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Record W4293067009 · doi:10.1093/sleep/zsac079.551

0554 Proteomic Approach for Understanding the Mechanisms of Periodic Limb Movements and Restless Legs Syndrome

2022· article· en· W4293067009 on OpenAlexaff
Katie L.J. Cederberg, Umaer Hanif, Eileen Leary, Logan Schneider, Anne Marie Morse, Adam Blackman, Paula K. Schweitzer, Suresh Kotagal, Richard Bogan, Clete A. Kushida, Emmanuel Mignot

Bibliographic record

VenueSLEEP · 2022
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteKlarman Family Foundation
KeywordsRestless legs syndromePolysomnographyMedicineInternal medicineInsomnia

Abstract

fetched live from OpenAlex

Abstract Introduction Periodic limb movements (PLMs) are episodes of involuntary, repetitive muscle movements that are highly associated with restless legs syndrome (RLS). Although PLMs and RLS are reportedly two separate phenomena, both are tightly correlated and may result from a similar pathology. The present study profiled plasma protein biomarkers of PLMs and RLS to contribute to the identification of mechanisms associated with each disorder/trait. Methods The SomaScan highly multiplexed aptamer assay was used to profile 5,000 proteins in 24–48-hour old EDTA plasma samples from the Stanford Technology Analytics and Genomics in Sleep (STAGES) study. PLMs per hour (PLMI) were derived from overnight polysomnography and RLS was classified based on affirmative responses to questions of the Alliance Sleep Questionnaire. Three linear regression models were conducted to examine significant protein markers of 1) PLMI in the sample as a whole; 2) PLMI controlling for the presence of RLS; and 3) RLS without PLMs (i.e.,PLMI<5). All models included log2-normalized relative protein expression as the dependent variable and important covariates such as age, gender, BMI, sample storage time, and blood draw period. False discovery rate (FDR) to control for multiple testing was applied with an a-priori p-value of 0.05 for identifying significant associations. Results PLMI was significantly associated with 253 proteins (219 positive,34 negative). The inclusion of RLS in the model mitigated the significance of most proteins, and only 8 proteins remained significant. Negatively associated proteins (LEAP-1, Ferritin, SELH, Caspase-8) included functions related to iron storage, absorption, and delivery and negative regulation of inflammatory/immune responses. Positively associated proteins (SFRP4, RANTES, CathepsinA, DKK1) included proteins with functions related to immune response, inflammatory response, bone formation, and protein stability. RLS without PLMs was associated with 7 upregulated proteins (megalin, RUFY1, TADBP, ANGL7, LRTM2, SNAPN, STOM) with functions related to vitamin D metabolism, calcium and zinc binding, circadian rhythm regulation, and calcium-dependent neurotransmitter secretion. Conclusion These large proteomic analyses identified independent differential protein expressions for PLMs and RLS that suggest different pathophysiological contributions. Support (If Any) This work was supported, in part, by the National Heart, lung, and Blood institute [T32HL110952] and the Klarman Family Foundation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.069
GPT teacher head0.303
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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