Dataset Information

Nutrient and food group intakes and skeletal muscle index in the Japanese elderly: a cross-sectional analysis of the NHNS 2017.

ABSTRACT:

Objective

To examine nutrient and food intakes according to the levels of skeletal muscle mass index (SMI) in the elderly.

Design

Cross-sectional study.

Setting

Data 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.

Participants

Men and women aged ≥60 years.

Results

Among 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).

Conclusions

Japanese 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.

SUBMITTER: Okada C

PROVIDER: S-EPMC10200537 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nutrient and food group intakes and skeletal muscle index in the Japanese elderly: a cross-sectional analysis of the NHNS 2017.

Okada Chika C Okada Emiko E Takimoto Hidemi H

Public health nutrition 20200529 16

<h4>Objective</h4>To examine nutrient and food intakes according to the levels of skeletal muscle mass index (SMI) in the elderly.<h4>Design</h4>Cross-sectional study.<h4>Setting</h4>Data 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 ...[more]

PMID: 32466809

Similar Datasets

Project description:BackgroundAccurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are often prone to recall error and social desirability bias, limiting their reliability. With the advancement of artificial intelligence (AI), there is potential to overcome these limitations through automated, objective, and scalable dietary assessment techniques. However, the effectiveness and challenges of AI applications in this domain remain inadequately explored.ObjectiveThis study aimed to conduct a scoping review to synthesize existing literature on the efficacy, accuracy, and challenges of using AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement.MethodsThis review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive literature search was conducted in 4 databases-PubMed, Web of Science, Cochrane Library, and EBSCO-covering publications from the databases' inception to June 30, 2023. Studies were included if they used modern AI approaches to assess food and nutrient intakes in human subjects.ResultsThe 25 included studies, published between 2010 and 2023, involved sample sizes ranging from 10 to 38,415 participants. These studies used a variety of input data types, including food images (n=10), sound and jaw motion data from wearable devices (n=9), and text data (n=4), with 2 studies combining multiple input types. AI models applied included deep learning (eg, convolutional neural networks), machine learning (eg, support vector machines), and hybrid approaches. Applications were categorized into dietary intake assessment, food detection, nutrient estimation, and food intake prediction. Food detection accuracies ranged from 74% to 99.85%, and nutrient estimation errors varied between 10% and 15%. For instance, the RGB-D (Red, Green, Blue-Depth) fusion network achieved a mean absolute error of 15% in calorie estimation, and a sound-based classification model reached up to 94% accuracy in detecting food intake based on jaw motion and chewing patterns. In addition, AI-based systems provided real-time monitoring capabilities, improving the precision of dietary assessments and demonstrating the potential to reduce recall bias typically associated with traditional self-report methods.ConclusionsWhile AI demonstrated significant advantages in improving accuracy, reducing labor, and enabling real-time monitoring, challenges remain in adapting to diverse food types, ensuring algorithmic fairness, and addressing data privacy concerns. The findings suggest that AI has transformative potential for dietary assessment at both individual and population levels, supporting precision nutrition and chronic disease management. Future research should focus on enhancing the robustness of AI models across diverse dietary contexts and integrating biological sensors for a holistic dietary assessment approach.

Project description:BackgroundHispanics are a heterogeneous group of individuals with a variation in dietary habits that is reflective of their cultural heritage and country of origin. It is important to identify differences in their dietary habits because it has been well established that nutrition contributes substantially to the burden of preventable diseases and early deaths in the United States.ObjectiveWe estimated the distribution of usual intakes (of both food groups and nutrients) by Hispanic and Latino backgrounds by using National Cancer Institute methodology.DesignThe Hispanic Community Health Study/Study of Latinos is a population-based cohort study that recruited participants who were 18-74 y of age from 4 US cities in 2008-2011 (Miami, Bronx, Chicago, and San Diego). Participants who provided at least one 24-h dietary recall and completed a food propensity questionnaire (n = 13,285) were included in the analyses. Results were adjusted for age, sex, field center, weekend, sequencing, and typical amount of intake.ResultsOverall, Cubans (n = 2128) had higher intakes of total energy, macronutrients (including all subtypes of fat), and alcohol than those of other groups. Mexicans (n = 5371) had higher intakes of vitamin C, calcium, and fiber. Lowest intakes of total energy, macronutrients, folate, iron, and calcium were reported by Dominicans (n = 1217), whereas Puerto Ricans (n = 2176) had lowest intakes of vitamin C and fiber. Food-group servings reflected nutrient intakes, with Cubans having higher intakes of refined grains, vegetables, red meat, and fats and Dominicans having higher intakes of fruit and poultry, whereas Puerto Ricans had lowest intakes of fruit and vegetables. Central and South Americans (n = 1468 and 925, respectively) were characterized by being second in their reported intakes of fruit and poultry and the highest in fish intake in comparison with other groups.ConclusionVariations in diet noted in this study, with additional analysis, may help explain diet-related differences in health outcomes observed in Hispanics and Latinos.

Dataset Information

Nutrient and food group intakes and skeletal muscle index in the Japanese elderly: a cross-sectional analysis of the NHNS 2017.

Objective

Design

Setting

Participants

Results

Conclusions

Publications

Nutrient and food group intakes and skeletal muscle index in the Japanese elderly: a cross-sectional analysis of the NHNS 2017.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets