Handgrip strength as an instrument for assessing the risk of malnutrition and inflammation in hemodialysis patients

Association of handgrip strength with anthropometry of a Brazilian healthy adolescent sample

The importance of dynamometric and anthropometric information for industry is known; however, few studies have investigated the relationship between anthropometry and handgrip strength (HGS) in adolescents. The aim of the present study was to investigate the association of anthropometric variables with the level of HGS production in adolescents. Participants were 541 adolescents aged 12-16 years from a public school in the city of Rio de Janeiro. The behavior of the following variables was examined: the ability to generate HGS, age, total body mass, height and hand dimensions (hand width, hand span, hand length). The results showed an increase in the ability to generate HGS in adolescence and a significant difference between the genders from 13 years old, where boys generated higher HGS values. Correlations between HGS and total body mass and the hand width stood out in both genders, indicating a greater association.

Phase Angle and Handgrip Strength as a Predictor of Disease-Related Malnutrition in Admitted Patients: 12-Month Mortality

Background: Phase Angle (PhA) value measured by bioelectrical impedance analysis (BIA) could be considered a good marker of the patient’s cell mass and cellular damage. Various studies have shown that the value of PhA is associated with an increased nutritional risk in several pathologies. However, not many studies have focused on the use of PhA as a screening tool in admitted patients. The aim of this study is to evaluate the prognostic value of PhA to determine disease-related malnutrition (DRM) and the risk that this entails for mortality and length of stay (LOS). Methods: 570 patients admitted to the hospital for different causes were included in this retrospective observational study. Patients’ nutritional risk was assessed by screening tests such as the Malnutrition Universal Screening tool (MUST) and Subjective Global Assessment (SGA), in addition to non-invasive functional techniques, such as BIA and handgrip strength (HGS), 24−48 h after admission. After performing an SGA as the gold standard to assess malnutrition, PhA and SPhA values were used to determine DRM. Furthermore, both samples: malnutrition status (MS) and non-malnutrition status (NMS) were compared, with SphA-Malnutrition corresponding to a diagnosis of malnutrition. Statistical analysis of the sample was conducted with JAMOVI version 2.2.2. Results: Patients with MS had lower PhA and SPhA than patients with NMS (p < 0.001). The ROC curve analysis (AUC = 0.81) showed a cut-off point for MS for PhA = 5.4° (sensitivity 77.51% and specificity 74.07%) and AUC = 0.776 with a cut-off point for SPhA = −0.3 (sensitivity 81.74% and specificity 63.53%). Handgrip strength (HGS) was also observed to be a good predictor in hospitalized patients. Carrying out a comparative analysis between MS and NMS, length of stay (LOS) was 9.0 days in MS vs. 5.0 days in NMS patients (OR 1.07 (1.04−1.09, p < 0.001)). A low SPhA-malnutrition value (SPhA < −0.3) was significantly associated with a higher mortality hazards ratio (HR 7.87, 95% CI 2.56−24.24, p < 0.001). Conclusion: PhA, SPhA and HGS are shown to be good prognostic markers of DRM, LOS and mortality and could therefore be useful screening tools to complement the nutritional assessment of admitted patients.

Geriatric nutritional risk index in prediction of muscular strength of elderly patients undergoing hemodialysis

PURPOSE

Geriatric nutritional risk index (GNRI) is one of the new tools to determine nutritional status in the elderly. This study assessed the association between GNRI and muscular strength through handgrip strength (HGS) in patients undergoing hemodialysis.

METHODS

This cross-sectional analytical study assessed 110 hemodialysis patients at Guilan, North of Iran, (mean age of 70.3 ± 6.93), 57 men and 53 women through simple random sampling. Demographic characteristics, GNRI, and HGS of patients were determined. Data were analyzed using descriptive and inferential statistics, including independent t test, AVOVA, Pearson correlation, and linear multiple regression tests.

RESULTS

The mean values of the GNRI and HGS were 93.90 ± 11.06 and 14.82 ± 3.72, respectively. Finally, it was identified that there is a direct and significant association between GNRI and HGS (p = 0.001, r = 0.734). Linear multiple regression showed that GNRI is an independent predictor of HGS (Adj.R² = 0.67, β_GNRI = 8.13).

CONCLUSION

GNRI can be used as a predictor of muscular strength in hemodialysis patients.