Evidence map›Paper›PMID 40696673›Full record

ArticleMedicine2025

Exploring the impact of smoking on coronary heart disease risk in women: Insights from the NHANES database.

Yang Mu, Jun Xia

Abstract read
In one paragraph

Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yang MuGoodwill Information Technology Co., Ltd., Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronary heart disease (CHD) is a widespread chronic condition. Its risk factors are numerous and complex, with smoking being a key factor. Recently, CHD risk in women has notably risen, partly due to increased smoking and lifestyle changes. This highlights the critical need for gender-specific CHD research. This study aims to assess CHD risk in smoking and nonsmoking women, identifying crucial biochemical markers influencing this risk. Our goal is to develop personalized risk assessment tools for improved clinical decision-making. We analyzed data from 41,482 female National Health and Nutrition Examination Survey participants (2011-2020), focusing on blood markers. Logistic regression models for smokers and nonsmokers were developed to predict CHD risk, assessed by the area under the curve of the receiver operating characteristic curve. We also created nomograms to translate biochemical indicator measurements into CHD risk probabilities, supporting clinical decisions. Univariate analysis showed significant correlations between age, biochemical markers, and CHD risk. The logistic regression models were highly predictive, with area under the curves of smoking CHD model and nonsmoking CHD model being 0.813 (95% confidence interval: 0.788-0.837) and 0.829 (95% confidence interval: 0.811-0.847), respectively. The nomograms effectively assessed risk across patient groups, confirmed by accurate calibration curves. This study presents distinct CHD risk assessment models for smoking and nonsmoking women, along with an innovative visual risk assessment tool. These insights underscore the role of gender in CHD risk and inform future public health strategies and clinical practices.

Indexed as

Coronary DiseaseSmokingAdultAgedBiomarkersFemaleHumansLogistic ModelsMiddle AgedNomogramsNutrition SurveysRisk AssessmentRisk FactorsROC CurveUnited StatesBiomarkersblood biochemical markerscoronary heart diseaselogistic regressionNHANESsmokingwomen

Identifiers

PMID40696673
PMCPMC12282803

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.