Response to: “Clonal Hematopoiesis of Indeterminate Potential and Diabetic Kidney Disease: A Nested Case-Control Study”
Bibliographic record
Abstract
We read the work of Denicolò et al.1Denicolò S. Vogi V. Keller F. et al.Clonal hematopoiesis of indeterminate potential and diabetic kidney disease: a nested case-control study.Kidney Int Rep. 2022; 7: 876-888https://doi.org/10.1016/j.ekir.2022.01.1064Abstract Full Text Full Text PDF PubMed Scopus (7) Google Scholar with great interest, reporting a lack of association between clonal hematopoiesis of indeterminate potential (CHIP) and incident or progressive diabetic kidney disease published in KI Reports. A major challenge when investigating CHIP is variant interpretation. Identified variants can represent pathogenic CHIP driver mutations, passenger variants, and variants of uncertain significance, or sequencing artefacts. At present, there is no universal consensus for what variants should be included (or excluded) as CHIP driver variants in the correct clinical context.2Steensma D.P. Bejar R. Jaiswal S. et al.Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes.Blood. 2015; 126: 9-16https://doi.org/10.1182/blood-2015-03-631747Crossref PubMed Scopus (1228) Google Scholar Nevertheless, to minimize false positives, CHIP calling criteria typically prespecify a list of allowable missense variants based on their reported frequencies in CHIP and cancer databases.3Jaiswal S. Natarajan P. Silver A.J. et al.Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.N Engl J Med. 2017; 377: 111-121https://doi.org/10.1056/NEJMoa1701719Crossref PubMed Scopus (1307) Google Scholar, 4Pascual-Figal D.A. Bayes-Genis A. Díez-Díez M. et al.Clonal hematopoiesis and risk of progression of heart failure with reduced left ventricular ejection fraction.J Am Coll Cardiol. 2021; 77: 1747-1759https://doi.org/10.1016/j.jacc.2021.02.028Crossref PubMed Scopus (75) Google Scholar, 5Dawoud A.A.Z. Gilbert R.D. Tapper W.J. Cross N.C.P. Clonal myelopoiesis promotes adverse outcomes in chronic kidney disease.Leukemia. 2022; 36: 507-515https://doi.org/10.1038/s41375-021-01382-3Crossref PubMed Scopus (30) Google Scholar As the pathogenicity of missense variants can be difficult to predict, in certain genes, only truncating (nonsense, frameshift, or splice site) variants are compatible with CHIP (e.g., BCOR, BCORL1, and CEBPA). By our estimation, 40 of the 127 variants reported by Denicolò et al.1Denicolò S. Vogi V. Keller F. et al.Clonal hematopoiesis of indeterminate potential and diabetic kidney disease: a nested case-control study.Kidney Int Rep. 2022; 7: 876-888https://doi.org/10.1016/j.ekir.2022.01.1064Abstract Full Text Full Text PDF PubMed Scopus (7) Google Scholar would not be considered CHIP driver variants using the cited conventional criteria.3Jaiswal S. Natarajan P. Silver A.J. et al.Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.N Engl J Med. 2017; 377: 111-121https://doi.org/10.1056/NEJMoa1701719Crossref PubMed Scopus (1307) Google Scholar, 4Pascual-Figal D.A. Bayes-Genis A. Díez-Díez M. et al.Clonal hematopoiesis and risk of progression of heart failure with reduced left ventricular ejection fraction.J Am Coll Cardiol. 2021; 77: 1747-1759https://doi.org/10.1016/j.jacc.2021.02.028Crossref PubMed Scopus (75) Google Scholar, 5Dawoud A.A.Z. Gilbert R.D. Tapper W.J. Cross N.C.P. Clonal myelopoiesis promotes adverse outcomes in chronic kidney disease.Leukemia. 2022; 36: 507-515https://doi.org/10.1038/s41375-021-01382-3Crossref PubMed Scopus (30) Google Scholar Inclusion of benign or misclassified variants would increase CHIP prevalence but may bias results toward the null hypothesis because they would be expected to be evenly represented across groups. Certainly, many challenges remain and there is much to learn about the classification and consequences of acquired variants in CHIP driver genes. Nevertheless, the conventional CHIP variant criteria have been used to establish various clinical consequences of CHIP.3Jaiswal S. Natarajan P. Silver A.J. et al.Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.N Engl J Med. 2017; 377: 111-121https://doi.org/10.1056/NEJMoa1701719Crossref PubMed Scopus (1307) Google Scholar, 4Pascual-Figal D.A. Bayes-Genis A. Díez-Díez M. et al.Clonal hematopoiesis and risk of progression of heart failure with reduced left ventricular ejection fraction.J Am Coll Cardiol. 2021; 77: 1747-1759https://doi.org/10.1016/j.jacc.2021.02.028Crossref PubMed Scopus (75) Google Scholar, 5Dawoud A.A.Z. Gilbert R.D. Tapper W.J. Cross N.C.P. Clonal myelopoiesis promotes adverse outcomes in chronic kidney disease.Leukemia. 2022; 36: 507-515https://doi.org/10.1038/s41375-021-01382-3Crossref PubMed Scopus (30) Google Scholar The significance to human health of somatic variants that fall outside of this list are less well understood. We hope to highlight some of the challenges in determining the pathogenicity of putative CHIP variants and the need for sensitivity analyses using strict and liberal CHIP definitions. ResponseKidney International ReportsVol. 7Issue 11PreviewAs the authors of the letter mention, it is a major challenge to interpret variants when investigating clonal hematopoiesis of indeterminate potential (CHIP). Undoubtedly, there are possibilities to identify passenger variants, benign variants, variants of uncertain significance, and sequencing artifacts. However, as the authors also mention, we currently do not have a universal consensus on which variants should be included or excluded as CHIP driver variants in which disease context. Furthermore, CHIP is not defined by a certain range above a variant allele frequency of 2%. Full-Text PDF Open Access
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".