Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Clinical Studies and Practice

Body size phenotypes comprehensively assess cardiometabolic risk and refine the association between obesity and gut microbiota



The gut microbiota associates with obesity and related disorders, but recent meta-analyses have found that this association is, at best, of small effect. We argue that such analyses are flawed by the use of body mass index (BMI) as sole proxy for disease, and explore a classification method that distinguishes the cardiometabolic health status of individuals to look for more comprehensive associations between gut microbes and health.


We analyzed a 441 community-dwelling cohort on which we obtained demographic and health information, anthropometry and blood biochemistry data that served to categorize participants according to BMI, cardiometabolic health status and body size phenotypes. In addition, the participants donated fecal samples from which we performed 16S rRNA gene sequencing to analyze the gut microbiota.


We observed that health-related variables deteriorate with increased BMI, and that there are further discrepancies within a given BMI category when distinguishing cardiometabolically healthy and unhealthy individuals. Regarding the gut microbiota, both obesity and cardiovascular disease associate with reductions in α-diversity; having lean, healthy individuals the most diverse microbiotas. Moreover, the association between the gut microbiota and health stems from particular consortia of microbes; the prevalence of consortia involving pathobionts and Lachnospiraceae are increased in obese and cardiometabolically abnormal subjects, whereas consortia including Akkermansia muciniphila and Methanobrevibacter, Oscillospira and Dialister have higher prevalence in cardiometabolically healthy and normoweight participants.


The incorporation of cardiometabolic data allows a refined identification of dissimilarities in the gut microbiota; within a given BMI category, marker taxa associated with obesity and cardiometabolic disease are exacerbated in individuals with abnormal health status. Our results highlight the importance of the detailed assessment and classification of individuals that should be carried out prior to the evaluation of obesity treatments targeting the gut microbiota.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2
Figure 3
Figure 4


  1. 1

    World Health Organization Global Status Report On Noncommunicable Diseases 2014 2014.

  2. 2

    Ichihara S, Yamada Y . Genetic factors for human obesity. Cell Mol Life Sci 2008; 65: 1086–1098.

    CAS  Article  Google Scholar 

  3. 3

    Leech RM, McNaughton SA, Timperio A . Clustering of children’s obesity-related behaviours: associations with sociodemographic indicators. Eur J Clin Nutr 2014; 68: 623–628.

    CAS  Article  Google Scholar 

  4. 4

    The Human Microbiome Project Consortium The Human Microbiome Project Consortium Huttenhower C The Human Microbiome Project Consortium Gevers D The Human Microbiome Project Consortium Knight R The Human Microbiome Project Consortium Abubucker S The Human Microbiome Project Consortium Badger JH et al. Structure, function and diversity of the healthy human microbiome. Nature 2012; 486: 207–214.

    Article  Google Scholar 

  5. 5

    Dugas LR, Fuller M, Gilbert J, Layden BT . The obese gut microbiome across the epidemiologic transition. Emerg Themes Epidemiol 2016; 13: 2.

    Article  Google Scholar 

  6. 6

    Tang WHW, Kitai T, Hazen SL . Gut microbiota in cardiovascular health and disease. Circ Res 2017; 120: 1183–1196.

    CAS  Article  Google Scholar 

  7. 7

    de la Cuesta-Zuluaga J, Mueller NT, Corrales-Agudelo V, Velásquez-Mejía EP, Carmona JA, Abad JM et al. Metformin is associated with higher relative abundance of mucin-degrading akkermansia muciniphila and several short- chain fatty acid – producing microbiota in the gut. Diabetes Care 2017; 40: 54–62.

    CAS  Article  Google Scholar 

  8. 8

    Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI et al. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 2005; 102: 11070–11075.

    CAS  Article  Google Scholar 

  9. 9

    Ley RE, Turnbaugh PJ, Klein S, Gordon JI . Microbial ecology: human gut microbes associated with obesity. Nature 2006; 444: 1022–1023.

    CAS  Article  Google Scholar 

  10. 10

    Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 2010; 18: 190–195.

    Article  Google Scholar 

  11. 11

    Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P et al. Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes 2008; 32: 1720–1724.

    CAS  Article  Google Scholar 

  12. 12

    Walters WA, Xu Z, Knight R . Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett 2014; 588: 4223–4233.

    CAS  Article  Google Scholar 

  13. 13

    Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS . A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS ONE 2014; 9: e84689.

    Article  Google Scholar 

  14. 14

    Sze MA, Schloss PD . Looking for a signal in the noise: revisiting obesity and the microbiome. MBio 2016; 7: e01018–e01116.

    Article  Google Scholar 

  15. 15

    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 2016; 23: 107–113.

    Article  Google Scholar 

  16. 16

    Goodrich JK, Davenport ER, Beaumont M, Jackson MA, Knight R, Ober C et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 2016; 19: 731–743.

    CAS  Article  Google Scholar 

  17. 17

    Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013; 500: 541–546.

    CAS  Article  Google Scholar 

  18. 18

    Nicholls SG . Standards and classification: a perspective on the ‘obesity epidemic’. Soc Sci Med 2013; 87: 9–15.

    Article  Google Scholar 

  19. 19

    Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes 2008; 32: 959–966.

    CAS  Article  Google Scholar 

  20. 20

    Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med 2008; 168: 1617–1624.

    Article  Google Scholar 

  21. 21

    Tomiyama AJ, Hunger JM, Nguyen-Cuu J, Wells C . Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005-2012. Int J Obes 2016; 40: 883–886.

    CAS  Article  Google Scholar 

  22. 22

    Escobar JS, Klotz B, Valdes BE, Agudelo GM . The gut microbiota of Colombians differs from that of Americans, Europeans and Asians. BMC Microbiol 2014; 14: 311.

    Article  Google Scholar 

  23. 23

    Siri WE . Body composition from fluid spaces and density: analysis of methods. Tech Meas body Compos 1961; 61: 223–244.

    Google Scholar 

  24. 24

    Manjarrés L, Manjarrés S Programa de Evaluación de Ingesta Dietética EVINDI v4. Universidad de Antioquia. 2008.

  25. 25

    Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35: 1381–1395.

    Article  Google Scholar 

  26. 26

    Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC . Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412–419.

    CAS  Article  Google Scholar 

  27. 27

    Gallo J, Aristizábal D, Segura Á, Correa M, Zapata N . Relationship of insulin resistance with heart structure, function, and metabolism in young, nonobese adults. Acta Medica Colomb 2008; 33: 117–126.

    Google Scholar 

  28. 28

    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.

    CAS  Article  Google Scholar 

  29. 29

    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.

    CAS  Article  Google Scholar 

  30. 30

    Friedman J, Alm EJ . Inferring Correlation Networks from Genomic Survey Data. PLoS Comput Biol 2012; 8: e1002687.

    CAS  Article  Google Scholar 

  31. 31

    Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 2012; 28: 2106–2113.

    CAS  Article  Google Scholar 

  32. 32

    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS et al. Metagenomic biomarker discovery and explanation. Genome Biol 2011; 12: R60.

    Article  Google Scholar 

  33. 33

    O’Hara RB, Kotze DJ . Do not log-transform count data. Methods Ecol Evol 2010; 1: 118–122.

    Article  Google Scholar 

  34. 34

    McGee DL . Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies. Ann Epidemiol 2005; 15: 87–97.

    Article  Google Scholar 

  35. 35

    Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet (London, England) 2006; 368: 666–678.

    Article  Google Scholar 

  36. 36

    Ortega FB, Sui X, Lavie CJ, Blair SN . Body mass index, the most widely used but also widely criticized index would a criterion standard measure of total body fat be a better predictor of cardiovascular disease mortality? Mayo Clin Proc 2016; 91: 443–455.

    Article  Google Scholar 

  37. 37

    Denis GV, Hamilton JA . Healthy obese persons: How can they be identified and do metabolic profiles stratify risk? Curr Opin Endocrinol Diabetes Obes 2013; 20: 369–376.

    CAS  Article  Google Scholar 

  38. 38

    Miller TM, Abdel-Maksoud MF, Crane LA, Marcus AC, Byers TE . Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutr J 2008; 7: 18.

    Article  Google Scholar 

  39. 39

    Fu J, Bonder MJ, Cenit MC, Tigchelaar EF, Maatman A, Dekens JAM et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipidsnovelty and significance. Circ Res 2015; 117: 817–824.

    CAS  Article  Google Scholar 

  40. 40

    Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM et al. Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes 2008; 57: 1470–1481.

    CAS  Article  Google Scholar 

  41. 41

    Verdam FJ, Fuentes S, de Jonge C, Zoetendal EG, Erbil R, Greve JW et al. Human intestinal microbiota composition is associated with local and systemic inflammation in obesity. Obesity 2013; 21: E607–E615.

    CAS  Article  Google Scholar 

  42. 42

    Derrien M, Belzer C, de Vos WM . Akkermansia muciniphila and its role in regulating host functions. Microb Pathog 2016; 106: 171–181.

    Article  Google Scholar 

  43. 43

    Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 2016; 65: 426–436.

    CAS  Article  Google Scholar 

  44. 44

    Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau L et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science (80-) 2013; 341: 1241214.

    Article  Google Scholar 

  45. 45

    Million M, Angelakis E, Maraninchi M, Henry M, Giorgi R, Valero R et al. Correlation between body mass index and gut concentrations of Lactobacillus reuteri, Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli. Int J Obes 2013; 37: 1460–1466.

    CAS  Article  Google Scholar 

  46. 46

    Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R et al. Human genetics shape the gut microbiome. Cell 2014; 159: 789–799.

    CAS  Article  Google Scholar 

  47. 47

    Konikoff T, Gophna U . Oscillospira: a Central, Enigmatic Component of the Human Gut Microbiota. Trends Microbiol 2016; 24: 523–524.

    CAS  Article  Google Scholar 

  48. 48

    Louis S, Tappu R-M, Damms-Machado A, Huson DH, Bischoff SC . Characterization of the gut microbial community of obese patients following a weight-loss intervention using whole metagenome shotgun sequencing. PLoS ONE 2016; 11: e0149564.

    Article  Google Scholar 

  49. 49

    Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R . Diversity, stability and resilience of the human gut microbiota. Nature 2012; 489: 220–230.

    CAS  Article  Google Scholar 

  50. 50

    Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le Chatelier E et al. Dietary intervention impact on gut microbial gene richness. Nature 2013; 500: 585–588.

    CAS  Article  Google Scholar 

  51. 51

    Tap J, Furet J-P, Bensaada M, Philippe C, Roth H, Rabot S et al. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ Microbiol 2015; 17: 4954–4964.

    CAS  Article  Google Scholar 

  52. 52

    Dao MC, Everard A, Clement K, Cani PD . Losing weight for a better health: Role for the gut microbiota. Clin Nutr Exp 2016; 6: 39–58.

    Article  Google Scholar 

  53. 53

    Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL . Diet-induced extinctions in the gut microbiota compound over generations. Nature 2016; 529: 212–215.

    CAS  Article  Google Scholar 

Download references


We thank the participants who took part in the study, and the Vidarium, EPS SURA and Dinámica IPS staff that helped with recruitment and field work; the APOLO Scientific Computing Center at EAFIT University hosted bioinformatics resources and the University of Michigan Medical School Host Microbiome Initiative for sequencing. This work was funded by Grupo Empresarial Nutresa, EPS SURA, and Dinámica I.P.S.

Author information



Corresponding author

Correspondence to J S Escobar.

Ethics declarations

Competing interests

JdlCZ, VCA and JSE are employees of a food company. JAC and JMA are employees of health provider companies..

Additional information


The funders of this work have not had any role in designing or conducting the study; in the collection, analysis, or interpretation of the data; in the preparation, review, or approval of the manuscript; or in the decision to submit the manuscript for publication.

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de la Cuesta-Zuluaga, J., Corrales-Agudelo, V., Carmona, J. et al. Body size phenotypes comprehensively assess cardiometabolic risk and refine the association between obesity and gut microbiota. Int J Obes 42, 424–432 (2018).

Download citation

Further reading


Quick links