Evidence map›Paper›PMID 39282603›Full record

ArticleInternational journal of cardiology. Cardiovascular risk and prevention2024

Higher neighborhood disadvantage is associated with weaker interactions among cardiometabolic drivers.

Joel Hernandez Sevillano, Masih A Babagoli, Yitong Chen, Shelley H Liu, Pranav Mellacheruvu, Janet Johnson, Borja Ibanez, Oscar Lorenzo, Jeffrey I Mechanick

Abstract read
In one paragraph

Article in International journal of cardiology. Cardiovascular risk and prevention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. The dominant driver paradigm of cardiometabolic care.American journal of preventive cardiology · 2026
    Article
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

9 authors.

Joel Hernandez SevillanoKravis Center for Clinical Cardiovascular Health at the Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Masih A BabagoliIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Yitong ChenDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Shelley H LiuDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Pranav MellacheruvuHospital of the University of Pennsylvania, Philadelphia, PA, USA.
Janet JohnsonKravis Center for Clinical Cardiovascular Health at the Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Borja IbanezCentro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
Oscar LorenzoIIS-Fundación Jiménez Díaz, Autónoma University, Madrid, Spain.
Jeffrey I MechanickKravis Center for Clinical Cardiovascular Health at the Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Funding

Improving precision in modeling childhood executive function trajectories using psychometricsK25HD104918 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LIU, SHELLEY HAN · 2021 to 2025
$651k
Endocrine disruptors and insulin resistance: quantifying impacts with a novel exposure burden scoreR03ES033374 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LIU, SHELLEY HAN · 2021 to 2022
$185k
NICHD NIH HHS K25 HD104918NIEHS NIH HHS R03 ES033374
6 · The paper itself

Abstract

Background: Adiposity, dysglycemia, and hypertension are metabolic drivers that have causal interactions with each other. However, the effect of neighborhood-level disadvantage on the intensity of interactions among these metabolic drivers has not been studied. The objective of this study is to determine whether the strength of the interplay between these drivers is affected by neighborhood-level disadvantage. Methods: This cross-sectional study analyzed patients presenting to a multidisciplinary preventive cardiology center in New York City, from March 2017 to February 2021. Patients' home addresses were mapped to the Area Deprivation Index to determine neighborhood disadvantage. The outcomes of interest were correlation coefficients (range from -1 to +1) among the various stages (0 - normal, 1 - risk, 2 - predisease, 3 - disease, and 4 - complications) of abnormal adiposity, dysglycemia, and hypertension at presentation, stratified by neighborhood disadvantage. Results: The cohort consisted of 963 patients (age, median [IQR] 63.8 [49.7-72.5] years; 624 [65.1 %] female). The correlation among the various stages of adiposity, dysglycemia, and hypertension was weaker with increasing neighborhood disadvantage (P for trend <0.001). Specifically, the correlation describing adiposity, dysglycemia, and hypertension interaction was weaker in the high neighborhood disadvantage group compared to the intermediate neighborhood disadvantage group (median [IQR]: 0.34 [0.27, 0.44] vs. median [IQR]: 0.39 [0.34, 0.45]; P < 0.001) and compared to the low neighborhood disadvantage group (median [IQR]: 0.34 [0.27, 0.44] vs. median [IQR]: 0.54 [0.52, 0.57]; P < 0.001), as well as weaker in the intermediate neighborhood disadvantage group compared to the low neighborhood disadvantage group (median [IQR]: 0.39 [0.34, 0.45] vs. 0.54 median [IQR]: 0.54 [0.52, 0.57]; P < 0.001). Conclusions: Interactions among the various stages of abnormal adiposity, dysglycemia, and hypertension with each other are weaker with increasing neighborhood disadvantage. Factors related to neighborhood-level disadvantage, other than abnormal adiposity, might play a crucial role in the development of dysglycemia and hypertension.

Indexed as

AdiposityDysglycemiaHypertensionSocial determinants of health

Identifiers

PMID39282603
PMCPMC11399558

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.