ABSTRACT
Background: There are few reports evaluating the relationship between undernutrition and the risk of sarcopenia in type 2 diabetes mellitus (T2DM) patients. Objective: We investigated whether undernutritional status assessed by the geriatric nutritional risk index (GNRI) and controlling nutritional status (CONUT) were associated with the diagnosis of sarcopenia. Methods: This was a cross-sectional study of Japanese individuals with T2DM. Univariate or multivariate logistic regression analysis was performed to assess the association of albumin, GNRI, and CONUT with the diagnosis of sarcopenia. The optimal cut-off values were determined by the receiver operating characteristic (ROC) curve to diagnose sarcopenia. Results: In 479 individuals with T2DM, the median age was 71 years [IQR 62, 77], including 264 (55.1%) men. The median duration of diabetes was 17 [11, 23] years. The prevalence of sarcopenia was 41 (8.6%) in all, 21/264 (8.0%) in men, and 20/215 (9.3%) in women. AUCs were ordered from largest to smallest as follows: GNRI > albumin > CONUT. The cut-off values of GNRI were associated with a diagnosis of sarcopenia in multiple logistic regression analysis (odds ratio 9.91, 95% confidential interval 5.72-17.2), P < 0.001. The superiority of GNRI as compared to albumin and CONUT for detecting sarcopenia was also observed in the subclasses of men, women, body mass index (BMI) < 22, and BMI ≥ 22. Conclusions: Results showed that GNRI shows a superior diagnostic power in the diagnosis of sarcopenia. Additionally, its optimal cut-off points were useful overall or in the subclasses. Future large and prospective studies will be required to confirm the utility of the GNRI cut-off for undernutrition individuals at risk for sarcopenia.
ABSTRACT
Intake of gamma-aminobutyric acid (GABA) from nutritionally controlled hospital diet was analyzed and compared with those estimated by calculation. Thirty meals provided at a hospital in Okinawa were sampled. GABA content per meal were measured by HPLC and calculated from GABA content data in foods as much as available. As a result, out of a total of 30 meals, only 49.3% of the weight of food that appeared in the meals could be calculated. The analyzed and calculated median daily GABA intake was 67.3 mg and 30.0 mg. Overall, the calculated values were lower than the analytical values, but there was a significant positive correlation (rs=0.618, p<0.001). The more complete the database on GABA content, the more accurate the GABA intake could be estimated by calculation.
