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Record W3112462662 · doi:10.1111/eci.13472

L‐homoarginine is associated with decreased cardiovascular‐ and all‐cause mortality

2020· article· en· W3112462662 on OpenAlexfundno aff
Maserame Cleopatra Mokhaneli, Shani Botha‐Le Roux, Carla Maria Theresia Fourie, Rainer H. Böger, Edzard Schwedhelm, Catharina M. C. Mels

Bibliographic record

VenueEuropean Journal of Clinical Investigation · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
FundersNorth-West UniversityPopulation Health Research Institute
KeywordsHazard ratioInternal medicineMedicineProportional hazards modelHomeostasisArgininePopulationGastroenterologyEndogenyEndocrinologyConfidence intervalPhysiologyAmino acidBiologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Increasing evidence suggests that L-homoarginine, an endogenous analogue of the amino acid L-arginine, may have beneficial effects on vascular homeostasis. We examined whether L-homoarginine is associated with 10-year risk of all-cause and cardiovascular mortality in a black South African population. METHODS: We included 669 black South African participants (mean age 59.5 years), 143 of whom died during the 10-year follow-up period. Mortality data were acquired via verbal autopsy. Plasma L-homoarginine (and other related markers) were analysed with liquid chromatography-tandem mass spectrometry. RESULTS: Survivors had higher L-homoarginine levels compared with nonsurvivors (1.25 µM vs. 0.89 µM; P < .001). Multivariable Cox regression analyses revealed that higher plasma L-homoarginine predicted a reduction in 10-year cardiovascular (hazard ratio [HR] per SD increment, 0.61; 95% CI 0.50 to 0.75) and all-cause (hazard ratio [HR] per SD increment, 0.59; 95% CI 0.41 to 0.84) mortality risk. CONCLUSION: Higher L-homoarginine levels are associated with reduced risk of 10-year cardiovascular and all-cause mortality. Regulation of L-homoarginine levels as a therapeutic target in the management of cardiovascular disease should be investigated.

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)0.003
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 score0.157
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.257
GPT teacher head0.360
Teacher spread0.103 · 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
Published2020
Admission routes1
Has abstractyes

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