https://orcid.org/0000-0002-0888-2875
Figures
Abstract
Aging is often accompanied by an increased risk of an array of diseases spanning the cardiovascular, nervous, and immune systems, among others. Despite remarkable progress in understanding the cellular and molecular mechanisms involved in aging, the role of the microbiome remains understudied. In this Essay, we highlight recent progress towards understanding if and how the microbiome contributes to aging and age-associated diseases. Furthermore, we discuss the need to consider sexually dimorphic phenotypes in the context of aging and the microbiome. We also highlight the broad implications for this emerging area of interdisciplinary research to address long-standing questions about host–microbiome interactions across the life span.
Citation: Rock RR, Turnbaugh PJ (2023) Forging the microbiome to help us live long and prosper. PLoS Biol 21(4): e3002087. https://doi.org/10.1371/journal.pbio.3002087
Published: April 5, 2023
Copyright: © 2023 Rock, Turnbaugh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the National Institutes of Health (P.J.T., R01HL122593) and the National Science Foundation (R.R., 203836). P.J.T. is a Chan Zuckerberg Biohub Investigator (7028823). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare no competing interests.
Abbreviations: CONV-R, conventionally raised; FMT, fecal microbiota transplantation; GF, germ-free; GI, gastrointestinal; L-dopa, levodopa; TyrDC, tyrosine decarboxylase
Introduction
Age represents the primary risk factor for some of the most prevalent diseases of high-income countries, including cancer, cardiovascular disease, and neurodegeneration [1]. Increased age is also associated with the risk and/or severity of numerous other diseases, including multiple sclerosis [2] and type 2 diabetes [3]. Defining mechanisms that contribute to health and disease in aging will therefore not only expand our knowledge of this universal process but also pave the way for interventions enabling us to optimize health in aging individuals.
The trillions of microorganisms found in and on the human body (the microbiota) offer tremendous potential in understanding aging. The microbiome (the aggregate genetic content of the microbiota) exceeds the human genome by multiple orders of magnitude [4]. Microorganisms colonize numerous sites in and on the body, with the greatest extent of colonization occurring within the gastrointestinal (GI) tract [4]. Extensive and rigorous prior research has emphasized the key role that the gut microbiota has in host health and disease, including contributions to diseases associated with aging such as cancer [5–7], Parkinson’s disease [8,9], obesity [10,11], and type 2 diabetes [12]. Yet, despite remarkable progress in understanding the cellular and molecular mechanisms through which the microbiome contributes to individual diseases linked to aging, the net effects of the microbiome for the aging process or the potential for manipulating the microbiome to promote healthy aging remain unclear.
This work is further complicated by the many demographic factors that contribute to aging and age-related phenotypes. For example, sex is an important factor in aging [13]. Women significantly outlive men in nearly all human populations around the world [14], and most of the World Health Organization’s common age-associated causes of death are sexually dimorphic [15]. The mechanisms responsible for these sexually dimorphic phenotypes remain poorly understood, emphasizing the need to better understand if and how differences in the microbiomes of male and female animal models influence their health and longevity as well as the conservation of these phenomena in humans.
In this Essay, we discuss the emerging literature indicating that the human microbiome is altered in aging individuals and that the microbiome impacts longevity in model organisms. We highlight recent studies in humans and model organisms that implicate the microbiome in multiple age-associated diseases, focusing on cancer, obesity, type 2 diabetes, and Parkinson’s disease. Then, we explain why biological sex is a key gap in understanding how the microbiome shapes aging. Together, these discussions emphasize the broad impact of the microbiome across the life span and the potential for rapid new discoveries in this interdisciplinary area.
The microbiome and aging
The human microbiome is altered in elderly adults.
The overall association between the human microbiome and age is strong enough that it is possible to predict biological age with striking precision with the microbiome. An initial proof-of-concept was demonstrated in early life, in which a “microbiota maturity index” established in healthy individuals was delayed in the context of malnutrition [16]. More recently, machine learning tools have enabled the accurate prediction of age in adults from distal gut metagenomic data with a mean absolute error of 6 to 8 years [17,18]. The composition of the microbiota found in other body habitats, including the skin and oral cavity, is also linked to age [19]. The skin microbiota has even been used postmortem to date bodies [20,21], emphasizing that the temporal relationships with the human microbiota encompass the entire life span and shortly thereafter. Continued progress in this area has clear implications for forensics, enabling new approaches to identifying suspects [22] and potentially even their age. Microbiome signatures have also been associated with survival in the elderly [23], further underscoring the importance of understanding how the microbiome is altered in aging.
Work on centenarians (individuals aged 100+ years) has provided valuable insights into components of the microbiome that may promote healthy aging [24] (Fig 1). Centenarians exhibit a higher bacterial diversity than younger individuals and are enriched for the bacterial genera Alistipes, Parabacteroides, and Clostridium. Consistent with these taxonomic shifts, multiple microbial metabolites are also enriched in centenarians, including anti-inflammatory bile acids produced by gut bacteria [24]. Follow-up studies testing the causal role of the specific bacterial species, genes, and metabolites in promoting healthy aging are needed; however, these data clearly demonstrate that individuals at the extremes of longevity harbor distinctive microbial taxa and metabolic end-products.
Aging can be considered as opposite sides of a balance tugged by the weights of various microbial factors, discussed in detail in this Essay. Created with BioRender.com.
Frailty (the condition of being weak or vulnerable to biological stressors) has also been linked to interindividual differences in the human gut microbiome [25,26] (Fig 1). Frail elderly individuals have decreased gut bacterial diversity relative to less frail individuals after adjusting for age [25]. Longitudinal analysis of community-dwelling and skilled nursing facility-dwelling older adults has revealed frailty-associated differences in the skin, oral, and gut microbiota [26]. Multiple potentially pathogenic bacterial species were observed on the skin of frail older adults, together with a vast repository of antibiotic resistance genes [26]. As in centenarians, the causal role of the microbiota in driving frailty remains to be established, especially given the many confounding factors that could potentially explain these frailty-associated differences in the human microbiota.
Age is associated with multiple aspects of lifestyle and changes in host biology that could feasibly explain many or all of the observed differences in the human microbiota. Aging is accompanied by impaired host immunity [27], which could lead to an expansion of microorganisms that were formerly held in check by the immune system, possibly explaining the enrichment of potential bacterial pathogens in frail elderly individuals [26]. Diet is also an obvious confounding factor, as the more restricted diet of nursing home residents may be a key driver of changes in the gut microbiota in some elderly individuals [28]. Gut motility also generally slows with age [29], which could have downstream consequences for the gut microbiota [30]. Finally, social determinants of health in aging such as solitary living [31], increased likelihood of residential care [32], reduced mobility [33,34], and loss of interpersonal relationships [35,36] could all potentially influence the microbiome. Given the numerous factors that could have a role, a recent study took a more integrative approach, demonstrating an association between the gut microbiome and overall life history, which encompasses information about medications, physical activity, diet, and blood markers [37]. Thus, microbiome shifts with respect to age appear to be driven by the net effects of numerous host and environmental factors.
These results emphasize that the human microbiome is an important but understudied aspect of the aging process. Given the complexity of this microbial ecosystem, disentangling causal relationships is intractable in humans, motivating the emerging work in model organisms that we discuss in the following section.
The microbiome impacts longevity across model organisms.
Research in germ-free (GF) model organisms has provided strong support for a causal role of the microbiome in determining host life span, including studies in worms, flies, fish, and mice. Considered together, the results of the studies discussed below imply that the human microbiome also has a causal role in life span; however, the direct “reverse translation” of the specific aspects of the human microbiome associated with aging to these model organisms remain to be explored.
Research across multiple model systems suggest that exposure to the microbiome in early life is beneficial in extending life span. This is most extreme in Danio rerio, which do not reach maturity under GF conditions due to an epidermal degeneration phenotype, likely driven by inadequate nutrition [38]. Similarly, bacterial colonization during embryonic development extends the life span of Drosophila melanogaster [39]. However, these results conflict with data from GF Caenorhabditis elegans [40], GF mice [41,42], and GF rats [42,43], which all live longer than conventionally raised (CONV-R) control animals. Thus, the potential benefits of microbial colonization in early life may be outweighed by detrimental effects later in life.
Consistent with this hypothesis, the microbiome can decrease life span in older animals. In C. elegans, GI accumulation of Escherichia coli contributes to age-related death [44]. Removal of GF D. melanogaster from sterile conditions reduces life span in adults [39]. More recently, the detrimental effects of the microbiome in aging animals has been studied using the African turquoise killifish [45]. Middle-aged (9.5-week-old) killifish treated with antibiotics outlived untreated fish, suggesting that the microbiota impairs life span in older killifish. Remarkably, inoculation with the GI microbiota of 6-week-old killifish significantly increased the life span of middle-aged killifish groups [45].
These findings are also relevant to mammals. Work in 2 mouse models of progeria (a human premature aging syndrome) supports the potential for microbiome-based interventions to extend life span [46]. The gut microbiota was altered in prematurely aging mice, including a significant decrease in Akkermansia muciniphila in the LmnaG609G/G609G model, which harbors the nuclear envelope lamin A/C point mutation responsible for the most common human progeria syndrome. As in killifish [45], fecal microbiota transplantation (FMT) from wild-type mice significantly increased the life span of transgenic prematurely aging recipient mice. Even more excitingly, the Verrucomicrobium A. muciniphila, a common member of the human gut microbiota, was sufficient to extend life span in the mice [46]. These results provide a major step towards identifying the cellular and molecular mechanisms responsible for microbiota-dependent changes in life span as well as an important step towards the potential translation of these results to humans.
One mechanism supported by results in multiple model organisms is that the microbiome may decrease life span by increasing the accessibility of dietary nutrients. Thus, differences in the microbiome may counteract or even compound the effects of caloric restriction, which extends life span in multiple species [47]. This area of study will benefit from the already extensive literature focused on the role of the gut microbiome in nutrition [48]. Briefly, the microbiome is critical for the digestion of plant polysaccharides [49], the absorption of lipids [50], and the uptake of amino acids [51]. Microbial colonization can also activate multiple pathways inhibited by caloric restriction (which extends life span), including insulin-like growth factor 1 [52] and AMP-activated protein kinase [53]. Notably, GF mice lose their life span advantage over CONV-R mice when calorie restricted [54]. Furthermore, recent studies in humans and mouse models have revealed that caloric restriction can perturb the human gut microbiome in a manner that promotes weight loss [55]. Extensive data have also implicated the microbiome in malnutrition [56]. More work is needed to untangle these complex interactions between diet and microbiome and their long-term implications for host health and longevity.
The microbiome and age-associated diseases
The data discussed in the previous section emphasize that the microbiome could influence longevity through shaping the risk and treatment of disease. Recent investigation supports that host age contributes to differences between disease-associated microbiomes and those of healthy individuals [57]. Given the vast literature spanning multiple disease areas, we opted to focus the following sections on 3 age-associated disease areas: cancer, metabolic disease (obesity and type 2 diabetes), and Parkinson’s disease (Fig 2). The studies discussed here highlight the potential to pair mechanistic and translational microbiome research and the generalizability of these approaches to other age-associated diseases.
Diagram summarizing some of the mechanisms through which the microbiome has been implicated in 3 distinct age-associated diseases. We propose that the net effect of all these pathways shapes life span by dictating the risk and treatment of disease. Amuc_1100, A. muciniphilia protein 1100; FAD-A, Fusobacterium adhesin A; ICAM-2, intercellular adhesion molecule 2; NF-κB, nuclear factor-κB; P9, carboxyl-terminal protease; TLR2, Toll-like receptor 2. Created with BioRender.com.
The microbiome influences cancer risk and treatment outcomes.
Cancer is associated with age, with under 25 cases per 100,000 under the age of 20, 350 cases per 100,000 among those aged 45 to 49, and over 1,000 cases per 100,000 in individuals 60 years and older [58]. The majority of cancer types, including breast [59], prostate [60], and colorectal [61], follow this trend. The causal role of the gut microbiome in cancer risk has been reviewed elsewhere [62], including seminal work on Helicobacter pylori [63]. More recently, comparisons of colorectal cancer tumors to adjacent, nonmalignant mucosa revealed a significant enrichment of Fusobacterium nucleatum [64]. Evidence for a causal role of F. nucleatum in colon cancer has come from mice, in which this bacterium activates signaling pathways that promote myeloid cell infiltration and expression of pro-inflammatory and oncogenic genes [65,66].
The entire microbiome, in addition to individual species such as F. nucleatum, can serve as a valuable biomarker for disease status. Using gut microbiome data as a screening tool improves colorectal adenoma prediction success by a factor of more than 50-fold [67]. The gut microbiome is also associated with cancers found in other organs, including the liver [68], prostate [69], and breast [70]. Furthermore, tumors found throughout the body often harbor detectable microorganisms, including both bacteria [71] and fungi [72], suggesting that the microbiome may have both local and systemic effects on tumor progression.
Work on cancer chemotherapy and immunotherapy has emphasized the broad role of the microbiome in shaping cancer treatment outcomes [73]. Remarkably, FMT from patients with melanoma who responded well to immunotherapy into other patients was followed by decreased tumor size in a subset of recipients [74,75]. These translational efforts were inspired by a series of elegant mechanistic studies highlighting how changes in the gut microbiome can alter host immunity and thus change responsiveness to immune checkpoint blockade [76,77]. In addition to immune interactions, the microbiome can also directly influence anticancer drugs by metabolizing them to downstream metabolites with increased [78,79] or decreased [79,80] activity. Selective inhibition of the bacterial enzyme that reactivates the anticancer drug irinotecan (β-glucuronidase) rescues GI toxicity [78], whereas high levels of expression of the bacterial preTA operon can interfere with the efficacy of capecitabine (an oral form of the anticancer drug 5-fluorouracil) [79]. Continued progress in understanding how the microbiome influences cancer risk, treatment, and survivorship has profound implications for addressing this devastating disease affecting the aging global population.
Reciprocal interactions between host metabolism and the microbiome.
Obesity and type 2 diabetes are both associated with age [3,81] and have extensive ties to the microbiome (reviewed in-depth elsewhere [82]). In humans, consistent microbiome correlations with these conditions have been elusive due in part to the confounding effects of the diabetes medication metformin [83,84], gastric bypass surgery [85,86], and weight loss diets [55,87]. Differences between ethnic groups may also have a role: for example, in a United States–based cohort, obesity-associated differences in the gut microbiotas of white individuals were not detected in East Asian individuals [88]. Taken together, these results emphasize that the common medical interventions meant to ameliorate metabolic disease have profound impacts on the gut microbiome, which could also be relevant to the aging process. Furthermore, the specific microbial species, genes, and pathways involved may vary between individuals [89] and across cohorts [90], motivating efforts towards microbiome-informed precision nutrition and medicine.
Mechanistic work in model organisms has highlighted the numerous pathways through which the microbiome can influence phenotypes related to obesity and type 2 diabetes [82]. As discussed above, the microbiome can contribute to caloric intake by aiding in the digestion of otherwise inaccessible components of the diet [11], consistent with recent data in humans showing a significant decrease in dietary energy harvest following treatment with the antibiotic vancomycin [91]. In turn, the microbiome also impacts host energy expenditure, in part through changing host gene expression and enzymatic activity [53]. More recently, work on A. muciniphila has led to the identification of a bacterial protein that is sufficient to rescue mice from diet-induced obesity [92]. Additional research has identified a separate A. muciniphila protein sufficient to improve glucose tolerance and rescue a metabolic disease phenotype in mice [93]. These findings are consistent with data from humans supporting the safety and beneficial effects of pasteurized A. muciniphila [94]. Moving forward, it will be critical to see how the impact of the microbiome on host energetics changes in aging individuals, especially given the concomitant changes in dietary intake [95] and pharmaceutical use.
Connections between the gut and brain provide insight into neurological disease.
The human microbiome may also have a causal role in the etiology and treatment of multiple neurological diseases whose risk and/or severity increase with age, including Alzheimer’s disease [96], multiple sclerosis [97], and Parkinson’s disease [98]. Here, we focus on Parkinson’s disease, given the recent advances towards understanding its relationship with the gut microbiome and the clear link to aging. More than 95% of Parkinson’s disease cases occur in individuals over the age of 50 years [99]; however, an aging population is insufficient to account for the rising incidence of Parkinson’s disease [100], implicating environmental factors like the microbiome. Furthermore, multiple lines of evidence implicate the GI tract in Parkinson’s disease: An early symptom to manifest is constipation [100]; the amyloid protein α-synuclein is found in the vagus nerve (which links the brain to the gut) before reaching the central nervous system [101]; and truncal vagotomy (removal of the vagus nerve at the gastroesophageal junction) is associated with a near 50% risk reduction of Parkinson’s disease [102,103]. Yet, despite these numerous links between the GI tract and Parkinson’s disease, the role of the microbiome has only recently come into focus.
Studies in mice have highlighted multiple mechanisms through which the gut microbiome communicates with the brain to impact Parkinson’s disease pathogenesis. The microbiota is altered in a mouse model of Parkinson’s disease wherein α-synuclein is overexpressed (the ASO model) [8]. Colonization of ASO GF mice [8], as well as an alternative mouse model of Parkinson’s disease [104], with the gut microbiota of affected mice or humans exacerbates brain pathology and motor dysfunction relative to controls. Disease can also be triggered by bacterial amyloids, as shown for the cell surface amyloid curli proteins made by E. coli [105]. Furthermore, a recent preprint demonstrates that gut bacteria can also influence the production of host amyloids, as bacterial nitrate reduction stimulates the intestinal aggregation of α-synuclein [106]. These results, combined with the growing amount of metagenomic data from patients with Parkinson’s disease and healthy individuals, suggest that multiple, distinct microbiome-dependent cellular and molecular mechanisms may combine to drive disease in patients with Parkinson’s disease [107].
The gut microbiome may also contribute to interindividual variations in Parkinson’s disease treatment outcomes. Parkinson’s disease treatment typically starts with the small molecule drug levodopa (L-dopa), which is converted in the central nervous system to dopamine, thus alleviating Parkinsonian symptoms resulting from neuronal dopamine depletion [108]. L-dopa is typically paired with carbidopa, a dehydroxylase inhibitor that reduces peripheral metabolism of the drug [109]. However, carbidopa does not inhibit the gut bacterial enzyme tyrosine decarboxylase (TyrDC) [110,111], which catalyzes the first step in a pathway for the gut bacterial metabolism of L-dopa to m-tyramine within the GI tract [111]. Instead, the compound (S)-α-fluoromethyltyrosine can be used to specifically inhibit bacterial TyrDC, leading to increased serum L-dopa in mice [111]. Notably, tyrDC levels increase over time in patients with Parkinson’s disease and are associated with GI adverse effects of treatment with multiple Parkinson’s disease medications [112]. TyrDC may be just one of multiple routes of gut bacterial metabolism; Clostridium sporogenes can also deaminate L-dopa [113]. More work is needed to understand the relative contributions of these and other pathways in model organisms and patients with Parkinson’s disease, as well as their downstream consequences for drug efficacy and adverse effect profiles. This concept could also be more broadly applied to other drugs used to treat neurological disease; for example, the Alzheimer’s disease medications galantamine and memantine, which are depleted by human gut bacterial isolates during in vitro growth [114].
Sex is a key gap in understanding how the microbiome shapes aging
Aging is fundamentally distinct in men and women, with broad differences in life span, frailty, and age-related diseases [13]. Frailty in women persists throughout the life span, with disability peaking in later years [115]. Yet, women outlive men in nearly all human populations around the world [14]. These data hold even when adjusting for socioeconomic status, ethnicity, and education. Multiple molecular mechanisms contribute to sexual dimorphism in aging, including endocrine and host genetic differences. For example, while contradictory findings reporting more modest or even opposite effects exist [116,117], several reports suggest that in humans and mice, oophorectomy decreases health span and life span [118–120]. In contrast, while also a topic of debate [121], several lines of literature support that male gonads and hormones can negatively influence life span. For example, research in eunuchs suggests that castration increases longevity in men [122,123], and research in rodents has shown certain exogenous androgens to decrease life span [124]. Furthermore, gonad swapping experiments in mice support the conclusion that ovaries (and potentially their hormones) can significantly prolong life span [125].
Most diseases associated with aging are also sexually dimorphic, including the 3 disease areas highlighted above. Cancer incidence and survival are higher in women and girls [126], and numerous nonreproductive cancers are strongly sex biased in incidence, most notably endocrine cancers (female bias) and Kaposi sarcoma (male bias) [126]. Women are at increased risk of obesity compared with men [127] yet exhibit a comparable risk of type 2 diabetes [128]. Finally, neurodegenerative disease severity and risk track with sex: For example, Parkinson’s disease risk is higher in men, but women exhibit more severe disease [129].
An emerging literature has also begun to reveal links between sex and the microbiome in humans [130,131] and mice [132–134]. While the mechanisms responsible remain poorly understood, initial data point towards sex hormones as important mediators of this relationship. In humans, sex is associated with gut microbiota differences from puberty until the mean age of menopause, consistent with the hypothesis that sex hormones are an important driver of the observed differences [130].
In turn, the microbiome may also have an important role in controlling sex hormone levels. GF mice have altered sex hormone levels relative to CONV-R animals: GF males have lower testosterone and higher β-estradiol, whereas GF females have lower progesterone and β-estradiol [132,135,136]. Gut bacterial β-glucuronidases can reactivate estrogen glucuronides [137], consistent with data in humans linking antibiotics to decreased serum sex hormone concentrations and increased fecal excretion of sex hormone conjugates [138]. Furthermore, circulating sex hormone levels are associated with the diversity and composition of the gut microbiota [139].
While literature at the intersection of sex, microbiome, and aging remains sparse, some initial observations highlight the value of this line of inquiry. Work in GF mice suggests that the female longevity advantage requires the microbiota [41]. A seminal study in the nonobese diabetic model of type 1 diabetes revealed that sex differences in the microbiome can impact autoimmune disease [132]. Male CONV-R mice were protected from diabetes, but this difference was lost in GF males due to decreased testosterone. Remarkably, transplantation of the male-associated gut microbiota into female recipients was sufficient to protect from disease [132]. These effects are likely relevant beyond testosterone: A recent study of diet-induced obesity in mice suggested that estrogen-induced differences in the gut microbiome may protect from metabolic disease [140]. Moving forward, it will be critical to identify the mechanisms through which sex alters the microbiome and the downstream consequences for age-associated diseases and overall life span. In doing so, investigators ought to take important considerations in understanding how biological sex influences the microbiome’s effects on phenotypes of aging (Table 1).
Conclusions
In this Essay, we discussed the emerging yet already compelling evidence supporting a role for the microbiome in aging and age-associated diseases. These findings have broad implications for biomedical science and other areas of biology. Microbiome researchers would do well to control for or otherwise account for age, sex, and other demographic variables in their studies, even if these variables do not represent the primary focus of their research program. In turn, researchers in the fields of aging and numerous age-associated disease areas should consider the potential role of the microbiome in their research; for example, by collecting exploratory samples for microbiome profiling, controlling for microbiome-associated variables like diet and cohousing, or using GF models [146]. Working together, this interdisciplinary research area is poised for rapid discovery and could address long-standing questions about the factors that control microbial community structure and function and the drivers of interindividual variations in age-related disease risk and treatment outcomes. Perhaps most importantly, it will be critical to avoid multiplying the hype in the microbiome and aging fields to prioritize rigorous, mechanistic, and experimentally tractable work aimed at understanding fundamental biological processes. The fountain of youth may be a long way off, but perhaps this line of research can still help us achieve more modest goals of living a bit longer and prospering a little bit more.
Acknowledgments
We thank the Turnbaugh Lab for critical feedback on the manuscript. Figures were created using the Procreate app.
References
- 1. Niccoli T, Partridge L. Ageing as a risk factor for disease. Curr Biol. 2012;22:R741–R752. pmid:22975005
- 2. Sanai SA, Saini V, Benedict RH, Zivadinov R, Teter BE, Ramanathan M, et al. Aging and multiple sclerosis. Mult Scler. 2016;22:717–725. pmid:26895718
- 3. Jaul E, Barron J. Age-Related Diseases and Clinical and Public Health Implications for the 85 Years Old and Over Population. Front Public Health. 2017;5:335.
- 4. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. pmid:20203603
- 5. Schwabe RF, Jobin C. The microbiome and cancer. Nat Rev Cancer. 2013;13:800–812. pmid:24132111
- 6. Dapito DH, Mencin A, Gwak G-Y, Pradere J-P, Jang M-K, Mederacke I, et al. Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4. Cancer Cell. 2012;21:504–516. pmid:22516259
- 7. Wong BC-Y, Lam SK, Wong WM, Chen JS, Zheng TT, Feng RE, et al. Helicobacter pylori eradication to prevent gastric cancer in a high-risk region of China: a randomized controlled trial. JAMA. 2004;291:187–194.
- 8. Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease. Cell. 2016;167:1469–1480.e12. pmid:27912057
- 9. Hill-Burns EM, Debelius JW, Morton JT, Wissemann WT, Lewis MR, Wallen ZD, et al. Parkinson’s disease and Parkinson’s disease medications have distinct signatures of the gut microbiome. Mov Disord. 2017;32:739–749. pmid:28195358
- 10. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–484. pmid:19043404
- 11. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–1031. pmid:17183312
- 12. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. pmid:23023125
- 13. Hägg S, Jylhävä J. Sex differences in biological aging with a focus on human studies. Elife. 2021;10:e63425. pmid:33982659
- 14. WHO. Life expectancy and healthy life expectancy data by country. In: World Health Organziation [Internet]. [cited 2021 Dec 4]. Available from: https://apps.who.int/gho/data/node.main.688.
- 15.
Global Health Estimates: Life expectancy and leading causes of death and disability. [cited 2022 Jan 17]. Available from: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates.
- 16. Subramanian S, Huq S, Yatsunenko T, Haque R, Mahfuz M, Alam MA, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature. 2014;510:417–421. pmid:24896187
- 17. Galkin F, Mamoshina P, Aliper A, Putin E, Moskalev V, Gladyshev VN, et al. Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience. 2020;23:101199. pmid:32534441
- 18. Chen Y, Wang H, Lu W, Wu T, Yuan W, Zhu J, et al. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes. 2022;14:2025016. pmid:35040752
- 19. Huang S, Haiminen N, Carrieri A-P, Hu R, Jiang L, Parida L, et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems. 2020;5:e00630–e00619. pmid:32047061
- 20. Metcalf JL, Xu ZZ, Weiss S, Lax S, Van Treuren W, Hyde ER, et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science. 2016;351:158–162. pmid:26657285
- 21. Johnson HR, Trinidad DD, Guzman S, Khan Z, Parziale JV, DeBruyn JM, et al. A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval. PLoS ONE. 2016;11:e0167370. pmid:28005908
- 22. Hampton-Marcell JT, Larsen P, Anton T, Cralle L, Sangwan N, Lax S, et al. Detecting personal microbiota signatures at artificial crime scenes. Forensic Sci Int. 2020;313:110351. pmid:32559614
- 23. Wilmanski T, Diener C, Rappaport N, Patwardhan S, Wiedrick J, Lapidus J, et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab. 2021;3:274–286. pmid:33619379
- 24. Sato Y, Atarashi K, Plichta DR, Arai Y, Sasajima S, Kearney SM, et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature. 2021;599:458–464. pmid:34325466
- 25. Jackson MA, Jeffery IB, Beaumont M, Bell JT, Clark AG, Ley RE, et al. Signatures of early frailty in the gut microbiota. Genome Med. 2016;8:8. pmid:26822992
- 26. Larson PJ, Zhou W, Santiago A, Driscoll S, Fleming E, Voigt AY, et al. Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults. Nat Aging. 2022;2:941–955. pmid:36398033
- 27. Weiskopf D, Weinberger B, Grubeck-Loebenstein B. The aging of the immune system. Transpl Int. 2009;22:1041–1050. pmid:19624493
- 28. O’Toole PW, Jeffery IB. Gut microbiota and aging. Science. 2015;350:1214–1215. pmid:26785481
- 29. Madsen JL. Effects of gender, age, and body mass index on gastrointestinal transit times. Dig Dis Sci. 1992;37:1548–1553. pmid:1396002
- 30. Reigstad CS, Kashyap PC. Beyond phylotyping: understanding the impact of gut microbiota on host biology. Neurogastroenterol Motil. 2013;25:358–372. pmid:23594242
- 31. Finnicum CT, Beck JJ, Dolan CV, Davis C, Willemsen G, Ehli EA, et al. Cohabitation is associated with a greater resemblance in gut microbiota which can impact cardiometabolic and inflammatory risk. BMC Microbiol. 2019;19:230. pmid:31640566
- 32. Jeffery IB, Lynch DB, O’Toole PW. Composition and temporal stability of the gut microbiota in older persons. ISME J. 2016;10:170–182. pmid:26090993
- 33. Bressa C, Bailén-Andrino M, Pérez-Santiago J, González-Soltero R, Pérez M, Montalvo-Lominchar MG, et al. Differences in gut microbiota profile between women with active lifestyle and sedentary women. PLoS ONE. 2017;12:e0171352. pmid:28187199
- 34. Barton W, Penney NC, Cronin O, Garcia-Perez I, Molloy MG, Holmes E, et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut. 2018;67:625–633. pmid:28360096
- 35. Dill-McFarland KA, Tang Z-Z, Kemis JH, Kerby RL, Chen G, Palloni A, et al. Close social relationships correlate with human gut microbiota composition. Sci Rep. 2019;9:703. pmid:30679677
- 36. Nguyen TT, Zhang X, Wu T-C, Liu J, Le C, Tu XM, et al. Association of Loneliness and Wisdom With Gut Microbial Diversity and Composition: An Exploratory Study. Front Psych. 2021;12:648475. pmid:33841213
- 37. Si J, Vázquez-Castellanos JF, Gregory AC, Decommer L, Rymenans L, Proost S, et al. Long-term life history predicts current gut microbiome in a population-based cohort study. Nat Aging. 2022;2:885–895.
- 38. Rawls JF, Samuel BS, Gordon JI. Gnotobiotic zebrafish reveal evolutionarily conserved responses to the gut microbiota. Proc Natl Acad Sci U S A. 2004;101:4596–4601. pmid:15070763
- 39. Brummel T, Ching A, Seroude L, Simon AF, Benzer S. Drosophila lifespan enhancement by exogenous bacteria. Proc Natl Acad Sci U S A. 2004;101:12974–12979. pmid:15322271
- 40. Houthoofd K, Braeckman BP, Lenaerts I, Brys K, De Vreese A, Van Eygen S, et al. Axenic growth up-regulates mass-specific metabolic rate, stress resistance, and extends life span in Caenorhabditis elegans. Exp Gerontol. 2002;37:1371–1378. pmid:12559406
- 41. Gordon HA, Bruckner-kardoss E, Wostmann BS. Aging in germ-free mice: life tables and lesions observed at natural death1. J Gerontol. 1966;21:380–387.
- 42. Festing M, Blackmore D. Life span of specified-pathogen-free (MRC category 4) mice and rats. Lab Anim. 1971;5:179–192. pmid:5166568
- 43. Snyder DL, Pollard M, Wostmann BS, Luckert P. Life span, morphology, and pathology of diet-restricted germ-free and conventional Lobund-Wistar rats. J Gerontol. 1990;45:B52–B58. pmid:2313040
- 44. Zhao Y, Gilliat AF, Ziehm M, Turmaine M, Wang H, Ezcurra M, et al. Two forms of death in ageing Caenorhabditis elegans. Nat Commun. 2017;8:15458. pmid:28534519
- 45. Smith P, Willemsen D, Popkes M, Metge F, Gandiwa E, Reichard M, et al. Regulation of life span by the gut microbiota in the short-lived African turquoise killifish. Elife. 2017;6:e27014. pmid:28826469
- 46. Bárcena C, Valdés-Mas R, Mayoral P, Garabaya C, Durand S, Rodríguez F, et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat Med. 2019;25:1234–1242. pmid:31332389
- 47. Masoro EJ. Overview of caloric restriction and ageing. Mech Ageing Dev. 2005;126:913–922. pmid:15885745
- 48. Carmody RN, Turnbaugh PJ. Host-microbial interactions in the metabolism of therapeutic and diet-derived xenobiotics. J Clin Invest. 2014;124:4173–4181. pmid:25105361
- 49. Koropatkin NM, Cameron EA, Martens EC. How glycan metabolism shapes the human gut microbiota. Nat Rev Microbiol. 2012;10:323–335. pmid:22491358
- 50. Semova I, Carten JD, Stombaugh J, Mackey LC, Knight R, Farber SA, et al. Microbiota Regulate Intestinal Absorption and Metabolism of Fatty Acids in the Zebrafish. Cell Host Microbe. 2012;12:277–288. pmid:22980325
- 51. Yamada R, Deshpande SA, Bruce KD, Mak EM, Ja WW. Microbes Promote Amino Acid Harvest to Rescue Undernutrition in Drosophila. Cell Rep. 2015;10:865–872. pmid:25683709
- 52. Yan J, Herzog JW, Tsang K, Brennan CA, Bower MA, Garrett WS, et al. Gut microbiota induce IGF-1 and promote bone formation and growth. Proc Natl Acad Sci U S A. 2016;113:E7554–E7563. pmid:27821775
- 53. Bäckhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A. 2007;104:979–984. pmid:17210919
- 54. Tazume S, Umehara K, Matsuzawa H, Aikawa H, Hashimoto K, Sasaki S. Effects of germfree status and food restriction on longevity and growth of mice. Jikken Dobutsu. 1991;40:517–522. pmid:1748169
- 55. von Schwartzenberg RJ, Bisanz JE, Lyalina S, Spanogiannopoulos P, Ang QY, Cai J, et al. Caloric restriction disrupts the microbiota and colonization resistance. Nature. 2021;595:272–277. pmid:34163067
- 56. Barratt MJ, Nuzhat S, Ahsan K, Frese SA, Arzamasov AA, Sarker SA, et al. Bifidobacterium infantis treatment promotes weight gain in Bangladeshi infants with severe acute malnutrition. Sci Transl Med. 2022;14:eabk1107. pmid:35417188
- 57. Ghosh TS, Das M, Jeffery IB, O’Toole PW. Adjusting for age improves identification of gut microbiome alterations in multiple diseases. Elife. 2020;9:e50240. pmid:32159510
- 58. Risk Factors: Age. In: National Cancer Institute [Internet]. 2015 Apr 29 [cited 2023 Mar 14]. Available from: https://www.cancer.gov/about-cancer/causes-prevention/risk/age.
- 59. Kelsey JL. A review of the epidemiology of human breast cancer. Epidemiol Rev. 1979;1:74–109. pmid:398270
- 60. Rawla P. Epidemiology of Prostate Cancer. World J Oncol. 2019;10:63–89. pmid:31068988
- 61. Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol. 2019;14:89–103. pmid:31616522
- 62. Helmink BA, Khan MAW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med. 2019;25:377–388. pmid:30842679
- 63. Blaser MJ, Perez-Perez GI, Kleanthous H, Cover TL, Peek RM, Chyou PH, et al. Infection with Helicobacter pylori strains possessing cagA is associated with an increased risk of developing adenocarcinoma of the stomach. Cancer Res. 1995;55:2111–2115. pmid:7743510
- 64. Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F, Earl AM, et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 2012;22:292–298. pmid:22009990
- 65. Kostic AD, Chun E, Robertson L, Glickman JN, Gallini CA, Michaud M, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 2013;14:207–215. pmid:23954159
- 66. Rubinstein MR, Wang X, Liu W, Hao Y, Cai G, Han YW. Fusobacterium nucleatum promotes colorectal carcinogenesis by modulating E-cadherin/β-catenin signaling via its FadA adhesin. Cell Host Microbe. 2013;14:195–206.
- 67. Zackular JP, Rogers MAM, Ruffin MT 4th, Schloss PD. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res. 2014;7: 1112–1121. pmid:25104642
- 68. Yu L-X, Schwabe RF. The gut microbiome and liver cancer: mechanisms and clinical translation. Nat Rev Gastroenterol Hepatol. 2017;14:527–539. pmid:28676707
- 69. Javier-DesLoges J, McKay RR, Swafford AD, Sepich-Poore GD, Knight R, Parsons JK. The microbiome and prostate cancer. Prostate Cancer Prostatic Dis. 2022;25:159–164. pmid:34267333
- 70. Kwa M, Plottel CS, Blaser MJ, Adams S. The Intestinal Microbiome and Estrogen Receptor-Positive Female Breast Cancer. J Natl Cancer Inst. 2016;108:djw029. pmid:27107051
- 71. Nejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, et al. The human tumor microbiome is composed of tumor type–specific intracellular bacteria. Science. 2020;368:973–980. pmid:32467386
- 72. Narunsky-Haziza L, Sepich-Poore GD, Livyatan I, Asraf O, Martino C, Nejman D, et al. Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions. Cell. 2022;185:3789–3806.e17. pmid:36179670
- 73. Elinav E, Garrett WS, Trinchieri G, Wargo J. The cancer microbiome. Nat Rev Cancer. 2019;19:371–376. pmid:31186547
- 74. Davar D, Dzutsev AK, McCulloch JA, Rodrigues RR, Chauvin J-M, Morrison RM, et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science. 2021;371:595–602. pmid:33542131
- 75. Baruch EN, Youngster I, Ben-Betzalel G, Ortenberg R, Lahat A, Katz L, et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science. 2021;371:602–609. pmid:33303685
- 76. Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K, Earley ZM, et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science. 2015;350:1084–1089. pmid:26541606
- 77. Vétizou M, Pitt JM, Daillère R, Lepage P, Waldschmitt N, Flament C, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015;350:1079–1084. pmid:26541610
- 78. Wallace BD, Wang H, Lane KT, Scott JE, Orans J, Koo JS, et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science. 2010;330:831–835. pmid:21051639
- 79. Spanogiannopoulos P, Kyaw TS, Guthrie BGH, Bradley PH, Lee JV, Melamed J, et al. Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism. Nat Microbiol. 2022;7:1605–1620. pmid:36138165
- 80. Geller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N, Nejman D, et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science. 2017;357:1156–1160. pmid:28912244
- 81. Cefalu WT, Wang ZQ, Werbel S, Bell-Farrow A, Crouse JR 3rd, Hinson WH, et al. Contribution of visceral fat mass to the insulin resistance of aging. Metabolism. 1995;44:954–959. pmid:7616857
- 82. Hartstra AV, Bouter KEC, Bäckhed F, Nieuwdorp M. Insights Into the Role of the Microbiome in Obesity and Type 2 Diabetes. Diabetes Care. 2014;38:159–165.
- 83. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannerås-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23:850–858. pmid:28530702
- 84. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528:262–266. pmid:26633628
- 85. Liou AP, Paziuk M, Luevano J-M Jr, Machineni S, Turnbaugh PJ, Kaplan LM. Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci Transl Med. 2013;5:178ra41. pmid:23536013
- 86. Shen N, Caixàs A, Ahlers M, Patel K, Gao Z, Dutia R, et al. Longitudinal changes of microbiome composition and microbial metabolomics after surgical weight loss in individuals with obesity. Surg Obes Relat Dis. 2019;15:1367–1373. pmid:31296445
- 87. Ang QY, Alexander M, Newman JC, Tian Y, Cai J, Upadhyay V, et al. Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. Cell. 2020;181:1263–1275.e16. pmid:32437658
- 88. Ang QY, Alba DL, Upadhyay V, Bisanz JE, Cai J, Lee HL, et al. The East Asian gut microbiome is distinct from colocalized white subjects and connected to metabolic health. Elife. 2021;10:e70349. pmid:34617511
- 89. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015;163:1079–1094. pmid:26590418
- 90. Deschasaux M, Bouter KE, Prodan A, Levin E, Groen AK, Herrema H, et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med. 2018;24:1526–1531. pmid:30150717
- 91. Basolo A, Hohenadel M, Ang QY, Piaggi P, Heinitz S, Walter M, et al. Effects of underfeeding and oral vancomycin on gut microbiome and nutrient absorption in humans. Nat Med. 2020;26:589–598. pmid:32235930
- 92. Plovier H, Everard A, Druart C, Depommier C, Van Hul M, Geurts L, et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat Med. 2017;23:107–113. pmid:27892954
- 93. Yoon HS, Cho CH, Yun MS, Jang SJ, You HJ, Kim J-H, et al. Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice. Nat Microbiol. 2021;6:563–573. pmid:33820962
- 94. Depommier C, Everard A, Druart C, Plovier H, Van Hul M, Vieira-Silva S, et al. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat Med. 2019;25:1096–1103. pmid:31263284
- 95. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012;488:178–184. pmid:22797518
- 96. Sochocka M, Donskow-Łysoniewska K, Diniz BS, Kurpas D, Brzozowska E, Leszek J. The gut microbiome alterations and inflammation-driven pathogenesis of Alzheimer’s disease-a critical review. Mol Neurobiol. 2019;56:1841–1851. pmid:29936690
- 97. Pröbstel A-K, Baranzini SE. The Role of the Gut Microbiome in Multiple Sclerosis Risk and Progression: Towards Characterization of the “MS Microbiome.”. Neurotherapeutics. 2018;15: 126–134.
- 98. Lionnet A, Leclair-Visonneau L, Neunlist M, Murayama S, Takao M, Adler CH, et al. Does Parkinson’s disease start in the gut? Acta Neuropathol. 2018;135:1–12. pmid:29039141
- 99. Van Den Eeden SK, Tanner CM, Bernstein AL, Fross RD, Leimpeter A, Bloch DA, et al. Incidence of Parkinson’s disease: variation by age, gender, and race/ethnicity. Am J Epidemiol. 2003;157:1015–1022. pmid:12777365
- 100. Bloem BR, Okun MS, Klein C. Parkinson’s disease. Lancet. 2021;397:2284–2303. pmid:33848468
- 101. Del Tredici K, Braak H. Review: Sporadic Parkinson’s disease: development and distribution of α-synuclein pathology. Neuropathol Appl Neurobiol. 2016;42:33–50.
- 102. Svensson E, Horváth-Puhó E, Thomsen RW, Djurhuus JC, Pedersen L, Borghammer P, et al. Vagotomy and subsequent risk of Parkinson’s disease. Ann Neurol. 2015;78:522–529. pmid:26031848
- 103. Liu B, Fang F, Pedersen NL, Tillander A, Ludvigsson JF, Ekbom A, et al. Vagotomy and Parkinson disease: A Swedish register-based matched-cohort study. Neurology. 2017;88:1996–2002. pmid:28446653
- 104. Sun M-F, Zhu Y-L, Zhou Z-L, Jia X-B, Xu Y-D, Yang Q, et al. Neuroprotective effects of fecal microbiota transplantation on MPTP-induced Parkinson’s disease mice: Gut microbiota, glial reaction and TLR4/TNF-α signaling pathway. Brain Behav Immun. 2018;70:48–60.
- 105. Sampson TR, Challis C, Jain N, Moiseyenko A, Ladinsky MS, Shastri GG, et al. A gut bacterial amyloid promotes α-synuclein aggregation and motor impairment in mice. Elife. 2020;9:e53111.
- 106. Ortiz de Ora L, Uyeda KS, Bess E. Discovery of a Gut Bacterial Metabolic Pathway that Drives α-Synuclein Aggregation and Neurodegeneration. bioRxiv. 2022.
- 107. Wallen ZD, Demirkan A, Twa G, Cohen G, Dean MN, Standaert DG, et al. Metagenomics of Parkinson’s disease implicates the gut microbiome in multiple disease mechanisms. Nat Commun. 2022;13:6958. pmid:36376318
- 108. Nutt JG, Fellman JH. Pharmacokinetics of levodopa. Clin Neuropharmacol. 1984;7:35–49. pmid:6367973
- 109. Burkhard P, Dominici P, Borri-Voltattorni C, Jansonius JN, Malashkevich VN. Structural insight into Parkinson’s disease treatment from drug-inhibited DOPA decarboxylase. Nat Struct Biol. 2001;8:963–967. pmid:11685243
- 110. van Kessel SP, Frye AK, El-Gendy AO, Castejon M, Keshavarzian A, van Dijk G, et al. Gut bacterial tyrosine decarboxylases restrict levels of levodopa in the treatment of Parkinson’s disease. Nat Commun. 2019;10:310. pmid:30659181
- 111. Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science. 2019;364:eaau6323. pmid:31196984
- 112. van Kessel SP, Auvinen P, Scheperjans F, El Aidy S. Gut bacterial tyrosine decarboxylase associates with clinical variables in a longitudinal cohort study of Parkinsons disease. NPJ Parkinsons Dis. 2021;7:115. pmid:34911958
- 113. van Kessel SP, de Jong HR, Winkel SL, van Leeuwen SS, Nelemans SA, Permentier H, et al. Gut bacterial deamination of residual levodopa medication for Parkinson’s disease. BMC Biol. 2020;18:137. pmid:33076930
- 114. Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570:462–467. pmid:31158845
- 115. Gordon EH, Peel NM, Samanta M, Theou O, Howlett SE, Hubbard RE. Sex differences in frailty: A systematic review and meta-analysis. Exp Gerontol. 2017;89:30–40. pmid:28043934
- 116. Arriola Apelo SI, Lin A, Brinkman JA, Meyer E, Morrison M, Tomasiewicz JL, et al. Ovariectomy uncouples lifespan from metabolic health and reveals a sex-hormone-dependent role of hepatic mTORC2 in aging. Elife. 2020;9:e56177. pmid:32720643
- 117. Mason JB, Cargill SL, Anderson GB, Carey JR. Transplantation of young ovaries to old mice increased life span in transplant recipients. J Gerontol A Biol Sci Med Sci. 2009;64:1207–1211. pmid:19776215
- 118. Rocca WA, Gazzuola-Rocca L, Smith CY, Grossardt BR, Faubion SS, Shuster LT, et al. Accelerated accumulation of multimorbidity after bilateral oophorectomy: A population-based cohort study. Mayo Clin Proc. 2016;91:1577–1589. pmid:27693001
- 119. Rocca WA, Grossardt BR, de Andrade M, Malkasian GD, Melton LJ. Survival patterns after oophorectomy in premenopausal women: a population-based cohort study. Lancet Oncol. 2006;7:821–828. pmid:17012044
- 120. Cargill SL, Carey JR, Müller H-G, Anderson G. Age of ovary determines remaining life expectancy in old ovariectomized mice. Aging Cell. 2003;2:185–190. pmid:12882411
- 121. Nieschlag E, Nieschlag S, Behre HM. Lifespan and testosterone. Nature. 1993;366:215–215. pmid:8232579
- 122. Hamilton JB, Mestler GE. Mortality and survival: comparison of eunuchs with intact men and women in a mentally retarded population. J Gerontol. 1969;24:395–411. pmid:5362349
- 123. Min K-J, Lee C-K, Park H-N. The lifespan of Korean eunuchs. Curr Biol. 2012;22:R792–R793. pmid:23017989
- 124. Bronson FH, Matherne CM. Exposure to anabolic-androgenic steroids shortens life span of male mice. Med Sci Sports Exerc. 1997;29:615–619. pmid:9140897
- 125. Davis EJ, Lobach I, Dubal DB. Female XX sex chromosomes increase survival and extend lifespan in aging mice. Aging Cell. 2019;18:e12871. pmid:30560587
- 126. Dong M, Cioffi G, Wang J, Waite KA, Ostrom QT, Kruchko C, et al. Sex Differences in Cancer Incidence and Survival: A Pan-Cancer Analysis. Cancer Epidemiol Biomarkers Prev. 2020;29:1389–1397. pmid:32349967
- 127. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387:1377–1396.
- 128. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–281. pmid:29496507
- 129. Cerri S, Mus L, Blandini F. Parkinson’s Disease in Women and Men: What’s the Difference? J Parkinsons Dis. 2019;9:501–515. pmid:31282427
- 130. Zhang X, Zhong H, Li Y, Shi Z, Ren H, Zhang Z, et al. Sex- and age-related trajectories of the adult human gut microbiota shared across populations of different ethnicities. Nat Aging. 2021;1:87–100.
- 131. de la Cuesta-Zuluaga J, Kelley ST, Chen Y, Escobar JS, Mueller NT, Ley RE, et al. Age- and Sex-Dependent Patterns of Gut Microbial Diversity in Human Adults. mSystems. 2019;4:e00261–e00219. pmid:31098397
- 132. Markle JGM, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, et al. Sex Differences in the Gut Microbiome Drive Hormone-Dependent Regulation of Autoimmunity. Science. 2013;339:1084–1088. pmid:23328391
- 133. Org E, Mehrabian M, Parks BW, Shipkova P, Liu X, Drake TA, et al. Sex differences and hormonal effects on gut microbiota composition in mice. Gut Microbes. 2016;7:313–322. pmid:27355107
- 134. Liang X, Bushman FD, FitzGerald GA. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc Natl Acad Sci U S A. 2015;112:10479–10484. pmid:26240359
- 135. Yurkovetskiy L, Burrows M, Khan AA, Graham L, Volchkov P, Becker L, et al. Gender bias in autoimmunity is influenced by microbiota. Immunity. 2013;39:400–412. pmid:23973225
- 136. Weger BD, Gobet C, Yeung J, Martin E, Jimenez S, Betrisey B, et al. The mouse microbiome is required for sex-specific diurnal rhythms of gene expression and metabolism. Cell Metab. 2019;29:362–382.e8. pmid:30344015
- 137. Ervin SM, Li H, Lim L, Roberts LR, Liang X, Mani S, et al. Gut microbial β-glucuronidases reactivate estrogens as components of the estrobolome that reactivate estrogens. J Biol Chem. 2019;294:18586–18599.
- 138. Adlercreutz H, Pulkkinen MO, Hämäläinen EK, Korpela JT. Studies on the role of intestinal bacteria in metabolism of synthetic and natural steroid hormones. J Steroid Biochem. 1984;20:217–229. pmid:6231418
- 139. Shin J-H, Park Y-H, Sim M, Kim S-A, Joung H, Shin D-M. Serum level of sex steroid hormone is associated with diversity and profiles of human gut microbiome. Res Microbiol. 2019;170:192–201. pmid:30940469
- 140. Kaliannan K, Robertson RC, Murphy K, Stanton C, Kang C, Wang B, et al. Estrogen-mediated gut microbiome alterations influence sexual dimorphism in metabolic syndrome in mice. Microbiome. 2018;6:205. pmid:30424806
- 141. Woitowich NC, Beery A, Woodruff T. A 10-year follow-up study of sex inclusion in the biological sciences. Elife. 2020;9:e56344. pmid:32513386
- 142. Nelson JF, Latham KR, Finch CE. Plasma testosterone levels in C57BL/6J male mice: effects of age and disease. Eur J Endocrinol. 1975;80:744–752. pmid:1103542
- 143. Manwani B, Liu F, Scranton V, Hammond MD, Sansing LH, McCullough LD. Differential effects of aging and sex on stroke induced inflammation across the lifespan. Exp Neurol. 2013;249:120–131. pmid:23994069
- 144. Villa A, Della Torre S, Stell A, Cook J, Brown M, Maggi A. Tetradian oscillation of estrogen receptor is necessary to prevent liver lipid deposition. Proc Natl Acad Sci U S A. 2012;109:11806–11811.
- 145. Vermeulen A, Goemaere S, Kaufman JM. Testosterone, body composition and aging. J Endocrinol Invest. 1999;22:110–116. pmid:10442580
- 146. Stappenbeck TS, Virgin HW. Accounting for reciprocal host–microbiome interactions in experimental science. Nature. 2016;534:191–199. pmid:27279212