Within nutrigenomics, clustering using data generated by microarray gene expression profiles can be used to identify sub-populations of subjects that respond differently to a given diet intervention. The use of clustering analyses is promising in obesity-related research as personalized nutrition is gaining popularity. This thesis focuses on clustering a human subcutaneous adipose tissue gene expression data set obtained during a low-calorie diet intervention to aid in the prediction of 6-month weight loss maintenance. The aims of the study were (1) to identify the best performing clustering method for clustering samples, (2) to identify differential responders to the low-calorie diet, and (3) to identify the biological pathways affected during the low-calorie diet by weight maintainers and weight regainers. MCLUST performed the best when clustering samples using relative weight change and either fasting insulin or insulin resistance change. Furthermore, it identified differences in the regulation of pathways between weight maintainers and regainers.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/2860 |
| Date | 25 August 2011 |
| Creators | Wong, Monica |
| Contributors | McNicholas, Paul, Mutch, David |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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