DANIELLE CRISTINA FONSECA CANDIAN

(Fonte: Lattes)
Índice h a partir de 2011
8
Lattes
Projetos de Pesquisa
Unidades Organizacionais
LIM/35 - Laboratório de Nutrição e Cirurgia Metabólica do Aparelho Digestivo, Hospital das Clínicas, Faculdade de Medicina

Filtros

Limpar filtros

Configurações

search.filters.applied.f.dateIssued: 2024 ×

Resultados de Busca

Agora exibindo 1 - 2 de 2
  • article 1 Citação(ões) na Scopus
    Gastrointestinal genetic reprogramming of vitamin A metabolic pathways in response of Roux-en-Y gastric bypass
    (2024) SAMPAIO, Priscilla; WAITZBERG, Dan Linetzky; MACHADO, Natasha Mendonca; TORRINHAS, Raquel Susana Matos de Miranda; FONSECA, Danielle C.; FERREIRA, Beatriz A. M.; MARQUES, Mariane; BARCELOS, Samira; ISHIDA, Robson Kiyoshi; GUARDA, Ismael Francisco Mota Siqueira; MOURA, Eduardo Guimaraes Hourneaux de; SAKAI, Paulo; SANTO, Marco Aurelio; HEYMSFIELD, Steven B.; CORREA-GIANNELLA, Maria Lucia; PASSADORE, Mariana Doce; SALA, Priscila
    Roux-en-Y gastric bypass (RYGB) is one of the most performed bariatric surgical techniques. However, RYGB commonly results, as side effects, in nutritional deficiencies. This study aimed to examine changes in the expression of vitamin A pathway encoding genes in the gastrointestinal tract (GI) and to evaluate the potential mechanisms associated with hypovitaminosis A after RYGB. Intestinal biopsies were obtained through double-balloon endoscopy in 20 women with obesity (age 46.9 +/- 6.2 years; body mass index [BMI] 46.5 +/- 5.3 kg/m(2) [mean +/- SD]) before and three months after RYGB (BMI, 38.2 +/- 4.2 kg/m(2)). Intestinal mucosal gene microarray analyses were performed in samples using a Human GeneChip 1.0 ST array (Affymetrix). Vitamin A intake was assessed from 7-day food records and serum retinot levels were evaluated by electrochemiluminescence immunoassay. Our results showed the following genes with significant downregulation (p <= 0.05): LIPF (-0.60), NPC1L1 (-0.71), BCO1 (-0.45), and RBP4 (-0.13) in duodenum: CD36 (-0.33), and ISX (-0.43) in jejunum and BCO1 (-0.29) in ileum. No significant changes in vitamin A intake were found (784 +/- 694 retinal equivalents [RE] pre-operative vs. 809 +/- 753 RE post-operative [mean +/- SD]). Although patients were routinely supplemented with 3500 international units IU/day (equivalent to 1050 mu gRE/day) of oral retinal palm itate, serum concentrations were lower in the post-operative when compared to pre-operative period (0.35 +/- 0.14 mu g/L vs. 0.52 +/- 0.33 mu g/L respectively - P=0.07), both within the normal range. After RYGB, the simultaneous change in expression of GI genes, may impair carotenoid metabolism in the enterocytes, formation of nascent chylomicrons and transport of retinol, resulting in lower availability of vitamin A.
  • article 0 Citação(ões) na Scopus
    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 Linetzky
    Gut 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.