Evidence map›Paper›PMID 32433046›Full record

ArticleChinese medical journal2020

Association of cardiovascular diseases with milk intake among general Chinese adults.

Xin-Yan Wang, Fang-Chao Liu, Xue-Li Yang, Jian-Xin Li, Jie Cao, Xiang-Feng Lu, Jian-Feng Huang, Ying Li, Ji-Chun Chen, Lian-Cheng Zhao and 7 more

Open access · goldAbstract read
In one paragraph

Article in Chinese medical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. The doseFrontiers in nutrition · 2026
    Pooled it
  2. Article
  3. Article
  4. 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

17 authors at 8 institutions in 1 country.

Xin-Yan WangKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Fang-Chao LiuKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Xue-Li YangKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Jian-Xin LiKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Jie CaoKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Xiang-Feng LuKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Jian-Feng HuangKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Ying LiKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Ji-Chun ChenKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Lian-Cheng ZhaoDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Chong ShenDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Dong-Sheng HuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, China.
Ying-Xin ZhaoShandong First Medical University, Jinan, Shandong 271000, China.
Ling YuDepartment of Cardiology, Fujian Provincial People's Hospital, Fuzhou, Fujian 350004, China.
Xiao-Qing LiuDivision of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou, Guangdong 510080, China.
Xian-Ping WuSichuan Center for Disease Control and Prevention, Chengdu, Sichuan 610041, China.
Dong-Feng GuKey Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNFujian Provincial People's Hospital · CNGuangdong Provincial People's Hospital · CNNanjing Medical University · CNShandong First Medical University · CNShenzhen University Health Science Center · CNSichuan Center for Disease Control and Prevention · CNSouthern University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe association of milk intake with cardiovascular disease (CVD) and cause-specific mortality remained controversial and evidence among the Chinese population was limited. We aimed to study the relationship between milk intake and CVDs among general Chinese adults.

methodsA total of 104,957 participants received questionnaire survey. Results of physical examination such as anthropometric measurements and biochemical tests during 2007 to 2008, demographic data and their information on milk intake were collected through standardized questionnaires. Cox proportional hazard regression models were used to calculate hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) of CVD incidence, cause-specific mortality and all-cause mortality related to milk intake. Restricted cubic splines (RCSs) were applied to examine dose-response associations.

resultsAmong the 91,757 participants with a median follow-up period of 5.8 years, we documented 3877 CVD cases and 4091 all-cause deaths. Compared with participants who never consumed milk, the multivariate-adjusted HRs (95% CIs) of CVD incidence for 1 to 150 g/day, 151 to 299 g/day, and ≥300 g/day were 0.94 (0.86-1.03) (P > 0.05), 0.77 (0.66-0.89) (P < 0.05), and 0.59 (0.40-0.89) (P < 0.05), respectively; each 100 g increase of daily milk intake was associated with 11% lower risk of CVD incidence (HR, 0.89; 95% CI: 0.85-0.94; P < 0.001), and 11% lower risk of CVD mortality (HR, 0.89; 95% CI: 0.82-0.97; P = 0.008) after adjustment for age, sex, residential area, geographic region, education level, family history of CVD, smoking, alcohol drinking, physical activity level, body mass index, and healthy diet status (ideal or not). RCS analyses also showed a linear dose-response relationship with CVD (P for overall significance of the curve <0.001; P for non-linearity = 0.979; P for linearity <0.001) and stroke (P for overall significance of the curve = 0.010; P for non-linearity = 0.998; P for linearity = 0.002) incidence, and CVD mortality (P for overall significance of the curve = 0.045; P for non-linearity = 0.768; P for linearity = 0.014) within the current range of daily milk intake.

conclusionsDaily milk intake was associated with lower risk of CVD incidence and mortality in a linear inverse relationship. The findings provide new evidence for dietary recommendations in CVD prevention among Chinese adults and people with similar dietary pattern in other countries.

Indexed as

Cardiovascular DiseasesAdultAnimalsChinaHumansIncidenceMilkProportional Hazards ModelsRisk Factors

Identifiers

PMID32433046
PMCPMC7249710
OpenAlexW3017031712

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.