Evidence map›Paper›PMID 33563289›Full record

SynthesisBMC medicine2021

Poor cardiovascular health is associated with subclinical atherosclerosis in apparently healthy sub-Saharan African populations: an H3Africa AWI-Gen study.

Engelbert A Nonterah, Nigel J Crowther, Abraham Oduro, Godfred Agongo, Lisa K Micklesfield, Palwendé R Boua, Solomon S R Choma, Shukri F Mohamed, Herman Sorgho, Stephen M Tollman and 7 more

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
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

16 citing papers in PubMed, 26 citations in OpenAlex.

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  8. Genes, environment, and African ancestry in cardiometabolic disorders.Trends in endocrinology and metabolism: TEM · 2023
    Review
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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

17 authors at 5 institutions in 5 countries.

Engelbert A NonterahClinical Sciences Department, Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana. engelbert.nonterah@navrongo-hrc.org.ORCID 0000-0001-8491-6478
Nigel J CrowtherDepartment of Chemical Pathology, National Health Laboratory Service, Faculty of Health Services, University of the Witwatersrand, Johannesburg, South Africa.
Abraham OduroClinical Sciences Department, Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana.
Godfred AgongoClinical Sciences Department, Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana.
Lisa K MicklesfieldMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Palwendé R BouaInstitut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Nanoro, Burkina Faso.
Solomon S R ChomaDIMAMO Health Demographic Surveillance Site, Department of Pathology and Medical Sciences, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa.
Shukri F MohamedAfrican Population Health Research Centre, Nairobi, Kenya.
Herman SorghoInstitut de Recherché en Sciences de la Santé, Clinical Research Unit of Nanoro, Nanoro, Burkina Faso.
Stephen M TollmanMRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Shane A NorrisMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Frederick J RaalCarbohydrate and Lipid Metabolism Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Diederick E GrobbeeJulius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Michelé RamsaySydney Brenner Institute of Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Michiel L BotsJulius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Kerstin Klipstein-GrobuschJulius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
as part of the H3Africa AWI-Gen study
University of the Witwatersrand · ZAAfrican Population and Health Research Center · KENational Health Laboratory Service · ZANavrongo Health Research Centre · GHInstitut de Recherche en Sciences de la Santé · BF

Funding

Shared Core: Biobanking, Bioinformatics and Data ManagementU54HG006938 · NHGRI · WITS HEALTH CONSORTIUM (PTY), LTD · PI RAMSAY, MICHELE MICHELE · 2012 to 2022
$12.2M
Department of Science and Technology, South Africa DST/CON 0056/2014NHGRI NIH HHS U54 HG006938
6 · The paper itself

Abstract

backgroundThe cardiovascular health index (CVHI) introduced by the American Heart Association is a valid, accessible, simple, and translatable metric for monitoring cardiovascular health in a population. Components of the CVHI include the following seven cardiovascular risk factors (often captured as life's simple 7): smoking, dietary intake, physical activity, body mass index, blood pressure, glucose, and total cholesterol. We sought to expand the evidence for its utility to under-studied populations in sub-Saharan Africa, by determining its association with common carotid intima-media thickness (CIMT).

methodsWe conducted a cross-sectional study involving 9011 participants drawn from Burkina Faso, Ghana, Kenya, and South Africa. We assessed established classical cardiovascular risk factors and measured carotid intima-media thickness of the left and right common carotid arteries using B-mode ultrasonography. Adjusted multilevel mixed-effect linear regression was used to determine the association of CVHI with common CIMT. In the combined population, an individual participant data meta-analyses random-effects was used to conduct pooled comparative sub-group analyses for differences between countries, sex, and socio-economic status.

resultsThe mean age of the study population was 51 ± 7 years and 51% were women, with a mean common CIMT of 637 ± 117 μm and CVHI score of 10.3 ± 2.0. Inverse associations were found between CVHI and common CIMT (β-coefficients [95% confidence interval]: Burkina Faso, - 6.51 [- 9.83, - 3.20] μm; Ghana, - 5.42 [- 8.90, - 1.95]; Kenya, - 6.58 [- 9.05, - 4.10]; and South Africa, - 7.85 [- 9.65, - 6.05]). Inverse relations were observed for women (- 4.44 [- 6.23, - 2.65]) and men (- 6.27 [- 7.91, - 4.64]) in the pooled sample. Smoking (p < 0.001), physical activity (p < 0.001), and hyperglycemia (p < 0.001) were related to CIMT in women only, while blood pressure and obesity were related to CIMT in both women and men (p < 0.001).

conclusionThis large pan-African population study demonstrates that CVHI is a strong marker of subclinical atherosclerosis, measured by common CIMT and importantly demonstrates that primary prevention of atherosclerotic cardiovascular disease in this understudied population should target physical activity, smoking, obesity, hypertension, and hyperglycemia.

Indexed as

Health StatusAdultAtherosclerosisBlood PressureBody Mass IndexBurkina FasoCarotid Intima-Media ThicknessCross-Sectional StudiesFemaleGhanaHumansHypertensionKenyaMaleMiddle AgedObesityCardiovascular diseasesCardiovascular health indexCarotid intima-media thicknessPrimary preventionScreeningSubclinical atherosclerosisSub-Saharan AfricaUnderstudied populations

Identifiers

PMID33563289
PMCPMC7874493
OpenAlexW3127594314

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.