Akademik Veri Yönetim Sistemi
18th Congress of the European Geriatric Medicine Society, London, İngiltere, 28 - 30 Eylül 2022, cilt.13, sa.1, ss.427
Introduction: Malnutrition and sarcopenia are geriatric syndromes that are common in older adults and associated with increased rates of disability, frailty and mortality. In diagnosis of both geriatric syndromes, skeletal muscle mass should be measured and determined whether it is low. After measuring the skeletal muscle mass (SMM) in kg, the skeletal muscle mass index (SMMI) should be determined according to the body structure/size of the individual, and then it should be evaluated whether it is within the normal limits. The most commonly used methods in SMMI calculation are the adjustments made with height2, weight and body mass index (BMI) ([SMM(kg)/height2 (m2)]; [SMM(kg)/weight(kg)]; [SMM(kg)/BMI (kg/m2), respectively]. In this study, our aim is to investigate the correlation of all 3 SMMI values with each other and with hand grip strength.
Methods: We included outpatients evaluated in the geriatrics outpatient clinic of a university between July 2012- July 2022 with muscle mass and handgrip strength (HGS) measurement. Muscle mass was evaluated with bio-electrical impedance analysis (TANITA BC532) in the morning after fasting and handgrip strength with JAMAR brand hydraulic hand dynamometer. National cut-off values were used for all parameters related to sarcopenia. EWGSOP2 (UK) cut-off values (males, 27/females, 16 kg) were used as alternative cut-offs for handgrip strength. Correlation coefficient (r):\0.3; negligible; r: 0.3–0.5; weak, r: 0.5–0.7; moderate, r:[0.7 were accepted as strong correlation.
Results: We included 1791 patients. The mean (SD) age was 75.1 ± 6.5 (min–max: 65–99). The prevalence of low muscle mass was 2.3% with SMMI (height 2), 45.8% with SMMI (weight), and 64.5% with SMMI (BMI). Low HGS prevalences were 38.6% with Turkish cut-offs (35/20 kg) and 12.1% with EWGSOP2 (England) cut-offs (27//16 kg). The best correlation between SMMI values was found between SMMI(BMI) and SMMMI(weight) which was a strong correlation (r = 0.857, p\0.001). No relationship was found between SMMI(height2) and SMMI(weight) or SMMI(height2) and SMMI(BMI). All three SMMI parameters were significantly associated with HGS (p\0.001, for all). The most strongly associated SMMI parameter with HGS was SMMI(BMI) (r = 0.584, p\0.001). The relationships between HGS and SMMI (weight) and SMMI (height2) were negligible (r = 0.302, r = 0.284; respectively).
Conclusions: There was a strong correlation between SMMI(BMI) and SMMMI(weight), while SMMI(height2) was not correlated with other SMMI parameters. The most related SMMI with HGS, which is the functional parameter of the muscle, was the adjustment of skeletal muscle mass with BMI [SMMI(BMI)]. Low muscle mass detected by SMMI(height2) had a negligible frequency and seems unsuitable for determining low muscle mass. Our study suggests that SMMI (BMI) is the most appropriate SMMI parameter in the determination of skeletal muscle mass index and subsequently in the evaluation of malnutrition and sarcopenia.