Evidence map›Paper›PMID 35631209›Full record

ArticleNutrients2022

Multilevel Understanding of the Impact of Individual- and School-Level Determinants on Lipid Profiles in Adolescents: The Cross-Level Interaction of Food Environment and Body Mass Index.

Wei-Ting Lin, Yu-Ting Chin, Pei-Wen Wu, Sharon Tsai, Meng-Hsueh Chen, Chiao-I Chang, Yu-Cheng Yang, Chun-Ying Lee, David W Seal, Chien-Hung Lee

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 3 institutions in 2 countries.

Wei-Ting LinDepartment of Social, Behavioral, and Population Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.ORCID 0000-0001-5556-2754
Yu-Ting ChinDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.ORCID 0000-0002-6270-0569
Pei-Wen WuDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Sharon TsaiDepartment of Laboratory Medicine, Kaohsiung Municipal Siaogang Hospital, Kaohsiung 81267, Taiwan.
Meng-Hsueh ChenDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Chiao-I ChangDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Yu-Cheng YangDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Chun-Ying LeeDepartment of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan.ORCID 0000-0001-8475-5205
David W SealDepartment of Social, Behavioral, and Population Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
Chien-Hung LeeDepartment of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.ORCID 0000-0002-0988-264X
Kaohsiung Medical University · TWTulane University · USKaohsiung Municipal Hsiao-Kang Hospital · TW

Funding

Taiwan Ministry of Science and Technology MOST 103-2314-B-037-019-MY3, MOST 106-2314-B-037-021-MY3, and MOST 109-2314-B-037-070-MY3The Research Center for Environmental Medicine, Kaohsiung Medical University KMU-TC111A01 and KMU-TC111IFSP01
6 · The paper itself

Abstract

Adolescents with comparable personal risk factors may have different lipid profiles because of the school’s context. Lipid determinants in adolescents should be considered using a multilevel perspective. This multilevel study investigated the effects of individual-level and school-level factors on lipid profiles in adolescents and evaluated the cross-level influence of lipid determinants. A representative adolescent cohort (n = 2727) was randomly selected from 36 schools in three diverse economic areas in Taiwan and assessed for their personal dietary patterns, physical parameters, and lipid profiles. For individual-level factors, both low physical activity and high body mass index (BMI) were associated with elevated triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels, and a sugar-sweetened beverage intake of >500 mL/day was associated with increases of 5.97 and 6.12 mg/dL in LDL-C and TC levels, respectively, compared with abstinence. Regarding school-level factors, students in schools with ≥2 health promotion programs per year had a 5.27 mg/dL lower level of LDL-C than those in schools with 0−1 program, and students in schools with ≥46 food outlets within 600 m of the school had 6.90 and 13.3 mg/dL higher levels of TG and TC, respectively, than those in schools with <46 food outlets. School context modified the individual-level positive correlation between BMI and TG level (the p-value for the random-slope effect was 0.003). In conclusion, individual-level and school-level factors exert a multilevel effect on adolescent lipid profiles. The food environment near the school has a stronger cross-level impact on individual TG levels in adolescents with a high BMI than in those with a normal BMI.

Indexed as

FoodSchoolsAdolescentBody Mass IndexCholesterol, LDLHumansStudentsTriglyceridesCholesterol, LDLTriglyceridesadolescentsbody mass indexcross-level influencefood environment near the schoollipid profilemultilevel effectphysical activitysugar-sweetened beverage

Identifiers

PMID35631209
PMCPMC9146111
OpenAlexW4280563434

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.