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Low‐dose Lithium is Associated with Left Ventricular Ejection Fraction in Healthy Young Men but not Women

2020· article· en· W3017384200 on OpenAlexaffabout
Kylie S. Dempster, Val A. Fajardo, Ryan W. Baranowski, Sophie I. Hamstra, Terrance J. Wade, Deborah D. O’Leary

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsBrock University
Fundersnot available
KeywordsEjection fractionInternal medicineCardiologyMedicineVentricleCardiac function curveParasternal lineHeart failureEndocrinology

Abstract

fetched live from OpenAlex

The impact of lithium (Li) exposure on the cardiovascular system is perplexing, as beneficial, negative, and null effects have been identified. Li is an inhibitor of glycogen‐synthase kinase 3β (GSK3‐β) which has been identified as a possible therapeutic target for cardiovascular disease (CVD) and mental health disorders. While earlier ecological studies suggest that trace amounts of Li may protect against CVD, no study to date has investigated the effects of low‐dose Li exposure on cardiac structure and function in healthy young adults without CVD and whether these associations are sex dependent. The purpose of the present investigation was to examine the association between Li exposure and left ventricular mass (LVM) and function in healthy young adults. This investigation represents a cross‐sectional analysis of the Niagara Longitudinal Heart Study (NLHS) at the midpoint of data collection for 156 participants aged 19 – 25 years. A final sample size of 117 had both valid Li and left ventricular data upon analyses. LVM was indexed to height 2.7 (LVMi), ejection fraction (EF), fractional shortening (FS), and stroke volume (SV) were assessed following echocardiography in the parasternal long‐axis view of the left ventricle. Serum Li levels were measured using inductively‐coupled plasma mass spectrometry. Li concentration was significantly associated with EF (r = −0.183, p = 0.05) and ESV (r = 0.197, p = 0.03) among the total sample. Li concentration did not differ when examined by sex (p > 0.05), however LVMi, ESV, EDV, SV, and EF were all significantly higher in males than females (p < 0.05). Li was only significantly correlated with EF in men (r = −0.320, p = 0.02) however no correlation was observed among women (p > 0.05). The novel result of our investigation was naturally occurring concentration of Li is associated with decreased EF in men, but not women, although, there was no significant difference in SV. Further, there was no effect of low‐dose Li on LVMi. This is in contrast to some animal studies which demonstrate long‐term exposure to Li has potent hypertrophic effect on the left ventricle, although, Li concentrations in our sample were considerably lower and fall within a normal physiological range. These findings may result from differential sensitivities of the GSK‐3β receptor to Li in the left ventricle in men and women, although further investigation is required. Support or Funding Information This research project is funded by the Canadian Institutes of Health Research (CIHR #s 363774, 399332). KSD is funded by a CIHR Doctoral Research Award – Frederick Banting and Charles Best Canada Graduate Scholarship (RFN#167014). VAF is supported by a Brock Explore Grant and an NSERC Discovery Grant (2019‐05833).

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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