Association between glycaemic control and malnutrition in older adults with type 2 diabetes mellitus: a cross-sectional study.
Rattiyaphon ThaenpramunNarucha KomolsuradejNapakkawat BuathongSiwaluk SrikrajangPublished in: The British journal of nutrition (2024)
Malnutrition is a major problem among older adults with type 2 diabetes mellitus (T2DM). Some studies suggest that well glycaemic control increases the risk of frailty due to reduced intake. Therefore, it could be hypothesised that adequate glycaemic controlled patients may be at risk of malnutrition. This study aimed to examine, in older adults with T2DM, the association between adequate glycaemic control and malnutrition as well as identify the risk factors for malnutrition. Data including general characteristics, health status, depression, functional abilities, cognition and nutrition status were analysed. Poor nutritional status is defined as participants assessed with the Mini Nutritional Assessment as being at risk of malnutrition or malnourished. Adequate glycaemic control refers to an HbA1c level that meets the target base in the American Diabetes Association 2022 guidelines with individualised criteria. There were 287 participants with a median (interquartile range) age of 64 (61-70) years, a prevalence of poor nutrition, 15 %, and adequate glycaemic control, 83·6 %. This study found no association between adequate glycaemic control and poor nutrition ( P = 0·67). The factors associated with poor nutritional status were low monthly income (adjusted OR (AOR) 4·66, 95 % CI 1·28, 16·98 for income < £118 and AOR 7·80, 95 % CI 1·74, 34·89 for income £118-355), unemployment (AOR 4·23, 95 % CI 1·51, 11·85) and cognitive impairment (AOR 5·28, 95 % CI 1·56, 17·93). These findings support the notion that older adults with T2DM should be encouraged to maintain adequate glycaemic control without concern for malnutrition, especially those who have low income, unemployment or decreased cognitive functions.
Keyphrases
- type diabetes
- physical activity
- glycemic control
- cognitive impairment
- mental health
- cardiovascular disease
- end stage renal disease
- chronic kidney disease
- depressive symptoms
- risk factors
- middle aged
- community dwelling
- machine learning
- skeletal muscle
- insulin resistance
- sleep quality
- peritoneal dialysis
- deep learning
- weight gain
- white matter
- artificial intelligence
- metabolic syndrome
- clinical evaluation