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. 2022 May 19;20(1):171.
doi: 10.1186/s12916-022-02376-3.

Temporal relationship among adiposity, gut microbiota, and insulin resistance in a longitudinal human cohort

Kui Deng  1   2   3 , Menglei Shuai  3 , Zheqing Zhang  1   4 , Zengliang Jiang  3   5 , Yuanqing Fu  3   5 , Luqi Shen  3   5 , Ju-Sheng Zheng  6   7   8 , Yu-Ming Chen  9
Affiliations

Temporal relationship among adiposity, gut microbiota, and insulin resistance in a longitudinal human cohort

Kui Deng et al. BMC Med. .

Abstract

Background: The temporal relationship between adiposity and gut microbiota was unexplored. Whether some gut microbes lie in the pathways from adiposity to insulin resistance is less clear. Our study aims to reveal the temporal relationship between adiposity and gut microbiota and investigate whether gut microbiota may mediate the association of adiposity with insulin resistance in a longitudinal human cohort study.

Methods: We obtained repeated-measured gut shotgun metagenomic and anthropometric data from 426 Chinese participants over ~3 years of follow-up. Cross-lagged path analysis was used to examine the temporal relationship between BMI and gut microbial features. The associations between the gut microbes and insulin resistance-related phenotypes were examined using a linear mixed-effect model. We examined the mediation effect of gut microbes on the association between adiposity and insulin resistance-related phenotypes. Replication was performed in the HMP cohort.

Results: Baseline BMI was prospectively associated with levels of ten gut microbial species. Among them, results of four species (Adlercreutzia equolifaciens, Parabacteroides unclassified, Lachnospiraceae bacterium 3 1 57FAA CT1, Lachnospiraceae bacterium 7 1 58FAA) were replicated in the independent HMP cohort. Lachnospiraceae bacterium 3 1 57FAA CT1 was inversely associated with HOMA-IR and fasting insulin. Lachnospiraceae bacterium 3 1 57FAA CT1 mediated the association of overweight/obesity with HOMA-IR (FDR<0.05). Furthermore, Lachnospiraceae bacterium 3 1 57FAA CT1 was positively associated with the butyrate-producing pathway PWY-5022 (p < 0.001).

Conclusions: Our study identified one potentially beneficial microbe Lachnospiraceae bacterium 3 1 57FAA CT1, which might mediate the effect of adiposity on insulin resistance. The identified microbes are helpful for the discovery of novel therapeutic targets, as to mitigate the impact of adiposity on insulin resistance.

Keywords: Adiposity; Gut microbiota; Insulin resistance; Longitudinal cohort study; Obesity; Temporal relationship; Weight change.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Study design and analytical method. A This study was based on the Guangzhou Nutrition and Health Study. A total of 741 stool samples were first collected between 2014 and 2017. Among them, 505 follow-up stool samples were repeatedly collected between 2018 and 2019. Metadata including sociodemographic factors, anthropometric parameters, lifestyle factors, medication use, habitual diet, and insulin resistance-related phenotypes were collected for the 505 participants. Shotgun metagenomic sequencing was performed for these 505 paired stool samples. After excluding participants who met the exclusion criterion and performing quality control for species, 426 participants with 171 species remained for subsequent analysis. B The cross-lagged panel analysis of BMI and gut microbiota. ρ1 and ρ2 are cross-lagged path coefficients; r1 and r2 are tracking correlations; r3 is the synchronous correlation between BMI and gut microbiota at baseline. C Gut microbiota mediates the association between overweight/obesity and insulin resistance
Fig. 2
Fig. 2
The prospective association between BMI and gut microbiota. A The associations between baseline adiposity and follow-up gut microbes. The cross-lagged path analysis was used to estimate the difference in the abundance of gut microbes (in SD unit of the log-transformed abundance) per 1-SD difference of BMI, adjusted for age, sex, smoking status, alcohol status, education, income, physical activity, total energy intake, Bristol stool score, and time interval. B Replication of the prospective associations of BMI with microbes in the HMP cohort. Multivariable linear regression models were used to estimate the difference in the abundance of gut microbes (in SD unit of the log-transformed abundance) per 1-SD difference in BMI, adjusted for age, sex, race (white/not white), time interval, and corresponding baseline microbe abundance. The meta-analysis with a random effects model was used to integrate the results from GNHS and HMP cohorts, and the heterogeneity was assessed using I2 and Cochran-Q test
Fig. 3
Fig. 3
The prospective association between long-term weight change and gut microbiota. A Gut microbes that were associated with the normal to adiposity group compared with the stable normal group. Multivariable linear regression models were used to estimate the difference in the abundance of gut microbes (in SD unit of the log-transformed abundance) comparing the normal to adiposity group with the stable normal group, adjusted for age, sex, smoking status, alcohol status, education, income, physical activity, total energy intake, Bristol stool score, time interval, and corresponding baseline microbe abundance. B Gut microbes that were associated with the stable adiposity group compared with the stable normal group. Multivariable linear regression models were used to estimate the difference in the abundance of gut microbes (in SD unit of the log-transformed abundance) comparing the stable adiposity group with the stable normal group, adjusted for age, sex, smoking status, alcohol status, education, income, physical activity, total energy intake, Bristol stool score, time interval, and corresponding baseline microbe abundance. CI, confidence interval; FDR, false discovery rate
Fig. 4
Fig. 4
The associations between the identified microbes and insulin resistance-related phenotypes. Linear mixed-effect models were used to estimate the difference in insulin resistance-related phenotypes (in SD unit) per 1-SD difference in the log-transformed abundance of gut microbes, adjusted for age, sex, smoking status, alcohol status, education, income, physical activity, and total energy intake. Fasting insulin, HOMA-IR, and fasting glucose were log-transformed. HOMA-IR, homeostasis model assessment of insulin resistance; HbA1c, hemoglobin A1c; FDR, false discovery rate
Fig. 5
Fig. 5
Mediation analysis for the role of gut microbes in the association between adiposity and insulin resistance-related phenotypes. A The mediation effect of Lachnospiraceae bacterium 3 1 57FAA CT1 on the association between adiposity and HOMA-IR. B The mediation effect of Clostridium hathewayi on the association between adiposity and HOMA-IR. C The mediation effect of Lachnospiraceae bacterium 3 1 57FAA CT1 on the association between adiposity and fasting insulin. D The mediation effect of Megamonas unclassified on the association between adiposity and HbA1c. In the mediation analysis, age, sex, smoking status, alcohol status, education, income, physical activity, total energy intake, Bristol stool score, time interval, and corresponding baseline gut microbes and insulin resistance-related phenotypes were adjusted. ACME, average causal mediation effects; ADE, average direct effect; FDR, false discovery rate; HOMA-IR, homeostasis model assessment of insulin resistance; HbA1c, hemoglobin A1c
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