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Record W4214768099 · doi:10.3389/fcvm.2022.821568

Arterial Stiffness in a Cohort of Young People Living With Perinatal HIV and HIV Negative Young People in England

2022· article· en· W4214768099 on OpenAlexfundno aff
Julie Mellin, Marthe Le Prevost, Julia Kenny, Kate Sturgeon, Lindsay C Thompson, Caroline Foster, Harald H. Kessler, Nandu Goswami, Nigel Klein, Ali Judd, Hannah Castro

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
FundersMedical Research CouncilMedizinische Universität GrazKarl-Franzens-Universität GrazImperial College Healthcare NHS TrustImperial College LondonKing's College LondonMcMaster UniversityPenta Foundation UKUniversity Hospitals Bristol NHS Foundation TrustHeart of England NHS Foundation TrustKing's College Hospital NHS Foundation TrustSt. George's, University of LondonUniversity Hospitals of Leicester NHS TrustMiddlesex UniversityRoyal Free London NHS Foundation TrustAlder Hey Children's NHS Foundation Trust
KeywordsMedicineArterial stiffnessCohortYoung adultPulse wave velocityBlood pressureRegimenInternal medicine

Abstract

fetched live from OpenAlex

Background Antiretroviral therapy (ART) has increased life expectancy and consequently the risk of cardiovascular disease (CVD) in adults living with HIV. We investigated the levels and predictors of arterial stiffness in young people (YP) living with perinatal HIV (PHIV) and HIV negative YP in the Adolescents and Adults Living with Perinatal HIV (AALPHI) study. Methods AALPHI was a prospective study evaluating the impact of HIV infection and exposure to ART on YP living with PHIV (aged 13–21 years) who had known their HIV status for at least 6 months, and HIV negative YP (aged 13–23 years) who either had a sibling, friend or parent living with HIV. Participants were enrolled from HIV clinics and community services in England. Two hundred and thirteen PHIV and 65 HIV negative YP (42% siblings of PHIV) had pulse wave velocity (PWV) measurements taken (Vicorder software) from the supra-sternal notch to the middle of the thigh cuff, at their second interview in the study between 2015 and 2017. Average PWV was calculated from the three closest readings (≥3 and ≤ 12 m/s) within 0.6 m/s of each other. Linear regression examined predictors of higher (worse) PWV, including age, sex, HIV status and height as a priori , ethnicity, born outside UK/Ireland, alcohol/nicotine/drug use, weight, waist-to-hip-ratio, mean arterial pressure (MAP), caffeine 2 h before PWV and nicotine on day of PWV. A separate PHIV model included CD4, viral load, years taking ART and ART regimen. Findings One hundred and twenty eight (60%) PHIV and 45 (69%) HIV negative YP were female ( p = 0.18), with median (IQR) age 18 (16, 20) and 18 (16, 21) years ( p = 0.48) respectively. Most PHIV were taking a combination of three ART drugs from two classes. There was a trend toward higher (worse) mean PWV in the PHIV group than the HIV negative group [unvariable analysis 6.15 (SD 0.83) m/s vs. 5.93 (0.70) m/s, respectively, unadjusted p = 0.058], which was statistically significant in the multivariable analysis [adjusted p (ap) = 0.020]. In multivariable analysis being male (ap = 0.002), older age (ap < 0.001), higher MAP (ap < 0.001) and nicotine use on day of measurement (ap = 0.001) were also predictors of higher PWV. The predictors were the same in the PHIV model. Interpretation By late adolescence PHIV had worse PWV in comparison to HIV negative peers, and traditional risk factors for CVD (higher arterial pressure, being male and older age) were associated with higher PWV values. Regular detailed monitoring of cardiovascular risk factors should become standard of care for every young person with PHIV worldwide.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.227
Teacher spread0.222 · 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 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

Citations14
Published2022
Admission routes1
Has abstractyes

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