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We read with interest the recent article by Ng et al. (Gut, 2022; 71:910–8), who reported inhibition of the gut microbiota trajectory in patients with autism spectrum disorder.1 Similarly, the human gut microbiota ages in adults.2 3 Transplantation of gut microbes from elderly hosts, compared with their younger counterparts, deteriorates recipients’ age-related metabolic alternations.4 5 However, the gut microbiota in humans could differ in its pace of ageing, namely, accelerated or delayed microbiota ageing, even among those with similar chronological ages (figure 1A); this process is analogous to biological ageing.6 Therefore, we wondered whether the gut microbiota ageing trajectory could be used as a biomarker for metabolic diseases in adults. We analysed the gut microbiota compositions of 6376 participants of a population-level survey7 with Quantitative Insights Into Microbial Ecology (QIIME),8 among whom the median chronological age of the healthy participants is 43 (IQR, 31–45, online supplemental table S1). A random forest algorithm was applied to the data of 1083 healthy individuals to model gut microbiota ageing in relation to chronological age. The model was then applied to subjects with metabolic diseases, matching healthy individuals by chronological age. See the online supplemental file 1 for additional information.
At the modelling phase, the microbiota age of the 1083 healthy individuals showed a significant and positive correlation with chronological age (online supplemental figure S1). The microbiota age in the healthy individuals was not significantly associated with gender or residing sites (developed vs less developed), but is significantly associated with grain, fruits and rice wine consumption (online supplemental table S2 and online supplemental figure S2). When the model was applied to individuals with metabolic diseases, the ageing trajectory of their microbiota was neither accelerated nor delayed but intersected with that of healthy individuals at nearly 50 years old (figure 1B–D). In individuals with metabolic disease, the microbiota age was older than that in healthy individuals at younger chronological ages, comparable with that in healthy individuals at middle chronological ages, and younger than that in healthy individuals at older chronological ages (online supplemental figure S3). Metabolic diseases (including hypertension, diabetes and metabolic syndrome) patients who took antibiotics or medication showed significantly lower microbiota age than the treatment-naive patients (online supplemental figure S4). We removed the medicated patients and the above intersecting patterns could still be reproduced (online supplemental figure S5).
To dissect the random forest ageing model, we analysed the ageing trajectories of the top 20 model-contributing taxa (figure 2A). The heatmap clearly showed that the relative taxonomic abundances correlated positively (upper half) or negatively (lower half) with chronological ageing in the healthy individuals, but the trajectories were disordered in hypertensive individuals. Typical microbial examples include Clostridium and Parabacteroides distasonis, with obvious intersecting patterns (figure 2B,C). Similar disordered taxonomic ageing trajectories were also observed in participants with diabetes, obesity, metabolic syndrome, hypercholesterolemia and hypertriglyceridaemia (online supplemental figure S6).
In conclusion, the results of the current and Ng’s study suggest that the ageing trajectory of the gut microbiota could be a potential biomarker for both paediatric and adult chronic diseases. However, accelerated microbiota ageing should not be simply considered a risk factor for metabolic diseases in adults or vice versa, as it might be chronological-age dependent. Our finding corresponds to Wilmanski’s observation that elderly individuals who carried younger microbiota signatures could have lower survival rates,9 but analyses to understand covariates (lifestyle, diet, medication, etc), subpopulation differences and longitudinal disease risks of gut microbiota ageing are warranted. Moreover, whether a chronological age-dependent bacterial function could be observed and understood in mechanism studies is worth investigating. Interestingly, a common practice in faecal microbiota transplantation (FMT) is to choose young donors for health reasons. Analysing whether a chronological age-matched healthy donor for FMT could promote additional health benefits in the recipient is worth further investigation.
Patient consent for publication
This study involves human participants and was approved by the ethical review committee of the Chinese Center for Disease Control and Prevention (no. 201519-A). Participants gave informed consent to participate in the study before taking part.
We thank Professor Lianmin Chen from Nanjing Medical University for an insightful discussion. YH (corresponding author) argued with Professor Lianmin Chen about whether a younger gut microbiota is always a healthier one, and then initiated the present analysis, in addition to the inspiration by Ng’s study. Based on the present observations, YH could be wrong for insisting that a younger microbiota is always better, but might be chronological-age dependent.
JF and WQ contributed equally.
Correction notice This article has been corrected since it published Online First. The first author's name in the citationof reference 9 has been corrected.
Contributors JF, YH and H-WZ designed the study and prepared the manuscript. YH, WW, W-JM and H-WZ provided the data. JF, WQ, H-MZ, HW, GW and YH analysed the data. PC, ZM and CZ provided crucial advice in analysing and interpreting the data.
Funding This study was supported by the National Key R&D Program of China (2019YFA0802300 (YH), 2017YFC1310600 (H-WZ)); National Natural Science Foundation of China (NSFC82022044 (YH), NSFC81925026 (H-WZ), 81800746 (YH)); and Natural Science Foundation of Guangdong Province China, Project No.-2018A0303130118 (WW).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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