Gut microbial characteristics in poor appetite and undernutrition: a cohort of older adults and microbiota transfer in germ-free mice

Kristina S Fluitman, Mark Davids, Louise E Olofsson, Madelief Wijdeveld, Valentina Tremaroli, Bart J F Keijser, Marjolein Visser, Fredrik Bäckhed, Max Nieuwdorp, Richard G IJzerman, Kristina S Fluitman, Mark Davids, Louise E Olofsson, Madelief Wijdeveld, Valentina Tremaroli, Bart J F Keijser, Marjolein Visser, Fredrik Bäckhed, Max Nieuwdorp, Richard G IJzerman

Abstract

Background: Older adults are particularly prone to the development of poor appetite and undernutrition. Possibly, this is partly due to the aged gut microbiota. We aimed to evaluate the gut microbiota in relation to both poor appetite and undernutrition in community-dwelling older adults. Furthermore, we studied the causal effects of the microbiota on body weight and body composition by transferring faecal microbiota from cohort participants into germ-free mice.

Methods: First, we conducted a cross-sectional cohort study of 358 well-phenotyped Dutch community-dwelling older adults from the Longitudinal Aging Study Amsterdam. Data collection included body measurements, a faecal and blood sample, as well as extensive questionnaires on appetite, dietary intake, and nutritional status. Appetite was assessed by the Council of Nutrition Appetite Questionnaire (CNAQ) and undernutrition was defined by either a low body mass index (BMI) (BMI < 20 kg/m2 if <70 years or BMI < 22 kg/m2 if ≥70 years) or >5% body weight loss averaged over the last 2 years. Gut microbiota composition was determined with 16S rRNA sequencing. Next, we transferred faecal microbiota from 12 cohort participants with and without low BMI or recent weight loss into a total of 41 germ-free mice to study the potential causal effects of the gut microbiota on host BMI and body composition.

Results: The mean age (range) of our cohort was 73 (65-93); 58.4% was male. Seventy-seven participants were undernourished and 21 participants had poor appetite (CNAQ < 28). A lower abundance of the genus Blautia was associated with undernutrition (log2 fold change = -0.57, Benjamini-Hochberg-adjusted P = 0.008), whereas higher abundances of taxa from Lachnospiraceae, Ruminococcaceae UCG-002, Parabacteroides merdae, and Dorea formicigenerans were associated with poor appetite. Furthermore, participants with poor appetite or undernutrition had reduced levels of faecal acetate (P = 0.006 and 0.026, respectively). Finally, there was a trend for the mice that received faecal microbiota from older adults with low BMI to weigh 1.26 g less after 3 weeks (P = 0.086) and have 6.13% more lean mass (in % body weight, P = 0.067) than the mice that received faecal microbiota from older adults without low BMI or recent weight loss.

Conclusions: This study demonstrates several associations of the gut microbiota with both poor appetite and undernutrition in older adults. Moreover, it is the first to explore a causal relation between the aged gut microbiota and body weight and body composition in the host. Possibly, microbiota-manipulating strategies will benefit older adults prone to undernutrition.

Keywords: Appetite; Germ-free mice; Gut microbiota; Microbiota transfer experiment; Older adults; Undernutrition.

Conflict of interest statement

The authors declare no competing interests.

© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.

Figures

Figure 1
Figure 1
Variables associated with alpha‐diversity and beta‐diversity in the human cohort. (A) Barplot showing the F‐statistic of the PERMANOVA model for each factor. Bars are coloured based on the type of variable. The R2 is noted in each bar, depicting the variance in Bray–Curtis beta‐diversity explained by each variable. Bold text indicated statistical significance (P < 0.05). (B and C) Boxplots of alpha‐diversity measures species richness, Shannon index, and FPD for participants with or without poor appetite (B) and with or without undernutrition (C). Boxplots show median (centre line), interquartile ranges (boxes), 1.5× the interquartile ranges (whiskers), and outliers. Differences in alpha‐diversity were tested with linear regression models, adjusted for age and Mediterranean Diet Score. ASMMI, appendicular skeletal muscle mass index; CESD, Centre for Epidemiologic Studies Depression; FPD, Faith's phylogenetic diversity; MMSE, mini‐mental state examination.
Figure 2
Figure 2
Log2 fold change of all bacterial taxa significantly associated with either poor appetite or undernutrition. Heatmap depicting the log2 fold change in bacterial abundance of taxa that are significantly associated with either undernutrition or poor appetite. Log2 fold change was calculated with DESeq models, either crude (first and fourth column), adjusted for age (second and fifth column), or adjusted for both age and Mediterranean Diet Score (third and sixth column). Blue cells depict a positive log2 fold change, indicating higher abundance in participants with undernutrition or poor appetite, whereas red cells depict a negative log2 fold change, indicating lower abundance in participants with undernutrition or poor appetite. Numbers behind taxa are arbitrary identifiers for clades within the specified rank. Asterisks indicate Benjamini–Hochberg‐adjusted P‐value < 0.01. Taxa were only considered if the mean abundance was >10. AC, age corrected; ADC, age and diet corrected.
Figure 3
Figure 3
Associations of faecal short‐chain fatty acids with poor appetite and undernutrition. (A) Ternary diagrams showing faecal propionate, acetate, and butyrate as compositional data. Each point represents a participant with or without poor appetite and with or without undernutrition. The closer the participant is plotted to one of the short‐chain fatty acids, the higher the concentration of that short‐chain fatty acid is relative to the concentrations of the other short‐chain fatty acids. (B and C) Boxplots of faecal acetate, butyrate, and propionate concentrations for participants with or without poor appetite (B) and with or without undernutrition (C). Boxplots show median (centre line), interquartile ranges (boxes), 1.5× the interquartile ranges (whiskers), and outliers. Differences were tested with Student's t‐test with Bonferroni correction.
Figure 4
Figure 4
Alpha‐diversity, beta‐diversity, and Blautia of each of the donor groups. (A) Boxplots of species richness, Shannon diversity index, and FPD for each of the donor groups and the remaining cohort. Differences among groups were tested with Kruskal–Wallis tests. Boxplots show median (centre line), interquartile ranges (boxes), 1.5× the interquartile ranges (whiskers), and outliers. (B) Principal coordinate plot based on Bray–Curtis dissimilarity, coloured for each of the donor groups and the remaining cohort. Donors seem equally distributed throughout the cohort; donor group does not explain a significant amount of variance based on PERMANOVA. (C) Boxplot of Blautia abundance. There is no difference between donor groups based on Kruskal–Wallis test. Boxplots indicate same parameters as (A). FPD, Faith's phylogenetic diversity; HC, mice that received faecal microbiota from human donors without low body mass index or substantial weight loss; LBMI, mice that received faecal microbiota from human donors with low body mass index; WL, mice that received faecal microbiota from human donors with substantial weight loss.
Figure 5
Figure 5
Weight, lean mass, and fat mass of mice during experiment. (A and B) Line graphs for average body weight in g (A), and % of baseline weight (B) of mice per group during experiment (error bars indicate standard error). (C and D) Boxplots of lean mass in % of body weight (C) and g (D) per group at baseline and after a 3 week follow‐up. (E and F) Same as (C) and (D), but for fat mass. Boxplot centre line indicates median, boxes indicate interquartile ranges, whiskers indicate 1.5× the interquartile ranges, and paired measurements are connected by grey lines. HC, mice that received faecal microbiota from human donors without low body mass index or substantial weight loss; LBMI, mice that received faecal microbiota from human donors with low body mass index; WL, mice that received faecal microbiota from human donors with substantial weight loss.
Figure 6
Figure 6
Blautia abundance in Week 3 mouse faecal samples. (A) Boxplot for Blautia abundance in Week 3 mouse faecal samples per group. (B) Scatterplot of Blautia abundance in Week 3 mouse faecal samples plotted against the weight difference in the mice from baseline to Week 3. Boxplot centre line indicates median, boxes indicate interquartile ranges, whiskers indicate 1.5× the interquartile ranges, and paired measurements are connected by grey lines. HC, mice that received faecal microbiota from human donors without low body mass index or substantial weight loss; LBMI, mice that received faecal microbiota from human donors with low body mass index; WL, mice that received faecal microbiota from human donors with substantial weight loss.

References

    1. Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, et al. Diagnostic criteria for malnutrition—an ESPEN Consensus Statement. Clin Nutr 2015;34:335–340.
    1. Leij‐Halfwerk S, Verwijs MH, van Houdt S, Borkent JW, Guaitoli PR, Pelgrim T, et al. Prevalence of protein‐energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults ≥ 65 years: a systematic review and meta‐analysis. Maturitas 2019;126:80–89.
    1. van der Pols‐Vijlbrief R, Wijnhoven HA, Schaap LA, Terwee CB, Visser M. Determinants of protein‐energy malnutrition in community‐dwelling older adults: a systematic review of observational studies. Ageing Res Rev 2014;18:112–131.
    1. Cox NJ, Bowyer RCE, Ni Lochlainn M, Wells PM, Roberts HC, Steves CJ. The composition of the gut microbiome differs among community dwelling older people with good and poor appetite. J Cachexia Sarcopenia Muscle 2021;12:368–377.
    1. Fluitman KS, De Clercq NC, Keijser BJF, Visser M, Nieuwdorp M, IJzerman RG. The intestinal microbiota, energy balance, and malnutrition: emphasis on the role of short‐chain fatty acids. Expert Rev Endocrinol Metab 2017;12:215–226.
    1. Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 2010;5:e10667.
    1. Rampelli S, Candela M, Turroni S, Biagi E, Collino S, Franceschi C, et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging (Albany NY) 2013;5:902–912.
    1. 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.
    1. Kang L, Li P, Wang D, Wang T, Hao D, Qu X. Alterations in intestinal microbiota diversity, composition, and function in patients with sarcopenia. Sci Rep 2021;11:4628.
    1. 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.
    1. Fluitman KS, Hesp AC, Kaihatu RF, Nieuwdorp M, Keijser BJF, IJzerman RG, et al. Poor taste and smell are associated with poor appetite, macronutrient intake, and dietary quality but not with undernutrition in older adults. J Nutr 2021;151:605–614.
    1. Hoogendijk EO, Deeg DJH, de Breij S, Klokgieters SS, Kok AAL, Stringa N, et al. The Longitudinal Aging Study Amsterdam: cohort update 2019 and additional data collections. Eur J Epidemiol 2020;35:61–74.
    1. Sergi G, De Rui M, Veronese N, Bolzetta F, Berton L, Carraro S, et al. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free‐living Caucasian older adults. Clin Nutr 2015;34:667–673.
    1. Hanisah R, Suzana S, Lee FS. Validation of screening tools to assess appetite among geriatric patients. J Nutr Health Aging 2012;16:660–665.
    1. Beukers MH, Dekker LH, de Boer EJ, Perenboom CW, Meijboom S, Nicolaou M, et al. Development of the HELIUS food frequency questionnaires: ethnic‐specific questionnaires to assess the diet of a multiethnic population in The Netherlands. Eur J Clin Nutr 2015;69:579–584.
    1. Panagiotakos DB, Pitsavos C, Arvaniti F, Stefanadis C. Adherence to the Mediterranean food pattern predicts the prevalence of hypertension, hypercholesterolemia, diabetes and obesity, among healthy adults; the accuracy of the MedDietScore. Prev Med 2007;44:335–340.
    1. Mueller CA, Pintscher K, Renner B. Clinical test of gustatory function including umami taste. Ann Otol Rhinol Laryngol 2011;120:358–362.
    1. Radloff L. The CES‐D scale: a self‐reported depression scale for research in the general population. Appl Psychol Measur 1977;1:385–401.
    1. Folstein MF, Folstein SE, McHugh PR. “Mini‐mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198.
    1. Sokol H, Pigneur B, Watterlot L, Lakhdari O, Bermudez‐Humaran LG, Gratadoux JJ, et al. Faecalibacterium prausnitzii is an anti‐inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci U S A 2008;105:16731–16736.
    1. 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.
    1. Mobini R, Tremaroli V, Stahlman M, Karlsson F, Levin M, Ljungberg M, et al. Metabolic effects of Lactobacillus reuteri DSM 17938 in people with type 2 diabetes: a randomized controlled trial. Diabetes Obes Metab 2017;19:579–589.
    1. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual‐index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 2013;79:5112–5120.
    1. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010;26:2460–2461.
    1. Edgar RC. UNOISE2: improved error‐correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016;081257.
    1. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;73:5261–5267.
    1. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web‐based tools. Nucleic Acids Res 2013;41:D590–D596.
    1. De Baere S, Eeckhaut V, Steppe M, De Maesschalck C, De Backer P, Van Immerseel F, et al. Development of a HPLC‐UV method for the quantitative determination of four short‐chain fatty acids and lactic acid produced by intestinal bacteria during in vitro fermentation. J Pharm Biomed Anal 2013;80:107–115.
    1. de Groot P, Scheithauer T, Bakker GJ, Prodan A, Levin E, Khan MT, et al. Donor metabolic characteristics drive effects of faecal microbiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time. Gut 2020;69:502–512.
    1. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013;8:e61217.
    1. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package. R package 2018. [Available from:
    1. Love MI, Wolfgang H, Anders S. Moderated estimation of fold change and dispersion for RNA‐Seq data with DESeq2. Genome Biol 2014;15:550.
    1. Wickham H. Elegant Graphics for Data Analysis. New York: Springer‐Verlag; 2016.
    1. Walter J, Armet AM, Finlay BB, Shanahan F. Establishing or exaggerating causality for the gut microbiome: lessons from human microbiota‐associated rodents. Cell 2020;180:221–232.
    1. Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T, et al. Population‐based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016;352:565–569.
    1. Mendez‐Salazar EO, Ortiz‐Lopez MG, Granados‐Silvestre MLA, Palacios‐Gonzalez B, Menjivar M. Altered gut microbiota and compositional changes in Firmicutes and Proteobacteria in Mexican undernourished and obese children. Front Microbiol 2018;9:2494.
    1. Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M, De Angelis M. The controversial role of human gut Lachnospiraceae. Microorganisms 2020;8:573.
    1. Stanislawski MA, Dabelea D, Lange LA, Wagner BD, Lozupone CA. Gut microbiota phenotypes of obesity. NPJ Biofilms Microbiomes 2019;5:18.
    1. Menni C, Jackson MA, Pallister T, Steves CJ, Spector TD, Valdes AM. Gut microbiome diversity and high‐fibre intake are related to lower long‐term weight gain. Int J Obes (Lond) 2017;41:1099–1105.
    1. Liu X, Mao B, Gu J, Wu J, Cui S, Wang G, et al. Blautia—a new functional genus with potential probiotic properties? Gut Microbes 2021;13:1–21.
    1. Frost G, Sleeth ML, Sahuri‐Arisoylu M, Lizarbe B, Cerdan S, Brody L, et al. The short‐chain fatty acid acetate reduces appetite via a central homeostatic mechanism. Nat Commun 2014;5:3611.
    1. Giezenaar C, Hutchison AT, Luscombe‐Marsh ND, Chapman I, Horowitz M, Soenen S. Effect of age on blood glucose and plasma insulin, glucagon, ghrelin, CCK, GIP, and GLP‐1 responses to whey protein ingestion. Nutrients 2017;10:2.
    1. Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R, Cheng J, et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 2013;339:548–554.
    1. von Haehling S, Morley JE, Coats AJS, Anker SD. Ethical guidelines for publishing in the Journal of Cachexia, Sarcopenia and Muscle: update 2021. J Cachexia Sarcopenia Muscle 2021;12:2259–2261.

Source: PubMed

3
Abonneren