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Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes
Open accessAbstract
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.
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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.
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35 Not checked yet · 21 Own result · 10 Own interpretation · 9 Own method · 1 Supported
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
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- Also stated in: § Attenuation of the red meat-associated diabetes risk by selected biomarkers · plain
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“Additional adjustment for lifestyle, medication, and diet (model 2) attenuated the relation (HR: 1.33; 95% CI: 1.09, 1.62).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
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In the paper
“The explainable proportion ranged between 8% and 33% for single biomarker adjustments.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“A higher total red meat consumption was related to a higher risk of developing type 2 diabetes [HR for 2 SD (11 g/MJ): 1.47; 95% CI: 1.24, 1.75] in the minimally adjusted model 1 (Table 4).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“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.”
- Also stated in: § Biomarker selection · plain
“For 6 of these 13 biomarkers-namely, ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxysphingomyelin 14:1-the associations with total red meat consumption and with diabetes risk were consistent with the mediation hypothesis (Table 2 and Table 3); that is, both associations were in the same direction.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
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In the paper
“Regarding lipid profiles, we found strong sex-consistent associations with total red meat consumption for 19 lipid metabolites from 4 classes.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
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“The marked attenuation of red meat-associated diabetes risk after adjustment for selected biomarkers suggests that these biomarkers reflect metabolic processes that link habitual red meat consumption to diabetes risk.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
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In the paper
“Habitual diet was assessed with validated semiquantitative food-frequency questionnaires.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
What the sources say
- Supportedcites [16] Kroke, 1999, Am J Clin Nutr source
The Food-frequency questionnaire section says the FFQ was used to assess habitual food intake and describes its frequency questions and portion-size estimates. The discussion following Table 4 concludes that its relative validity was acceptable. (LLM, conf=0.91)
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Biomarker selection
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
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In the paper
“In conclusion, high ferritin, low glycine, and altered hepaticderived lipids in the circulation were associated with both total red meat consumption and diabetes risk.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
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“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > INTRODUCTION
What the sources say
- Not checked yetcites [1] Feskens, 2013, Meat consumption, diabetes, and its complications, Curr Diab Rep source
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Statistical analysis
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“In EPIC-Potsdam, metabolomic data were used to identify lipid metabolites and amino acids as biomarkers of diabetes risk (12).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > INTRODUCTION
What the sources say
- Not checked yetcites [12] Floegel, 2013, Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach, Diabetes source
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > INTRODUCTION
What the sources say
- Not checked yetcites [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 yetcites [8] Ley, 2014, Associations between red meat intake and biomarkers of inflammation and glucose metabolism in women, Am J Clin Nutr source
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > INTRODUCTION
What the sources say
- Not checked yetcites [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
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Quantification of biomarkers
What the sources say
- Not checked yetcites [23] Floegel, 2011, Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach, PLoS ONE source
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In the paper
“We selected mediators of the red meat-diabetes association according to 4 predefined mediation criteria (25) (see Supplemental Figure 1A).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Statistical analysis
What the sources say
- Not checked yetcites [25] Baron, 1986, The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations, J Pers Soc Psychol source
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In the paper
“The case-cohort design was accounted for by Prentice weighting (26).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Assessment of the red meat association with type 2 diabetes
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > INTRODUCTION
What the sources say
- Not checked yetcites [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
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Biomarker selection
What the sources say
- Not checked yetcites [30] Benjamini, 1995, Controlling the false discovery rate—a practical and powerful approach to multiple testing, J R Stat Soc B source
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Assessment of the red meat association with type 2 diabetes
What the sources say
- Not checked yetcites [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 yetcites [3] Benedinelli, 2013, Association between dietary meat consumption and incident type 2 diabetes: the EPIC-InterAct study, Diabetologia source
- Not checked yetcites [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 yetcites [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
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In the paper
“The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
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- 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.
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In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [45] Hafeman, 2009, “Proportion explained”: a causal interpretation for standard measures of indirect effect?, Am J Epidemiol source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Thus, our results generate and underline experimentally testable hypotheses that can inform future dietary interventions in terms of design and biomarker assessment (46).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [46] Pearl, 2010, Causal Inference, Int J Biostat source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“described by us and others (12,41).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Underlying mechanisms seem to be manifold and likely include oxidative stress and modulation of intracellular signaling cascades (44).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [44] Simcox, 2013, Iron and diabetes risk, Cell Metab source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This should have reduced the likelihood of false discoveries.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, estrogen is a modulator of mitochondrial function, and associations of red meat consumption with acylcarnitines might be sex specific.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Estrogen is a modulator of mitochondrial function.
In the paper
“For example, advanced glycation end products, trans FA, nitrites and nitrates, and methylamines are discussed as meat type-specific mediators (1).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [1] Feskens, 2013, Meat consumption, diabetes, and its complications, Curr Diab Rep source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, we evaluated the robustness of our results in a bootstrapping procedure.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Still, random measurement error may have masked some associations.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, there is the possibility that cases remained undiagnosed during follow-up.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [49] Rothman, 2008
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [48] Krug, 2012, The dynamic range of the human metabolome revealed by challenges, FASEB J source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, FFQs showed good reliability and relative validity to assess meat intake, and investigated metabolites showed good reliability in validation studies (23).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [23] Floegel, 2011, Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach, PLoS ONE source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [47] Gelman, 2014, The statistical crisis in science, Am Sci source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Albeit large, the set of targeted metabolites was not exhaustive with regard to potential mediating mechanisms.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Furthermore, acylcarnitines have previously been linked to red meat intake (38).”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
What the sources say
- Not checked yetcites [38] O’Sullivan, 2011, Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies, Am J Clin Nutr source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > DISCUSSION
“| Energy, MJ/d | 7369 6 1845 | 7428 6 1664 | 7455 6 1709 | 10,539 6 2651 | 10,574 6 2386 | 10,911 6 2703 |”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Study population
“implausible self-reported energy intake (,3.35 MJ/d or .25.12 MJ/d)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Abstract
“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”
§ Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes 1-4 > Attenuation of the red meat-associated diabetes risk by selected biomarkers
“Therefore, mutual biomarker-adjusted models did not include diacyl phosphatidylcholine 36:4.”
Citations
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“Ferritin, glycine, four diacyl phosphatidylcholines, eleven acylalkyl phosphatidylcholines, two lysophosphatidylcholines and two sphingomyelins were associated with total red meat consumption and six of these biomarkers were also found to be associated with type-2 diabetes risk((33)).”
Backgroundon behalf of the China Kadoorie Biobank collaborative group, Du, H., Yu, C., Bennett, D., Bragg, F., Bian, Z., Chadni, M., Yu, C., Chen, Y., Tan, Y., et al. (2020) · Diabetologia · doi:10.1007/s00125-020-05091-x · Cited by 65
Wittenbecher, C., Kuxhaus, O., Boeing, H., Stefan, N., & Schulze, M. B. (2019) · Diabetologia · doi:10.1007/s00125-019-04978-8 · Cited by 65
Self-citationThe role of metabolomics in determination of new dietary biomarkers
O’Gorman, A., & Brennan, L. (2017) · Proceedings of The Nutrition Society · doi:10.1017/s0029665116002974 · Cited by 61
“fish((66)), red meat((67,68)), whole-grain bread((69)) and walnuts((70)).”
ResultYe, J., Yu, Q., Mai, W., Liang, P., Liu, X., & Wang, Y. (2019) · Acta Diabetologica · doi:10.1007/s00592-019-01320-x · Cited by 64
Chen, S., Zong, G., Wu, Q., Yun, H., Niu, Z., Zheng, H., Zeng, R., Sun, L., & Lin, X. (2021) · Diabetologia · doi:10.1007/s00125-021-05611-3 · Cited by 62
“For instance, the association between PC(36:4) and incident diabetes was positive in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study [9] but was negative in the Framingham Heart Study Offspring cohort [8].”
BackgroundWittenbecher, C., Cuadrat, R. R. C., Johnston, L., Eichelmann, F., Jäger, S., Kuxhaus, O., Prada, M., Fabiola Del Greco, M., Hicks, A. A., Hoffman, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28496-1 · Cited by 63
Self-citationIbsen, D. B., Steur, M., Imamura, F., Overvad, K., Schulze, M. B., Bendinelli, B., Guevara, M., Agudo, A., Amiano, P., Aune, D., et al. (2020) · Diabetes Care · doi:10.2337/dc20-1038 · Cited by 61
“Lastly, based on previous findings (9,10), we estimated the contribution of body iron storage (serum ferritin) as a potential mediator.”
BackgroundResultSelf-citationJackson, J. K., Williams, R. L., McEvoy, M., MacDonald‐Wicks, L. K., & Patterson, A. J. (2016) · Nutrients · doi:10.3390/nu8020089 · Cited by 56
“(2015) [72] Male (39%) and Female, 35–64 years Red meat: un-processed (beef, veal, pork and lamb) and processed meats (bacon, ham, sausages) Validated semi quantitative FFQ Blood sample, measured SF SF was significantly associated with total red meat consumption in both sexes: Women (p < 0.”
BackgroundResultInfluentialBlood metabolite profiles linking dietary patterns with health—Toward precision nutrition
Noerman, S., & Landberg, R. (2022) · Journal of Internal Medicine · doi:10.1111/joim.13596 · Cited by 59
Red meat consumption and risk of cardiovascular diseases—is increased iron load a possible link?
Pacheco, D. A. Q., Sookthai, D., Wittenbecher, C., Graf, M. E., Schübel, R., Johnson, T., Katzke, V. A., Jakszyn, P. G., Kaaks, R., & Kuhn, T. E. (2018) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqx014 · Cited by 57
“(32): 1) red meat consumption is associated with CVD risk; 2) red meat consumption is associated with ferritin concentrations; 3) ferritin concentrations are associated with CVD risk; and 4) associations between red meat consumption and CVD risk are significantly attenuated by adjustment for ferritin concentrations.”
BackgroundSelf-citationProgress in Metabonomics of Type 2 Diabetes Mellitus
Ma, Q., Li, Y., Wang, M., Tang, Z., Wang, T., Liu, C., Wang, C., & Zhao, B. (2018) · Molecules · doi:10.3390/molecules23071834 · Cited by 54
“Glycine is involved in the synthesis of glutathione, which, in turn, responds to oxidative stress and IR [51].”
BackgroundPolymorphisms, diet and nutrigenomics
Bonetti, G., Kiani, A. K., Donato, K., Kaftalli, J., Herbst, K. L., Stuppia, L., Fioretti, F., Nodari, S., Perrone, M. A., Chiurazzi, P., et al. (2022) · Journal of preventive medicine and hygiene · doi:10.15167/2421-4248/jpmh2022.63.2s3.2754 · Cited by 68
Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes
Antwi, J. (2023) · Current Nutrition Reports · doi:10.1007/s13668-023-00491-y · Cited by 53
“…the use of metabolomics in precision nutrition showed that dietary intake patterns were revealed in metabolomic profiles [66], and were associated with biomarkers such as high levels of lipid metabolites, amino acids, and ferritin that mediated red meat consumption and risk of type 2 diabetes [67].”
BackgroundMetabolic Signatures of Healthy Lifestyle Patterns and Colorectal Cancer Risk in a European Cohort
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“Second, serum ferritin and glycine concentrations are both associated with insulin resistance and oxidative stress, which was proposed as a potential mechanism [48,49].”
BackgroundWedekind, R., Kiss, A., Keski‐Rahkonen, P., Viallon, V., Rothwell, J. A., Cross, A. J., Rostgaard‐Hansen, A. L., Sandanger, T. M., Jakszyn, P. G., Schmidt, J. A., et al. (2020) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqaa140 · Cited by 43
“(30) found plasma concentrations of C18:0 to be associated with red meat intake in German men (n = 790) from the EPIC-Potsdam cohort, results consistent with our own findings.”
ResultSelf-citationGalbete, C., Nicolaou, M., Meeks, K. A. C., Klipstein‐Grobusch, K., Aikins, A. D., Addo, J., Amoah, S., Smeeth, L. L., Owusu‐Dabo, E., Spranger, J., et al. (2018) · Nutrition and Diabetes · doi:10.1038/s41387-018-0029-x · Cited by 42
Self-citationChronic Diseases and Lifestyle Biomarkers Identification by Metabolomics
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Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes
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“Similarly, low serum glycine levels were also found to be associated with total red meat consumption in the EPIC Potsdam cohort [47].”
BackgroundPotential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity
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“…6 biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidyl choline 17:0, and hydroxy-sphingomyelin 14:1) were associated with meat consumption and diabetes risk, suggesting that unique metabolomic signatures can be used to help identify obesity predisposition [71].”
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Urinary Metabolomics Profiles Associated to Bovine Meat Ingestion in Humans
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The source of dietary fat influences anti-tumour immunity in obese mice
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“This result is consistent with multiple studies examining the impact of diet composition on the metabolome in humans, which have shown increased CAR18:0 in the plasma of people who consume red meat 73–75 , particularly when compared to a plant-based diet, in a randomized controlled trial 73 .”
Serum metabolite profiles of habitual diet: evaluation by 1H-nuclear magnetic resonance analysis
Lindqvist, H. M., Rådjursöga, M., Malmodin, D., Winkvist, A., & Ellegård, L. (2019) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz032 · Cited by 31
“(3), which reported higher serum concentrations of glycine in vegans, but also other studies have reported an inverse relation between red meat intake and the glycine concentration in biofluids (5, 23).”
BackgroundMass Spectrometry as a Crucial Analytical Basis for Omics Sciences
Zaikin, V. G., & Борисов, Р. С. (2021) · Journal of Analytical Chemistry · doi:10.1134/s1061934821140094 · Cited by 28
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“A low serum glycine concentration, together with a high level of ferritin and altered hepatic derived lipid metabolite concentrations, has been associated with red meat intake and T2D risk (54).”
BackgroundFetal Serum Metabolites Are Independently Associated with Gestational Diabetes Mellitus
Lu, Y.-P., Reichetzeder, C., Prehn, C. P., von Websky, K., Slowinski, T., Chen, Y.-P., Yin, L., Kleuser, B., Yang, X., Adamski, J., et al. (2018) · Cellular Physiology and Biochemistry · doi:10.1159/000487119 · Cited by 27
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“It has been well documented that branched chain amino acids (BCAA) are more abundant in animal protein and elevated BCAA have been reported in obese humans and animal models of obesity [63,64].”
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Self-citationAn Overview of the Role of Metabolomics in the Identification of Dietary Biomarkers
Brennan, L., Gibbons, H., & O’Gorman, A. (2015) · Current Nutrition Reports · doi:10.1007/s13668-015-0139-1 · Cited by 21
Meidtner, K., Podmore, C., Kröger, J., van der Schouw, Y. T., Bendinelli, B., Agnoli, C., Arriola, L., Barricarte, A., Boeing, H., Cross, A. J., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1080 · Cited by 22
“Heme iron frommeat has been reported as the strongest dietary determinant of plasma ferritin concentrations (5), and part of the effect of redmeat on type 2 diabetes risk seemsmediated by ferritin (6).”
Self-citationSchulze, M. B., Haardt, J., Amini, A. M., Kalotai, N., Lehmann, A. C., Schmidt, A., Buyken, A. E., Egert, S., Ellinger, S., Kroke, A., et al. (2023) · European Journal of Nutrition · doi:10.1007/s00394-023-03234-5 · Cited by 22
“Furthermore, circulating glycine was found to be associated with a higher risk of T2D; as it is abundant in animal protein, it has been considered as a potential mediator which links higher intake of red meat to T2D [52].”
BackgroundSelf-citationPlant-Based Food for the Prevention of Type 2 Diabetes: Scoping Review
Silva, J. C. D. E., Anghinoni, I. C. A. B., & Gomes, M. B. (2024) · Nutrients · doi:10.3390/nu16111671 · Cited by 21
“Wittenbecher et al. [5] studied the total intake of red meat associated with the evaluation and concentration of biomarkers in the blood.”
BackgroundInfluentialOnwuka, S., Bravo-Merodio, L., Gkoutos, G. V., & Acharjee, A. (2024) · iScience · doi:10.1016/j.isci.2024.110298 · Cited by 20
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“Epidemiological studies have reported associations between intake of red meat and some amino acids (43), bread consumption and serum tryptophan and its associated amino acid metabolites (44), animal food-based diets (32), and a Western dietary pattern (45) and higher concentrations of plasma amino acids.”
BackgroundSelf-citationSlotting metabolomics into routine precision medicine
Mussap, M., Noto, A., Piras, C., Atzori, L., & Fanos, V. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1911639 · Cited by 18
“Metabolomics can predict the risk of T2D associated with the consumption of red meat, as demonstrated in a study involving more than 2,000 adults [108].”
BackgroundVíctor de la, O., Cruz, E. F., Valdés, A., Cifuentes, A., Walton, J., & Martínez, J. A. (2024) · Nutrition Reviews · doi:10.1093/nutrit/nuae133 · Cited by 15
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“Haem iron from meat intake has been reported as the strongest dietary determinant of plasma ferritin concentration [12], and it has been indicated that part of the effect of meat intake on T2DM risk is mediated by ferritin, a biomarker of excessive iron stores [13].”
BackgroundMorze, J., Wittenbecher, C., Schwingshackl, L., et al. (2022) · doi:10.2337/figshare.18857807.v1 · Cited by 13
Self-citationComparison of metabolite networks from four German population-based studies
Iqbal, K., Dietrich, S., Wittenbecher, C., Krumsiek, J., Kuhn, T. E., Lacruz, M. E., Kluttig, A., Prehn, C. P., Adamski, J., von Bergen�, M., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy119 · Cited by 13
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“This family of compounds has been previously reported as potential biomarkers of red meat consumption and was related to a higher risk of type 2 diabetes (Wittenbecher et al., 2015).”
BackgroundScalbert, A., Huybrechts, I., & Gunter, M. J. (2018) · Springer eBooks · doi:10.1007/978-3-319-89321-1_8 · Cited by 14
García-Gavilán, J. F., Nishi, S. K., Paz‐Graniel, I., Guasch‐Ferré, M., Razquín, C., Clish, C. B., Toledo, E., Ruiz‐Canela, M., Corella, D., Deik, A., et al. (2022) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202200145 · Cited by 12
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“Dietary protein, or a healthy diet (alternative healthy eating index) have been shown to regulate type 2 diabetes [9], renal disease [10], and cardiovascular disease [11] through amino acids and lipid metabolites.”
BackgroundXia, J., Yin, S., Yu, J., Wang, J. P., Jin, X., Wang, Y., Liu, H., & Sun, G. (2024) · Nutrients · doi:10.3390/nu16213755 · Cited by 12
“A high-carbohydrate diet may upregulate DNL, resulting in its unique fatty acyl chain, which may be an abundant substrate for GPL synthesis [29].”
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“For instance, an inverse association between SM (OH) C32:2 and incident T2D was reported in the European Prospective Investigation into Cancer and Nutrition–Potsdam cohort [35].”
BackgroundHuang, N. K., Matthan, N. R., Matuszek, G., & Lichtenstein, A. H. (2022) · Metabolites · doi:10.3390/metabo12030209 · Cited by 9
Genetic variants in ALDH1L1 and GLDC influence the serine-to-glycine ratio in Hispanic children
Krupenko, S. A., Cole, S. A., Hou, R., Haack, K., Laston, S., Mehta, N. R., Comuzzie, A. G., Butte, N. F., & Voruganti, V. S. (2022) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqac091 · Cited by 10
Exploring the Links between Diet and Health in an Irish Cohort: A Lipidomic Approach
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Metabolomics application for the design of an optimal diet
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“Pink dotted lines and arrows indicate cutoff values and points with HR equal to 1.”
Biotechnology applications in precision food
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Molecular Fingerprints of Iron Parameters among a Population-Based Sample
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“For instance, one previous study [15] showed that the association between red meat consumption and incident T2DM was mediated to a great amount by serum ferritin and several plasma metabolites.”
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Personalized nutrition and omics technologies
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“and hyperglycemia in healthy populations in the United States (13) and Sweden (27) as well as in European subjects with diabetes (28).”
BackgroundSimón, A., Bordonaba-Bosque, D., Montero, O., Solano-Castán, J., & Caro, I. (2025) · Metabolites · doi:10.3390/metabo15060395 · Cited by 5
Fan, B., & Zhao, J. V. (2022) · Nutrients · doi:10.3390/nu14245306 · Cited by 5
“Prior studies suggested red meat consumption may be related to higher gut microbiota-generated metabolite trimethylamine N-oxide [5], lipoprotein subclasses and lipids [6], ferritin, and lower glycine [7].”
BackgroundMutola, S., Gómez-Olivé, F. X., & Ng, N. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1011439 · Cited by 5
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“…more fat accumulation within muscle and liver cells [47]; (iv) greater presence of haem iron, a pro-oxidant that encourages the production of reactive oxygen species and may damage insulin-producing pancreatic cells [48]; (v) presence of Nitrates and their by-products such as peroxy-nitrite [49].”
BackgroundApplication of Chromatographic and Electrophoretic Techniques to Metabolomic Studies
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Metadata
- 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)
- 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
- bronze
- doi.org
- 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
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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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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