction), our results suggested that smoking alone may not significantly alter its abundance. Notably, the statistical methods employed in previous reports (such as Linear discriminant analysis Effect Size (LefSe) [17, 21, 22] and naive Kruskal-Wallis test [15, 16]) overlooked the confounding variables. The instability of these results might be attributed to unaddressed confounding factors. In our study, after restricting to male participants, mitigating the possible effects from passive smoking and medication, and then adjusting for age and BMI using MaAsLin2, the results provided answers with a higher degree of confidence. Furthermore, previous studies on cigarette smoking and gut microbiota have been less thoroughly investigated at the species level. Our study, utilizing metagenomic data, further unveiled that the smoking-induced elevation of Actinomyces[g] was primarily driven by some species, notably Actinomyces graevenitzii[s], considering both compositional ratio and effect size. Case reports of pulmonary abscess indicated that this organism is an opportunistic human pathogen [60, 61].

From the established consequences of smoking (such as direct antibacterial activity [62], chronic low-grade inflammation [63], and alteration of the intestinal micro-environment including pH [13, 64] and intestinal barrier [65, 66]), we can propose some hypotheses related to the observed changes in the microbial profile. Toxicology indicates that heavy metals in cigarette smoke (such as cadmium) can exert a direct antibiotic effect on the Lachnospiraceae[f], the family to which Lachnospira[g] belongs [13, 67]. Cadmium, on the other hand, elevates Turicibacter[g], although this may be an indirect result [13, 67]. Exposure to smoke components could elevate intestinal pH, reducing the production of organic acids as one of the ways involved [13, 64]. This may favor certain bacterial populations, enabling the thriving of specific genera. Actinomyces[g] can be categorized into acidophilic and basophilic strains, with the majority likely being basophilic [68]. As a result, the Actinomyces[g] in smokers tends to increase, with its some species showing an increase while some decline. There was also a significant correlation observed between the presence of Atopobium[g] and an elevated pH [69].

This study also aimed to explore the potential impact of smoking-induced changes in bacteria on diseases and their potential modulation by blood metabolites. A noteworthy finding in this regard was the association between Haemophilus[g] and T2D. The consistent negative correlation reported in various observational studies suggested that Haemophilus[g] could serve as a valuable microbiological marker for T2D [70–72]. A recent MR study in individuals of European descent provided further confirmation, identifying Haemophilus[g] as a defense element against T2D [73]. Moreover, a multi-omics study has elucidated crucial mechanisms through which gut microbiota not only aid in carbohydrate digestion, but also help to improve insulin resistance, thereby preventing the development of obesity and diabetes [74]. The link between Actinomyces[g] and cholecystitis is straightforward, as Actinomycosis of the gallbladder, although rare, is known to be directly caused by Actinomyces[g] [75]. MetOrigin is a bioinformatics tool designed to elucidate the cross-talk between bacteria and specific metabolic reactions [44]. The link between trans-3-hydroxycotinine and Actinomyces[g] was further supported through a simple quick search on this platform. The other four smoking-associated metabolites has not yet been studied directly with Actinomyces[g].

This study needs to acknowledge the limitations. Firstly, microbiome studies have often found that microbiota-phenotype associations identified in one location did not necessarily hold true when used elsewhere, underscoring the limitations of extrapolating such findings [47]. Additionally, the potential heterogeneity among the four studies (such as in terms of population demographics, and the definition of smoking status) necessitates caution when interpreting these results. Luckily, the consistency of findings across the studies in this investigation lends a relatively high level of confidence to the results. Secondly, residual confounding is somehow inevitable. Because the microbial composition is influenced by various factors, precisely quantifying the impact of a particular factor on the microbes is difficult. More importantly, as discussed earlier, host location accompanying types of staple foods and urbanization showed the strongest associations and contributions with microbiota variations [48]. Traits with smaller contributions, like smoking, may be overshadowed or confounded by these factors. Thirdly, the Bonferroni correction we applied may result in over-adjustment for p values. This correction method for multiple testing treats each genus as independent, overlooking the intricate inter-dependencies among bacterial organisms. The five genera highlighted in this study exhibit higher confidence levels, yet it remains plausible that there exist additional implicated genera. Fourthly, there was a risk of reversed causality when it comes to quitting smoking. Finally, the findings regarding microbial pathways lacked highly significance to be convincing. Our study offered suggestive evidence, emphasizing the need for more efforts in this area.

In summary, we uncovered significant alterations in several gut microbes between smokers and non-smokers. These genera were further identified to be associated with the risk of common diseases including cholecystitis and type 2 diabetes, providing clues that gut microbiome as a potential mediator between smoking and common diseases.

ASVs

Amplicon Sequence Variants

BBJ

BioBank Japan

BMI

body mass index

FDR

false discovery rate

GGMP

Guangdong Gut Microbiome Project

GNHS

Guangzhou Nutrition and Health Study

GWASs

Genome-Wide Association Studies

HUMAnN

HMP Unified Metabolic Analysis Network

IVW

inverse-variance weighted

linkage disequilibrium

LefSe

Linear discriminant analysis Effect Size

MaAsLin2

Multivariable Association with Linear Models

Mendelian randomization

MR-PRESSO

MR pleiotropy residual sum and outlier

MetaPhlAn

Metagenomic Phylogenetic Analysis

OTUs

Operational Taxonomic Units

PCoA

principal coordinate analysis

PERMANOVA

Permutational multivariate analysis of variance

PRINSEQ

PReprocessing and INformation of SEQuence data

QIIME2

Quantitative Insights into Microbial Ecology 2

SCFA

short-chain fatty acid production

SRRSHS

Sir Run Run Shaw Hospital Study

T2D

type2 diabetes

ZMSC

Zhejiang Metabolic Syndrome Cohort.

Acknowledgements

We thank all the researchers and participants involved in the four studies. We thank Zhoushan Municipal Medical Insurance Bureau, Precision Nutrition and Computational Medicine Lab, and Biobank Japan project for providing invaluable data. We thank Westlake University Supercomputer Center for the assistance in in storing and computing microbial data.

Author contributions

D Zhou, WJ Ma, YM Chen and L Wang were the major contributors in design and conceptualization. J Cai and WT Shen analyzed the data. CX Cheng, CF He, YJ Liu and Y Zhou verified the correctness of the data. JY Fan, FF Zeng and HL Zhong were major contributors in writing the manuscript. SJ Chen, YM Zhu, T Liu and JS Zheng critically revised the manuscript. All authors reviewed and approved the final manuscript.

Supplementary Information

The Supplementary Methods and Figures (provided in docx format) includes Description of studies and participants, Statistical analysis, Detailed information on stool sample collection, processing, and 16 rRNA/Shotgun metagenome sequencing of each study (Supplementary Table 1), and Supplementary Figure 1 through 8.

The Supplementary Tables (provided in xlsx format) includes Supplementary Table 2 through 34.

Availability of data and code

The raw data for 16S rRNA gene sequences and metagenome of the GNHS are accessible in the CNSA (https://db.cngb.org/cnsa/) of CNGBdb at accession numbers CNP0000829 and CNP0001510, respectively. The raw data for 16S rRNA gene sequences of the GGMP and SRRSHS are deposited in the European Nucleotide Archive (https://www.ebi.ac.uk/ena/) at accession number PRJEB18535 and the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) at accession number SRP238779, respectively. The code utilized to execute the analyses described in this study can be accessed on the GitHub repository located at https://github.com/zdangm/smoking_microbiome. Requests for the metadata from this study can be submitted via email to D Zhou ([email protected]).

Funding

This work was supported by the National Natural Sciences Foundation of China 82204118 (D.Z.).

Ethics approval and consent to participate

The ZMSC protocol received approval from the Ethics Committee of Zhejiang University School of Medicine. The GNHS protocol received ethical approval from both the Ethics Committee of the School of Public Health, Sun Yat-sen University, and the Ethics Committee of Westlake University. The GGMP was approved by the Ethical Review Committee of the Chinese Center for Disease Control and Prevention under Approval Notice No. 201519-A. The SRRSHS was conducted under the guidelines of the Ethics Committee of Sir Run Run Shaw Hospital and the China Association for Clinical Research. Written consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Potential Roles of Cigarette Smoking on Gut Microbiota Profile among Chinese Men

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Abstract

Figures

Introduction

Methods

Description of studies and participants

Metadata collection and definition of smoking status

Stool sample collection, DNA extraction, and 16S rRNA/shotgun metagenomic sequencing

Taxonomic profiling

Statistical analysis

The comparison of the gut microbial diversity (step1)

The identification of smoking-associated microbes and pathways (step2)

Results

Characteristics of study participants

Cigarette smoking and gut microbes at the genus level

Cigarette smoking and gut microbes at species level

Cigarette smoking and microbial pathway

Smoking-associated genera and health-related outcomes

Smoking-associated genera and smoking-associated metabolites

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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