Bibliographic record
Abstract
Decline in kidney function is a well-documented feature of human aging. This age-related decrease in kidney function can be partially attributed to other age-related comorbidities (e.g., diabetes, hypertension, and cardiovascular disease) and lead to CKD associated with adverse outcomes.1 Growing evidence has also suggested a connection between systemic inflammation and CKD. Indeed, population-based studies have reported that CKD incidence and progression are associated with higher baseline markers of inflammation, including IL-6, TNF-α, and fibrinogen.2 Clonal hematopoiesis of indeterminate potential (CHIP), which refers to the presence of blood cells harboring pathogenic clonal somatic mutations in the absence of hematologic neoplasia, has recently been described as a premalignant condition associated with an increased risk of developing myeloid neoplasms; indeed, the genes most frequently mutated in individuals with CHIP (e.g., DNMT3A, TET2, and ASXL1) are recurrent drivers of myeloid neoplasms.3,4 Analyses of exome and genome sequencing data from several large epidemiologic studies have demonstrated that the prevalence of CHIP increases with age, occurring in 10%–20% of elderly individuals without a hematologic neoplasm. These analyses also discovered that CHIP was associated with increased mortality due to cardiovascular disease. Analysis of human samples and mouse models have indicated that CHIP exacerbates cardiovascular disease via increased production of proinflammatory cytokines and chemokines.4 Subsequent studies have linked CHIP with several other age-related diseases, suggesting CHIP contributes significantly to inflammation-related disease pathogenesis in a wide variety of tissues.5 In this issue of JASN, Vlasschaert et al.6 present data supporting a correlation between CHIP and progression of CKD. To explore the relationship between CHIP and CKD progression, Vlasschaert et al. leveraged two well-annotated Canadian patient cohorts from the Kingston Health Sciences Centre and the Canadian Study of Prediction of Death, Dialysis, and Interim Cardiovascular Events. Together, the two cohorts comprised 172 patients (38% female) with a mean age of >60 years and mean eGFR of <30 ml/min per 1.73 m2. Targeted exome sequencing revealed the presence of CHIP in 43 patients (25%). Similar to prior studies, patients with CHIP were older than those without CHIP, DNMT3A and TET2 were the most frequently mutated genes, and the majority of patients with CHIP harbored a single pathogenic mutation. Compared with patients without CHIP, there was a trend toward decreased hemoglobin, increased use of erythropoiesis-stimulating agents, and increased parathyroid hormone levels among patients with CHIP. In line with these indicators of worse renal function in the setting of CHIP, baseline eGFR adjusted for age and sex tended to be lower in patients with CHIP. The authors next assessed whether the presence of CHIP affected the rate of CKD progression. In a model adjusted for age, sex, and baseline eGFR, patients with CHIP were at an approximately two-fold greater risk of a sustained 50% decline in eGFR or ESKD over 5 years. The authors also applied the internationally validated Kidney Failure Risk Equation (KFRE) to their cohort to determine if the presence of CHIP alters the risk of progression to ESKD7; the KFRE score estimates the risk of ESKD using a model that adjusts for age, sex, eGFR, and urine albumin-creatinine ratio. The KFRE-estimated risk of ESKD was increased in patients with CHIP. Remarkably, addition of CHIP to the KFRE score also led to a significant improvement in ESKD risk prediction. Altogether, Vlasschaert et al.7 add a new and provocative dataset to the growing corpus on the association of CHIP with nonmalignant diseases. In line with the findings presented here, a recent analysis of the UK Biobank also reported lower eGFR among individuals with CHIP and an association between CHIP and adverse outcomes in CKD.6 Similar to the UK Biobank analysis, the data presented by Vlasschaert et al.6 raises several key questions that should be explored further in epidemiologic datasets and experimental models. First, how does CHIP interact with other known drivers of CKD? CHIP has been associated with CVD and diabetes,3 suggesting the potential link between CHIP and CKD could be due, in part, to exacerbation of comorbidities known to promote CKD. Along these lines, it will be important to understand how CHIP affects CKD progression in the setting of different underlying causes of nephropathy (e.g., diabetic versus hypertensive versus autoimmune). Second, how do mutant hematopoietic cells contribute to tissue damage in the kidney? Eardley et al.9 reported that histologic and functional evidence of kidney damage correlated directly with the number of interstitial macrophages, although it remains unclear if increased macrophage abundance in the kidney is a cause or effect of tissue damage. The presence of mutant immune cells in situ could contribute directly to the initiation of kidney injury and/or amplify the recruitment of additional inflammatory cells via increased chemokine production.4 Development of new mouse models and characterization of the renal histopathologic findings in patients with CKD with and without CHIP could provide insight into the mechanisms and types of tissue damage associated with mutant immune cells. Third, do somatic mutations detected in individuals with lymphoid CHIP (L-CHIP) also promote the development of CKD?10 L-CHIP is strongly associated with incident lymphoid malignancies, but remains unexplored as a potential driver of nonmalignant diseases. Given the number of autoantibody-mediated etiologies of CKD, it is possible that L-CHIP enables or potentiates the function of autoreactive lymphocytes. Finally, how does CHIP affect CKD-related adverse outcomes? Analysis of CHIP and CKD in the UK Biobank revealed that CHIP was associated with increased noncancer mortality among patients with mild and moderate CKD.8 Whether there is an opportunity for novel therapeutic interventions in patients with CHIP and CKD will require additional studies aimed at identifying the cause of this mortality increase. Disclosures All authors have nothing to disclose. Funding A. Niroula is supported by Knut och Alice Wallenbergs Stiftelse grants. R. Belizaire is supported by American Society of Hematology, American Cancer Society, and Edward P. Evans Foundation grants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".