Abstract
Ni, Zhexin, Yongqiang Zhou, Mingyang Chang, Tiantian Xia, Wei Zhou, and Yue Gao. High-altitude impacts on gut microbiota: Accelerated aging and the urgency for targeted health interventions. High Alt Med Biol. 26:416–423, 2025.—The human gut microbiota is integral to the aging process, and its composition is notably influenced by the unique environmental pressures of high-altitude plateaus, characterized by hypobaric and hypoxic conditions. This study explores the correlation between physiological aging and gut microbiota among high-altitude plateau inhabitants, an essential aspect of health preservation in such regions. We conducted a metagenomic analysis of fecal samples from 105 individuals who migrated to high-altitude areas before the age of 20. Our results demonstrate that advancing age and prolonged high-altitude living significantly modify the gut microbiota, evidenced by reduced diversity and an elevated Firmicutes to Bacteroidetes (F/B) ratio in older subjects. Notably, the abundance of the anti-aging bacterium Akkermansia muciniphila (A. muciniphila) inversely correlates with age, showing a significant decline post the age of 25. A comparative analysis of 2,007 individuals from lower altitudes revealed a similar negative correlation between A. muciniphila and age, with a decline evident from age 38. These findings indicate that the high-altitude plateau environment may accelerate the decline of A. muciniphila by 10 years, underscoring the need for targeted health strategies for high-altitude populations.
Keywords
Introduction
In regions above 3,500 meters, environmental stressors like hypobaric hypoxia, low temperatures, and high UV radiation significantly impact the physiology and ecology of local inhabitants and gut microbiota (Han et al., 2024; Su et al., 2024). High-altitude exposure is linked to various pathological conditions, with gut microbiota disturbances being a significant issue (Zhao et al., 2024). These dysbiotic shifts can cause bacterial translocation, leading to potential end-organ damage (Zhou et al., 2025) and systemic inflammatory exacerbation (Khanna et al., 2018). Specifically, Song et al. highlighted hypoxia’s role in inducing acute intestinal injury (Song et al., 2024), while Pan et al. demonstrated that hypobaric hypoxia at 5,000 meters triggers pathological cardiac hypertrophy in rats, along with significant alterations in gut microbiota composition and diversity (Pan et al., 2022). These studies collectively emphasize the strong linkage between gut microbiota alterations and health in the hypoxic high-altitude environment. They reveal the complex interplay between the gut microbiome and the host’s physiological adaptations to high-altitude hypoxia, providing a basis for further exploration of the mechanisms behind these associations.
The age of gut microbiota is an important indicator for evaluating the health status of the host. Wang et al. demonstrated that the gut microbiota significantly influences our biological age, independent of chronological age. Consequently, elderly individuals with a healthy gut microbiota may exhibit “younger” biological characteristics, whereas a compromised gut microbiota can prematurely age younger individuals (Wang et al., 2024). This study has revealed that the high-altitude environments can accelerate the aging of gut microbiota, which may have potential adverse effects on overall health.
Results and Discussion
Our investigation enrolled 105 adult male migrants from the Chengdu Plain (Ethical Number AF/SC08/02.153), with a diet predominantly consisting of rice (90/105), mantou (10/105), and congee (5/105), alongside a uniform consumption of beef and mutton (105/105). Vegetable intake varied from once to seven times per week, and dietary analysis confirmed a consistent nutritional pattern among participants. A significant decline in gut microbiota diversity was observed in high-altitude immigrants aged 20–29, particularly around the age of 25 (Fig. 1A). The cohort was further stratified into a younger group (HNA, aged 20–24) and an older group (HYA, aged 25–29), exhibiting distinct gut microbiota clusters. The HYA group demonstrated reduced bacterial richness and homogeneity compared to the HNA, as evidenced by Shannon indices and Bray-Curtis distance (Fig. 1B and 1C). Phylum-level gut microbiota structure varied between subgroups (Fig. 1D), with a higher Firmicutes to Bacteroidetes ratio in HYA than HNA (Fig. 1E), suggesting accelerated aging in HYA. The Firmicutes to Bacteroidetes ratio (F/B ratio) is a critical indicator of gut microbiota health, with its alteration over age potentially correlating with age-related pathologies such as chronic inflammation, neurodegenerative diseases, and obesity (Mariat et al., 2009). The F/B ratio typically increases with age, with the most significant increase observed in individuals aged 60 to 69 (Vaiserman et al., 2020). The F/B ratio is also implicated in the production of short-chain fatty acids (SCFAs), which are essential for health maintenance (Kapoor et al., 2023). Our study indicates that high-altitude environments may induce aging-like changes characterized by an increased F/B ratio in individuals aged 25–29, which merits further attention.
Age-related decline in A. muciniphila diversity in high altitude plateau migrants.
In this study, we identified 98 differential genera with p < 0.05 and log2 (fold change) >1 (Fig. 1F). A random forest model cross-validated 20 genera as significant for subgroup discrimination (Fig. 1G). Partial Least Squares Discriminant Analysis (PLS-DA) confirmed the importance of these genera, with 15 exhibiting distinct abundance patterns between subgroups (Fig. 1H). The HNA group exhibited lower levels of Dalbergia, Deinococcus, Gymnostachys, Kageneckia, Kopsia, Pseudophegopteris, Pyracantha, Rhamnus, and Swingsia, and higher levels of Akkermansia, Andreesenia, Arachis, Desulfallas, Desulfonispora, and Glycine. Receiver Operating Characteristic (ROC) curve analysis indicated these 15 genera as potential aging biomarkers in high-altitude plateau migrants, with 10 genera showing AUC >0.5, and Akkermansia (AUC = 0.757) emerging as the most predictive (Fig. 1I). Species-level analysis of gut microbiota composition revealed standardized differences in relative abundance across age groups (Fig. 1J). Among six species—Akkermansia muciniphila (A. muciniphila), Arachis duranensis, Arachis hypogaea, Arachis ipaensis, Deinococcus metallilatus, and Glycine max—A. muciniphila’s decline with age mirrored that observed at the genus level for Akkermansia (Fig 1K). These findings suggest that microbial changes may significantly influence the aging process in high-altitude immigrants, with A. muciniphila potentially playing a key role in associated health outcomes. A. muciniphila, a gut bacterium, enhances host health by maintaining gut barrier integrity, modulating immune responses, and producing SCFAs that support glucose homeostasis (Zhao et al., 2024). It may also influence cognition and aging via the gut-brain axis and by bolstering the gut barrier through fimbriae-like proteins, reducing pathogen invasion and associated diseases (Jian et al., 2023; Kang et al., 2024). In addition, it promotes epithelial regeneration, potentially delaying aging (Zeng et al., 2023).
To substantiate the observed changes in gut microbiota associated with aging, a comprehensive analysis was performed on 2,007 healthy individuals aged 20 to 60 years, sourced from the European Nucleotide Archive (PRJEB18535) (He et al., 2018). Participants were stratified into 10-year age quartiles. Comparative analysis revealed significant differences in gut microbiota composition between the older (ages 40 and above) and younger (ages below 40) groups, marked by reduced levels of A. muciniphila, Faecalibacterium prausnitzii, and Bacteroides plebeius, and increased levels of Ruminococcus gnavus, Lactobacillus acidipiscis, and Shuttleworthia satelles (Fig. 1L). A significant negative correlation between age and the prevalence of A. muciniphila was identified (Fig. 1M), and ROC curve analysis indicated A. muciniphila’s moderate to good predictive power as a biomarker for biological aging (AUC = 0.692, Fig. 1N). The abundance of A. muciniphila declined significantly in individuals over 40 in plain regions, whereas in high-altitude regions, a similar decrease was noted as early as 25 years of age. These results suggest that high-altitude environments may hasten the aging of the gut microbiota, as evidenced by the reduced abundance of A. muciniphila.
Conclusion
Collectively, these results suggest that the high-altitude environment may precipitate an elevation in the F/B ratio and a decline in A. muciniphila abundance. The preliminary evidence indicates a possible link between high-altitude plateau environments and accelerated gut microbiota aging, but further detailed investigations are needed to establish causality and uncover the mechanisms involved.
Materials and Methods
Cohort description and sample collection
The study enrolled a cohort of high-altitude plateau migrants consisting of 105 adult males from Tibet who resided at altitudes ranging from 3,500 m to 4,000 m. The 105 samples represent the largest number we could possibly collect, and to our knowledge, they also constitute the largest sample size that has been collected in similar types of studies to date. These participants typically migrated to high-altitude plateau areas between the ages of 18 and 20, having previously lived in Chengdu Plain, Sichuan Province. All participants were Han Chinese with similar baseline dietary habits at low altitude. After high-altitude relocation, they maintained consistent Han dietary patterns: rice-based staples, equivalent weekly beef/lamb intake, and no special dietary variations. Each participant completed a comprehensive basic information questionnaire, covering aspects such as age, educational background, altitude of residence, duration of stay in Tibet, body mass index, dietary habits, history of smoking and alcohol consumption, antibiotic usage, and medical history. Precise inclusion and exclusion criteria were established to ensure that variations in gut microbiota were primarily influenced by environmental factors and age. Inclusion criteria involved selecting participants aged between 20 and 29 with no history of heart disease or genetic predisposition. Exclusion criteria encompassed individuals who had recently taken antibiotics, undergone bariatric surgery or intestinal resection (excluding appendectomy), had inflammatory bowel disease or autoimmune conditions, were affected by infectious diseases (e.g., hepatitis B or C or human immunodeficiency viruses), had a history of organ transplantation or were undergoing immunosuppressive therapy, or were struggling with substance abuse.
Fecal samples were collected from participants using fecal sampling tubes from Shenzhen Medico Biomedical Technology Co., Ltd., and stored at −80°C post-sampling. These samples were utilized for metagenomic sequencing. The study obtained approval from the Ethics Committee of Beijing Institute of Radiation Medicine, under approval number AF/SC-08/02.153. This study complied with the Declaration of Helsinki, and it was confirmed that informed consent had been obtained.
Metagenomic sequencing and analysis
In accordance with the manufacturer’s protocols, microbial DNA was extracted from stool samples using the stool DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). Each sample underwent shearing of 1 μg of genomic DNA by the Covaris S220 Focused-ultrasonicator (Woburn, MA, USA), and sequencing libraries were constructed by Shanghai Biozeron Biological Technology Co. Ltd. with a fragment length of approximately 450 bp. Subsequently, all samples were sequenced using the Illumina HiSeq X instrument in pair-end 150 bp (PE150) mode. Raw sequence reads were subjected to quality trimming using Trimmomatic to remove adaptor contaminants and low-quality reads, followed by quality control mapping against the human genome (version: hg19) using the BWA mem algorithm (parameters: -M -k 32 -t 16, http://bio-bwa.sourceforge.net/bwa.shtml). The resulting clean reads, devoid of host-genome contaminations and low-quality data, were utilized for further analysis.
To obtain reads-based phylogenetic annotation for each sample, Kraken2 was employed to determine the taxonomy of clean reads using a customized Kraken database, encompassing all bacteria, archaea, fungi, virus, protozoa, and algae genome sequences in the NCBI RefSeq database (release number: 90). Subsequently, the clean sequence reads were assembled into a set of contigs for each sample using MegaHit with “–min-contig-len 500” parameters. The open reading frames (ORFs) of the assembled contigs were predicted using Prodigal (v2.6.3), and all ORFs were clustered using CD-HIT (parameters: -n 9 -c 0.95 -G 0 -M 0 -d 0 -aS 0.9 -r 1) to generate a set of unique genes, with the longest sequence of each cluster considered as the representative sequence of each gene in the unique-gene set. Furthermore, the gene abundance within the total samples was calculated using the salmon software.
Alpha diversity was assessed using the Shannon index, and linear regression was employed to evaluate the change of the Shannon index with age. Bray-Curtis distance was utilized to evaluate the variations in species abundance between two groups. The analysis of similarities was performed to determine the significance index (p value), with a threshold of p < 0.05 indicating significant differences in species abundance between the groups. Differential genera were filtered by the Wilcoxon rank-sum test with p < 0.05, the absolute value of log2(fold change) >1, and VIP score >1 assessed by PLS-DA. In addition, the importance of genera in grouping was evaluated through 10-fold cross-validated classification using random forest, and the significance of importance scores was obtained through 1,000-times permutation analysis. ROC curve analysis was conducted to assess the predictive effect on grouping. Z-score normalization was applied to the abundance of gut microbiota at the species level.
Authors’ Contributions
Y.G. and W.Z. contributed to the study conception and design. Z.N., Y.Z., M.C., and T.X.: Material preparation, data collection and analysis. The first draft of the article was written by Z.N. and Y.Z. All authors read and approved the final article.
Footnotes
Acknowledgments
We thank to the volunteers for cooperating with this research and providing valuable samples.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
This work was supported by the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine (ZYYCXTD-D-202207, Y.G.), the High Level Traditional Chinese Medicine Key Discipline Construction Project of National Administration of Traditional Chinese Medicine (zyyzdxk-2023311, Y.G.) and the Young Elite Scientists Sponsorship Program by CAST (2021-QNRC1-03, W.Z.).
