Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis
Bibliographic record
Abstract
Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection are transforming traditional management approaches. This review explores how multiomics technologies and artificial intelligence (AI) are enhancing glucose management in this patient population. Continuous glucose monitoring (CGM) offers significant advantages over traditional markers, such as hemoglobin A1c (HbA1c). Unlike HbA1c, which reflects an mean glucose level, CGM provides real-time, dynamic glucose data that allow clinicians to make timely adjustments, leading to better glycemic control and outcomes. Multiomics approaches are valuable for understanding genetic factors that influence susceptibility to diabetic complications, particularly those related to advanced glycation end products (AGEs). Identifying genetic polymorphisms that modify a patient's response to AGEs allows for personalized treatments, potentially reducing the severity of diabetes-related pathologies. Metabolomic analyses of PD effluent are also promising, as they help identify early biomarkers of metabolic dysregulation. Early detection can lead to timely interventions and more tailored treatment strategies, improving long-term patient care. AI integration is revolutionizing diabetes management for PD patients by processing vast datasets from CGM, genetic, metabolic, and microbiome profiles. AI can identify patterns and predict outcomes that may be difficult for humans to detect, enabling highly personalized recommendations for diet, medication, and dialysis management. Furthermore, AI can assist clinicians by automating data interpretation, improving treatment plans, and enhancing patient education. Despite the promise of these technologies, there are limitations. CGM, multiomics, and AI require significant investment in infrastructure, training, and validation studies. Additionally, integrating these approaches into clinical practice presents logistical and financial challenges. Nevertheless, personalized, data-driven strategies offer great potential for improving outcomes in diabetes management for PD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".