Evidence map›Paper›PMID 30285642›Full record

ArticleBMC cardiovascular disorders2018

Using an introduced index to assess the association between food diversity and metabolic syndrome and its components in Chinese adults.

Wenzhi Zhao, Jian Zhang, Ai Zhao, Meichen Wang, Wei Wu, Shengjie Tan, Mofan Guo, Yumei Zhang

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMC cardiovascular disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% 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, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
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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 1 institution in 1 country.

Wenzhi ZhaoDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Jian ZhangDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Ai ZhaoDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Meichen WangDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Wei WuDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Shengjie TanDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Mofan GuoDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China.
Yumei ZhangDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University Health Science Center, Xueyuan Road 38, Haidian District, 100191, Beijing, China. zhangyumei@bjmu.edu.cn.ORCID 0000-0003-3621-5823
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is reported that an increase in food diversity would lower the risk of cardiac-cerebral vascular diseases.

methodsA new index was introduced to develop a Chinese healthy food diversity (HFD) index, exploring the association with metabolic syndrome (MetS) and its components among Chinese adults. Two sets of data were used. The primary data were from a cross-sectional survey conducted in 2016 called the Chinese Urban Adults Diet and Health Study (CUADHS); the verification data were from the China Health and Nutrition Survey (CHNS) of 2009. The Chinese HFD index was developed according to the Chinese Dietary Guideline, with food consumption information from 24-h dietary recalls. The association between the index and MetS and its components was explored in logistic regression models.

resultsAmong 1520 participants in the CUADHS, the crude prevalence of MetS was 36.4%, which was 29.0% after the standardisation of age and gender by the 2010 Chinese national census. In the CUADHS, the HFD index ranged from 0.04 to 0.63. The value of the index among participants who are male, young, poorly educated, drinking or smoking, and with high energy intakes was significantly lower than that of their counterparts. In the verification dataset of the CHNS, there were 2398 participants, and the distribution of different genders and age groups was more balanced. The crude prevalence of MetS in the CHNS was 27.3% and the standardised prevalence was 19.5%. The Chinese HFD index ranged from 0.02 to 0.62. In the CUADHS, the Chinese HFD index was not significantly associated with MetS in covariate-adjusted models or with its components. In the CHNS, the Chinese HFD index had a significantly negative correlation with MetS and its components (i.e., elevated fasting glucose and elevated waist circumference) in covariate-adjusted models.

conclusionsIncreased food diversity may decrease the risk of MetS, which is important in dietary interventions of cardiac-cerebral vascular disease. This underscores the necessity of continued investigation into the role of HFD in the prevention of MetS and provides an integral framework for ongoing research.

Indexed as

Diet, HealthyEatingFeeding BehaviorAdolescentAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMetabolic SyndromeMiddle AgedNutrition SurveysNutritive ValuePrevalenceAdultDietHealthy food diversityMetabolic syndrome

Identifiers

PMID30285642
PMCPMC6171175
OpenAlexW2894856639

What OpenQuestion holds

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LicenceCC BY
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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.