Gut and Liver is an international journal of gastroenterology, focusing on the gastrointestinal tract, liver, biliary tree, pancreas, motility, and neurogastroenterology. Gut atnd Liver delivers up-to-date, authoritative papers on both clinical and research-based topics in gastroenterology. The Journal publishes original articles, case reports, brief communications, letters to the editor and invited review articles in the field of gastroenterology. The Journal is operated by internationally renowned editorial boards and designed to provide a global opportunity to promote academic developments in the field of gastroenterology and hepatology. +MORE
Yong Chan Lee |
Professor of Medicine Director, Gastrointestinal Research Laboratory Veterans Affairs Medical Center, Univ. California San Francisco San Francisco, USA |
Jong Pil Im | Seoul National University College of Medicine, Seoul, Korea |
Robert S. Bresalier | University of Texas M. D. Anderson Cancer Center, Houston, USA |
Steven H. Itzkowitz | Mount Sinai Medical Center, NY, USA |
All papers submitted to Gut and Liver are reviewed by the editorial team before being sent out for an external peer review to rule out papers that have low priority, insufficient originality, scientific flaws, or the absence of a message of importance to the readers of the Journal. A decision about these papers will usually be made within two or three weeks.
The remaining articles are usually sent to two reviewers. It would be very helpful if you could suggest a selection of reviewers and include their contact details. We may not always use the reviewers you recommend, but suggesting reviewers will make our reviewer database much richer; in the end, everyone will benefit. We reserve the right to return manuscripts in which no reviewers are suggested.
The final responsibility for the decision to accept or reject lies with the editors. In many cases, papers may be rejected despite favorable reviews because of editorial policy or a lack of space. The editor retains the right to determine publication priorities, the style of the paper, and to request, if necessary, that the material submitted be shortened for publication.
Hyuk Yoon1 , Dong Ho Lee1
, Je Hee Lee2
, Ji Eun Kwon3
, Cheol Min Shin1
, Seung-Jo Yang2
, Seung-Hwan Park4
, Ju Huck Lee4
, Se Won Kang4
, Jung-Sook Lee4
, Byung-Yong Kim2
Correspondence to: Dong Ho Lee
ORCID https://orcid.org/0000-0002-6376-410X
E-mail dhljohn@yahoo.co.kr
Byung-Yong Kim
ORCID https://orcid.org/0000-0002-6376-410
E-mail bykim@chunlab.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gut Liver 2021;15(2):243-252. https://doi.org/10.5009/gnl19354
Published online May 13, 2020, Published date March 15, 2021
Copyright © Gut and Liver.
Background/Aims: South Korean soldiers are exposed to similar environmental factors. In this study, we sought to evaluate the gut microbiome of healthy young male soldiers (HYMS) and to identify the primary factors influencing the microbiome composition.
Methods: We prospectively collected stool from 100 HYMS and performed next-generation sequencing of the 16S rRNA genes of fecal bacteria. Clinical data, including data relating to the diet, smoking, drinking, and exercise, were collected.
Results: The relative abundances of the bacterial phyla Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria were 72.3%, 14.5%, 8.9%, and 4.0%, respectively. Fifteen species, most of which belonged to Firmicutes (87%), were detected in all examined subjects. Using cluster analysis, we found that the subjects could be divided into the two enterotypes based on the gut microbiome bacterial composition. Compared with enterotype 2 subjects, subjects classified as enterotype 1 tended to be characterized by higher frequencies of potentially harmful lifestyle habits (current smoker: 55.6% vs 36.6%, p=0.222; heavy drinker: 16.7% vs 3.7%, p=0.120; insufficient physical activity: 27.8% vs 14.6%, p=0.318). We identified a significant difference in the microbiome compositions of current and noncurrent smokers (p=0.008); the former differed from the latter mainly in a relatively lower abundance of Bifidobacterium species and a higher abundance of Negativicutes.
Conclusions: A high abundance of Actinobacteria and low abundance of Bacteroidetes were the main features distinguishing the gut microbiomes of HYMS, and current smokers could be differentiated from noncurrent smokers by their lower abundance of Bifidobacterium and higher abundance of Negativicutes.
Keywords: Microbiota, Health, Smokers, Military personnel
The development of next-generation sequencing technology has led to an exponential growth in the number of studies focusing on the gut microbiome, in many of which the gut microbiomes of patients with disease are compared with those of healthy control subjects. Numerous such studies have examined the composition and characteristics of gut microbiomes in healthy populations worldwide, including the Human Microbiome Project in the United States1 and the MetaHIT project in Europe.2 In contrast, there have been relatively few comparable investigations, particularly large-scale studies, in the Korean population. Therefore, in 2016, with financial support from the Ministry of Science and ICT, we inaugurated the Korean gut microbiome bank. The initial step in this huge project entailed collecting stool samples from healthy Korean adults and analyzing the gut microbiomes. During this phase, we also collected the stools of healthy young male Korean soldiers serving military duty. There is currently little data regarding the gut microbiomes of healthy young Koreans, and given that Korean soldiers represent a unique group in which individuals are exposed to a similar range of major environmental factors, we believe that this project might provide valuable insights regarding the factors that influence the gut microbiomes of healthy young adults.
Against this background, we sought in the present study to evaluate the composition of the gut microbiomes of healthy young male Korean soldiers and to determine the main factors contributing to differences in the composition of these gut microbiomes.
We recruited subjects from among male soldiers who were working in the Korean Armed Forces Capital Hospital in Seongnam, South Korea. All subjects were serving in the hospital as part of the compulsory military duty imposed on young Korean males. The inclusion criteria stipulated that the males should be healthy and without any of the specified exclusion criteria (see below). We performed history taking, physical examinations, and laboratory tests to screen volunteers. If laboratory test results obtained within a 1-year period prior to enrollment were available, these data were used to evaluate whether the subject met the inclusion criteria. The exclusion criteria were as follows: (1) the presence of acute illness; (2) a history of chronic illness, including hypertension, diabetes, angina, acute myocardial infarction, stroke, dyslipidemia, more than a moderate degree of fatty liver, cardiopulmonary disease, chronic liver disease, chronic renal disease, thyroid disease, asthma, or allergy; (3) a history of cancer within 5 years; (4) a history of abdominal surgery within 5 years; (5) a history of antibiotic or probiotic use within 3 months; (6) an abnormal stool form (a Bristol stool form score of 1, 2, 6, or 7); (7) body mass index <18.5 kg/m2 or ≥25 kg/m2; (8) systolic and diastolic blood pressure ≥140/90 mm Hg; (9) fasting glucose ≥126 mg/dL or random glucose ≥200 mg/dL; (10) creatinine ≥2-fold of the normal upper limit; (11) aspartate transaminase or alanine transaminase ≥3-fold of the normal upper limit; and (12) infection with hepatitis B virus, hepatitis C virus, or human immune deficiency virus.
Stool samples were collected from those subjects who met the inclusion criteria. In addition, we used questionnaires to collect clinical data relating to diet, smoking, drinking, physical activity, and socioeconomic status. To assess the intake of energy, carbohydrate, fat, protein, minerals, and vitamins, we used a semi-quantitative food frequency questionnaire that was developed and validated for Korean adults.3 For alcohol intake, subjects who drank more than eight standard drinks per week was classified as heavy drinkers.4 To access physical activity, we used a validated Korean version of the International Physical Activity Questionnaire Short Form.5 To analyze levels of physical activity, we followed guidelines for data processing and analysis of the International Physical Activity Questionnaire Short Form (www.ipaq.ki.se). Among the three designated levels of physical activity, we considered “inactive” individuals as being “insufficiently active,” whereas subjects who were “minimally active” or undertook “health-enhancing physical activity” were classified as being “sufficiently active.” All subjects provided informed consent and the study was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (IRB number: B-1701-380-304).
Total DNA was extracted from stool samples using a PowerSoil DNA Isolation Kit (MoBio, Solana Beach, CA, USA) according to the manufacturer’s instructions. Polymerase chain reaction (PCR) amplification was performed using primers targeting the V3 to V4 regions of the 16S rRNA gene. For bacterial 16S rRNA amplification, we used the primer pair 341F (5ʹ-TCGTCGGCAGCGTC-AGATGTGTATAAGAGACAG-
The PCR products were assessed using 2% agarose gel electrophoresis and visualized using a Gel Doc system (Bio-Rad, Hercules, CA, USA). The amplified products were purified using a QIAquick PCR purification kit (Qiagen, Valencia, CA, USA). Equal concentrations of purified products were pooled together and short fragments (non-target products) were removed using an Ampure beads kit (Agencourt Bioscience, Beverly, MA, USA). The quality and product size were assessed with an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) using a DNA 7500 chip. Mixed amplicons were pooled and sequencing was carried out by ChunLab Inc. (Seoul, Korea), using an Illumina MiSeq Sequencing system (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions.
Basic analyses were conducted according to previously described procedures.6-8 The reads obtained from different samples were sorted based on the unique barcodes of each PCR product. Barcode, linker, and primers sequences were subsequently removed from the original sequencing reads, and any reads with two or more ambiguous nucleotides, a low-quality score (average score <25), or sequences shorter than 300 bp were discarded. Potential chimeric sequences were detected using the Bellerophon method, which compares the BLASTN search results obtained for forward half and reverse half sequences.9 After removing chimeric sequences, the taxonomic classification of each read was assigned against the EzBioCloud database (http://ezbiocloud.net),10 which contains the 16S rRNA gene sequences of type strains that have valid published names and representative species-level phylotypes of either cultured or uncultured entries in the GenBank database, with complete hierarchical taxonomic classification from the phylum to the species level.
For analysis of alpha-diversity, the richness and diversity of samples were determined by abundance-based coverage estimators, Chao1. and Jackknife estimation. In addition, the Simpson and Shannon diversity indices at a 3% distance were calculated using the CL community program (ChunLab Inc.). Good’s method was used to calculate sequencing coverage.11 For comparisons of the composition of a selected taxon, we used the Wilcoxon rank-sum test.
For analysis of beta-diversity, the overall phylogenetic distance between communities was estimated and visualized using Jensen–Shannon-based principal coordinates analysis. We performed Permanova analysis to evaluate the set-difference between groups and linear discriminant analysis effect size (LEfSe) analysis to determine the features most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.12 Cluster analysis based on bacterial species composition was performed to classify the subjects into enterotypes.13
R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for the statistical analysis of clinical data. Continuous variables were analyzed using Student t-test. The chi-square test or Fisher exact test was used to analyze categorical variables. All results were considered statistically significant when p-values were less than 0.05.
Between April and November 2017, we prospectively enrolled 100 healthy male soldiers as study subjects. Table 1 shows the baseline characteristics of the subjects. The median age of the subjects was 21 years and all were unmarried. The daily energy intake (total calories and the proportions of carbohydrate, fat, and protein) was found to be similar to that previously determined for 19- to 29-year-old males.14 The proportions of current smokers and heavy drinkers among the study subjects were 40.0% and 6.0%, respectively, whereas 17.0% of the subjects were adjudged to undertake insufficient levels of physical activity.
The mean number of valid 16S rRNA reads and operational taxonomic units for each sample was 82,937 and 312, respectively. The median value for Good’s library coverage was 99.9. Fig. 1 shows the taxonomic composition of the gut microbiome. At the phylum level, the relative abundances of
Using cluster analysis, we found that we could divide the subjects into two enterotypes based on bacterial species compositions (Fig. 2). Table 3 shows the clinical characteristics of healthy male soldiers according to enterotype. Compared with enterotype 2 subjects, those classified as having enterotype 1 tended to be characterized by a greater frequency of potentially harmful lifestyle habits (current smoker: 55.6% vs 36.6%, heavy drinker: 16.7% vs 3.7%, insufficient physical activity: 27.8% vs 14.6%); however, the differences did not attain statistical significance.
We subsequently analyzed the gut microbiome according to the smoking and drinking habits of the subjects and their levels of physical activity. Among current smokers, we found that the abundance of
Previous studies have indicated that
Of the 15 bacterial species detected in each of the study subjects, all, with the exception of
Sex and age are invariably among the major determinants contributing to the differences observed in biological studies. Given that these variables were fixed in the subjects enrolled in the present study, who were also exposed to similar environments, we hypothesized that by classifying these subjects according to enterotype, we might obtain particularly meaningful information regarding the gut microbiome in healthy young adults. When we compared clinical factors between those classified as enterotype 1 and 2, it became apparent that a combination of potentially harmful lifestyle habits was a main major determining the differences in their gut microbiomes. However, because these differences did not attain a level of statistical significance, we performed further analyses of the relationship between gut microbiome composition and different lifestyle habits. When we compared stool microbiomes according to the smoking status of subjects, we obtained several interesting results. First, we found that the abundance of
The present study has several strengths. Notably, the study subjects were recruited from a population in which numerous factors that could potentially affect the composition of the gut microbiome are well controlled. All subjects were male and aged between 20 and 22 years old. Moreover, these individuals were exposed to similar environmental factors. During the daytime, they worked in a military hospital as administrative clerks and ate the same meals three times a day in a cafeteria. At nights, they slept together in barracks. The duration between the date on which they started their military service and that on which they provided a stool sample was on average 9 months, and therefore we can assume that these young men had been exposed to the same environment for a sufficient length of time. Furthermore, when we did subgroup analysis according to the length of military service in the hospital (less vs more than 9 months), we could not detect significant difference in the beta-diversity of gut microbial composition (Permanova: p=0.181). In addition, we selected the subjects using very strict inclusion and exclusion criteria in order to ensure that they were all very healthy. This could be verified to a certain extent by our observation that
There are, nevertheless, also certain limitations to the present study. First, the study was performed on a very specific group of individuals, and therefore it might be difficult to generalize these findings with respect to the Korean population as a whole. Second, although the daily energy intake of macronutrients was similar to that previously determined for males of the same age range, we could not include micronutrients data such as vitamins, minerals, and trace elements in the present analysis. Because the number and level of variables are too high, to perform it, more complex statistical methods are required. We are planning to perform it when we analyze gut microbiomes across a range of age groups in the Korean population. Third, we did not collect data on dental health of the subjects. Therefore, we cannot rule out the possibility that poor oral hygiene acted as a confounding factor in the relationship between smoking and increased abundance of
In conclusion, a high abundance of
This study was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MIST) of the Republic of Korea (Project number: 2016M3A9F3947027).
No potential conflict of interest relevant to this article was reported.
Conceptualization: D.H.L., B.Y.K. Subjects enrollment: J.E.K. Microbiome analysis: J.H.L., S.J.Y., H.Y. Data and statistical analysis: H.Y., C.M.S. Writing- original draft: H.Y. Writing- review & editing: S.H.P., J.H.L., S.W.K., J.S.L. Funding acquisition: D.H.L., B.Y.L., J.S.L. Approval of final manuscript: all authors.
Baseline Characteristics of 100 Healthy Male Soldiers
Variable | Value |
---|---|
Age, yr | 21.0 (20–22) |
Body mass index, kg/m2 | 22.7 (21.2–23.9) |
Systolic blood pressure, mm Hg | 120.0 (113.0–131.5) |
Diastolic blood pressure, mm Hg | 69.9±8.4 |
Laboratory results | |
White blood cells, ×103/μL | 6.1 (5.4–7.4) |
Hemoglobin, g/dL | 15.1±0.9 |
Platelets, ×103/μL | 254.3±54.3 |
Glucose, mg/dL | 92.9±6.9 |
Creatinine, mg/dL | 0.9±0.1 |
Total cholesterol, mg/dL | 159.0 (144.5–181.0) |
Aspartate transaminase, IU/L | 20.0 (17.0–23.0) |
Alanine transaminase, IU/L | 17.0 (14.0–24.0) |
Bristol stool form scale | |
3 | 48 (48.0) |
4 | 52 (52.0) |
Diet | |
Energy intake, kcal/day | 2,242.9 (1,844.9–2,800.7) |
Carbohydrate, % | 58.7 (53.1–63.1) |
Fat, % | 23.9 (21.4–27.1) |
Protein, % | 14.2 (13.3–15.3) |
Smoking status | |
Never | 52 (52.0) |
Past | 8 (8.0) |
Current | 40 (40.0) |
Drinking status, standard drinks/wk | |
Never | 10 (10.0) |
1–4 | 61 (61.0) |
5–8 | 23 (23.0) |
>8 | 6 (6.0) |
Exercise, MET-min/wk | 3,773.0 (2,541.0–5,933.0) |
Levels of physical activity | |
Inactive | 17 (17.0) |
Minimally active | 21 (21.0) |
Health-enhancing physical activity | 62 (62.0) |
Education level | |
High school | 82 (82.0) |
College & university or more | 18 (18.0) |
Income, dollars/mo | |
<270 | 20 (20.0) |
270–540 | 49 (49.0) |
>540 | 31 (31.0) |
Data are presented as median (interquartile range), mean±SD, or number (%). International Physical Activity Questionnaire Short Form.
MET, metabolic equivalent of task.
Taxonomy of the 15 Species Found in 100% of Healthy Male Soldiers
Phylum | Family | Species | Relative abundance, % |
---|---|---|---|
5.76 | |||
1.53 | |||
4.29 | |||
7.49 | |||
LN913006_s | 2.67 | ||
2.86 | |||
2.33 | |||
2.12 | |||
4.54 | |||
1.86 | |||
0.90 | |||
2.81 | |||
0.07 | |||
1.96 | |||
2.33 |
Clinical Characteristics of Healthy Male Soldiers According to Enterotype
Variable | Enterotype 1 (n=18) | Enterotype 2 (n=82) | p-value |
---|---|---|---|
Age, yr | 21.0 (21–22) | 21.0 (20–22) | 0.085 |
Body mass index, kg/m2 | 21.8 (20.5–23.6) | 22.8 (21.3–23.9) | 0.216 |
Systolic blood pressure, mm Hg | 115.0 (110.0–130.0) | 121.0 (114.0–132.0) | 0.106 |
Diastolic blood pressure, mm Hg | 69.0±9.6 | 70.1±8.1 | 0.616 |
Laboratory results | |||
White blood cells, ×103/μL | 5.7 (5.0–7.0) | 6.2 (5.4–7.6) | 0.207 |
Hemoglobin, g/dL | 15.2±0.8 | 15.1±1.0 | 0.748 |
Platelets, ×103/μL | 251.3±52.7 | 254.9±55.0 | 0.797 |
Glucose, mg/dL | 91.8±7.5 | 93.1±6.8 | 0.473 |
Creatinine, mg/dL | 0.9±0.1 | 0.9±0.1 | 0.739 |
Total cholesterol, mg/dL | 169.0 (146.0–179.0) | 158.0 (144.0–181.0) | 0.481 |
Aspartate transaminase, IU/L | 20.0 (19.0–24.0) | 19.0 (17.0–23.0) | 0.324 |
Alanine transaminase, IU/L | 20.0 (16.0–26.0) | 17.0 (14.0–23.0) | 0.191 |
Bristol stool form scale | 0.942 | ||
3 | 8 (44.4) | 40 (48.8) | |
4 | 10 (55.6) | 42 (51.2) | |
Diet | |||
Energy intake, kcal/day | 2,126.6 (1,653.9–2,531.0) | 2,257.1 (1,930.4–2,821.7) | 0.335 |
Carbohydrate, % | 62.2 (54.8–66.3) | 58.4 (53.0–62.3) | 0.159 |
Fat, % | 22.4 (18.2–25.9) | 24.4 (21.6–27.4) | 0.201 |
Protein, % | 13.8 (13.0–16.1) | 14.3 (13.4–15.3) | 0.427 |
Smoking status | 0.222 | ||
Never or past | 8 (44.4) | 52 (63.4) | |
Current | 10 (55.6) | 30 (36.6) | |
Drinking status | 0.120 | ||
Non-heavy drinker | 15 (83.3) | 79 (96.3) | |
Heavy drinker* | 3 (16.7) | 3 (3.7) | |
Levels of physical activity | 0.318 | ||
Insufficiently active | 5 (27.8) | 12 (14.6) | |
Sufficiently active | 13 (72.2) | 70 (85.4) | |
Education level | 0.616 | ||
High school | 16 (88.9) | 66 (80.5) | |
College & university or more | 2 (11.1) | 16 (19.5) | |
Income, dollars/mo | 0.911 | ||
<270 | 4 (22.2) | 16 (19.5) | |
270–540 | 8 (44.4) | 41 (50.0) | |
>540 | 6 (33.3) | 25 (30.5) |
Data are presented as median (interquartile range), mean±SD, or number (%).
*Drinking status, more than 8 standard drinks/wk.
Gut and Liver 2021; 15(2): 243-252
Published online March 15, 2021 https://doi.org/10.5009/gnl19354
Copyright © Gut and Liver.
Hyuk Yoon1 , Dong Ho Lee1
, Je Hee Lee2
, Ji Eun Kwon3
, Cheol Min Shin1
, Seung-Jo Yang2
, Seung-Hwan Park4
, Ju Huck Lee4
, Se Won Kang4
, Jung-Sook Lee4
, Byung-Yong Kim2
1Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, 2ChunLab Inc., Seoul, 3Armed Forces Capital Hospital, Seongnam, and 4Korean Collection for Type Cultures, Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Jeongeup, Korea
Correspondence to:Dong Ho Lee
ORCID https://orcid.org/0000-0002-6376-410X
E-mail dhljohn@yahoo.co.kr
Byung-Yong Kim
ORCID https://orcid.org/0000-0002-6376-410
E-mail bykim@chunlab.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background/Aims: South Korean soldiers are exposed to similar environmental factors. In this study, we sought to evaluate the gut microbiome of healthy young male soldiers (HYMS) and to identify the primary factors influencing the microbiome composition.
Methods: We prospectively collected stool from 100 HYMS and performed next-generation sequencing of the 16S rRNA genes of fecal bacteria. Clinical data, including data relating to the diet, smoking, drinking, and exercise, were collected.
Results: The relative abundances of the bacterial phyla Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria were 72.3%, 14.5%, 8.9%, and 4.0%, respectively. Fifteen species, most of which belonged to Firmicutes (87%), were detected in all examined subjects. Using cluster analysis, we found that the subjects could be divided into the two enterotypes based on the gut microbiome bacterial composition. Compared with enterotype 2 subjects, subjects classified as enterotype 1 tended to be characterized by higher frequencies of potentially harmful lifestyle habits (current smoker: 55.6% vs 36.6%, p=0.222; heavy drinker: 16.7% vs 3.7%, p=0.120; insufficient physical activity: 27.8% vs 14.6%, p=0.318). We identified a significant difference in the microbiome compositions of current and noncurrent smokers (p=0.008); the former differed from the latter mainly in a relatively lower abundance of Bifidobacterium species and a higher abundance of Negativicutes.
Conclusions: A high abundance of Actinobacteria and low abundance of Bacteroidetes were the main features distinguishing the gut microbiomes of HYMS, and current smokers could be differentiated from noncurrent smokers by their lower abundance of Bifidobacterium and higher abundance of Negativicutes.
Keywords: Microbiota, Health, Smokers, Military personnel
The development of next-generation sequencing technology has led to an exponential growth in the number of studies focusing on the gut microbiome, in many of which the gut microbiomes of patients with disease are compared with those of healthy control subjects. Numerous such studies have examined the composition and characteristics of gut microbiomes in healthy populations worldwide, including the Human Microbiome Project in the United States1 and the MetaHIT project in Europe.2 In contrast, there have been relatively few comparable investigations, particularly large-scale studies, in the Korean population. Therefore, in 2016, with financial support from the Ministry of Science and ICT, we inaugurated the Korean gut microbiome bank. The initial step in this huge project entailed collecting stool samples from healthy Korean adults and analyzing the gut microbiomes. During this phase, we also collected the stools of healthy young male Korean soldiers serving military duty. There is currently little data regarding the gut microbiomes of healthy young Koreans, and given that Korean soldiers represent a unique group in which individuals are exposed to a similar range of major environmental factors, we believe that this project might provide valuable insights regarding the factors that influence the gut microbiomes of healthy young adults.
Against this background, we sought in the present study to evaluate the composition of the gut microbiomes of healthy young male Korean soldiers and to determine the main factors contributing to differences in the composition of these gut microbiomes.
We recruited subjects from among male soldiers who were working in the Korean Armed Forces Capital Hospital in Seongnam, South Korea. All subjects were serving in the hospital as part of the compulsory military duty imposed on young Korean males. The inclusion criteria stipulated that the males should be healthy and without any of the specified exclusion criteria (see below). We performed history taking, physical examinations, and laboratory tests to screen volunteers. If laboratory test results obtained within a 1-year period prior to enrollment were available, these data were used to evaluate whether the subject met the inclusion criteria. The exclusion criteria were as follows: (1) the presence of acute illness; (2) a history of chronic illness, including hypertension, diabetes, angina, acute myocardial infarction, stroke, dyslipidemia, more than a moderate degree of fatty liver, cardiopulmonary disease, chronic liver disease, chronic renal disease, thyroid disease, asthma, or allergy; (3) a history of cancer within 5 years; (4) a history of abdominal surgery within 5 years; (5) a history of antibiotic or probiotic use within 3 months; (6) an abnormal stool form (a Bristol stool form score of 1, 2, 6, or 7); (7) body mass index <18.5 kg/m2 or ≥25 kg/m2; (8) systolic and diastolic blood pressure ≥140/90 mm Hg; (9) fasting glucose ≥126 mg/dL or random glucose ≥200 mg/dL; (10) creatinine ≥2-fold of the normal upper limit; (11) aspartate transaminase or alanine transaminase ≥3-fold of the normal upper limit; and (12) infection with hepatitis B virus, hepatitis C virus, or human immune deficiency virus.
Stool samples were collected from those subjects who met the inclusion criteria. In addition, we used questionnaires to collect clinical data relating to diet, smoking, drinking, physical activity, and socioeconomic status. To assess the intake of energy, carbohydrate, fat, protein, minerals, and vitamins, we used a semi-quantitative food frequency questionnaire that was developed and validated for Korean adults.3 For alcohol intake, subjects who drank more than eight standard drinks per week was classified as heavy drinkers.4 To access physical activity, we used a validated Korean version of the International Physical Activity Questionnaire Short Form.5 To analyze levels of physical activity, we followed guidelines for data processing and analysis of the International Physical Activity Questionnaire Short Form (www.ipaq.ki.se). Among the three designated levels of physical activity, we considered “inactive” individuals as being “insufficiently active,” whereas subjects who were “minimally active” or undertook “health-enhancing physical activity” were classified as being “sufficiently active.” All subjects provided informed consent and the study was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (IRB number: B-1701-380-304).
Total DNA was extracted from stool samples using a PowerSoil DNA Isolation Kit (MoBio, Solana Beach, CA, USA) according to the manufacturer’s instructions. Polymerase chain reaction (PCR) amplification was performed using primers targeting the V3 to V4 regions of the 16S rRNA gene. For bacterial 16S rRNA amplification, we used the primer pair 341F (5ʹ-TCGTCGGCAGCGTC-AGATGTGTATAAGAGACAG-
The PCR products were assessed using 2% agarose gel electrophoresis and visualized using a Gel Doc system (Bio-Rad, Hercules, CA, USA). The amplified products were purified using a QIAquick PCR purification kit (Qiagen, Valencia, CA, USA). Equal concentrations of purified products were pooled together and short fragments (non-target products) were removed using an Ampure beads kit (Agencourt Bioscience, Beverly, MA, USA). The quality and product size were assessed with an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) using a DNA 7500 chip. Mixed amplicons were pooled and sequencing was carried out by ChunLab Inc. (Seoul, Korea), using an Illumina MiSeq Sequencing system (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions.
Basic analyses were conducted according to previously described procedures.6-8 The reads obtained from different samples were sorted based on the unique barcodes of each PCR product. Barcode, linker, and primers sequences were subsequently removed from the original sequencing reads, and any reads with two or more ambiguous nucleotides, a low-quality score (average score <25), or sequences shorter than 300 bp were discarded. Potential chimeric sequences were detected using the Bellerophon method, which compares the BLASTN search results obtained for forward half and reverse half sequences.9 After removing chimeric sequences, the taxonomic classification of each read was assigned against the EzBioCloud database (http://ezbiocloud.net),10 which contains the 16S rRNA gene sequences of type strains that have valid published names and representative species-level phylotypes of either cultured or uncultured entries in the GenBank database, with complete hierarchical taxonomic classification from the phylum to the species level.
For analysis of alpha-diversity, the richness and diversity of samples were determined by abundance-based coverage estimators, Chao1. and Jackknife estimation. In addition, the Simpson and Shannon diversity indices at a 3% distance were calculated using the CL community program (ChunLab Inc.). Good’s method was used to calculate sequencing coverage.11 For comparisons of the composition of a selected taxon, we used the Wilcoxon rank-sum test.
For analysis of beta-diversity, the overall phylogenetic distance between communities was estimated and visualized using Jensen–Shannon-based principal coordinates analysis. We performed Permanova analysis to evaluate the set-difference between groups and linear discriminant analysis effect size (LEfSe) analysis to determine the features most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.12 Cluster analysis based on bacterial species composition was performed to classify the subjects into enterotypes.13
R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for the statistical analysis of clinical data. Continuous variables were analyzed using Student t-test. The chi-square test or Fisher exact test was used to analyze categorical variables. All results were considered statistically significant when p-values were less than 0.05.
Between April and November 2017, we prospectively enrolled 100 healthy male soldiers as study subjects. Table 1 shows the baseline characteristics of the subjects. The median age of the subjects was 21 years and all were unmarried. The daily energy intake (total calories and the proportions of carbohydrate, fat, and protein) was found to be similar to that previously determined for 19- to 29-year-old males.14 The proportions of current smokers and heavy drinkers among the study subjects were 40.0% and 6.0%, respectively, whereas 17.0% of the subjects were adjudged to undertake insufficient levels of physical activity.
The mean number of valid 16S rRNA reads and operational taxonomic units for each sample was 82,937 and 312, respectively. The median value for Good’s library coverage was 99.9. Fig. 1 shows the taxonomic composition of the gut microbiome. At the phylum level, the relative abundances of
Using cluster analysis, we found that we could divide the subjects into two enterotypes based on bacterial species compositions (Fig. 2). Table 3 shows the clinical characteristics of healthy male soldiers according to enterotype. Compared with enterotype 2 subjects, those classified as having enterotype 1 tended to be characterized by a greater frequency of potentially harmful lifestyle habits (current smoker: 55.6% vs 36.6%, heavy drinker: 16.7% vs 3.7%, insufficient physical activity: 27.8% vs 14.6%); however, the differences did not attain statistical significance.
We subsequently analyzed the gut microbiome according to the smoking and drinking habits of the subjects and their levels of physical activity. Among current smokers, we found that the abundance of
Previous studies have indicated that
Of the 15 bacterial species detected in each of the study subjects, all, with the exception of
Sex and age are invariably among the major determinants contributing to the differences observed in biological studies. Given that these variables were fixed in the subjects enrolled in the present study, who were also exposed to similar environments, we hypothesized that by classifying these subjects according to enterotype, we might obtain particularly meaningful information regarding the gut microbiome in healthy young adults. When we compared clinical factors between those classified as enterotype 1 and 2, it became apparent that a combination of potentially harmful lifestyle habits was a main major determining the differences in their gut microbiomes. However, because these differences did not attain a level of statistical significance, we performed further analyses of the relationship between gut microbiome composition and different lifestyle habits. When we compared stool microbiomes according to the smoking status of subjects, we obtained several interesting results. First, we found that the abundance of
The present study has several strengths. Notably, the study subjects were recruited from a population in which numerous factors that could potentially affect the composition of the gut microbiome are well controlled. All subjects were male and aged between 20 and 22 years old. Moreover, these individuals were exposed to similar environmental factors. During the daytime, they worked in a military hospital as administrative clerks and ate the same meals three times a day in a cafeteria. At nights, they slept together in barracks. The duration between the date on which they started their military service and that on which they provided a stool sample was on average 9 months, and therefore we can assume that these young men had been exposed to the same environment for a sufficient length of time. Furthermore, when we did subgroup analysis according to the length of military service in the hospital (less vs more than 9 months), we could not detect significant difference in the beta-diversity of gut microbial composition (Permanova: p=0.181). In addition, we selected the subjects using very strict inclusion and exclusion criteria in order to ensure that they were all very healthy. This could be verified to a certain extent by our observation that
There are, nevertheless, also certain limitations to the present study. First, the study was performed on a very specific group of individuals, and therefore it might be difficult to generalize these findings with respect to the Korean population as a whole. Second, although the daily energy intake of macronutrients was similar to that previously determined for males of the same age range, we could not include micronutrients data such as vitamins, minerals, and trace elements in the present analysis. Because the number and level of variables are too high, to perform it, more complex statistical methods are required. We are planning to perform it when we analyze gut microbiomes across a range of age groups in the Korean population. Third, we did not collect data on dental health of the subjects. Therefore, we cannot rule out the possibility that poor oral hygiene acted as a confounding factor in the relationship between smoking and increased abundance of
In conclusion, a high abundance of
This study was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MIST) of the Republic of Korea (Project number: 2016M3A9F3947027).
No potential conflict of interest relevant to this article was reported.
Conceptualization: D.H.L., B.Y.K. Subjects enrollment: J.E.K. Microbiome analysis: J.H.L., S.J.Y., H.Y. Data and statistical analysis: H.Y., C.M.S. Writing- original draft: H.Y. Writing- review & editing: S.H.P., J.H.L., S.W.K., J.S.L. Funding acquisition: D.H.L., B.Y.L., J.S.L. Approval of final manuscript: all authors.
Table 1 Baseline Characteristics of 100 Healthy Male Soldiers
Variable | Value |
---|---|
Age, yr | 21.0 (20–22) |
Body mass index, kg/m2 | 22.7 (21.2–23.9) |
Systolic blood pressure, mm Hg | 120.0 (113.0–131.5) |
Diastolic blood pressure, mm Hg | 69.9±8.4 |
Laboratory results | |
White blood cells, ×103/μL | 6.1 (5.4–7.4) |
Hemoglobin, g/dL | 15.1±0.9 |
Platelets, ×103/μL | 254.3±54.3 |
Glucose, mg/dL | 92.9±6.9 |
Creatinine, mg/dL | 0.9±0.1 |
Total cholesterol, mg/dL | 159.0 (144.5–181.0) |
Aspartate transaminase, IU/L | 20.0 (17.0–23.0) |
Alanine transaminase, IU/L | 17.0 (14.0–24.0) |
Bristol stool form scale | |
3 | 48 (48.0) |
4 | 52 (52.0) |
Diet | |
Energy intake, kcal/day | 2,242.9 (1,844.9–2,800.7) |
Carbohydrate, % | 58.7 (53.1–63.1) |
Fat, % | 23.9 (21.4–27.1) |
Protein, % | 14.2 (13.3–15.3) |
Smoking status | |
Never | 52 (52.0) |
Past | 8 (8.0) |
Current | 40 (40.0) |
Drinking status, standard drinks/wk | |
Never | 10 (10.0) |
1–4 | 61 (61.0) |
5–8 | 23 (23.0) |
>8 | 6 (6.0) |
Exercise, MET-min/wk | 3,773.0 (2,541.0–5,933.0) |
Levels of physical activity | |
Inactive | 17 (17.0) |
Minimally active | 21 (21.0) |
Health-enhancing physical activity | 62 (62.0) |
Education level | |
High school | 82 (82.0) |
College & university or more | 18 (18.0) |
Income, dollars/mo | |
<270 | 20 (20.0) |
270–540 | 49 (49.0) |
>540 | 31 (31.0) |
Data are presented as median (interquartile range), mean±SD, or number (%). International Physical Activity Questionnaire Short Form.
MET, metabolic equivalent of task.
Table 2 Taxonomy of the 15 Species Found in 100% of Healthy Male Soldiers
Phylum | Family | Species | Relative abundance, % |
---|---|---|---|
5.76 | |||
1.53 | |||
4.29 | |||
7.49 | |||
LN913006_s | 2.67 | ||
2.86 | |||
2.33 | |||
2.12 | |||
4.54 | |||
1.86 | |||
0.90 | |||
2.81 | |||
0.07 | |||
1.96 | |||
2.33 |
Table 3 Clinical Characteristics of Healthy Male Soldiers According to Enterotype
Variable | Enterotype 1 (n=18) | Enterotype 2 (n=82) | p-value |
---|---|---|---|
Age, yr | 21.0 (21–22) | 21.0 (20–22) | 0.085 |
Body mass index, kg/m2 | 21.8 (20.5–23.6) | 22.8 (21.3–23.9) | 0.216 |
Systolic blood pressure, mm Hg | 115.0 (110.0–130.0) | 121.0 (114.0–132.0) | 0.106 |
Diastolic blood pressure, mm Hg | 69.0±9.6 | 70.1±8.1 | 0.616 |
Laboratory results | |||
White blood cells, ×103/μL | 5.7 (5.0–7.0) | 6.2 (5.4–7.6) | 0.207 |
Hemoglobin, g/dL | 15.2±0.8 | 15.1±1.0 | 0.748 |
Platelets, ×103/μL | 251.3±52.7 | 254.9±55.0 | 0.797 |
Glucose, mg/dL | 91.8±7.5 | 93.1±6.8 | 0.473 |
Creatinine, mg/dL | 0.9±0.1 | 0.9±0.1 | 0.739 |
Total cholesterol, mg/dL | 169.0 (146.0–179.0) | 158.0 (144.0–181.0) | 0.481 |
Aspartate transaminase, IU/L | 20.0 (19.0–24.0) | 19.0 (17.0–23.0) | 0.324 |
Alanine transaminase, IU/L | 20.0 (16.0–26.0) | 17.0 (14.0–23.0) | 0.191 |
Bristol stool form scale | 0.942 | ||
3 | 8 (44.4) | 40 (48.8) | |
4 | 10 (55.6) | 42 (51.2) | |
Diet | |||
Energy intake, kcal/day | 2,126.6 (1,653.9–2,531.0) | 2,257.1 (1,930.4–2,821.7) | 0.335 |
Carbohydrate, % | 62.2 (54.8–66.3) | 58.4 (53.0–62.3) | 0.159 |
Fat, % | 22.4 (18.2–25.9) | 24.4 (21.6–27.4) | 0.201 |
Protein, % | 13.8 (13.0–16.1) | 14.3 (13.4–15.3) | 0.427 |
Smoking status | 0.222 | ||
Never or past | 8 (44.4) | 52 (63.4) | |
Current | 10 (55.6) | 30 (36.6) | |
Drinking status | 0.120 | ||
Non-heavy drinker | 15 (83.3) | 79 (96.3) | |
Heavy drinker* | 3 (16.7) | 3 (3.7) | |
Levels of physical activity | 0.318 | ||
Insufficiently active | 5 (27.8) | 12 (14.6) | |
Sufficiently active | 13 (72.2) | 70 (85.4) | |
Education level | 0.616 | ||
High school | 16 (88.9) | 66 (80.5) | |
College & university or more | 2 (11.1) | 16 (19.5) | |
Income, dollars/mo | 0.911 | ||
<270 | 4 (22.2) | 16 (19.5) | |
270–540 | 8 (44.4) | 41 (50.0) | |
>540 | 6 (33.3) | 25 (30.5) |
Data are presented as median (interquartile range), mean±SD, or number (%).
*Drinking status, more than 8 standard drinks/wk.