Focus on Nutritional Aspects of Sarcopenia in Diabetes: Current Evidence and Remarks for Future Research

Geriatric Nutritional Risk Index is associated with adverse outcomes in patients with hypertension: the Fukushima Cohort study

Malnutrition is reportedly associated with adverse clinical outcomes in various populations. However, associations between nutritional status and adverse outcomes in patients with hypertension have not been sufficiently elucidated. We therefore aimed to investigate the impact of nutritional status as evaluated by the Geriatric Nutritional Risk Index (GNRI) on adverse outcomes in patients with hypertension. We conducted a retrospective cohort study of 1588 hypertensive patients enrolled in the Fukushima Cohort Study. Participants were categorized into tertiles (T1-T3) according to GNRI at baseline. The primary endpoint of the present study was a kidney event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease requiring kidney replacement therapy. Associations between GNRI and kidney events were assessed using Kaplan-Meier curves and multivariate Cox regression analyses. Median age was 64 years, 55% were men, median eGFR was 63.1 mL/min/1.73 m2, and median GNRI was 101.3. The lower GNRI group (T1) showed an increased incidence of kidney events in the Kaplan-Meier curve analysis. Compared to the highest GNRI group (T3), lower GNRI carried a higher risk of kidney events for both T2 (hazard ratio [HR] 1.38, 95% confidence interval [CI] 0.71-2.68) and T1 (HR 3.59, 95%CI 1.96-6.63). Similar relationships were observed for risks of all-cause death and cardiovascular events. Lower GNRI was associated with kidney events, all-cause death, and cardiovascular events in patients with hypertension. Nutritional status as evaluated by GNRI could offer a simple and useful predictor of adverse outcomes in this population.

A nutritional assessment tool, GNRI, predicts sarcopenia and its components in type 2 diabetes mellitus: A Japanese cross-sectional study

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.

Mathematical model of insulin kinetics accounting for the amino acids effect during a mixed meal tolerance test

Amino acids (AAs) are well known to be involved in the regulation of glucose metabolism and, in particular, of insulin secretion. However, the effects of different AAs on insulin release and kinetics have not been completely elucidated. The aim of this study was to propose a mathematical model that includes the effect of AAs on insulin kinetics during a mixed meal tolerance test. To this aim, five different models were proposed and compared. Validation was performed using average data, derived from the scientific literature, regarding subjects with normal glucose tolerance (CNT) and with type 2 diabetes (T2D). From the average data of the CNT and T2D people, data for two virtual populations (100 for each group) were generated for further model validation. Among the five proposed models, a simple model including one first-order differential equation showed the best results in terms of model performance (best compromise between model structure parsimony, estimated parameters plausibility, and data fit accuracy). With regard to the contribution of AAs to insulin appearance/disappearance (kAA model parameter), model analysis of the average data from the literature yielded 0.0247 (confidence interval, CI: 0.0168 - 0.0325) and -0.0048 (CI: -0.0281 - 0.0185) μU·ml-1/(μmol·l-1·min), for CNT and T2D, respectively. This suggests a positive effect of AAs on insulin secretion in CNT, and negligible effect in T2D. In conclusion, a simple model, including single first-order differential equation, may help to describe the possible AAs effects on insulin kinetics during a physiological metabolic test, and provide parameters that can be assessed in the single individuals.

Bioelectrical Impedance Analysis for the Assessment of Body Composition in Sarcopenia and Type 2 Diabetes

Sarcopenia is emerging as a severe complication in type 2 diabetes (T2DM). On the other hand, it has been documented that nutritional aspects, such as insufficient protein or total energy intake, increase sarcopenia risk. The analysis of body composition is a relevant approach to assess nutritional status, and different techniques are available. Among such techniques, bioelectrical impedance analysis (BIA) is particularly interesting, since it is non-invasive, simple, and less expensive than the other techniques. Therefore, we conducted a review study to analyze the studies using BIA for body composition analysis in T2DM patients with sarcopenia or at risk of catching it. Revised studies have provided important information concerning relationships between body composition parameters (mainly muscle mass) and other aspects of T2DM patients’ conditions, including different comorbidities, and information on how to avoid muscle mass deterioration. Such relevant findings suggest that BIA can be considered appropriate for body composition analysis in T2DM complicated by sarcopenia/muscle loss. The wide size of the patients’ cohort in many studies confirms that BIA is convenient for clinical applications. However, studies with a specific focus on the validation of BIA, in the peculiar population of patients with T2DM complicated by sarcopenia, should be considered.