Evidence map›Paper›PMID 38056943›Full record

ArticleBMJ open2023

Association between dietary knowledge and muscle mass in Chinese older adults: a cross-sectional and longitudinal study.

Qiaoling Liu, Luonan Wang, Yuge Ma, Yunjie Geng

Abstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
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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

4 authors.

Qiaoling LiuSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK q.liu.3@research.gla.ac.uk.ORCID 0000-0003-2109-3974
Luonan WangFaculty of Business, Economics and Law, The University of Queensland, Saint Lucia, Queensland, Australia.
Yuge MaThe First School of Clinical Medicine, Hainan Medical University, Haikou, Hainan, China.
Yunjie GengResearch Institute of Statistical Sciences, National Bureau of Statistics of China, Beijing, 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
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
Program Development (Pilot) CoreP30AG066615 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELIZABETH A FRANKENBERG · 2020 to 2026
$4.1M
Monitoring Social Change: Dynamics Of Aging And Cognitive FunctionR01AG065357 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 2020 to 2024
$3.6M
Emergence of Cardiometabolic Risk Across the Lifecycle in ChinaR01HL108427 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2012 to 2015
$2.7M
Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiomeR01DK104371 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2015 to 2018
$2.5M
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
FIC NIH HHS D43 TW009077NHLBI NIH HHS R01 HL108427NIA NIH HHS P30 AG066615NIA NIH HHS R01 AG065357NICHD NIH HHS P2C HD050924NICHD NIH HHS R01 HD030880NICHD NIH HHS R01 HD038700NIDDK NIH HHS P30 DK056350NIDDK NIH HHS R01 DK104371
6 · The paper itself

Abstract

objectivesThis study aims to explore the possible association between dietary knowledge and muscle mass in a Chinese population aged 60 years and above.

designCross-sectional and longitudinal studies.

settingData from the 2006 and 2011 China Health and Nutrition Survey (CHNS) were used for this study.

participantsA total of 1487 Chinese participants (44.38% males) aged 60 and above in the 2006 survey were included in the cross-sectional study. From the same study population, a total of 1023 participants (46.82% males) with normal muscle mass on the interview date of 2006 were included in the longitudinal study. OUTCOME MEASURES: Dietary knowledge was accessed by a validated CHNS questionnaire. Appendicular skeletal muscle mass was calculated using a validated anthropometric equation derived from a representative Chinese population. Based on the 2021 Chinese consensus on sarcopenia, the appendicular skeletal muscle mass was categorised as 'normal' or 'low' using sex-specific cut-off values.

resultsThe prevalence of low muscle mass in the study population was 31.20%, with a higher prevalence in females (34.22%). People with low muscle mass have a significantly lower dietary knowledge score (mean difference: -1.74, 95% CI -2.20 to -1.29). In the cross-sectional analysis, one score higher in dietary knowledge score was associated with a 4% lower odds of low muscle mass (OR=0.96, 95% CI 0.93 to 0.99). Compared with people in the lowest quartile of dietary knowledge, people in the highest quartile have a 44% lower odds of low muscle mass (OR=0.56, 95% CI 0.35 to 0.91). In the longitudinal analysis, no significant association was found between dietary knowledge and low muscle mass, yet the upper 95% CI was close to one (HR=0.97, 95% CI 0.93 to 1.01).

conclusionsSufficient dietary knowledge may play a protective role in maintaining normal muscle mass in Chinese adults aged 60 or above.

Indexed as

DietEast Asian PeopleSarcopeniaAgedCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansLongitudinal StudiesMaleMusclesMuscle, SkeletalAgedEPIDEMIOLOGIC STUDIESNUTRITION & DIETETICSRetrospective Studies

Identifiers

PMID38056943
PMCPMC10711816

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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.