Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the disease. However, response to dietary interventions varies, potentially due to genetic variations influencing metabolic pathways. This review highlights key gene–diet interactions relevant to CKD management, including risk factors and comorbidities such as hypertension, diabetes, and proteinuria. Variants in the ACE gene influence salt sensitivity and blood pressure responses, while TCF7L2 polymorphisms affect the relationship between dietary glycemic load and diabetes risk, impacting kidney complications. Protein intake, a key modifier of CKD, correlates with proteinuria risk, moderated by a PPM1K polymorphism. Dietary bioactives, such as caffeine, may also alter the progression rate of proteinuria and hypertension, with effects contingent upon CYP1A2 genotype. Additional markers of cardiovascular disease, CKD-associated bone-mineral disease, and CKD anemia are also discussed as well as role of the gut microbiome in nutrition modulation and vice versa. The review concludes with the potential of artificial intelligence as a clinical tool to refine precision nutrition, enabling clinicians to adopt targeted approaches, stratified by genetic-metabolic patient profiles that match best nutritional interventions for prevention and management of CKD. Vitamin D is used as a model nutrient to illustrate a simulated framework for precision nutrition, incorporating molecular mechanisms, genetic variation, epigenetic modifications, and translational tools applicable to both population health and clinical practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".