Evidence map›Paper›PMID 34528018›Full record

ArticleThe Lancet regional health. Western Pacific2021

Dietary patterns and cardiometabolic risks in diverse less-developed ethnic minority regions: results from the China Multi-Ethnic Cohort (CMEC) Study.

Xiong Xiao, Zixiu Qin, Xiaoyan Lv, Yingxue Dai, Zhuoga Ciren, Yangla Yangla, Peibin Zeng, Yue Ma, Xiang Li, Lina Wang and 9 more

Open access · goldAbstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
13.1field-weighted citation impact, top 1% 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

53 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 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

19 authors at 8 institutions in 1 country.

Xiong XiaoWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Zixiu Qinthe key Laboratory of Environmental Pollution Monitoring and Disease Control, School of Public Health, Guizhou Medical University, Guiyang, China.
Xiaoyan LvChongqing Municipal Center for Disease Control and Prevention, Chongqing, China.
Yingxue DaiChengdu Center for Disease Control and Prevention, Chengdu, China.
Zhuoga CirenTibet Center for Disease Control and Prevention, Lhasa, China.
Yangla YanglaTibet University, Lhasa, China.
Peibin ZengTibet University, Lhasa, China.
Yue MaWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Xiang LiInstitute of Surveying and Mapping, Information Engineering University, Zhengzhou, China.
Lina WangSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Yifan HuWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Fan YangWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Chaonan FanWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Dan TangWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Suyao DaiWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Ning ZhangWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Xiaofen XieWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Jianzhong YinSchool of Public Health, Kunming Medical University; Baoshan College of Traditional Chinese Medicine, Kunming, China.
Xing ZhaoWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Sichuan University · CNTibet University · CNChengdu Center for Disease Control and Prevention · CNGuiyang Medical University · CNKunming Medical University · CNPLA Information Engineering University · CNTibet Autonomous Region People's Hospital · CNZhengzhou University of Light Industry · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background In Western developed countries, food-based dietary patterns have been associated with the risk of cardiometabolic diseases, but little is known about such associations in less developed ethnic minority regions (LEMRs), where the cardiometabolic disease burden is growing rapidly and food patterns differ substantially. Methods Between May 2018 and September 2019, we recruited 99556 participants aged 30-79 years from the China Multi-Ethnic Cohort (CMEC) Study. We measured habitual dietary intake with validated food frequency questionnaire (FFQ) and then calculated dietary pattern scores for two of the most studied a priori dietary patterns, i.e., Dietary Approaches to Stop Hypertension (DASH) and alternative Mediterranean (aMED) style diets, and three a posteriori dietary patterns. Four cardiometabolic risks, including hypertension, diabetes, dyslipidaemia and metabolic syndrome (MetS), were newly diagnosed by medical examination and blood tests. We estimated adjusted odds ratios (OR) relating various dietary pattern scores to cardiometabolic risks using marginal structural models under the guidance of directed acyclic graphs. For the above associations, we further calculated the proportion mediated by overweight (PM) using regression-based mediation analysis for better public health implications. Findings The final study sample consisted of 68834 participants. Among them, we newly diagnosed 12803 hypertension, 3527 diabetes, 16342 hyperlipidaemia, and 8198 MetS cases. Overall, all 5 dietary patterns showed considerable associations with risks of hypertension and MetS. Comparing the highest with the lowest quintiles, the DASH score showed the strongest inverse associations with risks of hypertension (OR=0.74, 95% CI:0.70-0.79; PM=10%) and MetS (OR=0.79, 95% CI:0.74-0.85; PM=35%); conversely, scores of the localized a posteriori Yunnan-Guizhou plateau dietary pattern in LEMRs showed the strongest positive associations with risks of hypertension (OR=1.44, 95% CI:1.35-1.52; PM=10%) and MetS (OR=1.35, 95% CI:1.26-1.46; PM=33%), with all

Indexed as

Cardiometabolic risksDietary patternepidemiological studyethnic disparitysocioeconomic status disparity

Identifiers

PMID34528018
PMCPMC8383007
OpenAlexW3193584937

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.