Evidence map›Paper›PMID 27469620›Full record

ArticlePublic health nutrition2016

Association between dietary patterns and blood lipid profiles among Chinese women.

Jiguo Zhang, Zhihong Wang, Huijun Wang, Wenwen Du, Chang Su, Ji Zhang, Hongru Jiang, Xiaofang Jia, Feifei Huang, Fengying Zhai and 1 more

Abstract read
In one paragraph

Article in Public health nutrition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  19. Association between nutrient patterns and serum lipids in Chinese adult women: A cross-sectional study.Nutrition & dietetics : the journal of the Dietitians Association of Australia · 2019
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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

11 authors.

Jiguo Zhang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Zhihong Wang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Huijun Wang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Wenwen Du1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Chang Su1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Ji Zhang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Hongru Jiang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Xiaofang Jia1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Feifei Huang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Fengying Zhai1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.
Bing Zhang1National Institute for Nutrition and Health,Chinese Center for Disease Control and Prevention,No. 29 Nanwei Road,Xicheng District,Beijing 100050,People's Republic of China.

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SUSAN M. SMITH · 1999 to 2026
$31.6M
MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Carolina Population CenterR24HD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MORGAN, SAMUEL PHILIP · 2005 to 2014
$10.0M
CHILD HEALTH EFFECTS OF RAPID SOCIO-DEMOGRAPHIC CHANGER01HD038700 · NICHD · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2000 to 2004
$1.5M
Nutrition-Related NCD Prevention Training in ChinaD43TW009077 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2011 to 2015
$1.1M
Nutrition-Related NCD Prevention Training in ChinaD43TW007709 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2006 to 2010
$723k
FIC NIH HHS D43 TW007709FIC NIH HHS D43 TW009077NICHD NIH HHS R01 HD030880NICHD NIH HHS R01 HD038700NICHD NIH HHS R24 HD050924NIDDK NIH HHS P30 DK056350
6 · The paper itself

Abstract

objectiveThe present study aimed to identify dietary patterns and explore their associations with blood lipid profiles among Chinese women.

designIn a cross-sectional study, we identified dietary patterns using principal component analysis of data from three consecutive 24 h dietary recalls. The China Health and Nutrition Survey (CHNS) collected blood samples in the morning after an overnight fast and measured total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C) and TAG.

settingData were from the 2009 wave of the CHNS. SUBJECTS: We studied 2468 women aged 18-80 years from the CHNS.

resultsWe identified three dietary patterns: traditional southern (high intakes of rice, pork and vegetables), snack (high intakes of fruits, eggs and cakes) and Western (high intakes of poultry, fast foods and milk). The traditional southern pattern was inversely associated with HDL-C (β=-0·68; 95 % CI -1·22, -0·14; P<0·05). The snack pattern was significantly associated with higher TAG (β=4·14; 95 % CI 0·44, 7·84; P<0·05). The Western pattern was positively associated with TC (β=2·52; 95 % CI 1·03, 4·02; P<0·01) and LDL-C (β=2·26; 95 % CI 0·86, 3·66; P<0·01).

conclusionsWe identified three dietary patterns that are significantly associated with blood lipid profiles. This information is important for developing interventions and policies addressing dyslipidaemia prevention among Chinese women.

Indexed as

DietAdultAgedAged, 80 and overAnimalsChinaCross-Sectional StudiesFemaleHumansLipidsMiddle AgedRed MeatYoung AdultLipidsChinaDietary patternsLipid profilesPrincipal component analysisWomen

Identifiers

PMID27469620
PMCPMC6383153

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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.