MétaCan
Menu
Back to cohort
Record W4414382211 · doi:10.1016/j.jacl.2025.05.018

Hypertension in youth with metabolic syndrome

2025· review· en· W4414382211 on OpenAlexaff
Jhanahan Sriranjan, Claire Adams, Rahul Chanchlani, Manish D. Sinha

Bibliographic record

VenueJournal of clinical lipidology · 2025
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMcMaster Children's HospitalMcMaster University
Fundersnot available
KeywordsMetabolic syndromePsychological interventionMultidisciplinary approachPharmacotherapyIntervention (counseling)Lifestyle modification

Abstract

fetched live from OpenAlex

BACKGROUND: Hypertension continues to increase in its prevalence in children and adolescents globally. This evolving health issue has been associated with the concurrent world-wide rise in childhood obesity. SOURCES OF MATERIAL: In this review, we aim to improve the awareness of hypertension in youth with metabolic syndrome (MetS) highlighting key published data. This review examines the interplay between MetS and hypertension during childhood and adolescence, covering its epidemiology, pathophysiology, screening, diagnosis, and potential management strategies. ABSTRACT OF FINDINGS: Hypertension and obesity are integral features of MetS, which describes a clustering of metabolic risk factors hallmarked by insulin resistance that significantly increases the risk of cardiovascular disease in adulthood. Children at risk of developing hypertension and MetS share many modifiable and non-modifiable risk factors, which are derived from common pathophysiological mechanisms. Identifying at-risk individuals through targeted screening is crucial, given their long-term adverse cardiovascular outcomes in young adulthood. CONCLUSION: Management of hypertension and MetS requires a stepwise, multidisciplinary approach, central to which is the recognition that adoption of healthy lifestyle intervention over the lifespan is key to prevention, with consideration for pharmacotherapy and other medical interventions as indicated.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.218
GPT teacher head0.440
Teacher spread0.221 · 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.

Study designOther design
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of clinical lipidologySame topicBlood Pressure and Hypertension StudiesFrench-language works237,207