Research Questions:
This study aims to systematically explore the relationships between the digestive, absorptive, and metabolic characteristics of the elderly population and their nutritional requirement patterns. We will utilize population health data, biochemical indicators, lifestyle information, dietary habit data, etc., from the UK Biobank, combined with machine learning algorithms (including Recurrent Neural Networks – RNN and Long Short-Term Memory networks – LSTM) to build predictive models for the nutritional requirements of the elderly population. The research will focus on common age-related health issues such as sarcopenia, osteoporosis, memory decline, constipation, and intestinal bloating, mining the associations between these conditions and nutritional metabolic characteristics through big data analysis and multi-omics technologies.
Research Objectives:
1.Construct a database related to the use of nutritional health products, nutrient intake, and health/disease status in the elderly population.
2.Establish a predictive model for the nutritional requirements of the elderly population.
3.Identify key biomarkers associated with nutrition and metabolism in the elderly.
4.Provide a scientific basis for the development of specialized nutritional formula foods for the elderly.
Scientific Rationale:
The scientific rationale of this study is based on real-world data analysis and artificial intelligence modeling. By integrating multi-source data, it systematically elucidates the digestive, absorptive, and metabolic characteristics of the elderly population, providing theoretical support for improving their quality of life and nutritional health status.