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Cited by 120Nutritional Studies and DietMetabolomics and Mass Spectrometry StudiesDiet, Metabolism, and Disease

Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes

Open access
Wittenbecher, Clemens, Mühlenbruch, Kristin, Kröger, Janine, Jacobs, Simone, Kuxhaus, Olga, Floegel, Anna, Fritsche, Andreas, Pischon, Tobias +5 more·2015·The American Journal of Clinical Nutrition
DOI 10.3945/ajcn.114.099150
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Abstract

Background Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear.Objective This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes.Design Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates.Results After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%).Conclusion In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.

AI snapshot

Written from the paper’s full text.

TL;DR

In the EPIC-Potsdam prospective cohort, higher total red meat consumption was associated with higher type 2 diabetes risk, and this association was largely attenuated after adjustment for selected serum biomarkers. Ferritin, glycine, and specific lipid metabolites were identified as potential mediators linking red meat intake to diabetes risk.

Key findings

  • After adjustment for age, sex, lifestyle, diet, and BMI, total red meat consumption was directly related to diabetes risk: HR for 2 SD (11 g/MJ) = 1.26 (95% CI: 1.01, 1.57).
  • Six biomarkers were associated with both red meat consumption and diabetes risk: ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1.
  • Simultaneous adjustment for selected biomarkers attenuated the red meat–diabetes association to HR 1.09 (95% CI: 0.86, 1.38); the proportion of diabetes risk explainable by biomarkers was 69% (IQR: 49%, 106%) in the BMI-adjusted model.
  • In sensitivity analyses, the explainable proportion was 49% (IQR: 29%, 89%) for unprocessed red meat and 71% (IQR: 45%, 115%) for processed meat.

Methods

  • Prospective EPIC-Potsdam cohort (n = 27,548) with a nested case-cohort design (n = 2681, including 688 incident type 2 diabetes cases; mean follow-up 7.0 y).
  • Habitual diet was assessed by validated semiquantitative food-frequency questionnaires; total red meat was energy-standardized summed intake of unprocessed and processed red meat.
  • Serum concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were measured; Cox models with Prentice weighting and a bootstrapping procedure with 1000 replicates estimated mediation/attenuation.

Limitations

  • The paper states that residual confounding cannot be ruled out because of the observational design, and that the results cannot prove causality.
  • The paper notes dietary information relied on FFQ estimates and metabolites were measured at a single time point; the targeted metabolite set was not exhaustive, and selection was somewhat sensitive for lysophosphatidylcholine 17:0 and hydroxy-sphingomyelin 14:1.
  • Evident from the text: the study population was middle-aged adults from Potsdam, Germany, so generalizability to other populations is not established.

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

Integrity signals

Registry

Crossref · 10.3945/ajcn.114.099150

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 48 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    The red meat-associated diabetes risk was significantly attenuated after simultaneous adjustment for the selected biomarkers (biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38; P < 0.001).

    “The red meat-associated diabetes risk was significantly (P , 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarkeradjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38].”
    • Also stated in: § Attenuation of the red meat-associated diabetes risk by selected biomarkers · plain
      “After mutually including the 5 other selected biomarkers as covariates, the HR (per 2 SD) of total red meat consumption was significantly attenuated from 1.33 (95% CI: 1.09, 1.62) to 1.04 (0.83, 1.30) for the non-BMI-adjusted model 2 and from 1.26 (95% CI: 1.01, 1.57) to 1.09 (0.86, 1.38) for the BMI-adjusted model 3.”
    Quote in the paperSubstantial claim
  • Findingown data

    After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk (HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57).

    “After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57].”
    • Also stated in: § Attenuation of the red meat-associated diabetes risk by selected biomarkers · plain
      “This association was further attenuated after also adjusting for BMI and again remained significant (HR for 2 SD: 1.26; 95% CI: 1.01, 1.57).”
    Quote in the paperSubstantial claim
  • Findingown data

    If the non-BMI-adjusted model 2 was defined as reference, the red meat-associated diabetes risk was attenuated by 89% (IQR: 69%, 121%) after simultaneous adjustment for the selected biomarkers.

    “If the non-BMI-adjusted model 2 was defined as reference, the red meat-associated diabetes risk was attenuated by 89% (IQR: 69%, 121%) after simultaneous adjustment for the selected biomarkers (Figure 1A).”
    Quote in the paperSubstantial claim
  • Findingown data

    Additional adjustment for lifestyle, medication, and diet (model 2) attenuated the relation between total red meat consumption and diabetes risk (HR: 1.33; 95% CI: 1.09, 1.62).

    “Additional adjustment for lifestyle, medication, and diet (model 2) attenuated the relation (HR: 1.33; 95% CI: 1.09, 1.62).”
    Quote in the paperSubstantial claim
  • Findingown data

    The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%).

    “The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%).”
    • Also stated in: § Attenuation of the red meat-associated diabetes risk by selected biomarkers · plain
      “If the BMIadjusted model 3 was defined as reference, the red meatassociated diabetes risk was attenuated by 69% (IQR: 49%, 106%) after simultaneous adjustment for selected biomarkers (Figure 1B).”
    Quote in the paperSubstantial claim
  • May combine two claims. 15
  • Classified as probably not a substantial claim. 28

  • May combine two claims.

    Findingown data

    The attenuation of the associated diabetes risk after mutual adjustment for all selected biomarkers was also significant if only unprocessed red meat (P = 0.007) or processed meat (P = 0.002) consumption was considered the exposure variable (reference: model 3).

    “Our sensitivity analysis evaluating different subtypes of exposure revealed that the attenuation of the associated diabetes risk after mutual adjustment for all selected biomarkers was also significant if only unprocessed red meat (P = 0.007) or processed meat (P = 0.002) consumption was considered the exposure variable (reference: model 3).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    When using the BMI-adjusted model 3 as reference, the attenuation was significant after adjustment for diacyl phosphatidylcholine 36:4, hydroxy-sphingomyelin 14:1, and glycine and borderline significant after adjustment for diacyl phosphatidylcholine 38:4 (P = 0.05) and ferritin (P = 0.06).

    “When using the BMI-adjusted model 3 as reference, the attenuation was significant after adjustment for diacyl phosphatidylcholine 36:4, hydroxy-sphingomyelin 14:1, and glycine and borderline significant after adjustment for diacyl phosphatidylcholine 38:4 (P = 0.05) and ferritin (P = 0.06).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Diacyl phosphatidylcholines 36:4 and 38:4 were highly correlated, and adjustment for diacyl phosphatidylcholine 36:4 did not further attenuate the association between total red meat consumption and type 2 diabetes risk if diacyl phosphatidylcholine 38:4 was also included in the same model.

    “Diacyl phosphatidylcholines 36:4 and 38:4 were highly correlated, and adjustment for diacyl phosphatidylcholine 36:4 did not further attenuate the association between total red meat consumption and type 2 diabetes risk if diacyl phosphatidylcholine 38:4 was also included in the same model (data not shown).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the fully adjusted model 3, the proportion of associated diabetes risk explainable by simultaneous biomarker adjustment was 49% (IQR: 29%, 89%) for unprocessed red meat consumption and 71% (IQR: 45%, 115%) for processed meat consumption.

    “In the fully adjusted model 3, the proportion of associated diabetes risk explainable by simultaneous biomarker adjustment (median and IQR from 1000 bootstrap replicates) was 49% (IQR: 29%, 89%) for unprocessed red meat consumption, whereas it was 71% (IQR: 45%, 115%) for processed meat consumption (Supplemental Figure 8).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Results were robust to the choice of selection strategy and P value adjustment method for glycine, ferritin, and phosphatidylcholines 36:4 and 38:4 and were somewhat sensitive to that choice for lysophosphatidylcholine 17:0 and hydroxy-sphingomyelin 14:1.

    “Thus, results were robust to the choice of selection strategy and P value adjustment method for glycine, ferritin, and phosphatidylcholines 36:4 and 38:4 and were somewhat sensitive to that choice for lysophosphatidylcholine 17:0 and hydroxy-sphingomyelin 14:1.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Sensitivity analysis applying different P value adjustments and differently defined selection criteria showed that selection was very robust for most potential mediators but was somewhat sensitive to these choices for lysophosphatidylcholine 17:0 and hydroxy-sphingomyelin 14:1.

    “Our sensitivity analysis applying different P value adjustments and differently defined selection criteria showed that selection was very robust for most potential mediators but was somewhat sensitive to these choices for lysophosphatidylcholine 17:0 and hydroxy-sphingomyelin 14:1.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    High ferritin, low glycine, and altered hepatic-derived lipids in circulation were associated with both total red meat consumption and diabetes risk.

    “In conclusion, high ferritin, low glycine, and altered hepaticderived lipids in the circulation were associated with both total red meat consumption and diabetes risk.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Components of red meat proposed to affect diabetogenic pathways include heme iron, cholesterol, fatty acids, amino acids, and advanced glycation and lipoxidation end products.

    “Components of red meat that were proposed to affect diabetogenic pathways include heme iron, cholesterol, fatty acids (FAs), amino acids, and advanced glycation and lipoxidation end products (1), among others.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Feskens, 2013, Meat consumption, diabetes, and its complications, Curr Diab Rep source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Habitual diet was assessed with validated semiquantitative food-frequency questionnaires.

    “Habitual diet was assessed with validated semiquantitative food-frequency questionnaires.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [16] Kroke, 1999, Am J Clin Nutr source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    To reduce the probability of spurious associations, biomarkers were selected only if they were associated with total red meat consumption in both sexes in stratified analyses.

    “To reduce the probability of spurious associations, we selected biomarkers only if they were associated with total red meat consumption in both sexes in stratified analyses.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    In EPIC-Potsdam, metabolomic data were used to identify lipid metabolites and amino acids as biomarkers of diabetes risk.

    “In EPIC-Potsdam, metabolomic data were used to identify lipid metabolites and amino acids as biomarkers of diabetes risk (12).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [12] Floegel, 2013, Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach, Diabetes source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Reported associations of red meat consumption with biomarkers of inflammation, glucose metabolism, and hepatocellular and oxidative stress were mostly explainable by differences in BMI.

    “Reported associations of red meat consumption with biomarkers of inflammation, glucose metabolism, and hepatocellular and oxidative stress were mostly explainable by differences in BMI (7,8).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [7] Montonen, 2013, Consumption of red meat and whole-grain bread in relation to biomarkers of obesity, inflammation, glucose metabolism and oxidative stress, Eur J Nutr source

      Not checked in the free run.

    • cites [8] Ley, 2014, Associations between red meat intake and biomarkers of inflammation and glucose metabolism in women, Am J Clin Nutr source

      Not checked in the free run.

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

    Premisecitation

    Change in red meat consumption was related to a change in diabetes risk among 3 large US cohorts, suggesting a dose-response relation.

    “Recently, change in red meat consumption was observed to be related to a change in diabetes risk among 3 large US cohorts (4), suggesting a dose-response relation.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] Pan, 2013, Changes in red meat consumption and subsequent risk of type 2 diabetes mellitus: three cohorts of US men and women, JAMA Intern Med source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    On the basis of a previous evaluation of measurement reliability, metabolites below the limit of detection and those with very high analytic variance were excluded.

    “On the basis of a previous evaluation of the reliability of measurements (23), we excluded metabolites below the limit of detection (n = 30) and those with very high analytic variance (n = 6).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Floegel, 2011, Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach, PLoS ONE source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Mediators of the red meat-diabetes association were selected according to 4 predefined mediation criteria.

    “We selected mediators of the red meat-diabetes association according to 4 predefined mediation criteria (25) (see Supplemental Figure 1A).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [25] Baron, 1986, The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations, J Pers Soc Psychol source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    The case-cohort design was accounted for by Prentice weighting.

    “The case-cohort design was accounted for by Prentice weighting (26).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [26] Prentice, 1988, Aspects of the use of relative risk models in the design and analysis of cohort studies and prevention trials, Stat Med source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The red meat-associated diabetes risk was observed in models in which red meat was substituted by foods with a similar macronutrient content, suggesting the relation to be independent of macronutrient composition of the diet.

    “Furthermore, the red meat-associated diabetes risk was also observed in models in which red meat was substituted by foods with a similar macronutrient content (2), suggesting the relation to be independent of macronutrient composition of the diet.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Pan, 2011, Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis, Am J Clin Nutr source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    P values were controlled for false discovery rate within metabolite class using the linear step-up method by Benjamini and Hochberg.

    “The P values were controlled for false discovery rate (FDR) within metabolite class by using the linear step-up method by Benjamini and Hochberg (30).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [30] Benjamini, 1995, Controlling the false discovery rate—a practical and powerful approach to multiple testing, J R Stat Soc B source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Potential confounders were a priori selected based on the literature on red meat intake and diabetes risk and prior investigations in EPIC-Potsdam.

    “Potential confounders to be included as covariates were a priori selected based on the literature on the association between red meat intake and diabetes risk (2, 3) and prior investigations in EPIC-Potsdam regarding risk factors of type 2 diabetes (5), dietary patterns (27), and determinants of metabolic profiles (28,29).”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [2] Pan, 2011, Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis, Am J Clin Nutr source

      Not checked in the free run.

    • cites [3] Benedinelli, 2013, Association between dietary meat consumption and incident type 2 diabetes: the EPIC-InterAct study, Diabetologia source

      Not checked in the free run.

    • cites [5] Mühlenbruch, 2014, Update of the German Diabetes Risk Score and external validation in the German MONICA/KORA study, Diabetes Res Clin Pract source

      Not checked in the free run.

    • cites [27] Schulze, 2001, Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study, Br J Nutr source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.

    “The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Lipid metabolites, amino acids, and ferritin are likely influenced by total red meat consumption in a relatively direct manner based on its nutrient composition.

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

    Premisecitation

    Experimental evidence relates lipid metabolism to the pathogenesis of type 2 diabetes via challenged mitochondrial metabolism, altered cellular signaling at the membrane, and modulation of gene expression.

    “In the literature, several lines of experimental evidence relate lipid metabolism to the pathogenesis of type 2 diabetes, among them a challenged mitochondrial metabolism, altered cellular signaling processes at the membrane, and modulation of gene expression (34,35).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [34] Ye, 2013, Mechanisms of insulin resistance in obesity, Front Med source

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    • cites [35] Amati, 2012, Revisiting the diacylglycerol-induced insulin resistance hypothesis, Obes Rev source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    High ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk.

    “In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationcitation

    Interpretation of our findings with regard to biological paths is hypothesis generating rather than able to prove their causal nature, given that an observational study is generally prone to confounding and other sources of bias.

    “Given that an observational study is generally prone to confounding and other sources of bias, interpretation of our findings with regard to biological paths is rather hypothesis generating than able to proof their causal nature (45).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [45] Hafeman, 2009, “Proportion explained”: a causal interpretation for standard measures of indirect effect?, Am J Epidemiol source

      Not checked in the free run.

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

    Interpretationreasoning

    Our results generate and underline experimentally testable hypotheses that can inform future dietary interventions in terms of design and biomarker assessment.

    “Thus, our results generate and underline experimentally testable hypotheses that can inform future dietary interventions in terms of design and biomarker assessment (46).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [46] Pearl, 2010, Causal Inference, Int J Biostat source

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

    Premisecitation

    An independent inverse association of glycine with diabetes risk was previously described by us and others.

    “described by us and others (12,41).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [12] Floegel, 2013, Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach, Diabetes source

      Not checked in the free run.

    • cites [41] Wang-Sattler, 2012, Novel biomarkers for pre-diabetes identified by metabolomics, Mol Syst Biol source

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

    Premisecitation

    Endogenous fatty acid composition in several lipid compartments has been related to the risk of type 2 diabetes in several prospective cohorts, including EPIC-Potsdam.

    “Consistently, endogenous FA composition in several lipid compartments has been related to the risk of type 2 diabetes in several prospective cohorts, including EPIC-Potsdam (36).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Kröger, 2011, Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study, Am J Clin Nutr source

      Not checked in the free run.

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

    Premisecitation

    Underlying mechanisms linking iron status to diabetes risk seem manifold and likely include oxidative stress and modulation of intracellular signaling cascades.

    “Underlying mechanisms seem to be manifold and likely include oxidative stress and modulation of intracellular signaling cascades (44).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Simcox, 2013, Iron and diabetes risk, Cell Metab source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    This approach should have reduced the likelihood of false discoveries.

    “This should have reduced the likelihood of false discoveries.”
    Quote in the paperSubstantial claim

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

    Interpretationreasoning

    Associations of red meat consumption with acylcarnitines might be sex specific.

    “Furthermore, estrogen is a modulator of mitochondrial function, and associations of red meat consumption with acylcarnitines might be sex specific.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Estrogen is a modulator of mitochondrial function.

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

    Premisecitation

    Advanced glycation end products, trans fatty acids, nitrites and nitrates, and methylamines are discussed as meat type-specific mediators.

    “For example, advanced glycation end products, trans FA, nitrites and nitrates, and methylamines are discussed as meat type-specific mediators (1).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] Feskens, 2013, Meat consumption, diabetes, and its complications, Curr Diab Rep source

      Not checked in the free run.

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

    Study design and conductnone given

    We evaluated the robustness of our results in a bootstrapping procedure.

    “Furthermore, we evaluated the robustness of our results in a bootstrapping procedure.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Random measurement error may have masked some associations.

    “Still, random measurement error may have masked some associations.”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    There is a possibility that cases remained undiagnosed during follow-up.

    “In addition, there is the possibility that cases remained undiagnosed during follow-up.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductcitation

    Undiagnosed cases misclassification should not bias the associations between red meat consumption and diabetes because false-positive case definitions should have been rare due to strict verification procedures in EPIC-Potsdam.

    “However, this misclassification should not bias the associations between red meat consumption and diabetes given that false-positive case definitions should have been rare due to the strict verification procedures in EPIC-Potsdam (49).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [49] Rothman, 2008

      Not checked in the free run.

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

    Interpretationcitation

    The link between acylcarnitines and red meat intake may reflect mitochondrial metabolism under acute dietary challenge rather than effects of long-term habitual diet as assessed in our study.

    “This effect, however, may reflect mitochondrial metabolism under acute dietary challenge (48) rather than effects of long-term habitual diet as assessed in our study.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] Krug, 2012, The dynamic range of the human metabolome revealed by challenges, FASEB J source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    FFQs showed good reliability and relative validity to assess meat intake, and investigated metabolites showed good reliability in validation studies.

    “However, FFQs showed good reliability and relative validity to assess meat intake, and investigated metabolites showed good reliability in validation studies (23).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Floegel, 2011, Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach, PLoS ONE 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 conductcitation

    The a priori design of our search strategy included arbitrary decisions, and P values lose straightforward interpretability regarding type I error probability in a step-by-step selection procedure such as ours.

    “Still, the a priori design of our search strategy included arbitrary decisions, and P values lose their straightforward interpretability with regard to the probability of type I errors in a step-by-step selection procedure such as ours (47).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [47] Gelman, 2014, The statistical crisis in science, Am Sci source

      Not checked in the free run.

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

    Study design and conductnone given

    The set of targeted metabolites was not exhaustive with regard to potential mediating mechanisms.

    “Albeit large, the set of targeted metabolites was not exhaustive with regard to potential mediating mechanisms.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    Acylcarnitines have previously been linked to red meat intake.

    “Furthermore, acylcarnitines have previously been linked to red meat intake (38).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] O’Sullivan, 2011, Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies, Am J Clin Nutr source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationnone given

    Our results cannot prove causality of the observed associations.

    “Our results, however, cannot prove causality of the observed associations and, where possible, the suggested single relations should inform the design and the biomarker assessment of interventional studies.”
    Quote in the paperSubstantial claim
40 of 40

  • The paper relies on this without support.

    Mechanisms underlying the relation of red meat consumption with diabetes risk remain largely unknown.

    “Yet, mechanisms underlying the relation of red meat consumption with diabetes risk remain largely unknown.”

  • The authors state this as an assumption.

    Lipid metabolites, amino acids, and ferritin are likely influenced by total red meat consumption in a relatively direct manner based on its nutrient composition.

    “Lipid metabolites, amino acids, and ferritin were included as potential mediators of the red meat-associated diabetes risk because these biomarkers were assumed to be likely influenced by total red meat consumption in a relatively direct manner based on its nutrient composition (Supplemental Figure 1B).”

  • The paper relies on this without support.

    Circulating lipid metabolites reflect synthesis activity of lipid classes as well as availability and metabolism of specific fatty acids in the liver.

    “Circulating lipid metabolites reflect synthesis activity of lipid classes as well as availability and metabolism of specific FAs in the liver.”

  • The paper relies on this without support.

    Total red meat as defined in the study is a large and heterogeneous food group.

    “Total red meat as defined in our study is a large and heterogeneous food group.”

  • The paper relies on this without support.

    Estrogen is a modulator of mitochondrial function.

    “Furthermore, estrogen is a modulator of mitochondrial function, and associations of red meat consumption with acylcarnitines might be sex specific.”

  • The paper relies on this without support.

    This is the first study evaluating a large set of metabolites as potential mediators of the association between red meat intake and diabetes risk, including several lipid metabolites, amino acids, and ferritin.

    “To our knowledge, this is the first study evaluating a large set of metabolites as potential mediators of the association between red meat intake and diabetes risk, including several lipid metabolites, amino acids, and ferritin.”
6 of 6

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?

  • Risk for diabetes from red and processed meats may be linked to iron content as well as possibly lipid and amino acid metabolites, advanced glycation end products, trimethylamine N-oxide, and nitrates/nitrites.

    “Mechanisms require further study, but risk for diabetes may be linked to iron content179, 180 as well as possibly lipid and amino acid metabolites, advanced glycation end products, trimethylamine N-oxide, and nitrates/nitrites.181, 182”

    From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [181]

  • Intake of yogurt, but not milk, is consistently associated with lower incidence of diabetes; cheese intake also associates with lower diabetes risk in several although not all studies.

    “For example, intake of yogurt, but not milk, is consistently associated with lower incidence of diabetes; while intake of cheese, which has high calorie, fat, and saturated fat content, also associates with lower diabetes risk in several although not all studies.57, 179-184, 224, 226-228”

    From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [181]

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.

  • Table 1 energy-intake units conflict with the exclusion threshold

    Minor

    Table 1 labels mean energy intakes of 7369 to 10,911 as MJ/d, although the analysis excludes participants reporting more than 25.12 MJ/d. The table values appear to be in kJ/d rather than the stated MJ/d.

  • Abstract says the reported HR adjusts for six biomarkers, but the model uses five

    Minor

    The abstract describes the HR of 1.09 as resulting from simultaneous adjustment for the six listed biomarkers. The methods and results state that diacyl phosphatidylcholine 36:4 was excluded from the mutual biomarker-adjusted models, leaving five.

2 of 2

Cites

Flagged first, then by citations

1 of the 54 references couldn’t be matched to a known paper.

Cited by

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Related

Authors
Wittenbecher, C., Mühlenbruch, K., Kröger, J., Jacobs, S., Kuxhaus, O., Floegel, A., Fritsche, A., Pischon, T., Prehn, C., Adamski, J., Joost, H.-G., Boeing, H., & Schulze, M. B.
Affiliations
Max Delbrück Center (DE)
Corresponding author
Schulze, Matthias B
ORCID
Venue
The American Journal of Clinical Nutrition
Publisher
Elsevier BV
Published
2015-06
Volume
101
Issue
6
Pages
1241-1250
ISSN
0002-9165 (ISSN-L)
DOI
10.3945/ajcn.114.099150
OpenAlex
W2128199390
Semantic Scholar
787e907852c477230bad79180e71e6df48f3d1d4
Cited by
120
References
54
Citations by year
2026: 4 · 2025: 8 · 2024: 7 · 2023: 7 · 2022: 15 · 2021: 10 · 2020: 14 · 2019: 15 · 2018: 12 · 2017: 11 · 2016: 11 · 2015: 6
FWCI
14.25
Type
journal article
Language
en
Open access
Licence
Locations
  • American Journal of Clinical Nutrition (publishedVersion)
  • PubMed (publishedVersion)
  • MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
  • Site cant be reached (submittedVersion)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders
  • Federal Ministry of Science and Research (01 EA 9401)
  • European Union (SOC 95201408 05 F02)
  • EPIC–Potsdam Study
  • German Cancer Aid (70-2488-Ha I)
  • European Community (SOC 98200769 05 F02)
  • German Federal Ministry of Education and Research (BMBF)
  • German Center for Diabetes Research
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
Keywords
consumption, type 2 diabetes, amino acids, glycine, lipid metabolites, acylcarnitines, glycerophospholipids, sphingomyelin, ferritin, processed meat, hepatic lipid metabolism, blood metabolites, EPIC-Potsdam cohort, biomarkers of diabetes risk, saturated fatty acids, lysophosphatidylcholine
MeSH terms
Adult, Amino Acids, Energy Intake, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Diet, Female, Ferritins, Follow-Up Studies, Humans, Life Style, Male, Meat, Middle Aged, Prospective Studies, Risk Factors, Reproducibility of Results, Multicenter Studies as Topic, Biomarkers, Body Mass Index, Incidence, Linear Models, Proportional Hazards Models, Lipid Metabolism

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 2026-10-10.

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

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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