RESUMEN
Postmenopausal osteoporosis stands as the predominant bone disorder in the developed world, posing a significant public health challenge. Nutritional factors play a crucial role in bone health and may contribute to its prevention or treatment. Calcium and vitamin D, extensively studied with robust scientific evidence, are integral components of the non-pharmacological treatment for this disorder. Nevertheless, other less-explored nutritional elements appear to influence bone metabolism. This review provides a comprehensive summary of the latest evidence concerning the relationship between various nutrients, such as phosphorus, magnesium, vitamins, phytate, and phytoestrogens; specific foods like dairy or soy, and dietary patterns such as the Mediterranean diet with bone health and osteoporosis.
RESUMEN
Objectives: To assess the link between ultrasonographic measurements of the biceps brachii and total muscle mass measured by bio-impedancemetry in hospitalized older patients. Methods: A prospective observational study was conducted. The study included patients older than 65 years admitted in internal medicine, acute geriatrics, orthogeriatrics and rehabilitation departments. All measurements, ultrasonographic measurements and muscle mass and function by bio-impedancemetry and dynamometry, were taken within the first 48 hours of admission. Results: In total 19 patients were included, the mean age was 85.4 ± 3.9 years and 7 (36.8%) were females. Very strong direct correlations were obtained in the entire cohort in both biceps brachii cross-sectional area and muscle thickness with skeletal muscle mass displayed in kilograms. Conclusion: Biceps brachii looks like a very good muscle measuring tool: easy, comfortable, fast, good correlated with total body muscle mass. This muscle could effectively be used for the assessment of muscle mass in the diagnosis of sarcopenia since it reflects muscle mass precisely, however more studies are needed to provide reference values in all age cohorts.
RESUMEN
Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL ≥ 30% were incorrectly classified as "unsuccessful procedures". The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.