BIANCA DEPIERI BALMANT AZEVEDO
Índice h a partir de 2011
3
Projetos de Pesquisa
Unidades Organizacionais
LIM/35 - Laboratório de Nutrição e Cirurgia Metabólica do Aparelho Digestivo, Hospital das Clínicas, Faculdade de Medicina
3 resultados
Resultados de Busca
Agora exibindo 1 - 3 de 3
- Dys-R Questionnaire: A Novel Screening Tool for Dysbiosis Linked to Impaired Gut Microbiota Richness(2023) BALMANT, Bianca Depieri; FONSECA, Danielle Cristina; ROCHA, Ilanna Marques; CALLADO, Leticia; TORRINHAS, Raquel Susana Matos de Miranda; WAITZBERG, Dan LinetzkyPractical and affordable tools to screen intestinal dysbiosis are needed to support clinical decision making. Our study aimed to design a new subjective screening tool for the risk of intestinal dysbiosis from a previously described nonvalidated questionnaire (DYS/FQM) and based on subjective and objective data. A total of 219 individuals comprised the chronic diseases (CD; n = 167) and healthy control (HC; 52 subjects) groups. Sociodemographic, anthropometric, body composition, lifestyle, past history, intestinal health, and dietary data were collected. The gut microbiota (GM) profile was assessed from fecal samples using the 16S rRNA sequencing. Scores for the new tool (Dys-R Questionnaire) were assigned using discrete optimization techniques. The association between Dys-R scores and dysbiosis risk was assessed through correlation, simple linear models, sensitivity, specificity, as well as positive and negative predictive values. We found significant differences in the Chao1 Index between CD and HC groups (adjusted p-value = 0.029), highlighting lower GM richness as the primary marker for intestinal dysbiosis. DYS/FQM showed poor performance in identifying poor GM richness. Dys-R exhibited a 42% sensitivity, 82% specificity, 79% positive predictive value (PPV), and 55% negative predictive value (NPV) to identify poor GM richness. The new Dys-R questionnaire showed good performance in ruling out dysbiosis.
- Megamonas funiformis, Plasma Zonulin, and Sodium Intake Affect C3 Complement Levels in Inactive Systemic Lupus Erythematosus(2023) BALMANT, Bianca Depieri; FONSECA, Danielle Cristina; PRUDENCIO, Ana Paula Aguiar; ROCHA, Ilanna Marques; CALLADO, Leticia; ALVES, Juliana Tepedino Martins; TORRINHAS, Raquel Susana Matos de Miranda; BORBA, Eduardo Ferreira; WAITZBERG, Dan LinetzkyThe etiology of systemic lupus erythematosus (SLE) remains unclear, with both genetic and environmental factors potentially contributing. This study aimed to explore the relationship among gut microbiota (GM), intestinal permeability, and food intake with inflammatory markers in inactive SLE patients. A total of 22 women with inactive SLE and 20 healthy volunteers were enrolled, and dietary intake was assessed through 24-h dietary recalls. Plasma zonulin was used to evaluate intestinal permeability, while GM was determined by 16S rRNA sequencing. Regression models were used to analyze laboratory markers of lupus disease (C3 and C4 complement and C-reactive protein). Our results showed that the genus Megamonas was significantly enriched in the iSLE group (p < 0.001), with Megamonas funiformis associated with all evaluated laboratory tests (p < 0.05). Plasma zonulin was associated with C3 levels (p = 0.016), and sodium intake was negatively associated with C3 and C4 levels (p < 0.05). A combined model incorporating variables from each group (GM, intestinal permeability, and food intake) demonstrated a significant association with C3 complement levels (p < 0.01). These findings suggest that increased Megamonas funiformis abundance, elevated plasma zonulin, and higher sodium intake may contribute to reduced C3 complement levels in women with inactive SLE.
- Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases(2024) FONSECA, Danielle Cristina; ROCHA, Ilanna Marques Gomes da; BALMANT, Bianca Depieri; CALLADO, Leticia; PRUDENCIO, Ana Paula Aguiar; ALVES, Juliana Tepedin Martins o; TORRINHAS, Raquel Susana; FERNANDES, Gabriel da Rocha; WAITZBERG, Dan LinetzkyGut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions. Design: We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction. Results: We evaluated 52 bacterial taxa and pre-selected PV (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%). Conclusion: Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.