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

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

Open access
Fu, Jingyuan, Bonder, Marc Jan, Cenit, María Carmen, Tigchelaar, Ettje F., Maatman, Astrid, Dekens, Jackie A.M., Brandsma, Eelke, Marczynska, Joanna +9 more·2015·Circulation Research
DOI 10.1161/circresaha.115.306807PMC PMC4596485
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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.

AI snapshot

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1161/circresaha.115.306807

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 33 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    No significant association at OTU level was detected for LDL or TC.

    “We did not detect any significant association at OTU level for LDL or TC.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Decreased bacterial diversity was correlated with increased BMI.

    “We also identified a correlation of decreased bacterial diversity with increased BMI, which is in line with previous observations.40”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] Le Chatelier E Nielsen T Qin J MetaHIT consortium Richness of human gut microbiome correlates with metabolic markers. Nature 2013 500 541 546 doi: 10.1038/nature12506 23985870 source

      Not checked in the free run.

  • Findingown data

    The study established associations for 34 taxonomies with BMI and blood lipid levels.

    “We established associations for 34 taxonomies with BMI and blood lipid levels”
    Quote in the paperSubstantial claim
  • Findingown data

    Gut microbiota composition can explain 4.57% to 6% of the variation in BMI, triglycerides, and HDL.

    “we have been able to estimate that the microbiome could explain 4.57% to 6% of the variation in BMI, triglyceride and HDL, respectively.”
    Quote in the paperSubstantial claim
  • Findingcitation

    Increased abundance of genus Akkermansia (N34) is associated with a decrease in BMI (P=0.0005).

    “An increased abundance of genus Akkermansia (N34) has been associated with a decrease in BMI (P=0.0005).37”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Everard A Belzer C Geurts L Ouwerkerk JP Druart C Bindels LB Guiot Y Derrien M Muccioli GG Delzenne NM de Vos WM Cani PD Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci U S A 2013 110 9066 9071 doi: 10.1073/pnas.1219451110 23671105 PMC3670398 source

      Not checked in the free run.

  • May combine two claims. 38
  • Classified as probably not a substantial claim. 27

  • May combine two claims.

    Findingown data

    When BMI was included as a risk factor, the total explained variation in lipids increased to 25% in triglycerides, 37.4% in HDL, 22.3% in LDL, and 22.3% in total cholesterol.

    “When we included BMI as a risk factor, the total explained variation in lipids increased to 25% in triglycerides, 37.4% in HDL, 22.3% in LDL, and 22.3% in TC (Online Figure IV).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    Order Bacteroidales and family Clostridiaceae were negatively correlated with BMI and triglycerides.

    “In particular, order Bacteroidales (phylum Bacteroidetes) and family Clostridiaceae (phylum Firmicutes) are both negatively correlated with BMI and triglycerides and known to be involved in bile acid metabolism.41”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Family Christensenellaceae (N18) and phylum Tenericutes (N33) are associated with low BMI (P=9.8×10−7 and P=0.0002, respectively), as reported in the TwinsUK cohort.

    “We also confirmed the association of both the family Christensenellaceae (phylum Firmicutes; N18) and the phylum Tenericutes (mainly represented by order RF-39; N33) with low BMI (P=9.8×10−7 and P=0.0002, respectively), as reported in the TwinsUK cohort.38”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] Goodrich JK Waters JL Poole AC Sutter JL Koren O Blekhman R Beaumont M Van Treuren W Knight R Bell JT Spector TD Clark AG Ley RE Human genetics shape the gut microbiome. Cell 2014 159 789 799 doi: 10.1016/j.cell.2014.09.053 25417156 PMC4255478 source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Christensenellaceae and Tenericutes are associated with lower triglyceride (P=2.1×10−5 and P=2.7×10−7, respectively) and higher HDL (P=0.0047 and P=0.0006, respectively).

    “In addition, we identified a novel and strong association of these particular bacteria with lower levels of triglyceride (P=2.1×10−5 and P=2.7×10−7, respectively) and higher levels of HDL (P=0.0047 and P=0.0006, respectively).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Age and the proportion of women were higher in richer OTU groups.

    “When individuals were grouped into different bins based on their richness, we observed that age and the proportion of women were higher in the richer OTU groups (Figure 1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The gut microbiome made a significant contribution to the explained variation in BMI (P=4.1×10−3), triglycerides (P=4.5×10−4), and HDL (P=2.7×10−3).

    “with the microbiome making a significant contribution to the explained variation in BMI (P=4.1×10−3), triglycerides (P=4.5×10−4), and HDL (P=2.7×10−3; Figure 3B).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Of the 148 associated OTUs, 12 were shared by all 3 traits (BMI, triglyceride, and HDL), 29 shared by BMI and triglycerides, 4 by BMI and HDL, while 21, 64, and 9 were specifically associated with BMI, triglyceride, and HDL, respectively.

    “Of the 148 associated OTUs, 12 were shared by all 3 traits (BMI, triglyceride, and HDL), 29 OTUs were shared by BMI and triglycerides, and 4 by BMI and HDL, whereas 21, 64, and 9 OTUs were specifically associated with BMI, triglyceride, and HDL, respectively (Online Figure II).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    After correcting for age and sex, OTU richness was negatively correlated with BMI (P=3.8×10−4) and triglycerides (P=1.37×10−4), but positively correlated with HDL (P=8.3×10−4).

    “After correcting for age and sex, OTU richness was negatively correlated with BMI (P=3.8×10−4) and triglycerides (P=1.37×10−4), but positively correlated with HDL (P=8.3×10−4).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At the taxonomic level, 50 significant associations for 34 unique taxonomies were identified at FDR=0.05: 22 with BMI, 23 with triglycerides, 4 with HDL, and 1 with LDL.

    “At the taxonomic level, we identified 50 significant associations for 34 unique taxonomies at FDR=0.05: 22 were associated with BMI, 23 with triglycerides, 4 with HDL, and 1 with LDL (Figure 2; Online Table V).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Family Clostridiaceae/Lachnospiracease (N16) was specifically associated with LDL (P=9.1×10−5) and not detected for BMI nor other lipids.

    “the family Clostridiaceae/Lachnospiracease (N16 in Figure 2) was specially associated with LDL (P=9.1×10−5; Online Table V) and not detected for BMI nor other lipids.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The gut microbiome makes a significant contribution, beyond clinical risk factors and genetics, to the individual variance in BMI and to blood levels of triglycerides and HDL.

    “We observed that the gut microbiome makes a significant contribution, beyond that of clinical risk factors and genetics, to the individual variance seen in BMI and to the blood levels of triglycerides and HDL”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    When BMI was included, the gut microbiome made a lesser but still significant contribution to triglycerides (P=4×10−3) and HDL (P=0.026).

    “The microbiome made a lesser, but still significant, contribution to triglycerides (P=4×10−3) and HDL (P=0.026).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Genus Eggerthella (N3) is associated with increased triglyceride (P=4.1×10−5) and decreased HDL (P=6.3×10−5), and family Pasteurellaceae (N32) is associated with decreased triglyceride (P=6.2×10−5).

    “such as genus Eggerthella (N3) with increased triglyceride (P=4.1×10−5) and decreased HDL (P=6.3×10−5), and family Pasteurellaceae (N32) with decreased triglyceride (P=6.2×10−5).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    When association significance decreased and the risk model included more OTUs, the explained variation increased to 4.57% in BMI, 6.0% in triglycerides, 4.0% in HDL, but was only 1.5% in LDL and 0.7% in TC.

    “When the association significance decreased and the risk model included more (but less-significant) OTUs, the explained variation increased to 4.57% in BMI, 6.0% in triglycerides, 4.0% in HDL, but was only 1.5% in LDL and 0.7% in TC (Figure 3A).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The cohort had on average 238 OTUs per individual, ranging from 44 to 355.

    “The cohort had on average 238 OTUs per individual, ranging from 44 to 355.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Shannon diversity showed similar significant correlations with age and sex.

    “we computed the microbial diversity (Shannon’s diversity index) and observed similar significant correlations for age and sex (Online Table I).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Genus Butyricimonas (N9) is strongly associated with decreased triglyceride (P=4.7×10−6) and nominally associated with BMI and HDL in humans.

    “Our study shows that this genus is strongly associated with decreased triglyceride (P=4.7×10−6) and nominally associated with BMI and HDL in humans.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Most associated taxonomies were shared across lipid metabolites and BMI, but several microbes were predominantly linked to lipids rather than BMI.

    “Although most of the associated taxonomies were shared across lipid metabolites and BMI, several microbes were predominantly linked to lipids rather than BMI.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Family Pasteurellaceae (N32), genus Coprococcus (N24), and genus Collinsella species Stercoris (N2) showed strong association with triglyceride levels (P=6.2×10−5, P=4.6×10−5, and P=0.0006, respectively), nominal significance to other lipids, and no association with BMI (P>0.1).

    “the family Pasteurellaceae (N32; Proteobacteria), genus Coprococcus (N24; Firmicutes), and genus Collinsella species Stercoris (N2) showed strong association with triglyceride levels (P=6.2×10−5, P=4.6×10−5, and P=0.0006, respectively), a nominal significance to other lipids, and no association with BMI (P>0.1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    After adjusting for age and sex, 148 OTUs were associated at FDR=0.05: 66 with BMI, 114 with triglycerides, and 34 with HDL.

    “After adjusting for age and sex, we identified 148 associated OTUs at FDR=0.05: 66 OTUs were associated with BMI, 114 with triglycerides, and 34 with HDL (Online Tables II–IV).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Of 22 independent taxa associated with BMI in this study, 16 were also assessed in the TwinsUK study, and 11 (68.8%) showed significant association with BMI with the same direction of effect.

    “Of 22 independent taxa associated with BMI by our study, 16 were also accessed in the TwinsUK study: 11 (68.8%) showed significant association with BMI (P<0.05) with the same direction of effect as we found (Online Table V).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] Goodrich JK Waters JL Poole AC Sutter JL Koren O Blekhman R Beaumont M Van Treuren W Knight R Bell JT Spector TD Clark AG Ley RE Human genetics shape the gut microbiome. Cell 2014 159 789 799 doi: 10.1016/j.cell.2014.09.053 25417156 PMC4255478 source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Microbial richness was significantly higher in women (P=0.0055) and increased with age (P=5.87×10−12).

    “The Spearman correlation showed that the richness was significantly higher in women (P=0.0055) and increased with age (P=5.87×10−12; Online Table I).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Of the taxonomic associations, 18 (36%) were detected by binary analysis, 4 (8%) by quantitative model, and 28 (56%) by meta-analysis.

    “We found that 18 associations (36%) were detected by binary analysis (presence/absence); 4 associations (8%) were detected by the quantitative model; and 28 associations (56%) were detected by the meta-analysis of binary and quantitative analyses (Online Table V).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Age, sex, genetics, and the gut microbiome collectively explained 11.3% of variation in BMI, 17.1% in triglycerides, and 25.9% in HDL cholesterol.

    “Our analysis unambiguously showed that age, sex, genetics, and the gut microbiome collectively explained 11.3% of the variation in BMI, 17.1% in triglycerides, and 25.9% in HDL cholesterol”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    OTUs identified at P=1×10−5 in the discovery set explained 2.74% variation in BMI in the validation set, 3.83% in triglycerides, 2.46% in HDL, 0.01% in LDL, and 0.01% in TC.

    “The OTUs identified at P=1×10−5 level in the discovery set explained 2.74% variation in BMI in the validation set, 3.83% in triglycerides, 2.46% in HDL, 0.01% variation in LDL, and 0.01% in TC.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the LifeLines-Deep cohort, the 157 lipid-associated SNPs and 97 BMI-associated SNPs collectively explained 2.1% of variation in BMI, 3.4% in triglycerides, 7.5% in HDL, 4.6% in LDL, and 5.6% in total cholesterol, after correcting for age and sex.

    “In our cohort, these SNPs collectively explained 2.1% variation in BMI (P=1.66×10−5), 3.4% in triglycerides (P=3.22×10−8), 7.5% in HDL (P<2.2×10−16), 4.6% in LDL (P=8.0×10−11), and 5.6% in TC (P=7.7×10−13), after correcting for age and sex.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [33] Global Lipids Genetics Consortium Discovery and refinement of loci associated with lipid levels. Nat Genet 2013 45 1274 1283 24097068 PMC3838666 source

      Not checked in the free run.

    • cites [34] Locke AE Kahali B Berndt SI LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium Genetic studies of body mass index yield new insights for obesity biology. Nature 2015 518 197 206 doi: 10.1038/nature14177 25673413 PMC4382211 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Gut microbiota comprise thousands of microbial species that are involved in host metabolism by regulating energy extraction, activation of the immune system, drug metabolism, and other processes.

    “Gut microbiota comprise thousands of microbial species that are involved in host metabolism by regulating energy extraction, activation of the immune system, drug metabolism, and other processes.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] Huttenhower C Gevers D Knight R Structure, function and diversity of the healthy human microbiome. Nature 2012 486 207 214 22699609 PMC3564958 source

      Not checked in the free run.

    • cites [4] Qin J Li R Raes J MetaHIT Consortium A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010 464 59 65 doi: 10.1038/nature08821 20203603 PMC3779803 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Association of bacterial composition with many diseases has been observed, including immune, inflammatory, and metabolic phenotypes.

    “Association of bacterial composition with many diseases has been observed, including immune, inflammatory, and metabolic phenotypes.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [5] Henao-Mejia J Elinav E Jin C Hao L Mehal WZ Strowig T Thaiss CA Kau AL Eisenbarth SC Jurczak MJ Camporez JP Shulman GI Gordon JI Hoffman HM Flavell RA Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 2012 482 179 185 doi: 10.1038/nature10809 22297845 PMC3276682 source

      Not checked in the free run.

    • cites [6] Karlsson FH Tremaroli V Nookaew I Bergström G Behre CJ Fagerberg B Nielsen J Bäckhed F Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 2013 498 99 103 doi: 10.1038/nature12198 23719380 source

      Not checked in the free run.

    • cites [7] Kau AL Ahern PP Griffin NW Goodman AL Gordon JI Human nutrition, the gut microbiome and the immune system. Nature 2011 474 327 336 doi: 10.1038/nature10213 21677749 PMC3298082 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Dysbiosis in the gut induces increased permeability of the intestine, leading to increased systemic levels of bacterial products causing low-grade chronic inflammation.

    “dysbiosis in the gut has been shown to induce increased permeability of the intestine, leading to increased systemic levels of bacterial products causing low-grade chronic inflammation.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] Frazier TH DiBaise JK McClain CJ Gut microbiota, intestinal permeability, obesity-induced inflammation, and liver injury. JPEN J Parenter Enteral Nutr 2011 35 14S 20S doi: 10.1177/0148607111413772 21807932 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The study confined analysis to 645 OTUs, each comprising ≥0.05% of reads and present in at least 1% of the population.

    “we filtered on the OTU abundance and confined our analysis to 645 OTUs, each of which comprised ≥0.05% of reads and was present in at least 1% of the population.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The observation provides insight into the microbiome's role in regulating metabolic processes during the development of cardiovascular disease.

    “This observation provides insight into the microbiome’s role in regulating metabolic processes during the development of CVD.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The results highlight the potential of therapies that alter the gut microbiome to control body mass, triglycerides, and HDL in cardiovascular disease prevention.

    “Our results highlight the potential of therapies that alter the gut microbiome to control body mass, triglycerides, and HDL in CVD prevention.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: An individual's microbiota composition can be modified by prebiotics, probiotics, and fecal transplantation.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    The study used a two-part model to account for both binary and quantitative features of microbial data.

    “We therefore adopted a novel, 2-part model that was developed to account for both binary (detected/undetected) and quantitative features.32”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Keurentjes JJ Fu J de Vos CH Lommen A Hall RD Bino RJ van der Plas LH Jansen RC Vreugdenhil D Koornneef M The genetics of plant metabolism. Nat Genet 2006 38 842 849 doi: 10.1038/ng1815 16751770 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Altering the gut microbiome in humans and mice has shown improvement in metabolic syndrome.

    “altering the gut microbiome in humans and mice has shown improvement in metabolic syndrome.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13] Turnbaugh PJ Ley RE Mahowald MA Magrini V Mardis ER Gordon JI An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006 444 1027 1031 doi: 10.1038/nature05414 17183312 source

      Not checked in the free run.

    • cites [14] Ridaura VK Faith JJ Rey FE Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 2013 341 1241214 doi: 10.1126/science.1241214 24009397 PMC3829625 source

      Not checked in the free run.

    • cites [15] Vrieze A Van Nood E Holleman F Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 2012 143 913 916.e7 doi: 10.1053/j.gastro.2012.06.031 22728514 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Bile acid activity of commensal bacteria is involved in a complex interplay with host hepatic enzymes, and together they promote digestion and absorption of dietary lipids.

    “Bile acid activity of commensal bacteria is involved in a complex interplay with host hepatic enzymes, and together they promote digestion and absorption of dietary lipids.42”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [42] Ridlon JM Kang DJ Hylemon PB Bile salt biotransformations by human intestinal bacteria. J Lipid Res 2006 47 241 259 doi: 10.1194/jlr.R500013-JLR200 16299351 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The study removed ethnic outliers and genetically related participants.

    “We removed ethnic outliers and genetically related participants from our study.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Gut microbiota can influence the efficiency of energy harvest from ingested food and play a crucial role in metabolic processes and development of obesity.

    “They can also influence the efficiency of energy harvest from ingested food12,13 and play a crucial role in the metabolic processes and development of obesity.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [12] Bäckhed F Ding H Wang T Hooper LV Koh GY Nagy A Semenkovich CF Gordon JI The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 2004 101 15718 15723 doi: 10.1073/pnas.0407076101 15505215 PMC524219 source

      Not checked in the free run.

    • cites [13] Turnbaugh PJ Ley RE Mahowald MA Magrini V Mardis ER Gordon JI An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006 444 1027 1031 doi: 10.1038/nature05414 17183312 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The study rarefied the library size to 15,000 read-depth to make library sizes equal.

    “We rarefied the library size to 15 000 read-depth using the rarefy function in R package vegan (version 2.3-0).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The study excluded 99 samples from participants taking antibiotic or other potential microbiome-modifying drugs or lipid-lowering medication.

    “We excluded 99 samples from participants who were taking antibiotic or other potential microbiome-modifying drugs or who were on lipid-lowering medication.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Several small-scale studies reported lowered cholesterol on using probiotics with bile salt hydrolytic activity.

    “Interestingly, several small-scale studies reported lowered cholesterol on using probiotics with bile salt hydrolytic activity.43,44”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [43] Hepner G Fried R St Jeor S Fusetti L Morin R Hypocholesterolemic effect of yogurt and milk. Am J Clin Nutr 1979 32 19 24 581636 source

      Not checked in the free run.

    • cites [44] Hlivak P Odraska J Ferencik M Ebringer L Jahnova E Mikes Z One-year application of probiotic strain Enterococcus faecium M-74 decreases serum cholesterol levels. Bratisl Lek Listy 2005 106 67 72 16026136

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    The study rerarefied the OTU library 100× and repeated the whole analysis, yielding similar results and confirming robustness.

    “we rerarefied the OTU library 100× and repeated the whole analysis. This approach yielded similar results, thereby confirming the robustness of our estimation”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    The study performed 100× cross validation, splitting data into 80% discovery and 20% validation sets.

    “we performed a 100× cross validation. Each time we split the data randomly into an 80% discovery set and a 20% validation set.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The study adjusted for age and sex in association analyses.

    “After adjusting for age and sex, we identified 148 associated OTUs at FDR=0.05”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The microbiota play an important role in choline diet-induced trimethylamine N-oxide production, which has been implicated in CVD.

    “The microbiota play an important role in choline diet–induced trimethylamine N-oxide production, which has been implicated in CVD.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Tang WH Wang Z Levison BS Koeth RA Britt EB Fu X Wu Y Hazen SL Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med 2013 368 1575 1584 doi: 10.1056/NEJMoa1109400 23614584 PMC3701945 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The study performed 1000× permutation tests to control the false discovery rate (FDR) at 0.05.

    “we therefore performed 1000× permutation tests to control the false discovery rate (FDR).”
    • Also stated in: § Statistical Analysis · plain
      “We controlled the FDR at 0.05.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Many of the taxonomies identified in this study are novel findings.

    “However, many of the taxonomies we identified are novel findings.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    The study supports the potential of microbiota-modifying intervention to correct lipid disbalance and thereby help prevent cardiovascular disease.

    “Our study supports the potential of microbiota-modifying intervention to correct lipid disbalance and thereby help prevent CVD.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: An individual's microbiota composition can be modified by prebiotics, probiotics, and fecal transplantation.

  • May combine two claims.

    Premisecitation

    Both orders Bacteroidales and Clostridiales, identified in this study, are involved in short chain fatty acid metabolism.

    “Both orders Bacteroidales and Clostridiales, identified in our study, are involved in short chain fatty acid metabolism.41”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] Caspi R Altman T Billington R The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 2014 42 D459 D471 doi: 10.1093/nar/gkt1103 24225315 PMC3964957 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Studies have shown that diet can alter the gut microbiome.

    “Studies have shown that diet can alter the gut microbiome.48”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] David LA Maurice CF Carmody RN Gootenberg DB Button JE Wolfe BE Ling AV Devlin AS Varma Y Fischbach MA Biddinger SB Dutton RJ Turnbaugh PJ Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014 505 559 563 doi: 10.1038/nature12820 24336217 PMC3957428 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Short chain fatty acids are produced by microbiota from dietary fibers, affect host body energy homeostasis, and are protective against metabolic syndrome, type 2 diabetes mellitus, and atherosclerosis.

    “Short chain fatty acids are produced by microbiota from dietary fibers, effect host body energy homeostasis, and are protective against metabolic syndrome, type 2 diabetes mellitus, and atherosclerosis.15,45–47”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [15] Vrieze A Van Nood E Holleman F Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 2012 143 913 916.e7 doi: 10.1053/j.gastro.2012.06.031 22728514 source

      Not checked in the free run.

    • cites [45] Karlsson FH Fåk F Nookaew I Tremaroli V Fagerberg B Petranovic D Bäckhed F Nielsen J Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat Commun 2012 3 1245 doi: 10.1038/ncomms2266 23212374 PMC3538954 source

      Not checked in the free run.

    • cites [46] Qin J Li Y Cai Z A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012 490 55 60 doi: 10.1038/nature11450 23023125 source

      Not checked in the free run.

    • cites [47] Kimura I Ozawa K Inoue D Imamura T Kimura K Maeda T Terasawa K Kashihara D Hirano K Tani T Takahashi T Miyauchi S Shioi G Inoue H Tsujimoto G The gut microbiota suppresses insulin-mediated fat accumulation via the short-chain fatty acid receptor GPR43. Nat Commun 2013 4 1829 doi: 10.1038/ncomms2852 23652017 PMC3674247 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The study adopted a novel and powerful 2-part model to account for both binary and quantitative features of microbial data.

    “Moreover, we adopted a novel and powerful 2-part model to account for both the binary and quantitative features of microbial data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Order Bacteroidales and family Clostridiaceae are known to be involved in bile acid metabolism.

    “In particular, order Bacteroidales (phylum Bacteroidetes) and family Clostridiaceae (phylum Firmicutes) are both negatively correlated with BMI and triglycerides and known to be involved in bile acid metabolism.41”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] Caspi R Altman T Billington R The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 2014 42 D459 D471 doi: 10.1093/nar/gkt1103 24225315 PMC3964957 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The gut microbiomes were profiled by 16S ribosomal RNA gene sequencing, which can identify microbial taxonomies and composition but has limitations in identifying genetically specific species and strains and provides little information on bacterial genes and their functions.

    “The gut microbiomes in our study were profiled by 16s ribosomal RNA gene sequencing. This technology can identify microbial taxonomies and composition, but has limitations in identifying genetically specific species and strains. Furthermore, 16s ribosomal RNA gene sequencing provides little information on bacterial genes and their functions.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Detailed medication information per individual allowed exclusion of those taking lipid-lowering or antibiotic medication.

    “We also had detailed medication information per individual and could exclude those taking lipid-lowering or antibiotic medication.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The study has not addressed how much of the observed association between gut microbiome and blood lipids might be explained by diet.

    “Our study has not addressed how much of the association we observed between gut microbiome and blood lipids might be explained by diet.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductcitation

    The genetic risk score was limited to 157 lipid-associated SNPs and 97 BMI-associated SNPs, which together explain only a small proportion of the heritability of lipid levels, so effects of other undiscovered SNPs might have been missed.

    “It should be noted, however, that the genetic risk score was limited to our established 157 lipid-associated SNPs33 and 97 BMI-associated SNPs,34 which together only explain a small proportion of the heritability of lipid levels. We might have missed the effect of other, not yet discovered SNPs.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [33] Global Lipids Genetics Consortium Discovery and refinement of loci associated with lipid levels. Nat Genet 2013 45 1274 1283 24097068 PMC3838666 source

      Not checked in the free run.

    • cites [34] Locke AE Kahali B Berndt SI LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium Genetic studies of body mass index yield new insights for obesity biology. Nature 2015 518 197 206 doi: 10.1038/nature14177 25673413 PMC4382211 source

      Not checked in the free run.

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.”
8 of 8

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A two part model for association analysis between BMI and lipids with either OTUs or taxonomic units adjusting for age and gender was performed as described by Fu et al.

    “A two part model for association analysis between BMI and lipids with either OTUs or taxonomic (at the genus level) units adjusting for age and gender was performed as described by Fu et al. [37].”

    From Intestinal Microbiota Is Influenced by Gender and Body Mass Index (2016) · cites this paper as [37]

  • In a recent study by Fu et al., gut microbiota composition has little effect on LDL or TC levels, but makes a significant contribution to the individual variance seen in BMI and to the blood levels of triglycerides and HDL.

    “In a recent study by Fu et al. [37], it has been shown that gut microbiota composition has little effect on LDL or TC levels. However, it makes a significant contribution to the individual variance seen in BMI and to the blood levels of triglycerides and HDL.”

    From Intestinal Microbiota Is Influenced by Gender and Body Mass Index (2016) · cites this paper as [37]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Discussion's variance range excludes the reported HDL value

    Minor

    The discussion gives a range of 4.57% to 6% for BMI, triglycerides, and HDL, but the results report 4.0% for HDL.

  • Incorrect percentage for frequently detected taxonomies

    Minor

    The stated 32 of 136 taxonomies is 23.5%, not 9.5%.

    Reported 9.5 · recomputed 23.529412

2 of 2

Cites

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1 of the 52 references couldn’t be matched to a known paper.

Cited by

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Related

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
ORCID
Venue
Circulation Research
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2015-10-09
Volume
117
Issue
9
Pages
817-824
ISSN
  • 0009-7330 (ISSN-L)
  • 1524-4571
DOI
10.1161/circresaha.115.306807
PMC
PMC4596485
OpenAlex
W1918132278
Semantic Scholar
9c012329acd98bf9278665f78bb7541a27215c8f
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 · 2014: 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

Statistics and arithmetic are recomputed, not judged.

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