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Abstract 134: Does Low-Level Cumulative Lead Play a Role in Resistant-Hypertension?

2018· article· en· W2886343681 on OpenAlexaff
Alexander R. Zheutlin, Howard Hu, Marc G. Weisskopf, David Sparrow, Pantel Vokonas, Sung Kyun Park

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsMedicineConfoundingBlood pressureLogistic regressionLead (geology)Risk factorInternal medicineTibiaSurgery

Abstract

fetched live from OpenAlex

Purpose: Deposition of lead into bone offers a better method over conventional blood lead measurement to discern long-term lead exposure and its insidious accumulation within the body. Bone lead deposition has been identified as an independent risk factor for hypertension (HTN). Yet, little is known how bone lead as a risk factor for HTN can be translated into clinical utility. We examined the association between bone lead levels and resistant-HTN. Methods: All subjects were males, participating in the Veterans Affairs Normative Aging Study (NAS) with an age variation of 48-93 years old. Participants were included if there was complete data on HTN (systolic blood pressure, diastolic blood pressure, and anti-HTN medication), lead (blood, bone-patella, bone-tibia), as well as demographic and confounding variables. Cases of resistant-HTN were identified by meeting criteria for a) inadequate SBP (≥140 mmHg) or DBP (≥90 mmHg) on 3 medications or b) requiring ≥ 4 medications for blood pressure control. Resistant-HTN was categorized as a dichotomous variable, based upon meeting the noted criteria, while tibia and patella bone lead were treated as continuous variables. The data was analyzed using a binomial logistic regression, accounting for demographic and confounding variables. Results: Of the 871 total study participants, 111 cases of resistant-HTN (12.7%) were identified. Amongst the cases of resistant-HTN, the mean tibia and patella lead levels were 23.1 μg/g and 31.5 μg/g, respectively. Both mean levels were higher than those among the participants without resistant-HTN (21.5 μg/g and 30.9 μg/g, respectively). Tibia lead levels demonstrated a significant association with resistant-HTN (OR=1.27 (95% CI, 1.01-1.59) per one IQR increase in tibia lead (15μg/g), p=0.04) after adjusting for age, BMI, cigarette pack-year burden, income, education, and ethnicity. A weak, non-significant association was observed between patella lead and resistant-HTN (OR = 1.16 (95% CI, 0.92-1.46) per one IQR increase in patella lead (21μg/g), p=0.21). Conclusion: Lead has been long-studied for its effect on blood pressure. Yet, lead has not previously been assessed for the role it plays in clinical outcomes. Difficulty in attaining goal blood pressure may be influenced by environmental exposures. Our study demonstrates an increased association between tibia lead and resistant-HTN status, with an OR of 1.27 per one IQR increase in tibia lead. Tibia lead represents a novel risk factor for resistant-HTN. Future research should consider screening and mitigation strategies for populations with resistant-HTN exposed to long-term low-levels of lead.

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)0.005
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.058
GPT teacher head0.297
Teacher spread0.239 · 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
Published2018
Admission routes1
Has abstractyes

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