ArticlePublic health nutrition2020
Nutrient and food group intakes and skeletal muscle index in the Japanese elderly: a cross-sectional analysis of the NHNS 2017.
Article in Public health nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Evaluation of the nutrient content of dishes by the dish-version nutrient profile model in Japanese adults: the NHNS 2019.BMC nutrition · 2026Article
- Seasonal variation in fresh fruit consumption: insights from the Sukoyaka health survey 2019-2020 in Japan.BMC nutrition · 2026Article
- Multiple regression model for ascertaining the skeletal muscle mass index using grip strength and lifestyle factors in older outpatients.Journal of physical therapy science · 2025Article
- Dietary characteristics of urban community-dwelling older adults with low muscle mass: the bunkyo health study: a cross-sectional study.BMC geriatrics · 2024Article
- Satellite Cells Exhibit Decreased Numbers and Impaired Functions on Single Myofibers Isolated from Vitamin B6-Deficient Mice.Nutrients · 2021Article
- Low Protein Diets and Plant-Based Low Protein Diets: Do They Meet Protein Requirements of Patients with Chronic Kidney Disease?Nutrients · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo examine nutrient and food intakes according to the levels of skeletal muscle mass index (SMI) in the elderly.
designCross-sectional study.
settingData were derived from the 2017 National Health and Nutrition Survey in Japan. SMI was calculated by dividing appendicular skeletal muscle (or lean) mass (kg) by height squared (m2). We calculated the multivariable-adjusted means of individuals' dietary intake. Dietary intake of energy, nutrients and food categories was assessed by examining dietary records using a semi-weighed method and compared according to the sex-specific quartiles of SMI.
participantsMen and women aged ≥60 years.
resultsAmong 797 men and 969 women, individuals with a higher SMI consumed more energy and more nutrients than did those with a lower SMI after adjusting for age, lifestyle and physical activity factors. After further adjusting for energy intake, total dietary fibre, vitamin A, vitamin B6, K, Fe and Cu were positively associated with higher SMI in men (Pfor trend < 0·05). For food categories, men with a higher SMI consumed more vegetables and meats, but the associations were attenuated after adjustment for energy and remained significant for vegetable only (Pfor trend = 0·018).
conclusionsJapanese elderly people with a higher SMI consumed more energy and nutrients and more vegetables than did those with a lower SMI. This finding shows that diet is important in preventing muscle loss among the elderly in an ageing society.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.