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Factors Influencing Muscle Sympathetic Nerve Activity in Human Heart Failure

2022· article· en· W4225422713 on OpenAlexaff
Mark B. Badrov, Daniel A. Keir, George Tomlinson, Evan Keys, Catherine F. Notarius, John S. Floras

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineHeart failureEjection fractionCardiologyInternal medicineBlood pressureHeart rateStroke volumeSympathetic nervous systemEtiology

Abstract

fetched live from OpenAlex

In heart failure (HF), the magnitude of sympathetic activation predicts both disease progression and mortality. Elevated cardiac norepinephrine (NE) spillover is the earliest documented perturbation, followed later by augmented renal NE spillover and muscle sympathetic nerve activity (MSNA). To date, there is limited understanding of the mechanism(s) responsible for between‐patient variation in MSNA and cohorts studied have been small. To characterize the magnitude of MSNA discharge in HF, its inter‐patient variance, and its principal determinants, we assembled microneurographic data from 177 HF patients (28 females; 53±13 yr; LVEF: 25±11%, range 5‐60%) studied in our laboratory. Data from 658 non‐medicated, normotensive volunteers (CTRL; 260 females; 53±14 yr) acquired contemporaneously under similar conditions served as a reference group. We analyzed, at rest, fibular MSNA, blood pressure (BP), heart rate (HR), LVEF, and etiology of disease, and in a sub‐group (n=63), echocardiographically‐derived stroke volume (SV), cardiac output (CO), and total peripheral resistance (TPR). In HF, there was marked inter‐patient variance in MSNA, with burst frequency (BF) ranging from 7 to 90 bursts/minandburst incidence (BI) from 9 to 100 bursts/100 heartbeats. BF was similar in patients dispensed or not dispensed each of the three principal classes of prevailing HF therapy (all P>0.05). BF related directly (R 2 =0.17), and BI inversely (R 2 =0.09), to HR (all P<0.001); neither correlated with BP. For each kg/m 2 rise in BMI, BF increased, on average, by 0.20 bursts/min (95% CI, 0.02‐0.37; P=0.03); its relation to BI was less pronounced (P=0.05). BF increased non‐linearly when LVEF fell below ~21% (P<0.01); above this inflection point, BF displayed no relationship with LVEF. Overall, LVEF accounted for only 9.8% of the variance in BF (P=0.001), while no relationship existed between LVEF and BI (R 2 =0.007). BI (P=0.01), but not BF (P=0.14), was greater in patients with ischemic vs. dilated cardiomyopathy. BF and BI exhibited non‐linear, inverse relationships with SV (R 2 =0.31 and 0.12; all P<0.01) and CO (R 2 =0.17 and 0.20; all P<0.01), and a non‐linear, direct relationship with TPR (R 2 =0.18 and 0.21; all P<0.01). Unadjusted BF (52±15 vs. 26±13 bursts/min; P<0.001) was elevated in HF vs. CTRL. With age, BF exhibited little or no change in HF, in contrast to its non‐linear increase in CTRL. BF, adjusted for age, sex, BMI, and HR, was significantly greater throughout the lifespan (estimate 14.20 bursts/min; 95% CI, 12.11‐16.28; P<0.001) in HF, relative to CTRL, and higher in men than in women (estimate 5.01 bursts/min; 95% CI, 3.42‐6.61; P<0.001). Overall, the extent of sympathetic excess in HF, relative to CTRL, diminished with age. In conclusion, resting MSNA in HF is a function of etiology, BMI, HR, LVEF and SV, but not age; these relationships are significant but moderate to weak, as is that between sympathetic vasoconstrictor discharge and TPR. These observations provide insight into the complexity and individual specificity of mechanisms eliciting muscle sympathetic excitation in human HF.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.265
Teacher spread0.239 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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