ABSTRACT
Purpose: Obesity is a multifactorial condition with a relevant genetic correlation. Recent advances in genomic research have identified several single nucleotide polymorphisms (SNPs) in genes such as FTO, MCM6, HLA, and MC4R, associated with obesity. This study aimed to evaluate the association of 102 SNPs with BMI and weight loss treatment response in a multi-ethnic population. Methods: The study analyzed 9,372 patients for the correlation between SNPs and BMI (dataset A). The correlation between SNP and weight loss was accessed in 474 patients undergoing different treatments (dataset B). Patients in dataset B were further divided into 3 categories based on the type of intervention: dietary therapy, intragastric balloon procedures, or surgeries. SNP association analysis and multiple models of inheritance were performed. Results: In dataset A, ten SNPs, including rs9939609 (FTO), rs4988235 (MCM6), and rs2395182 (HLA), were significantly associated with increased BMI. Additionally, other four SNPs, rs7903146 (TCF7L2), (rs6511720), rs5400 (SLC2A2), and rs7498665 (SH2B1), showed sex-specific correlation. For dataset B, SNPs rs2016520 (PPAR-Delta) and rs2419621 (ACSL5) demonstrated significant correlation with weight loss for all treatment types. In patients who adhered to dietary therapy, SNPs rs6544713 (ABCG8) and rs762551 (CYP1A2) were strongly correlated with weight loss. Patients undergoing surgical or endoscopic procedures exhibited differential correlations with several SNPs, including rs1801725 (CASR) and rs12970134 (MC4R), and weight loss. Conclusion: This study provides valuable insights into the genetic factors influencing BMI and weight loss response to different treatments. The findings highlight the potential for personalized weight management approaches based on individual genetic profiles.
ABSTRACT
Background: Diabetes mellitus is a global epidemic affecting 346 million people in the world. The glycemic control is the key for diabetes prevention and management. Some proteins can stimulate insulin release and modulate glycemic response. Objectives: To assess the effect of the consumption of different types of protein (whey protein, soy protein and egg white) on a second meal postprandial glycaemia innormal weight and normoglycemic subjects. Methodology: Randomized crossover clinical trial. After an overnight fast of 12-hours, ten subjects attended the laboratory to drink one of the protein shakes (whey, soy or egg white) or the control drink. Thirty minuteslater, the subjects consumed a glucose solution (25 gglucose). Glycemic response was monitored at times 0(before glucose solution) and 15, 30, 45, 60, 90 and 120min (after glucose solution consumption). Incremental area under the glycemic curve (iAUC) was calculated by the trapezoidal method. Furthermore, glycemic response was assessed by a new method using iG equation. Results: Compared with control, whey and soy protein drinks reduced postprandial iAUC in 56.5% (p = 0.004)and 44.4% (p = 0.029), respectively. Whey protein was the only protein capable of avoiding great fluctuations and a peak in postprandial glycemia. The assessment of glycemic response by iG equation showed positive correlation with iAUC (Pearson 0.985, p < 0.05).Conclusion: The consumption of whey and soy protein30 minutes before a glucose load resulted in lower iAUC compared with control drink. Whey protein maintained postprandial glycemia more stable (AU)
Introducción: La diabetes mellitus es una enfermedad epidémica que afecta a 346 millones de personas en el mundo. El control glicémico es la clave para la prevención y el control de la diabetes. Algunas proteínas pueden estimularla liberación de insulina y modular la respuesta glicémica. Objetivos: Evaluar el efecto del consumo de diferentes tipos de proteínas (proteína de suero de leche, proteína de soja y la clara de huevo) de la glicemia postprandial en una segunda comida en individuos de peso normal y normoglicémicos Metodología: Este fue un ensayo clínico aleatorizado cruzado. Después de un ayuno nocturno de 12 horas, diez individuos asistieron al laboratorio para beber uno de los batidos de proteínas (suero de leche, soja o clara de huevo) o la bebida control. Treinta minutos más tarde, los individuos consumieron una solución de glucosa (25 gde glucosa). La respuesta glicémica fue monitorizada enlos tiempos 0 (antes de solución de glucosa) y 15, 30, 45,60, 90 y 120 min (después del consumo de la solución de glucosa). El área incrementada bajo la curva de glicemia(iAUC) fue calculada por el método trapezoidal. Por otra parte, la respuesta glicémica se evaluó mediante un nuevo método que utiliza la ecuación de iG. Resultados: En comparación con el control, las bebidas de suero de leche y de proteína de soja reducen iAUC postprandial en 56,5% (p = 0,004) y 44,4% (p = 0,029),respectivamente. La proteína de suero es la única proteína capaz de evitar grandes fluctuaciones y un picode glicemia postprandial. La evaluación de la respuesta glicémica por la ecuación iG mostró correlación positiva con iAUC (Pearson 0,985, p < 0,05).Conclusión: El consumo de suero de leche y proteína de soja 30 minutos antes de una carga de glucosa resultó en menor iAUC en comparación con la bebida control. La proteína del suero mantiene la glucemia postprandial más estable (AU)
