Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes

Abstract

Background: Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression.

Methods: We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background.

Results: We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities.

Conclusions: These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions.

Fig. 1

Study design for sub-clinical gut microbiome analysis in obesity and type 2 diabetes. a Stool and blood samples were collected at one to two time points from 10 MZ twin pairs. DNA was extracted from the stool samples and used for shotgun metagenomic sequencing, from which community composition and function were profiled using MetaPhlAn [18] and HUMAnN [19], respectively. Clinical biomarkers including sugar metabolism measurements (fasting blood sugar (FBS) and insulin (FBI)), inflammation markers (hsCRP) and others (Additional file 1: Table S1) were derived from accompanying blood samples. Finally, we determined significant associations between these clinical biomarkers and microbial taxa and functions using MaAsLin [13]. b Overall covariation of taxonomic profiles and the clinical biomarkers and taxa enriched among distinct sample subsets. Points represent samples ordinated using metric multidimensional scaling (MDS) by Bray-Curtis dissimilarity, colored by twin pair, with lines connecting samples from the same individual at different time points. Taxa and metadata are labeled at the point of maximum enrichment among samples. c Absolute BMI differences between any two ‘Unrelated’ (at time point 1), ‘Twins’ (at time point 1), and the same individuals at the two different time points (‘Self’). Comparisons are colored by the maximal BMI of the participants involved; P values were calculated using a t-test. d Taxonomic profile similarities of unrelated, twins, and individuals over time. Comparisons are colored by the maximal age of the participants involved; P values were calculated using a t-test

Fig. 2

Taxonomic and functional profiles of twin gut microbiomes accompanied by T2D/obesity clinical indicators. a Clustered taxonomic profiles, discretized and with groups of tightly covarying taxa binned into 15 clusters for visualization. Each row represents a cluster of one or more species, and each column is one sample colored by the twin variable. Values indicate relative abundances from the medoid member of each cluster (see Additional file 5: Figure S3 for full matrix). b As (a) for metabolic modules derived from metagenomic functional profiling. Sample clustering retains ordering from (a). c Corresponding clustering of selected discretized clinical biomarkers, again retaining ordering from (a)

Fig. 3

Selected significant associations of clinical markers with clade and pathway abundances. Lines represent linear model fit after transform to accommodate compositional, non-normally distributed data (see Methods) and account for age, sex, smoking, and twin relationships as covariates. Nominal P values and FDR corrected q-values are assigned by MaAsLin [13]. See Additional file 5: Figure S3 for complete list of significant associations

Fig. 4

Association of taxa with microbial metabolic modules. The relative abundances of 56 total species were Spearman correlated against those of 87 functional profiles to identify covariation between taxa and metabolic modules (either due to genetic carriage or shared environment). Pluses and stars indicate nominal P value <0.01 or FDR q-value <0.2, respectively. Yellow marks indicate correlations also found in the corresponding analysis of HMP data [21] (see Additional file 11: Figure S8). Highlighted boxes are discussed in the main text

Fig. 5

Strain profiles between unrelated, twins, and samples from the same individual over time. a Bray-Curtis dissimilarities of marker gene profiles indicative of strain similarity between unrelated, twins, and the same person over time (P values comparing unrelated vs. twins and twins vs. self are calculated by t-test). b, c The abundances of (b) Methanobrevibacter smithii and (c) Prevotella copri marker gene [18] profiles (see Methods). Each row represents one sample, colored by twin, containing vertical lines each representing one species-specific marker gene’s abundance as binned into four levels. Markers that are present only in one of the twins appear in black, with a triangle pointing to the absent (white) marker. Markers that differ between samples of the same person over time are marked with a gray dot

Fig. 6

Microbial functional dysbioses common among this study and the gut microbiome in IBD and T2D. Several microbial metabolic pathways were determined to be significantly enriched (red) or depleted (green) relative to the obesity-related clinical markers collected in our study (KTwin) and in inflammatory bowel disease (IBD [13]) and/or type 2 diabetes (T2D [3]). Directionality of association in these common dysbioses was near-uniformly consistent, as indicated by box color