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Record W3108621242 · doi:10.1093/ehjci/ehaa946.2767

Associations of grip strength with cardiovascular disease and all-cause mortality in people with and without hypertension: findings from the Prospective Urban Rural Epidemiology (PURE) China Study

2020· article· en· W3108621242 on OpenAlexaboutno aff
Wen-Huang Li and Ping-Yen Liu, Wenyuan Li, C.S Wang, Bo Hu, Yibo Wang, X.Y Liu

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInterquartile rangeInternal medicineEpidemiologyProportional hazards modelIncidence (geometry)Prospective cohort studyBlood pressureHazard ratioCardiologyConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Hypertension and grip strength (GS) are predictors of mortality and cardiovascular disease (CVD), but whether these risk factors interact to affect both CVD and all-cause mortality is unknown. The study aimed to examine whether the associations between hypertension and GS with the risk of major CVD incidence, CVD mortality, and all-cause mortality differed between people with and without hypertension. Methods GS was measured using a Jamar dynamometer in participants aged 35–70 years from 12 provinces in the Prospective Urban Rural Epidemiology (PURE) China study. Hypertension was defined as a baseline systolic and diastolic blood pressure of at least 140/90 mm Hg, a self-reported history of hypertension, or treatment with antihypertensive medications. Cox proportional hazards models were used to examine the associations of GS and hypertension and with the outcomes of all-cause mortality and CVD incidence/mortality, and to test the multiplicative interactions between hypertension and GS. Results Among 39,862 participants included in this study, 15,964 reported having hypertension at baseline and 9095 had high GS. After a median follow-up of 8.9 years [interquartile range (IQR) 6.7–9.9 years], 1822 participants developed major CVD, and 1250 deaths occurred (388 as a result of CVD). Compared with normotensive participants with high GS, hypertensive patients with high GS had a higher risk of major CVD incidence (HR 2.36 [95% CI: 1.84–3.02]; P<0.0001) or CVD mortality (HR 3.05 [95% CI: 1.56–5.95]; P<0.0001) but did not have a significantly increased risk of all-cause mortality (HR 1.23 [95% CI: 0.91–1.67]; P=0.181); these risks were further increased if hypertensive participants whose GS level was low (major CVD incidence (HR 3.33 [95% CI: 2.61, 4.24]; P<0.0001), CVD mortality (HR: 5.20 [95% CI: 2.76, 9.82]; P<0.0001), and all-cause mortality (HR 2.00 [95% CI: 1.53, 2.62]; P<0.0001)). Conclusions The present study demonstrates that hypertensive patients with low GS are associated with the highest risk of major CVD incidence, CVD mortality, and all-cause mortality. High levels of GS appear to mitigate long-term mortality risk among hypertensive patients. Association of adverse outcomes Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): The main PURE study and its components are funded by the Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, and through unrestricted grants from several pharmaceutical companies. Besides funding from global PURE, this work was also sponsored by CAMS Innovation Fund for Medical Sciences (CIFMS): 2016-I2M-2-004, Construction of Basic Information Technology Support System and Platform for National Prevention and Treatment of Cardiovascular Diseases.

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)0.002
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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.110
GPT teacher head0.338
Teacher spread0.228 · 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
Published2020
Admission routes1
Has abstractyes

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