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Introduction: Introduction: food type represents higher odds of having obesity (OB), especially in overweight (OW) subjects. Minimally and ultra-processed foods can be associated with the odds of having OB in OW subjects. Objective: to investigate the association of minimally and ultra-processed food consumption with OB in OW adults. Methods: we included 15,024 participants (9,618 OW [25.0-29.9 kg/m2], 5,406 OB [≥ 30 kg/m2]) with ages ranging from 18 to 59 years from the 2019 baseline survey of the Surveillance of Risk Factors and Protection for Chronic Diseases by Telephone Survey (VIGITEL, Brazil). Minimally and ultra-processed food daily consumption scores and confounding variables (age, sex, scholarly, physical activity, hypertension, and diabetes) were measured. Binary logistic regression analyzes the association of minimally and ultra-processed food consumption scores with OB (odds ratio [OR]). Results: minimally processed food consumption score quartiles (1st = 1[food-score/day]; 2nd = 6[food-score/day]; 3rd = 7[food-score/day]; 4th = 8[food-score/day]) presented higher values compared to ultra-processed food (1st = 1[food-score/day]; 2nd = 1[food-score/day]; 3rd = 2[food-score/day]; 4th = 4[food-score/day]). For each score of minimally processed food consumed, there was a -5.9 % odds of OB. Thus, the higher quartile (4th) of minimally processed food consumption score represents less odds of OB (OR: -47.2 %; p < 0.001). Each ultra-processed food score consumed presented odds of 3.7 % of OB. Therefore, higher consumption of ultra-processed food (4th quartile) shows higher odds of OB (OR: +14.8 %; p < 0.001). All associations remained significatively even after being adjusted by the confounders. Conclusion: the consumption scores of minimally processed and ultra-processed foods presented a magnitude capable of impacting OW adults' odds of OB, even when controlled by sociodemographic factors, physical activity, hypertension, and diabetes.

Introduction: Introduction: functional limitation is a result of sarcopenia and is associated with loss of skeletal muscle mass (SMM). Cost-effective methods are important for the identification of sarcopenia. Objective: to propose cutoff points for normalized calf circumference (CC) in order to identify low SMM in older women based on their functional limitation. Methods: in this descriptive, cross-sectional study the CC values of a young female sample (n = 78) were used to establish cutoff points (-2 SD) for low SMM in older women (n = 67). Functional limitation was identified by the six-minute walk test (≤ 400 m). CC was normalized by body mass, height, and BMI. The diagnostic accuracy of CC was calculated with a ROC curve, using functional limitation as standard. Results: cutoff points and area under the curve (AUC) were: CC (≤ 28.5; 0.62); CC·body mass-1 (≤ 0.40; 0.63); CC·height-2 (≤ 8.52; 0.55) and CC·BMI-1 (≤ 1.10; 0.73). Only CC·BMI-1 achieved a desirable accuracy (AUC > 0.7) to distinguish functional limitation. Conclusion: the accuracy attained supports the use of CC·BMI-1 to identify low SMM in older women. In the clinical context it is possible to predict the risk of sarcopenia when sophisticated methods for determining SMM are not available.

Introduction: Introduction: the estimation of appendicular lean soft tissue by DXA (ALSTDXA) is one of the criteria for the diagnosis of sarcopenia. However, this method is expensive and not readily avaiable in clinical practice. Anthropometric equations are low-cost and able to accurate predict ALST, but such equations have not been validated for male Brazilian older adults between the ages of 60 to 79 years. To this end, this study sought to validate the existing predictive anthropometric equations for ALST, and to verify its accuracy for the diagnosis of sarcopenia in male Brazilian older adults. Methods: this cross-sectional study recruited and enrolled 25 male older adults (69.3 ± 5.60 years). ALSTDXA and anthropometric measures were determined. ALST estimations with 13 equations were compared to ALSTDXA. The validity of the equations was established when: p > 0.05 (paired t-test); standard error of the estimate (SEE) < 3.5 kg; and coefficient of determination r² > 0.70. Results: two Indian equations met the criteria (Kulkarini 1: 22.19 ± 3.41 kg; p = 0.134; r² = 0.78; EPE = 1.3 kg. Kulkarini 3: 22.14 ± 3.52 kg; p = 0.135; r² = 0.82; SEE = 1.2 kg). However, these equations presented an average bias (Bland-Altman: 0.54 and 0.48 kg) and 'false negative' classification for the ALST index. Thus, three explanatory equations were developed. The most accurate equation demonstrated a high level of agreement (r2adj = 0.87) and validity (r²PRESS = 0.83), a low predictive error (SEEPRESS = 1.53 kg), and an adequate ALST classification. Conclusion: anthropometric models for predicting ALST are valid alternatives for the diagnosis and monitoring of sarcopenia in older adults; however, population specificity affects predictive validity, with risks of false positive/negative misclassification.

Introduction: Introduction: appropriate appendicular lean soft tissue (ALST) parameters to identify sarcopenia in Brazil are scarce. The use of international references may lead to a false positive diagnosis. The objective was to propose cut-off points to identify sarcopenia in older men and women using DXA-derived ALST values from a young adult population. Methods: this was an observational study with a cross-sectional analysis. University students of both sexes (n = 125), aged 20 to 30 years, underwent anthropometric measurements and DXA scanning to obtain their ALST (kg). Cut-off points for sarcopenia were set at -2 standard deviations (-2SDs) away from the mean ALST of a young sample. Absolute values and indices (ALST/height2, ALST/weight, and ALST/body mass index [BMI]) were considered as recommended by international consensus. Results: compared to women, men presented higher values of weight, height, BMI, bone mineral content, lean soft tissue, ALST, and ALST index (kg/m²). Only fat mass was higher in women, and age was not different between genders. The -2SD ALST indices obtained were ≤ 6.56 kg/m² for men and ≤ 4.67 kg/m² for women. They were below international and national values, which tended to classify false positives. Conclusions: the -2SD ALST values proposed here are validated cut-offs for identifying low muscle in older adults and to prevent misdiagnosis with sarcopenia. In addition, they contribute to efficient monitoring and control of this disease in geriatric populations.

Introduction: Introduction: currently, there is no consensus regarding accurate and low-cost methods for diagnosing lipodystrophy in people living with HIV/AIDS (PLWHA). The aim of this study was to propose anthropometric cutoff points for the diagnosis of lipodystrophy among PLWHA. Methods: we included 106 PLWHA (men = 65, women = 41) who are under antiretroviral therapy and have been clinically classified into either a "lipodystrophy" or "non-lipodystrophy" group. Anthropometric measurements included 19 regions of body perimeters and 6 skinfold thickness measures. The Youden index was used to establish anthropometric cutoff points for the diagnosis of lipodystrophy, using the mean values of the anthropometric data (referred to as "original") along with the "Z index" (ZI) values, which were adjusted by the "Phantom Strategy." The cutoff points were proposed when "original" anthropometric measurements and ZI values had a statistical significance of p < 0.01 and an area under the curve (AUC) higher than 70%. The size effect was assessed to verify the influence of lipodystrophy on each anthropometric measure. Results: our data analysis proposes sex-specific cutoff points for the diagnosis of lipodystrophy in PLWHA - 17 points using the "original" anthropometric measurements, and 20 using the ZI values (average effect size between 1.0 and 1.1, and AUC = 76.7% and 78%). Conclusions: our study proposes accurate cutoff points for the diagnosis of lipodystrophy using "original" anthropometric measurements and ZI values adjusted by the "Phantom Strategy." Our findings support the use of anthropometric measurements as a simplified method for diagnosing lipodystrophy and monitoring body composition alterations in people living with HIV/AIDS.