Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study
Bibliographic record
Abstract
Rationale & Objective While urine excretion of nitrogen estimates total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of six protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study Design Prospective cohort study. Setting & Participants 3,726 participants from the Atherosclerosis Risk in Communities (ARIC) study without CKD at baseline. Exposure Dietary intake of six protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, poultry), serum metabolites. Outcomes Incident CKD [eGFR <60 mL/min/1.73 m 2 with ≥25% eGFR decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease. Analytical Approach Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C-statistics assessed the metabolites' ability to improve discrimination of highest versus lower three quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident chronic kidney disease (CKD). Results Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n=8; nuts, n=5; legumes, n=0; red and processed meat, n=5; eggs, n=3; poultry, n=9). Metabolites collectively significantly improved discrimination of high intake of protein-rich foods compared to covariates alone (difference in C-statistics=0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; p<1.00 x 10 -16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR 0.82, 95% CI 0.75-0.89, p=7.81×10 -6 ). Limitations Residual confounding and sample storage duration. Conclusions We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with lower risk of CKD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".