Surfactant protein D is a causal risk factor for COPD: results of Mendelian randomisation
Bibliographic record
Abstract
Surfactant protein D (SP-D) is produced primarily in the lung and is involved in regulating pulmonary surfactants, lipid homeostasis and innate immunity. Circulating SP-D levels in blood are associated with chronic obstructive pulmonary disease (COPD), although causality remains elusive. In 4061 subjects with COPD, we identified genetic variants associated with serum SP-D levels. We then determined whether these variants affected lung tissue gene expression in 1037 individuals. A Mendelian randomisation framework was then applied, whereby serum SP-D-associated variants were tested for association with COPD risk in 11 157 cases and 36 699 controls and with 11 years decline of lung function in the 4061 individuals. Three regions on chromosomes 6 (human leukocyte antigen region), 10 (SFTPDgene) and 16 (ATP2C2gene) were associated with serum SP-D levels at genome-wide significance. In Mendelian randomisation analyses, variants associated with increased serum SP-D levels decreased the risk of COPD (estimate −0.19, p=6.46×10−03) and slowed the lung function decline (estimate=0.0038, p=7.68×10−3). Leveraging genetic variation effect on protein, lung gene expression and disease phenotypes provided novel insights into SP-D biology and established a causal link between increased SP-D levels and protection against COPD risk and progression. SP-D represents a very promising biomarker and therapeutic target for COPD.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".