Cited by 748Gut microbiota and healthOral microbiology and periodontitis researchNutrition, Genetics, and Disease

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index; none of the 33 sources its claims cite is checked for support yet.

The Gut Microbiome Contributes to a Substantial Proportion of the Variation in Blood Lipids

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Abstract

Rationale: Evidence suggests that the gut microbiome is involved in the development of cardiovascular disease, with the host–microbe interaction regulating immune and metabolic pathways. However, there was no firm evidence for associations between microbiota and metabolic risk factors for cardiovascular disease from large-scale studies in humans. In particular, there was no strong evidence for association between cardiovascular disease and aberrant blood lipid levels. Objectives: To identify intestinal bacteria taxa, whose proportions correlate with body mass index and lipid levels, and to determine whether lipid variance can be explained by microbiota relative to age, sex, and host genetics. Methods and Results: We studied 893 subjects from the LifeLines-DEEP population cohort. After correcting for age and sex, we identified 34 bacterial taxa associated with body mass index and blood lipids; most are novel associations. Cross-validation analysis revealed that microbiota explain 4.5% of the variance in body mass index, 6% in triglycerides, and 4% in high-density lipoproteins, independent of age, sex, and genetic risk factors. A novel risk model, including the gut microbiome explained ≤25.9% of high-density lipoprotein variance, significantly outperforming the risk model without microbiome. Strikingly, the microbiome had little effect on low-density lipoproteins or total cholesterol. Conclusions: Our studies suggest that the gut microbiome may play an important role in the variation in body mass index and blood lipid levels, independent of age, sex, and host genetics. Our findings support the potential of therapies altering the gut microbiome to control body mass, triglycerides, and high-density lipoproteins.

Overview

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Written from the paper’s full text.

TL;DR

In 893 Dutch LifeLines-DEEP participants, gut microbiome composition was associated with BMI, triglycerides, and HDL cholesterol, and explained a significant portion of their variation beyond age, sex, and host genetics. The authors estimate the microbiome explains up to 6% of lipid variation, with little effect on LDL or total cholesterol.

Key findings

  • After adjustment for age and sex, 148 OTUs were associated with traits at FDR=0.05: 66 with BMI, 114 with triglycerides, and 34 with HDL; no OTUs were significantly associated with LDL or total cholesterol. At the taxonomic level, 34 unique taxonomies yielded 50 significant associations: 22 with BMI, 23 with triglycerides, 4 with HDL, and 1 with LDL.
  • Microbial richness averaged 238 OTUs per individual (range 44–355) and was negatively correlated with BMI (P=3.8×10−4) and triglycerides (P=1.37×10−4), and positively correlated with HDL (P=8.3×10−4), but not with LDL or total cholesterol.
  • At P=1×10−5, microbiome-associated OTUs explained 2.74% of BMI variation, 3.83% of triglycerides, 2.46% of HDL, 0.01% of LDL, and 0.01% of total cholesterol; with less-stringent thresholds, explained variation increased to 4.57% for BMI, 6.0% for triglycerides, 4.0% for HDL, 1.5% for LDL, and 0.7% for total cholesterol.
  • Age, sex, genetics, and microbiome together explained 11.3% of BMI, 17.1% of triglycerides, and 25.9% of HDL variation, with the microbiome contributing significantly to BMI (P=4.1×10−3), triglycerides (P=4.5×10−4), and HDL (P=2.7×10−3). No significant association was found between the microbiome and genetic risk scores at FDR=0.05.

Methods

  • Cross-sectional analysis of 893 LifeLines-DEEP subjects (380 men, 513 women) with lipid measurements, genotype data, and gut microbiome data after excluding antibiotic, microbiome-modifying, or lipid-lowering medication users and applying rarefaction to 15,000 reads.
  • Fecal microbiome profiling used 16S rRNA gene V4 sequencing; OTU picking used QIIME/UCLUST with GreenGenes 13.5; analysis was confined to 645 OTUs present at ≥0.05% of reads in ≥1% of the population.
  • Associations were tested with a two-part model for binary presence/absence and quantitative abundance, with 1000× permutations to control FDR at 0.05; variance explained was estimated by 100× cross-validation, and genetic risk scores were computed from 155 lipid-associated and 96 BMI-associated SNPs.

Limitations

  • The paper states that the genetic risk score was limited to 157 established lipid-associated SNPs and 97 BMI-associated SNPs, which explain only a small proportion of heritability, so undiscovered SNPs may have been missed.
  • The paper states that 16S rRNA gene sequencing has limited ability to identify species and strains and provides little information on bacterial genes and functions.
  • The paper states that diet was not addressed, and it calls for validation in independent cohorts and functional studies to establish causality; the cross-sectional observational design also cannot prove causation.

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Integrity signals

Registry

Crossref · 10.1161/circresaha.115.306807

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1. The richness of the gut microbiome. The microbial richness associated with age and sex. The bar plot shows the distribution of individuals binned to different groups of richness. The black and gray colors indicate the proportion of men and women in each group, and the dark gray line indicates the correlation between the average age and richness, whereas the light gray shadow indicates the SD of the age per richness bin. OTU indicates operational taxonomy unit.
Figure 1. The richness of the gut microbiome. The microbial richness associated with age and sex. The bar plot shows the distribution of individuals binned to different groups of richness. The black and gray colors indicate the proportion of men and women in each group, and the dark gray line indicates the correlation between the average age and richness, whereas the light gray shadow indicates the SD of the age per richness bin. OTU indicates operational taxonomy unit.CC BY-NC-ND · PubMed Central
Figure 2. The effect of taxonomies on body mass index (BMI) and lipids. The effects of 34 taxonomies associated with BMI, triglycerides (TG), and high-density lipoprotein (HDL) are shown as Z scores. Red sectors indicate positive associations and blue negative associations. Brighter colors indicate that the association was significant at false discovery rate (FDR) 0.05 level. Dashed circles indicate the scale of Z values from 1 to 5.
Figure 2. The effect of taxonomies on body mass index (BMI) and lipids. The effects of 34 taxonomies associated with BMI, triglycerides (TG), and high-density lipoprotein (HDL) are shown as Z scores. Red sectors indicate positive associations and blue negative associations. Brighter colors indicate that the association was significant at false discovery rate (FDR) 0.05 level. Dashed circles indicate the scale of Z values from 1 to 5.CC BY-NC-ND · PubMed Central
Figure 3. The contribution of the gut microbiome to body mass index (BMI) and lipids. A, The variation explained by gut microbes at different levels of significance. B, The variation explained by different risk models, including age, sex, genetic risk, and microbial risk. The significance of microbial contribution is indicated as the P value of the ANOVA test that compared the performance of the risk models r2 and r3. HDL indicates high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; and TG, triglycerides.
Figure 3. The contribution of the gut microbiome to body mass index (BMI) and lipids. A, The variation explained by gut microbes at different levels of significance. B, The variation explained by different risk models, including age, sex, genetic risk, and microbial risk. The significance of microbial contribution is indicated as the P value of the ANOVA test that compared the performance of the risk models r2 and r3. HDL indicates high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; and TG, triglycerides.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

28 Own result · 22 Not checked yet · 13 Own method · 11 Own interpretation · 1 No basis given

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Show all 19 claims

  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 27
56 of 56
  • The paper relies on this without support.

    Gut microbiota are linked with lipid metabolism through their role in bile acid metabolism.

    “Gut microbiota have also been linked with lipid metabolism through their role in bile acid metabolism.”
  • The paper relies on this without support.

    Evidence for a causal relationship between the gut microbiome and the development of CVD has not been firmly established for lack of large-scale human studies.

    “However, the evidence for a causal relationship between the gut microbiome and the development of CVD has not been firmly established for lack of large-scale human studies.”
  • The paper relies on this without support.

    Atherosclerosis, a lipid-driven disease, is the main underlying cause of CVD.

    “Atherosclerosis, a lipid-driven disease, is the main underlying cause of CVD.”
  • The paper relies on this without support.

    To date, no studies of sufficient size have been done to assess the association between lipids and microbiota.

    “However, to date, no studies of sufficient size have been done to assess the association between lipids and microbiota.”
  • The paper relies on this without support.

    No large-scale studies have been performed in humans linking the gut microbiome to blood lipids.

    “Although animal studies have shown that microbiota can influence lipid metabolism,39 no large-scale studies have been performed in humans thus far.”
  • The paper relies on this without support.

    This study is the largest association study linking the gut microbiome to blood lipids in humans to date.

    “First, to our knowledge, it is the largest association study linking the gut microbiome to blood lipids in humans to date.”
  • The paper relies on this without support.

    Blood lipids and BMI are highly correlated with each other.

    “Because blood lipids and BMI are highly correlated with each other and many associated bacteria were shared, we investigated whether the observed effect of the gut microbiome on lipids might just be the confounded effect of BMI.”
  • The paper relies on this without support.

    An individual's microbiota composition can be modified by prebiotics, probiotics, and fecal transplantation.

    “In contrast to genetics, sex, and age (all fixed characteristics), an individual’s microbiota composition can be modified by diet, prebiotics and probiotics, and fecal transplantation.”
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Metadata

Authors
Fu, J., Bonder, M. J., Cenit, M. C., Tigchelaar, E. F., Maatman, A., Dekens, J. A. M., Brandsma, E., Marczynska, J., Imhann, F., Weersma, R. K., Franke, L., Poon, T. W., Xavier, R. J., Gevers, D., Hofker, M. H., Wijmenga, C., & Zhernakova, A.
Affiliations
  • Broad Institute (US)
  • From the Department of Pediatrics (J.F., E.B., M.H.H.), Department of Genetics (J.F., M.J.B., M.C.C., E.F.T., A.M., J.A.M.D., J.M., L.F., C.W., A.Z.), and Department of Gastroenterology and Hepatology (F.I., R.K.W.), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Top Institute Food and Nutrition, Wageningen, The Netherlands (E.F.T., J.A.M.D., A.Z.); Department of Immunology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University,...
Corresponding author
  • Hofker, Marten H.
  • Wijmenga, Cisca
  • Zhernakova, Alexandra
Venue
Circulation Research
Published
2015-10-09
Volume
117
Issue
9
Pages
817-824
ISSN
  • 0009-7330 (ISSN-L)
  • 1524-4571
OpenAlex
W1918132278
Cited by
748
References
52
Citations by year
2026: 30 · 2025: 62 · 2024: 83 · 2023: 83 · 2022: 83 · 2021: 109 · 2020: 90 · 2019: 63 · 2018: 62 · 2017: 46 · 2016: 34 · 2015: 1
FWCI
20.69
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Circulation Research (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc-nd)
Funders
  • Fundación Alfonso Martín Escudero
  • European Commission (259867)
  • Rijksuniversiteit Groningen
  • ZonMw (016.136.308, 917.14.374)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (FP7 2007-2013, ZonMW-VIDI 016.136.308, 864.13.013, 917.14.374, 016.136.308, ZonMW-VIDI 917.14.374)
  • National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
Keywords
gut microbiota, serum lipids, body mass index, triglycerides, high-density lipoprotein, low-density lipoprotein, total cholesterol, cardiovascular risk, host-microbe interactions, metabolic risk factors
MeSH terms
Gastrointestinal Microbiome, Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Bacteria, Cardiovascular Diseases, Cholesterol, Female, Humans, Lipids, Cholesterol, HDL, Cholesterol, LDL, Male, Middle Aged, Risk Factors, RNA, Ribosomal, 16S, Triglycerides, Cohort Studies, Body Mass Index, Risk Assessment, Polymorphism, Single Nucleotide, Host-Pathogen Interactions, Young Adult

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