Evidence map›Paper›PMID 28588111›Full record

ArticleBMJ open2017

Dietary determinants of serum total cholesterol among middle-aged and older adults: a population-based cross-sectional study in Dar es Salaam, Tanzania.

Sujay S Kakarmath, Rachel M Zack, Germana H Leyna, Saman Fahimi, Enju Liu, Wafaie W Fawzi, Zohra Lukmanji, Japhet Killewo, Frank Sacks, Goodarz Danaei

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  4. Prediction of hypercholesterolemia using machine learning techniques.Journal of diabetes and metabolic disorders · 2023
    Article
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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

10 authors at 5 institutions in 4 countries.

Sujay S KakarmathDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Rachel M ZackDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Germana H LeynaDepartment of Epidemiology and Biostatistics, School of Public Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of Tanzania.
Saman FahimiDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Enju LiuDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Wafaie W FawziDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Zohra LukmanjiWorld food program, Dar es Salaam and Tumaini Hospital, Dar es Salaam, Tanzania.
Japhet KillewoDepartment of Epidemiology and Biostatistics, School of Public Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of Tanzania.
Frank SacksDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Goodarz DanaeiDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Harvard Global Health Institute · USHarvard University · USFaculty of Public Health · GBMuhimbili University of Health and Allied Sciences · TZTumaini University · TZ

Funding

Training Program in Cancer EpidemiologyT32CA009001 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI A. Heather Eliassen, Meir Stampfer · 1985 to 2026
$17.3M
The Center for the Global Demography of AgingP30AG024409 · NIA · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI BLOOM, DAVID E · 2004 to 2019
$7.9M
Reducing disparities in diabetes through expanded insurance coverage.R01DK090435 · NIDDK · HARVARD SCHOOL OF PUBLIC HEALTH · PI DANAEI, GOODARZ · 2011 to 2014
$1.8M
NCI NIH HHS T32 CA009001NIA NIH HHS P30 AG024409NIDDK NIH HHS R01 DK090435
6 · The paper itself

Abstract

objectiveTo assess the dietary determinants of serum total cholesterol.

designCross-sectional population-based study.

settingPeri-urban region of Dar es Salaam, Tanzania.

participants347 adults aged 40 years and older from the Dar es Salaam Urban Cohort Hypertension Study.

main outcome measureSerum total cholesterol measured using a point-of-care device.

resultsMean serum total cholesterol level was 204 mg/dL (IQR 169-236 mg/dL) in women and 185 mg/dL (IQR 152-216 mg/dL) in men. After adjusting for demographic, socioeconomic, lifestyle and dietary factors, participants who reported using palm oil as the major cooking oil had serum total cholesterol higher by 15 mg/dL (95% CI 1 to 29 mg/dL) compared with those who reported using sunflower oil. Consumption of one or more servings of meat per day (p for trend=0.017) and less than five servings of fruits and vegetables per day (p for trend=0.024) were also associated with higher serum total cholesterol. A combination of using palm oil for cooking, eating more than one serving of meat per day and fewer than five servings of fruits and vegetables per day, was associated with 46 mg/dL (95% CI 16 to 76 mg/dL) higher serum total cholesterol.

conclusionsUsing palm oil for cooking was associated with higher serum total cholesterol levels in this peri-urban population in Dar es Salaam. Reduction of saturated fat content of edible oil may be considered as a population-based strategy for primary prevention of cardiovascular diseases.

Indexed as

CookingDietAdultAgedBiomarkersCholesterolCohort StudiesCross-Sectional StudiesFemaleFood AnalysisHumansHypertensionLinear ModelsMaleMiddle AgedMultivariate AnalysisBiomarkersCholesterolPalm Oilcholesteroldietnutritionpalm oilSub-Saharan AfricaTanzania

Identifiers

PMID28588111
PMCPMC5663012
OpenAlexW2624183036

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.