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dc.contributor.authorPersky, Leah B.en_US
dc.date.accessioned2018-02-13T14:43:03Z
dc.date.available2018-02-13T14:43:03Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/2144/26973
dc.description.abstractOBJECTIVE: T2D is a metabolic disease that is a significant health problem throughout many populations. Increased incidence of T2D across the age spectrum makes preventive measures for this disease a top healthcare priority. Physiological changes such as expression of pro-inflammatory T cell cytokines, insulin resistance, and pancreatic beta cell dysfunction play major roles in the onset of T2D. Current treatments include lifestyle changes with oral medications and/or synthetic insulin therapy. While treatments aim to normalize blood glucose and increase insulin sensitivity in patients diagnosed with T2D, efforts are growing to find preventative therapies for prediabetes, a condition where blood glucose levels are higher than normal but are under the threshold determined for a diabetes diagnosis. Metformin, a well-known first-line recommendation for treating T2D, in conjunction with lifestyle modification may be a viable preventative measure to delay the onset of T2D. Previous study results have created momentum to generate data promoting metformin use as an off-label preventative drug for T2D. To identify a therapeutic intervention that may help to shift T cell cytokine profiles from being pro-inflammatory and diabetogenic to anti-inflammatory, we investigated the effects of metformin on immune cell function in prediabetic patients. It is known that one effect of metformin is activating AMPK, which secondarily decreases inflammation. We therefore hypothesized metformin affects immune cell function by modulating genes in the AMPK pathway. METHODS: We recruited 49 subjects using EPIC database to screen patients with appointments at the Nutrition and Weight Management Center at Boston Medical Center. Forty-nine pre-metformin and 13 post- metformin blood samples were collected from subjects at baseline and after three months of taking metformin, respectively. Ficoll was utilized to separate and extract PBMCs. I activated PBMCs with LPS or CpG for 24 hours, and anti-CD3/CD28 for 24 or 40 hours. Then I isolated and reverse transcribed RNA, producing cDNA. We ran a human AMPK signaling qRT-PCR array on the 40-hour anti-CD3/CD28 activated PBMCs from 4 randomly chosen subjects and analyzed data to investigate candidate targets in the AMPK pathway possibly modulated by metformin. I designed primers for six chosen targets and ran qRT-PCR comparing the pre- and post-metformin dataset of 13 subjects, using the generated human gene-specific primers to see if these genes were affected across the dataset. RESULTS: Total sample population was n=13. The majority of subjects were African American females. The study participants were considered prediabetic when A1C measured between 5.7-6.4%. Median A1C and BMI averaged at 5.8% and 38.6 kg/m2  2.48 (mean  SEM), respectively. There was an expected decrease in BMI as metformin is associated with weight loss. To understand how metformin may affect genes in the AMPK pathway, qRT-PCR array analysis of the 40-hour anti-CD3/CD28 activated PBMCs in a subset of 4 subjects was used to create a volcano plot. The plot demonstrated that out of the possible gene candidates, SLC2A4, LIPE, INSR, CRY1, GAPDH, and STK11 had the greatest log2 fold change and –log (p-value). Further analysis on the 4 subjects compared delta Ct values and relative gene expression showing CRY1, a circadian function gene, had a significant decrease in expression (p=0.03, n=4, paired t-test). Primers were designed for the six candidate genes and used to run qRT-PCR on the entire dataset of 13 subjects. There was a significant decrease in expression of STK11 in 24-hour non-stimulated PBMCs (p=0.008, n=12, paired t-test) and CRY1 in 24-hour anti-CD3/CD28 activated PBMCs (p=0.04, n=12, paired t-test). There was a significant increase in expression of SLC2A4 in 24-hour CpG activated PBMCs (p=0.02, n=12, paired t-test). Furthermore, GLUT4 was detected in CpG activated immune cells and gene expression was increased in cells from subjects post-metformin treatment. CONCLUSIONS: Further investigation is required to examine how metformin decreases the expression of CRY1 and how this decrease associates with pro-inflammatory cytokine expression. STK11 expression was decreased in non-stimulated cells but did not show any trend in the activated conditions. Additional research is warranted to see if these results can be repeated, and if so, more work will be needed to define the link between CRY1/STK11 and metformin-driven AMPK activation in immune cells. Protein expression analysis will be required to support our gene expression data. Overall, these findings initiate our understanding of how AMPK activation and changes in cellular metabolism activate pathways leading to cytokine secretion by immune cells. Further study of the downstream effects of metformin and how it may change inflammatory cytokine profiles will strengthen the evidence identifying metformin as a viable preventative therapy for prediabetic patients.en_US
dc.language.isoen_US
dc.subjectNutritionen_US
dc.subjectCRY1en_US
dc.subjectDiabetesen_US
dc.subjectGLUT4en_US
dc.subjectMetforminen_US
dc.subjectPrediabetesen_US
dc.subjectImmune cellsen_US
dc.titleThe effects of metformin on immune cell function in prediabetic patientsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2017-11-02T19:15:38Z
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineNutrition and Metabolismen_US
etd.degree.grantorBoston Universityen_US


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