Publications by authors named "A Granic"

Purpose: To investigate associations between glycaemic measures (HbA1c, random glucose), and grip strength (GS) in adults without prevalent diabetes.

Methods: We included 381,715 UK Biobank participants aged 38-73 years without diabetes (any type) with complete baseline measures for GS and HbA1c (main analyses), and glucose (supplementary analyses). Cross-sectional sex- and age-stratified associations between each glycaemic measure, GS, and probable sarcopenia (low GS) were examined with regression analyses.

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Loss of skeletal muscle strength and mass (sarcopenia) is common in older adults and associated with an increased risk of disability, frailty and premature death. Finding cost-effective prevention and treatment strategies for sarcopenia for the growing ageing population is therefore of great public health interest. Although nutrition is considered an important factor in the aetiology of sarcopenia, its potential for sarcopenia prevention and/or treatment is still being evaluated.

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Background: Older adults living with multiple long-term conditions (MLTC, also known as multimorbidity) and frailty are more likely to experience a deterioration in their health requiring specialist referral or hospital admission than individuals without these syndromes. However, this group of older people are underserved by research meaning that there is a limited evidence base for their care. This study therefore aimed (1) to determine if it is feasible to recruit and collect quantitative data to describe the health and lifestyle of older adults living with MLTC, frailty and a recent deterioration in health and (2) to assess if taking part in research is acceptable to this group of older adults.

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Maximising the potential benefit of resistance exercise (RE) programs by ensuring optimal recovery is an important aim of exercise prescription. Despite this, research surrounding recovery from RE in older adults is limited and inconsistent. The following randomised controlled trial was designed to investigate the efficacy of milk consumption for improving recovery from RE in older adults.

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Objectives: This study aims to determine whether machine learning can identify specific combinations of long-term conditions (LTC) associated with increased sarcopenia risk and hence address an important evidence gap-people with multiple LTC (MLTC) have increased risk of sarcopenia but it has not yet been established whether this is driven by specific combinations of LTC.

Design: Decision trees were used to identify combinations of LTC associated with increased sarcopenia risk. Participants were classified as being at risk of sarcopenia based on maximum grip strength of <32 kg for men and <19 kg for women.

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