The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. ABSTRACT The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

[1]  S. Lynch,et al.  The Human Intestinal Microbiome in Health and Disease. , 2016, The New England journal of medicine.

[2]  R. Knight,et al.  Bacterial Community Variation in Human Body Habitats Across Space and Time , 2009, Science.

[3]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[4]  K. Pearson Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs , 1897, Proceedings of the Royal Society of London.

[5]  Jiachao Zhang,et al.  A phylo-functional core of gut microbiota in healthy young Chinese cohorts across lifestyles, geography and ethnicities , 2015, The ISME Journal.

[6]  David R. Lovell,et al.  propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis , 2017, Scientific Reports.

[7]  P. Bork,et al.  Enterotypes of the human gut microbiome , 2011, Nature.

[8]  Gregory B. Gloor,et al.  Displaying Variation in Large Datasets: Plotting a Visual Summary of Effect Sizes , 2016 .

[9]  S. Rampelli,et al.  Gut Microbiota and Extreme Longevity , 2016, Current Biology.

[10]  Susan M. Huse,et al.  Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle , 2016, Microbiome.

[11]  S. Podolsky Metchnikoff and the microbiome , 2012, The Lancet.

[12]  Javier Palarea-Albaladejo,et al.  zCompositions — R package for multivariate imputation of left-censored data under a compositional approach , 2015 .

[13]  P. O’Toole,et al.  Composition and temporal stability of the gut microbiota in older persons , 2015, The ISME Journal.

[14]  Cédric Notredame,et al.  How should we measure proportionality on relative gene expression data? , 2016, Theory in Biosciences.

[15]  B. Marsland,et al.  The Gut-Lung Axis in Respiratory Disease. , 2015, Annals of the American Thoracic Society.

[16]  Jessika Weiss,et al.  Graphical Models In Applied Multivariate Statistics , 2016 .

[17]  Gregory B. Gloor,et al.  Compositional uncertainty should not be ignored in high-throughput sequencing data analysis , 2016 .

[18]  J. Aitchison,et al.  Biplots of Compositional Data , 2002 .

[19]  Vera Pawlowsky-Glahn,et al.  It's all relative: analyzing microbiome data as compositions. , 2016, Annals of epidemiology.

[20]  Jean M. Macklaim,et al.  Microbiome Profiling by Illumina Sequencing of Combinatorial Sequence-Tagged PCR Products , 2010, PLoS ONE.

[21]  Christian L. Müller,et al.  Sparse and Compositionally Robust Inference of Microbial Ecological Networks , 2014, PLoS Comput. Biol..

[22]  Robert C. Edgar,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2001 .

[23]  Zoubin Ghahramani,et al.  Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.

[24]  T. Spector,et al.  Signatures of early frailty in the gut microbiota , 2016, Genome Medicine.

[25]  Jean M. Macklaim,et al.  Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis , 2014, Microbiome.

[26]  Y. Li,et al.  Gut microbiota signatures of longevity , 2016, Current Biology.

[27]  Jonathan Friedman,et al.  Inferring Correlation Networks from Genomic Survey Data , 2012, PLoS Comput. Biol..

[28]  D. Sinderen,et al.  Gut microbiota composition correlates with diet and health in the elderly , 2012, Nature.

[29]  William A. Walters,et al.  Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms , 2012, The ISME Journal.

[30]  Aly A. Khan,et al.  Gender bias in autoimmunity is influenced by microbiota. , 2013, Immunity.

[31]  A. Gasbarrini,et al.  Gut microbiota and aging. , 2018, European review for medical and pharmacological sciences.

[32]  John Aitchison,et al.  The Statistical Analysis of Compositional Data , 1986 .

[33]  Robert C. Edgar,et al.  Error filtering, pair assembly and error correction for next-generation sequencing reads , 2015, Bioinform..

[34]  Jean M. Macklaim,et al.  ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq , 2013, PloS one.

[35]  S. Shen,et al.  The statistical analysis of compositional data , 1983 .

[36]  Gregory B. Gloor,et al.  Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. , 2016, Canadian journal of microbiology.

[37]  C. Huh,et al.  Comparative analysis of gut microbiota in elderly people of urbanized towns and longevity villages , 2015, BMC Microbiology.

[38]  D. Curran‐Everett,et al.  The fickle P value generates irreproducible results , 2015, Nature Methods.

[39]  P. Bork,et al.  Human gut microbes impact host serum metabolome and insulin sensitivity , 2016, Nature.

[40]  D. Beckwée,et al.  Frailty and the Prediction of Negative Health Outcomes: A Meta-Analysis. , 2016, Journal of the American Medical Directors Association.

[41]  Yoav Gilad,et al.  Sex-specific genetic architecture of human disease , 2008, Nature Reviews Genetics.

[42]  Daniel G. Brown,et al.  PANDAseq: paired-end assembler for illumina sequences , 2012, BMC Bioinformatics.

[43]  Jürg Bähler,et al.  Proportionality: A Valid Alternative to Correlation for Relative Data , 2014, bioRxiv.

[44]  J. Clemente,et al.  Human gut microbiome viewed across age and geography , 2012, Nature.