{"id":51160,"date":"2024-12-14T14:25:23","date_gmt":"2024-12-14T14:25:23","guid":{"rendered":""},"modified":"2024-12-14T14:25:23","modified_gmt":"2024-12-14T14:25:23","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-51160","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-51160\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0637\u0631 \u0628\u0627\u0644\u06cc\u0646\u06cc \u06cc\u06a9\u067e\u0627\u0631\u0686\u0647 \u062f\u06cc\u0627\u0628\u062a \u0646\u0648\u0639 2 \u0628\u0627 \u0627\u0645\u062a\u06cc\u0627\u0632 \u062e\u0637\u0631 \u0686\u0646\u062f \u0639\u0627\u0645\u0644\u06cc \u0686\u0646\u062f \u0639\u0627\u0645\u0644\u06cc"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nIntegrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0637\u0631 \u0628\u0627\u0644\u06cc\u0646\u06cc \u06cc\u06a9\u067e\u0627\u0631\u0686\u0647 \u062f\u06cc\u0627\u0628\u062a \u0646\u0648\u0639 2 \u0628\u0627 \u0627\u0645\u062a\u06cc\u0627\u0632 \u062e\u0637\u0631 \u0686\u0646\u062f \u0639\u0627\u0645\u0644\u06cc \u0686\u0646\u062f \u0639\u0627\u0645\u0644\u06cc<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n ProfileScott C. Ritchie, ProfileHenry J. Taylor, Yujian Liang, ProfileHasanga D. Manikpurage, Lisa Pennells, ProfileCarles Foguet, ProfileGad Abraham, Joel T. Gibson, ProfileXilin Jiang, Yang Liu, Yu Xu, ProfileLois G. Kim, ProfileAnubha Mahajan, ProfileMark I. McCarthy, Stephen Kaptoge, ProfileSamuel A Lambert, ProfileAngela Wood, Xueling Sim, Francis S. Collins, Joshua C. Denny, John Danesh, ProfileAdam S. Butterworth, ProfileEmanuele Di Angelantonio, ProfileMichael Inouye<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
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\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n27<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nEndocrinology (including Diabetes Mellitus and Metabolic Disease)
\n\u063a\u062f\u062f \u062f\u0631\u0648\u0646 \u0631\u06cc\u0632 (\u0627\u0632 \u062c\u0645\u0644\u0647 \u062f\u06cc\u0627\u0628\u062a \u0648 \u0628\u06cc\u0645\u0627\u0631\u06cc \u0645\u062a\u0627\u0628\u0648\u0644\u06cc\u06a9)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
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\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.<\/p>\n

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\u062f\u0647\u06cc\u0645 \u06a9\u0647 \u067e\u062a\u0627\u0646\u0633\u06cc\u0644 PRS \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0637\u0631 T2D \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u063a\u0631\u0628\u0627\u0644\u06af\u0631\u06cc \u0631\u0627\u0647\u0646\u0645\u0627\u06cc\u06cc \u0648 \u06a9\u0645\u06a9 \u0628\u0647 \u0627\u0646\u06af\u0644\u0633\u062a\u0627\u0646 \u0648 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0637\u0631 \u0628\u0627 \u0646\u0645\u0631\u0647 \u062e\u0637\u0631 \u0628\u0627\u0644\u06cc\u0646\u06cc \u0627\u0633\u062a.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
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