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Record W4377819052 · doi:10.1016/j.ebiom.2023.104619

Global distributions of age- and sex-related arterial stiffness: systematic review and meta-analysis of 167 studies with 509,743 participants

2023· review· en· W4377819052 on OpenAlexaboutno aff
Yao Lu, Sophia J. Kiechl, Jie Wang, Qingbo Xu, Stefan Kiechl, Raimund Pechlaner, David Aguilar, Khamis Al-Hashmi, Rafael de Oliveira Alvim, Ibrahim Al‐Zakwani, Christina Antza, Arrigo F.G. Cicero, Maja Avramovska, Petar Avramovski, Hyun Jae Baek, Magnus Bäck, Kent R. Bailey, Marcelo Perim Baldo, Rosângela Fernandes Lucena Batista, Athanasios Benetos, Emelia J. Benjamin, Daniel Bia, Claudio Borghi, Shani Botha‐Le Roux, Yolandi Breet, David Burgner, Viviane Cunha Cardoso, Marina Cecelja, Indrė Čeponienė, Chen‐Huan Chen, Michael Cheung, Hao‐Min Cheng, Jaegeol Cho, Phil Chowienczyk, Eduardo Barbosa Coelho, Orsolya Cseprekál, Amilcar BT Da Silva, Frédéric Dallaire, Roberto de Sá Cunha, Alejandro Díaz, Albano Vicente Lopes Ferreira, Jean Ferrières, Yoshihiko Furuta, Manuel A. Gómez‐Marcos, Leticia Gómez‐Sánchez, Julian Halcox, Craig L. Hanis, Karl‐Heinz Herzig, Edgar Jaeggi, Maryam Kavousi, Ursula Kiechl‐Kohlendorfer, Hack‐Lyoung Kim, Mi Kyung Kim, Yu‐Mi Kim, Éva Kis, Michael Knoflach, Vasilios Kotsis, Teruhide Koyama, M. Kozàkovà, Ruan Kruger, Iftikhar J. Kullo, Sun‐Seog Kweon, Ιrene Lambrinoudaki, Chang Liu, Markus Loeffler, Jeongok G. Logan, Jane Maddock, Pedro Magalhães, João Maldonado, Francesco Mattace‐Raso, Alex Messner, Michelle L. Meyer, Jie Mi, José Geraldo Mill, Gary F. Mitchell, Jianjun Mu, Iram Faqir Muhammad, Johannes Nairz, Atsushi Nakagomi, Mieko Nakamura, Peter M. Nilson, Toshiharu Ninomiya, Carlo Palombo, Alexandre C. Pereira, Telmo Pereira, Daniel Pires Capingana, Anna K. Poon, Nicole Probst‐Hensch, Arshed A. Quyyumi, György Reusz, Moo‐Yong Rhee, Cecília Cláudia Costa Ribeiro, Ernst Rietzschel, Paulo Ricardo Higassiaraguti Rocha, Enrique Rodilla, Marta Rojek, Jean‐Bernard Ruidavets, Joost H.W. Rutten, Yasuaki Saijo, Paolo Salvi, Arno Schmidt‐Trucksäss, Markus Scholz, Min‐Ho Shin, Patrick Segers, Kimon Stamatelopoulos, И. Д. Стражеско, Minoru Sugiura, Hirofumi Tomiyama, Elaine M. Urbina, Inge van den Munckhof, Ramachandran S. Vasan, Melissa Wake, S. Goya Wannamethee, Andrew Wong, Akira Yamashina, Yinkun Yan, Divanei Zaniqueli, Fang Zhu, Yanina Zócalo

Bibliographic record

VenueEBioMedicine · 2023
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilScience and Technology Program of Hunan ProvinceNational Heart, Lung, and Blood InstituteState Government of VictoriaFinancial Markets Foundation for ChildrenSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of MelbourneDepartment of Social Services, Australian GovernmentNational Heart Foundation of AustraliaMurdoch Children's Research InstituteÖsterreichische ForschungsförderungsgesellschaftRoyal Children's Hospital FoundationNational Science FoundationChildren's Hospital FoundationDementias Platform UKNational Natural Science Foundation of ChinaMedical Research CouncilChildren’s Hospital of Wisconsin Research Institute
KeywordsPulse wave velocityArterial stiffnessMedicineConfidence intervalInternal medicineDemographyCardiologyMeta-analysisBlood pressure

Abstract

fetched live from OpenAlex

BACKGROUND: Arterial stiffening is central to the vascular ageing process and a powerful predictor and cause of diverse vascular pathologies and mortality. We investigated age and sex trajectories, regional differences, and global reference values of arterial stiffness as assessed by pulse wave velocity (PWV). METHODS: Measurements of brachial-ankle or carotid-femoral PWV (baPWV or cfPWV) in generally healthy participants published in three electronic databases between database inception and August 24th, 2020 were included, either as individual participant-level or summary data received from collaborators (n = 248,196) or by extraction from published reports (n = 274,629). Quality was appraised using the Joanna Briggs Instrument. Variation in PWV was estimated using mixed-effects meta-regression and Generalized Additive Models for Location, Scale, and Shape. FINDINGS: The search yielded 8920 studies, and 167 studies with 509,743 participants from 34 countries were included. PWV depended on age, sex, and country. Global age-standardised means were 12.5 m/s (95% confidence interval: 12.1-12.8 m/s) for baPWV and 7.45 m/s (95% CI: 7.11-7.79 m/s) for cfPWV. Males had higher global levels than females of 0.77 m/s for baPWV (95% CI: 0.75-0.78 m/s) and 0.35 m/s for cfPWV (95% CI: 0.33-0.37 m/s), but sex differences in baPWV diminished with advancing age. Compared to Europe, baPWV was substantially higher in the Asian region (+1.83 m/s, P = 0.0014), whereas cfPWV was higher in the African region (+0.41 m/s, P < 0.0001) and differed more by country (highest in Poland, Russia, Iceland, France, and China; lowest in Spain, Belgium, Canada, Finland, and Argentina). High vs. other country income was associated with lower baPWV (-0.55 m/s, P = 0.048) and cfPWV (-0.41 m/s, P < 0.0001). INTERPRETATION: China and other Asian countries featured high PWV, which by known associations with central blood pressure and pulse pressure may partly explain higher Asian risk for intracerebral haemorrhage and small vessel stroke. Reference values provided may facilitate use of PWV as a marker of vascular ageing, for prediction of vascular risk and death, and for designing future therapeutic interventions. FUNDING: This study was supported by the excellence initiative VASCage funded by the Austrian Research Promotion Agency, by the National Science Foundation of China, and the Science and Technology Planning Project of Hunan Province. Detailed funding information is provided as part of the Acknowledgments after the main text.

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)0.015
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0160.025
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.157
GPT teacher head0.433
Teacher spread0.276 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations103
Published2023
Admission routes1
Has abstractyes

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