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

Reporting of nutritional screening, status, and intake in trials of nutritional and physical rehabilitation following critical illness: a systematic review

BACKGROUND

Surviving critical illness leads to prolonged physical and functional recovery with both nutritional and physical rehabilitation interventions for prevention and treatment being investigated. Nutritional status and adequacy may influence outcome, but no consensus on which nutritional-related variables should be measured and reported in clinical trials exists.

OBJECTIVES

This study aimed to undertake a systematic review investigating the reporting of nutritional screening, nutritional status, and nutritional intake/delivery in randomized controlled trials (RCTs) evaluating nutritional and/or physical rehabilitation on physical and functional recovery during and following critical illness.

METHODS

Five electronic databases (MEDLINE, Web of Science, EMBASE, CINAHL, and Cochrane) were searched (last update 9 August, 2023). Search terms included both free text and standardized indexed terms. Studies included were RCTs assessing nutritional and/or physical interventions either during or following intensive care unit (ICU) admission in adults (18 y or older) with critical illness, and who required invasive mechanical ventilation for any duration during ICU admission. Study quality was assessed using the Cochrane Collaboration Risk of Bias tool for RCTs and descriptive data synthesis was performed and presented as counts (%). n t RESULTS: In total, 123 RCTs (30 nutritional, 87 physical function, and 6 combined) were included. Further, ≥1 nutritional variable was measured and/or reported in 99 (80%) of the studies including BMI (n = 69), body weight (n = 57), nutritional status (n = 11), nutritional risk (n = 10), energy delivery (n = 41), protein delivery (n = 35), handgrip strength (n = 40), and other nutritional-related muscle variables (n = 41). Only 3 studies were considered to have low risk of bias in all categories.

CONCLUSIONS

Few RCTs of physical rehabilitation measure and report nutritional or related variables. Future studies should measure and report specific nutritional factors that could impact physical and functional recovery to support interpretation where studies do not show benefit. This protocol was preregistered at PROSPERO as CRD42022315122.

Correlation of physical function and physical activity with muscle mass measured with computed tomography in adult hemodialysis patients

Introduction

Background: muscle mass (MM) plays an important role in the physical function of hemodialysis patients; however, muscle mass measurement can be unreliable and expensive. In contrast, the measurement of physical function (PF) is simple and inexpensive and may serve as an alternative. The aim of this study was to correlate the measurement of MM by computed tomography (CT) with physical function measurements and physical activity (PA) levels in HD patients. Methods: this was a cross-sectional study that included 38 HD patients from a single HD clinic. Each participant had a CT scan to measure mid-thigh muscle mass. Physical function tests were assessed using the six-minute walk test (SMWT), handgrip strength (HGS) test, 5 x sit-to-stand test (STS5), timed up and go test (TUGT) and Short Physical Performance Battery (SPPB), while physical activity levels were measured using the Godin-Shephard leisure-time physical activity questionnaire. Correlation analysis was used to examine the relationship between variables. Results: handgrip strength was strongly positively correlated with thigh muscle area (r = 0.656, p ≤ 0.001) and weakly correlated with arm muscle area (r = 0.396, p = 0.002), SMWT (r = 0.373, p = 0.004), SPPB (r = 0.269, p = 0.041) and physical activity (r = 0.323, p = 0.013). There was also a trend for an inverse correlation between handgrip strenght and TUGT (r = -0.235, p = 0.076). Positive correlations were found between the thigh muscle area and the SPPB (r = 0.339, p = 0.009) and PA (r = 0.293, p = 0.025), while there was a trend for an inverse correlation between thigh muscle area and STS5 (r = -0.256, p = 0.052). Conclusion: several measures of PF and strenght were correlated with objectives measurements of MM, thus provide options for low-cost measurements related to muscle mass.

Association of Handgrip Strength and Nutritional Status in Non-Dialysis-Dependent Chronic Kidney Disease Patients: Results from the KNOW-CKD Study

Handgrip strength (HGS) is suggested as an indirect assessment of nutritional status in chronic kidney disease (CKD) patients, but evidence is limited for non-dialysis-dependent CKD (NDD-CKD) patients. This cross-sectional study included 404 patients from the Phase II KoreaN Cohort Study for Outcome in Patients With CKD. HGS, measured twice in each hand, was the exposure, and malnutrition status was defined by a malnutrition-inflammation score (MIS) of 6 or higher. A logistic regression analysis adjusted for age, sex, diabetes mellitus (DM), hypertension, CKD stages, smoking, overhydration, education, and income status was used to assess malnutrition risk. The predictability of HGS for malnutrition was evaluated using the area under the curve (AUC). Patients with lower HGS were older, had a higher prevalence of DM, and lower estimated glomerular filtration rate. Higher HGS was significantly associated with lower malnutrition risk after adjustment (per 1 standard deviation increase, adjusted odds ratio, 0.47 [0.30-0.75]). Subgroup analyses showed no significant interaction between HGS and malnutrition risk across age, sex, DM, and CKD stage. HGS showed fair predictability for malnutrition in men (AUC 0.64 [0.46-0.83]) and women (AUC 0.71 [0.55-0.86]). In conclusion, HGS is a useful diagnostic indicator of malnutrition in NDD-CKD 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.