肥胖指標與糖尿病、高血壓與高血脂症遺傳傾向之關聯性

The association of obesity indicators with the genetic predisposition to diabetes, hypertension, and hyperlipidemia

李宗澔(Tsung-Hao Lee)

指導教授 ： 林菀俞

國立臺灣大學/公共衛生學院/流行病學與預防醫學研究所/碩士(2020年)

https://doi.org/10.6342/NTU202002854

摘要

糖尿病、高血壓與高血脂症 (簡稱「三高」) 已成為全球重要的健康議題。三高疾病同時受到遺傳因子與生活方式的影響，對糖尿病指標的遺傳度估計範圍位在20%到80%之間；對高血壓為30%到70%之間；對高血脂症為28%到78%之間。許多研究顯示三高疾病與肥胖相關，但尚不清楚哪種肥胖指標可能改變糖尿病、高血壓與高脂血症的遺傳風險，因此在本研究中，我們使用了五種肥胖指標來探討三高疾病中基因與肥胖的交互作用，五種肥胖指標包括身體質量指數(BMI)、體脂肪率(BFR)、腰圍(WC)、臀圍(HC)以及腰臀比(WHR)。
我們分析「臺灣人體生物資料庫」之資料，受試者由TWB1或TWB2晶片來定出基因型。研究中納入TWB1的25,460位個案作為檢測組與TWB2的58,774位個案作為驗證組，其中各包含了597,644與606,096個「單核苷酸多態性」(SNPs)。吾人使用空腹血糖值(FG)與糖化血色素(HbA1c)作為糖尿病指標；舒張壓(DBP)與收縮壓(SBP)作為高血壓指標；三酸甘油脂(TG)、總膽固醇(TC)、低密度脂蛋白膽固醇(LDL-C)以及高密度脂蛋白膽固醇(HDL-C)作為高血脂症指標。我們回顧了這些性狀的相關文獻，以文獻中的SNP主效應作為權重，建構各性狀的加權多基因分數(weighted polygenic score)，以此分析五種肥胖指標與基因之間的交互作用。檢測組中的顯著水準設為0.00125 (即0.05/40，40為檢測組中檢定的交互作用總數)，驗證組中的顯著水準設為0.00556 (即0.05/9，9為檢測組中交互作用達到顯著水準的個數)。
檢測組與驗證組均顯示全部肥胖指標皆與八種性狀顯著相關，其中體脂肪率(BFR)的主效應最為強烈。除此之外，在高血脂症的兩個性狀，三酸甘油脂(TG)與高密度脂蛋白膽固醇(HDL-C)中，發現5個能夠重複驗證的交互作用結果，這些結果表示肥胖與三酸甘油脂(TG)的基因風險上升有關，並與高密度脂蛋白膽固醇(HDL-C)的基因保護作用減弱有關。

關鍵字

基因-肥胖交互作用 ； 臺灣人體生物資料庫 ； 肥胖指標 ； 加權多基因分數 ； 單核苷酸多態性

並列摘要

Diabetes, hypertension, and hyperlipidemia have been important health issues around the world. Genetic and lifestyle factors are responsible for these diseases. The heritability values were estimated to be ~20% to 80% for diabetes; ~30% to 70% for hypertension; and ~28% to 78% for hyperlipidemia. Many studies have shown that these chronic diseases are related to obesity, but it is not clear which obesity measure may modify the genetic risk of diabetes, hypertension, and hyperlipidemia. In this study, we used five obesity measures to investigate the gene-by-obesity interactions on these diseases. The five obesity measures included body mass index (BMI), body fat rate (BFR), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR).
We analyzed data from the Taiwan Biobank (TWB), where subjects were genotyped by TWB1 or TWB2 array. This study includes 25,460 TWB1 participants as discovery cohort and 58,774 TWB2 participants as replication cohort, each with around 600,000 genotyped single-nucleotide polymorphisms (SNPs). In this study, fasting glucose and glycated hemoglobin (HbA1c) were used as indicators for diabetes; diastolic blood pressure (DBP) and systolic blood pressure (SBP) for hypertension; and triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) for hyperlipidemia. We reviewed previous studies related to these phenotypes, and used SNP main effects in those studies as weights of our weighted polygenic score (PS). Then we analyzed PS-by-obesity interactions on these eight phenotypes. The significance level in the discovery cohort was set at 0.000125 (i.e., 0.05/40, where 40 is the number of interaction tests performed in the discovery cohort), whereas the significance level in replication cohort was set at 0.00556 (i.e., 0.05/9, where 9 is the number of significant PS-by-obesity interactions detected from the discovery cohort).
The discovery cohort and the replication cohort both show that all the five obesity measures are significantly associated with the eight phenotypes, where body fat rate (BFR) provides the strongest effects among all the five obesity measures. Moreover, 5 significant PS-by-obesity interactions were discovered from 2 phenotypes, TG and HDL-C. These results indicate that obesity is associated with an exacerbation of the detrimental genetic effects of TG and an attenuation of the beneficial genetic effects of HDL-C.

並列關鍵字

Gene-by-obesity interaction ； Taiwan biobank ； Obesity measures ； Weighted polygenic score ； Single nucleotide polymorphism

參考文獻

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