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Record W2512384933 · doi:10.1113/jp272979

The impacts of age and frailty on heart rate and sinoatrial node function

2016· article· en· W2512384933 on OpenAlexafffund
Motahareh Moghtadaei, Hailey J. Jansen, Martin Mackasey, Sara A. Rafferty, Oleg Bogachev, John L. Sapp, Susan E. Howlett, Robert A. Rose

Bibliographic record

VenueThe Journal of Physiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsMedicineSinoatrial nodeAgeingInternal medicineHeart rateFrailty IndexCardiologyGerontologyIntracardiac injectionBiological agePhysiologyBlood pressure

Abstract

fetched live from OpenAlex

KEY POINTS: Sinoatrial node (SAN) function declines with age; however, not all individuals age at the same rate and health status can vary from fit to frail. Frailty was quantified in young and aged mice using a non-invasive frailty index so that the impacts of age and frailty on heart rate and SAN function could be assessed. SAN function was impaired in aged mice due to alterations in electrical conduction, changes in SAN action potential morphology and fibrosis in the SAN. Changes in SAN function, electrical conduction, action potential morphology and fibrosis were correlated with, and graded by, frailty. This study shows that mice of the same chronological age have quantifiable differences in health status that impact heart rate and SAN function and that these differences in health status can be identified using our frailty index. ABSTRACT: Sinoatrial node (SAN) dysfunction increases with age, although not all older adults are affected in the same way. This is because people age at different rates and individuals of the same chronological age vary in health status from very fit to very frail. Our objective was to determine the impacts of age and frailty on heart rate (HR) and SAN function using a new model of frailty in ageing mice. Frailty, which was quantified in young and aged mice using a frailty index (FI), was greater in aged vs. young mice. Intracardiac electrophysiology demonstrated that HR was reduced whereas SAN recovery time (SNRT) was prolonged in aged mice; however, both parameters showed heteroscedasticity suggesting differences in health status among mice of similar chronological age. Consistent with this, HR and corrected SNRT were correlated with, and graded by, FI score. Optical mapping of the SAN demonstrated that conduction velocity (CV) was reduced in aged hearts in association with reductions in diastolic depolarization (DD) slope and action potential (AP) duration. In agreement with in vivo results, SAN CV, DD slope and AP durations all correlated with FI score. Finally, SAN dysfunction in aged mice was associated with increased interstitial fibrosis and alterations in expression of matrix metalloproteinases, which also correlated with frailty. These findings demonstrate that age-related SAN dysfunction occurs in association with electrical and structural remodelling and that frailty is a critical determinant of health status of similarly aged animals that correlates with changes in HR and SAN function.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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

Citations93
Published2016
Admission routes2
Has abstractyes

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