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Objective: Hand grip strength (HGS) is frequently used in clinical practice, resulting in a potential marker of nutritional status. This study aimed to develop reference values of HGS in Italian women with different categories of body mass index (BMI). Additionally, the main predictors of HGS were identified., Methods: A cross-sectional study was conducted in Italian women between ages 16 and 55 y with different categories of BMI at the Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples Italy. The whole sample was divided into tertiles according to BMI: 15 to 17.29 kg/m 2 (T1), 17.3 to 19.9 kg/m 2 (T2), and 20 to 25 kg/m 2 (T3). Anthropometry, bioimpedance analysis, and muscle strength by an HGS test were evaluated. The cut-off values for HGS were developed for all participants and stratified by age group. Finally, a multivariate linear regression analysis was performed to assess the main predictors of HGS., Results: A total of 529 women with a mean age of 23.2 ± 7.0 y and an average BMI of 18.9 ± 2.5 kg/m 2 were analyzed. HGS was higher for the dominant hand than for the non-dominant hand in all BMI tertiles. On both sides, according to age groups, HGS increased with increasing age in T1 and T3, whereas it increased in the women between ages 20 and 30 y in T2 only. Multivariate linear regression analysis showed that predictors of HGS varied according to tertiles. Specifically, we found that body weight (R 2  = 0.252) was the main predictor in T1, whereas phase angle (PhA) was the main determinant in both T2 (R 2  = 0.240) and T3 (R 2  = 0.216)., Conclusion: This study defined the normal reference values of HGS in Italian women with different BMI ranges, stratifying the sample group by age. Additionally, the main predictors of HGS were assessed for each BMI tertile. In primary malnutrition (T1), the main predictor of HGS was body weight, whereas in the other two tertiles (T2, T3), the PhA was the main predictor of HGS., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: The aim of this study was to develop and validate new predictive equations for estimating resting energy expenditure (REE) in subjects with normal weight and overweight, considering anthropometric parameters as well as raw variables from bioimpedance analysis (BIA)., Methods: Adult participants with normal weight and overweight were recruited and randomly split into calibration and validation groups. Indirect calorimetry (IC) and BIA were performed in all subjects. New predictive equations were developed using the following models: model 1 with age, weight, stature, and body mass index (BMI) as predictors; and model 2: model 1 + raw BIA variables (bioimpedance index and phase angle). The accuracy of the new equations at both the group (bias) and individual (within ±10%) levels was tested in the validation group. Three published predictive equations were also compared, with the REE values measured by IC., Results: A total of 2483 adults were included for developing and validating the new equations. All selected formulas, including the new ones, showed a bias of <5% in estimating REE at the group level. Accuracy at the individual level was slightly higher for the new equations, especially for the equation based on raw BIA variables (men = 70.3%; women = 72.3%)., Conclusions: Compared to the equations in the literature, the new equations showed good accuracy at both the group and individual levels, with a slight improvement in individual accuracy for the formula including raw BIA variables. However, future research is required to verify the role of the raw BIA variables in predicting REE in subjects with normal weight and overweight., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)