Infectious Disease Risk in Dialysis Patients: A Transdisciplinary Approach
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infections are a major contributor to morbidity and mortality in end-stage renal disease (ESRD) patients. A better understanding of the interplay between infectious processes and ESRD may eventually lead to the development of targeted treatment strategies aimed at lowering overall disease morbidity and mortality. Monogenic causes are a major contributor to the development of adult chronic kidney disease (CKD). Recent studies identified a genetic cause in 10% to 20% of adults with CKD. With the introduction of whole-exome sequencing (WES) into clinical mainstay, this proportion is expected to increase in the future. Once patients develop CKD/ESRD due to a genetic cause, secondary changes, such as a compromised immune status, affect overall disease progression and clinical outcomes. Stratification according to genotype may enable us to study its effects on secondary disease outcomes, such as infectious risk. Moreover, this knowledge will enable us to better understand the molecular interplay between primary disease and secondary disease outcomes. SOURCES OF INFORMATION: We conducted a literature review using search engines such as PubMed, PubMed central, and Medline, as well as cumulative knowledge from our respective areas of expertise. METHODS: This is a transdisciplinary perspective on infectious complications in ESRD due to monogenic causes, such as autosomal dominant polycystic kidney disease (ADPKD), combining expertise in genomics and immunology. KEY FINDINGS: In ADPKD, infection is a frequent complication manifesting primarily as lower urinary tract infection and less frequently as renal infection. Infectious episodes may be a direct consequence of a specific underlying structural abnormality, for example the characteristic cysts, among others. However, evidence suggests that infectious disease risk is also increased in ESRD due to secondary not-well-understood disease mechanisms. These disease mechanisms may vary depending on the underlying nature of the primary disease. While the infectious disease risk is well documented in ADPKD, there are currently insufficient data on the risk in other monogenic causes of ESRD. WES in combination with novel technologies, such as RNA sequencing and single-cell RNA sequencing, can provide insight into the molecular mechanisms of disease progression in different monogenic causes of CKD/ESRD and may lead to the development of novel risk-stratification profiles in the future. LIMITATIONS: This is not a systematic review of the literature and the proposed perspective is tainted by the authors' point of view on the topic. IMPLICATIONS: WES in combination with novel technologies such as RNA sequencing may enable us to fully unravel underlying disease mechanisms and secondary disease outcomes in monogenic causes of CKD and better characterize individual risk profiles. This understanding will hopefully facilitate the development of novel targeted therapies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".