Sources of Variation in Sweat Chloride Measurements in Cystic Fibrosis
Bibliographic record
Abstract
Abstract Rationale Expanding the use of cystic fibrosis transmembrane conductance regulator (CFTR) potentiators and correctors for the treatment of cystic fibrosis (CF) requires precise and accurate biomarkers. Sweat chloride concentration provides an in vivo assessment of CFTR function, but it is unknown the degree to which CFTR mutations account for sweat chloride variation. Objectives To estimate potential sources of variation for sweat chloride measurements, including demographic factors, testing variability, recording biases, and CFTR genotype itself. Methods A total of 2,639 sweat chloride measurements were obtained in 1,761 twins/siblings from the CF Twin-Sibling Study, French CF Modifier Gene Study, and Canadian Consortium for Genetic Studies. Variance component estimation was performed by nested mixed modeling. Measurements and Main Results Across the tested CF population as a whole, CFTR gene mutations were found to be the primary determinant of sweat chloride variability (56.1% of variation) with contributions from variation over time (e.g., factors related to testing on different days; 13.8%), environmental factors (e.g., climate, family diet; 13.5%), other residual factors (e.g., test variability; 9.9%), and unique individual factors (e.g., modifier genes, unique exposures; 6.8%) (likelihood ratio test, P < 0.001). Twin analysis suggested that modifier genes did not play a significant role because the heritability estimate was negligible (H2 = 0; 95% confidence interval, 0.0–0.35). For an individual with CF, variation in sweat chloride was primarily caused by variation over time (58.1%) with the remainder attributable to residual/random factors (41.9%). Conclusions Variation in the CFTR gene is the predominant cause of sweat chloride variation; most of the non-CFTR variation is caused by testing variability and unique environmental factors. If test precision and accuracy can be improved, sweat chloride measurement could be a valuable biomarker for assessing response to therapies directed at mutant CFTR.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".