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Record W3023831923 · doi:10.2217/pgs-2020-0028

Differences in Telomere Length Between Patients with Bipolar Disorder and Controls are Influenced by Lithium Treatment

2020· article· en· W3023831923 on OpenAlexaff
Claudia Pisanu, Donatella Congiu, Mirko Manchia, Paola Caria, Cristina Cocco, Tinuccia Dettori, Daniela Virginia Frau, Elias Manca, Anna Meloni, Mariella Nieddu, Barbara Noli, Federica Pinna, Renato Robledo, Valeria Sogos, Gian‐Luca Ferri, Bernardo Carpiniello, Roberta Vanni, Alberto Bocchetta, Giovanni Severino, Raffaella Ardau, Caterina Chillotti, Maria Del Zompo, Alessio Squassina

Bibliographic record

VenuePharmacogenomics · 2020
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsDalhousie University
FundersFondazione di Sardegna
KeywordsTelomereLithium (medication)Mendelian randomizationBipolar disorderInternal medicineGenome-wide association studyGenetic associationMedicineOncologyGeneticsBiologyGenotypeSingle-nucleotide polymorphismGeneGenetic variants

Abstract

fetched live from OpenAlex

Aim: To assess the role of lithium treatment in the relationship between bipolar disorder (BD) and leukocyte telomere length (LTL). Materials & methods: We compared LTL between 131 patients with BD, with or without a history of lithium treatment, and 336 controls. We tested the association between genetically determined LTL and BD in two large genome-wide association datasets. Results: Patients with BD with a history lithium treatment showed longer LTL compared with never-treated patients (p = 0.015), and similar LTL compared with controls. Patients never treated with lithium showed shorter LTL compared with controls (p = 0.029). Mendelian randomization analysis showed no association between BD and genetically determined LTL. Conclusion: Our data support previous findings showing that long-term lithium treatment might protect against telomere shortening.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.250
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2020
Admission routes1
Has abstractyes

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