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Alakwaa, F.

Publications and source records attributed to Alakwaa, F..

2 recordsLinked to original sources

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Lilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi

bioinformatics

Metabolomics profile of umbilical cord blood is associated with maternal pre-pregnant obesity in a prospective multi-ethnic cohort displaying health disparities

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal pre-pregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using coupled untargeted and targeted metabolomics approach, from the newborn cord blood metabolomes associated with a matched maternal pre-pregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multi-ethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using elastic net regularization based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age and ethnicity, parity and gravidity). We validated the model results in a subsequent set of samples (N=30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese vs. norm-weight groups. Interestingly, Hydroxy-3-methylbutyric acid showed significnatly higher levels in cord blood from the NHPI group, compared to asian and caucasian groups. In summary, significant associations were observed between maternal pre-pregnant obesity and offspring metabolomics alternation at birth, revealing the inter-generational impact of maternal obesity.

bioinformatics