Association Study of Serotonin Transporter Gene (SLC6A4) in Systemic Sclerosis in European Caucasian Populations
Bibliographic record
Abstract
OBJECTIVE: Serotonin is a key contributing factor in pulmonary arterial hypertension (PAH) by inducing pulmonary arterial smooth muscle cell (PA-SMC) proliferation. This relates specifically to the internalization process in PA-SMC of the serotonin transporter (SLC6A4 or 5-HTT). A long (L)/short (S) (44 base pair insertion) functional polymorphism within the promoter of the transporter SLC6A4 gene has been reported to be associated with familial and idiopathic PAH. Our objective was to determine whether polymorphisms of SLC6A4 confer susceptibility to SSc and its vascular phenotype. METHODS: Three Tag single-nucleotide polymorphisms (SNP) (rs2066713, rs1042173, rs6354) chosen using Hapmap and linkage disequilibrium data were genotyped in a total cohort of 667 SSc patients (56 with PAH, 207 with digital ulcerations) and 447 controls. All individuals were of French Caucasian origin. L/S polymorphism genotyping was determined by polymerase chain reaction in a random subgroup of 364 SSc patients (34 with PAH, 138 with digital ulcerations) and 218 controls. RESULTS: Three polymorphisms (L/S, rs2066713, rs1042173) were in Hardy-Weinberg equilibrium in the control population, but rs6354 deviated. Allelic and genotypic frequencies for these 3 polymorphisms were similar in SSc patients and controls. Subphenotype analyses of subsets with PAH and digital ulceration did not detect any difference between SSc patients compared to controls. CONCLUSION: These results from a large cohort of European Caucasian SSc patients do not support the implication of SLC6A4 in the pathogenesis of SSc and its vascular subphenotypes. However, serotonin pathways remain good candidates to contribute to the vasculopathy of SSc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".