Weight Regain after Metabolic Surgery: Beyond the Surgical Failure.

Patients undergoing metabolic surgery have factors ranging from anatomo-surgical, endocrine metabolic, eating patterns and physical activity, mental health and psychological factors. Some of the latter can explain the possible pathophysiological neuroendocrine, metabolic, and adaptive mechanisms that cause the high prevalence of weight regain in postbariatric patients. Even metabolic surgery has proven to be effective in reducing excess weight in patients with obesity; some of them regain weight after this intervention. In this vein, several studies have been conducted to search factors and mechanisms involved in weight regain, to stablish strategies to manage this complication by combining metabolic surgery with either lifestyle changes, behavioral therapies, pharmacotherapy, endoscopic interventions, or finally, surgical revision. The aim of this revision is to describe certain aspects and mechanisms behind weight regain after metabolic surgery, along with preventive and therapeutic strategies for this complication.

Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy.

BACKGROUND: Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. METHODS: The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. RESULTS: Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. CONCLUSIONS: The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

Asociación entre obesidad e infecciones: un estudio de corte transversal / Association between obesity and infection: a study of cross section / Associação entre obesidade e infecções: um estudo transversal

Antecedentes: Desde hace más de dos décadas se reportan observaciones que plantean que la obesidad se asocia a la presencia de infecciones. Sin embargo, los diferentes hallazgos han sido contradictorios y la dirección de la posible asociación no ha sido clarificada. En nuestro país no se encontraron trabajos al respecto que contribuyan a aclarar esta pregunta. Métodos: se realizó una serie de casos retrospectivo (N= 4840 con muestreo por conglomerados n= 100), evaluando Índice de Masa Corporal (IMC) e infección por Helicobacter pylori, Streptococcus β Hemolítico, Infección Urinaria (IVU) y Vaginosis. Se obtuvieron estadísticos descriptivos y se realizaron cruces de variables para obtener OR. Resultados: 85% sexo femenino, procedentes de la zona andina en un 77% que consultan en su mayoría para lipólisis laser, IMC: 32 (DS 5,4), la relación entre infección y obesidad fue: IVU OR 1,4 (IC 1,02-3,62), (p:0,042), VAGINITIS: OR 1,4 (IC: 1,09-3,019), (p:0,028), Helicobacter pylori: Pearson 0,25 (p:0,064) y Streptocccus: 0,56 (p:0.046). Conclusiones: La presencia de infección Urinaria y la vaginitis se asocian a la obesidad. Estos hallazgos confirman previos estudios, se discuten las implicaciones de los mismos.

Background: From almost two decades ago, some reports had suggested that obesity is associated with active infections, however, these findings have been contradictory and the address of possible associations has not been clarified. In our country, no search was founded to clarify the question. Methods: In a retrospective case series fashion (N = 4840 with conglomerate sampling n = 100) we evaluate Body Mass Index (BMI) and infection by Helicobacter pylori , β Hemolytic Streptococcus , Urinary Tract Infection (UTI ) and Vaginosis. We get descriptive statistics and Odds Ratios. Results: 85 % female, 77% from the Andean region 77 % ,consulting mostly for laser lipolysis, BMI: 32 (SD.5.4) , the relationship between infection and obesity was : UTI OR: 1 4 (CI 1.02 to 3.62 ) (p : 0.042 , vaginitis : OR 1.4 ( IC: 1 ,09 -3, 019), (p : 0.028), Helicobacter pylori: Pearson 0.25 (p : 0.064 ) Streptococcus 0.56 (p = 0.046 ) These findings confirm previous studies. Implications are discussed. Conclusions: in our search Urinary tract infections and vaginitis are associated with obesity, this data confirm previous reports.

Justificativa: Observações durante duas décadas sugerem uma relação entre obesidade e a presença de infecções, no entanto, os diferentes achados têm sido contraditórios e a associação ainda não foi verificada. No nosso país, não foram achadas pesquisas no tema. Métodos: Numa série de casos retrospectiva (N = 4840 amostragem por conglomerados com n = 100), avaliando do índice de massa corporal (IMC) e infecção pelo H. pylori. Métodos: uma série de casos retrospectiva (N = 4840 amostragem por conglomerados com n = 100), a avaliação do índice de massa corporal (IMC) e infecção pelo Helicobacter Pylori, Estreptococcus β Hemolítico, infecção do trato urinário (ITU) e vaginose. As estatísticas descritivas foram obtidas e variáveis transversais foram realizadas para o odds ratio (OR). Resultados: 85% do sexo feminino, 77% da região andina consultantes principalmente de lipólise por laser, IMC: 32 (DE 5.4), a relação entre infecção e obesidade foi: IVU OR 1,4 (IC 1,02-3,62) (p: 0,042), VAGINITE: OU 1,4 (IC: 1,09-3,019) (p: 0,028), H. pylori: Pearson de 0,25 (p = 0,064) e Estreptococos 0,56 (p: 0,046). Conclusões: A presença de infecção urinária e vaginite estão associadas com a obesidade. Estas constatações confirmam estudos anteriores, as suas conseqüências são discutidas.