건강 및 질병감시 지표의 대규모 발굴 및 활용

초록

Purpose : Using high-throughput biotechnology, we aim to discover biomarkers efficiently for monitoring the disease presentation and for the Metabolic Syndrome. Methods : Target disease are hypertension and Metabolic syndrome. The Study designed Case-controls study. Normal group in Hypertension study were selected persons who had normal in not only initial epidemiology data but also follow-up data. Patient group were selected persons who had normal in initial epidemiology data but had hypertension in follow-up data. Normal group in Metabolic Syndrome study were selected persons who had normal in not only initial epidemiology data but also follow-up data. Patient group were selected persons who had metabolic syndrome in not only initial epidemiology data but also follow-up data. We selected artifical 1250 RNAs those have coherence with human serum and had sensitivity with human disease in random sequence RNA library, we look for 100 spot had mean difference with normal and patient group. The validation of clustering with 2 groups were established artificial neural network 10-fold validation method. Results : In hypertension study, 100 spots clustering initial patient group and initial normal group were established validation that is 99.09% and follow-up patient group and follow-up normal group were established validation that is 98.33%. In metabolic syndrome study, 100 spots clustering initial patient group and initial normal group were established validation that is 86.84% and follow-up patient group and follow-up normal group were established validation that is 88.95%. Conclusions : Throughout using aptamer chip based high-throughput biotechnology, we can diagonosis hypertension and metabolic syndrome. Especially we can predict that genomic factor of hypertension before a persons has an attack of a hypertension, but we must discover an unknown proteoms that has coherence with RNAs used this study.

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