Evidence map›Paper›PMID 31434763›Full record

ArticleBMJ open2019

Prevalence and associations of behavioural risk factors with blood lipids profile in Lebanese adults: findings from WHO STEPwise NCD cross-sectional survey.

Megali Mansour, Hani Tamim, Lara Nasreddine, Christelle El Khoury, Nahla Hwalla, Monique Chaaya, Antoine Farhat, Abla M Sibai

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
0.6field-weighted citation impact, top 28% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

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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

8 authors at 3 institutions in 2 countries.

Megali MansourDepartment of Epidemiology and Population Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon.
Hani TamimBiostatistics Unit, Clinical Research Institute, American University of Beirut, Beirut, Lebanon.
Lara NasreddineDepartment of Nutrition and Food Sciences, Faculty of Agriculture and Food Sciences, American University of Beirut, Beirut, Lebanon.
Christelle El KhouryDepartment of Public Health, School of Medicine, Tufts University, Boston, Massachusetts, USA.
Nahla HwallaDepartment of Nutrition and Food Sciences, Faculty of Agriculture and Food Sciences, American University of Beirut, Beirut, Lebanon.
Monique ChaayaDepartment of Epidemiology and Population Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon.
Antoine FarhatDepartment of Nursing and Health Sciences, Notre Dame University, Louaize, Lebanon.
Abla M SibaiDepartment of Epidemiology and Population Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon am00@aub.edu.lb.
American University of Beirut · LBNotre Dame University – Louaize · LBTufts University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo examine associations of behavioural risk factors, namely cigarette smoking, physical activity, dietary intakes and alcohol consumption, with blood lipids profile. DESIGN AND

participantsData drawn from a cross-sectional study involving participants aged 18 years and over (n=363) from the nationwide WHO STEPwise Nutrition and Non-communicable Disease Risk Factor survey in Lebanon. MEASURES: Demographic characteristics, behaviours and medical history were obtained from participants by questionnaire. Dietary assessment was performed using a 61-item Culture-Specific Food Frequency Questionnaire that measured food intake over the past year. Lipid levels were measured by the analysis of fasting blood samples (serum total cholesterol (TC), triglycerides (TG), very low-density lipoprotein (VLDL), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C)).

resultsCurrent cigarette smoking, alcohol consumption and low physical activity were prevalent among 33.3%, 39.7% and 41.6% of the sample, respectively. The contributions of fat and saturated fat to daily energy intake were high, estimated at 36.5% and 11.4%, respectively. Abnormal levels of TC, TG, VLDL, LDL-C and HDL-C were observed for 55.4%, 31.4%, 29.2%, 47.5% and 21.8% of participants, respectively. Adjusting for potential confounders, cigarette smoking was positively associated with higher odds of TG and VLDL (OR=4.27; 95% CI 1.69 to 10.77; and 3.26; 95% CI 1.33 to 8.03, respectively) with a significant dose-response relationship (p value for trend=0.010 and 0.030, respectively). Alcohol drinking and high saturated fat intake (≥10% energy intake) were associated with higher odds of LDL-C (OR=1.68; 95% CI 1.01 to 2.82 and OR= 1.73; 95% CI 1.02 to 2.93). Physical activity did not associate significantly with any blood lipid parameter.

conclusionThe demonstrated positive associations between smoking, alcohol drinking and high saturated fat intake with adverse lipoprotein levels lay further evidence for clinical practitioners, public health professionals and dietitians in the development of preventive strategies among subjects with a high risk of cardiovascular diseases in Lebanon and other neighbouring countries with similar epidemiological profile.

Indexed as

Nutritional StatusAdolescentAdultBiomarkersCardiovascular DiseasesCross-Sectional StudiesExerciseFemaleHealth BehaviorHumansLebanonLipidsMaleMiddle AgedPrevalencePrognosisBiomarkersLipidsalcohol consumptionblood lipidscardiovascular diseasedietary intakeLebanonphysical inactivitysmoking

Identifiers

PMID31434763
PMCPMC6707694
OpenAlexW2969456502

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.