The atorvastatin metabolic phenotype shift is influenced by interaction of drug-transporter polymorphisms in Mexican population: results of a randomized trial

Atorvastatin (ATV) is a blood cholesterol-lowering drug used to prevent cardiovascular events, the leading cause of death worldwide. As pharmacokinetics, metabolism and response vary among individuals, we wanted to determine the most reliable metabolic ATV phenotypes and identify novel and preponderant genetic markers that affect ATV plasma levels. A controlled, randomized, crossover, single-blind, three-treatment, three-period, and six-sequence clinical study of ATV (single 80-mg oral dose) was conducted among 60 healthy Mexican men. ATV plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed by real-time PCR with TaqMan probes. Four ATV metabolizer phenotypes were found: slow, intermediate, normal and fast. Six gene polymorphisms, SLCO1B1-rs4149056, ABCB1-rs1045642, CYP2D6-rs1135840, CYP2B6-rs3745274, NAT2-rs1208, and COMT- rs4680, had a significant effect on ATV pharmacokinetics (P < 0.05). The polymorphisms in SLCO1B1 and ABCB1 seemed to have a greater effect and were especially important for the shift from an intermediate to a normal metabolizer. This is the first study that demonstrates how the interaction of genetic variants affect metabolic phenotyping and improves understanding of how SLCO1B1 and ABCB1 variants that affect statin metabolism may partially explain the variability in drug response. Notwithstanding, the influence of other genetic and non-genetic factors is not ruled out.Fil: León Cachón, Rafael B. R.. Universidad de Monterrey.; MéxicoFil: Bamford, Aileen Diane. Universidad de Monterrey.; MéxicoFil: Meester, Irene. Universidad de Monterrey.; MéxicoFil: Barrera Saldaña, Hugo Alberto. Vitagenesis S.A.; México. Innbiogem S.C.; MéxicoFil: Gómez Silva, Magdalena. Universidad Autonoma de Nuevo Leon.; MéxicoFil: Garcia Bustos, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentin

Seropositividad a Helicobacter pylori entre estudiantes universitarios y sus familias: Estudio comparativo

Objetivo: asociar la respuesta serológica a Helicobacter pylori (Hp) entre estudiantes universitarios seropositivos y sus familias en el occidente de México. Métodos: estudio transversal comparativo aleatorizado de 30 núcleos familiares de 14 estudiantes universitarios seropositivos para Hp, y 16 negativos. Se realizó determinación de seropositividad (IgG) a Hp por método de ELISA. El análisis se hizo utilizando chi cuadrado y U de Mann Whitney, con la ayuda de los programas EPI INFO 2000 y SIGMA STAT 3.1 Resultados: la seropositividad global del núcleo familiar de los estudiantes infectados fue del 57 vs. el 13% de los familiares de los estudiantes no infectados (p = 0,000002). En las familias de los estudiantes positivos a Hp se encontró una frecuencia de: binomio paterno (padre y madre) 70%, madres 71%, hermanos 42%, mientras que en los seronegativos fue: binomio paterno 17% (p = 0,00005), madres 12% (p = 0,001), hermanos 10% (p = 0,0076). Conclusiones: existió mayor prevalencia de anticuerpos a Hp en los familiares de los alumnos seropositivos

Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

BACKGROUND: The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. METHODS: We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. RESULTS: The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMCs delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. CONCLUSIONS: DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers