Evidence map›Paper›PMID 34791509›Full record

ArticleEuropean journal of nutrition2022

Ultra-processed food intake is associated with grip strength decline in middle-aged and older adults: a prospective analysis of the TCLSIH study.

Shunming Zhang, Yeqing Gu, Sabina Rayamajhi, Amrish Thapa, Ge Meng, Qing Zhang, Li Liu, Hongmei Wu, Tingjing Zhang, Xuena Wang and 16 more

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 8% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
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  13. Navigating Ultra-Processed Foods with Insight.Diabetes & metabolism journal · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. The role of nutrition in the prevention of sarcopenia.The American journal of clinical nutrition · 2023
    Review
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  19. Review
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

26 authors at 6 institutions in 2 countries.

Shunming Zhang *Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yeqing Gu *Nutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Sabina RayamajhiNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Amrish ThapaNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Ge MengNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Qing ZhangHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Li LiuHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Hongmei WuNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Tingjing ZhangNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Xuena WangNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Jun DongNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Xiaoxi ZhengNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Zhixia CaoNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Xu ZhangNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Xinrong DongNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
Shaomei SunHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Xing WangHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Ming ZhouHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Qiyu JiaHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Kun SongHealth Management Centre, Tianjin Medical University General Hospital, Tianjin, China.
Jian HuangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Junsheng HuoNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Bing ZhangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Gangqiang DingNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Lu QiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA. lqi1@tulane.edu.
Kaijun NiuNutrition and Radiation Epidemiology Research Center, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. nkj0809@gmail.com.ORCID http://orcid.org/0000-0002-8772-2481
Tianjin Medical University · CNTianjin Medical University General Hospital · CNChinese Center For Disease Control and Prevention · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNTianjin University · CNTulane University · US

Funding

National Natural Science Foundation of China 81372118National Natural Science Foundation of China 81673166National Natural Science Foundation of China 91746205
6 · The paper itself

Abstract

purposeUltra-processed foods make up more than 50% of daily energy consumed in Western countries and are rapidly increasing in China. However, little is known about the association between ultra-processed food intake and muscle strength, a predictor for physical disability in senior years. We aimed to investigate the association of ultra-processed food intake with longitudinal changes in grip strength among middle-aged and older Chinese adults.

methodsThis prospective cohort study included a total of 5409 adults aged 40 years and over (61.3% men). Ultra-processed food intake was obtained by means of a validated food frequency questionnaire and classified according to the NOVA classification system. Grip strength was measured annually using a handheld digital dynamometer. Multivariable linear regression models were used to examine the association between ultra-processed food intake and annualized change in grip strength and weight-adjusted grip strength.

resultsIn the fully adjusted models, annualized changes in grip strength and weight-adjusted grip strength per 10% increment in the proportion of ultra-processed foods in the diet were - 0.3708 kg (95% confidence interval - 0.5687, - 0.1730; P < 0.001) and - 0.0057 kg/kg (95% confidence interval - 0.0086, - 0.0029; P < 0.0001), respectively. In analyses stratified by age, sex, body mass index, physical activity, hypertension, hyperlipidemia, diabetes, and healthy diet score, such associations were largely consistent in most subgroups (all P for interaction > 0.05).

conclusionsOur data indicate that higher ultra-processed food intake was associated with faster grip strength decline in middle-aged and older Chinese adults.

Indexed as

Energy IntakeFast FoodsAdultAgedDietEatingFemaleFood HandlingHand StrengthHumansMaleMiddle AgedProspective StudiesDietEpidemiologyGrip strengthNutritionUltra-processed foods

Identifiers

PMID34791509
OpenAlexW3211882664

What OpenQuestion holds

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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.