曹嵐，李志強，師詠勇，劉赟

研究報告

端粒長度與2型糖尿?。好系聽栯S機化研究與多基因風(fēng)險評分分析

曹嵐1,3，李志強2,3，師詠勇3，劉赟4

1. 上海市婦幼保健中心，上海 200062 2. 青島大學(xué)生物醫(yī)學(xué)研究院(暨上海交通大學(xué)Bio-X研究院青島分院)，青島 266003 3. 上海交通大學(xué)Bio-X研究院，遺傳發(fā)育與精神神經(jīng)疾病教育部重點實驗室，上海 200030 4. 復(fù)旦大學(xué)生物醫(yī)學(xué)研究院，上海 200032

多項觀察性研究表明，端粒長度縮短與2型糖尿病(type 2 diabetes, T2D)之間存在關(guān)聯(lián)。然而，傳統(tǒng)觀察性研究結(jié)果常受到混雜因素和反向因果關(guān)聯(lián)的影響，端粒長度與T2D是否存在因果關(guān)聯(lián)尚不明確。本研究在中國漢族人群中利用孟德爾隨機化(Mendelian randomization, MR)和多基因風(fēng)險評分(polygenic risk score, PRS)方法探索端粒長度與T2D的因果關(guān)系。MR研究選取8個與端粒長度相關(guān)的獨立遺傳變異作為工具變量，利用2632例中國漢族人群T2D全基因組關(guān)聯(lián)研究(genome-wide association study, GWAS)數(shù)據(jù)，檢驗遺傳預(yù)測的端粒長度與T2D的關(guān)系。利用中國漢族人群GWAS數(shù)據(jù)，采用PRS分析評價端粒長度PRS與T2D的關(guān)系。MR研究共納入1318例T2D患者和1314例正常對照，逆方差加權(quán)、MR-Egger回歸、簡單中位數(shù)和加權(quán)中位數(shù)法估計的OR值分別為0.78 (95%: 0.36~1.68,= 0.522)、0.23 (95%: 0.01~7.64,= 0.412)、0.60 (95%: 0.28~ 1.28,= 0.185)和0.64 (95%: 0.31~1.33,= 0.233)，遺傳預(yù)測的較長端粒長度與T2D之間不存在關(guān)聯(lián)。PRS分析未發(fā)現(xiàn)端粒長度PRS與T2D顯著關(guān)聯(lián)的一致結(jié)果。本研究采用MR和PRS方法未發(fā)現(xiàn)端粒長度與T2D具有因果關(guān)聯(lián)，后續(xù)研究中增大樣本量有助于得出更可靠的結(jié)論。

孟德爾隨機化；多基因風(fēng)險評分；端粒長度；2型糖尿病

過去幾十年中，糖尿病患病率和病例數(shù)在全球范圍內(nèi)持續(xù)升高[1]。2017年，全球有約4.51億成人患有糖尿病[2]，而中國估計有超過1億成人患糖尿病[3]。2型糖尿病(type 2 diabetes, T2D)是一種由遺傳和環(huán)境因素相互作用導(dǎo)致的復(fù)雜疾病[4~6]。T2D的患病率隨年齡增加而上升[7]。糖尿病及其并發(fā)癥給患者家庭和國家造成了巨大的衛(wèi)生經(jīng)濟負(fù)擔(dān)。

端粒是真核細(xì)胞染色體末端的DNA-蛋白質(zhì)復(fù)合體，其功能是維持染色體的完整性[8]。由于DNA末端不能完全復(fù)制，正常體細(xì)胞端粒會隨著細(xì)胞分裂逐漸縮短，導(dǎo)致細(xì)胞老化[9]。細(xì)胞老化是生物老化的重要方面，而端粒長度是細(xì)胞老化的重要標(biāo)志物。端粒長度經(jīng)常在白細(xì)胞中進行測量。白細(xì)胞端粒長度(leukocyte telomere length, LTL)具有遺傳性，遺傳度在36%~84%之間[10]。

多項觀察性研究表明，LTL縮短與T2D之間存在關(guān)聯(lián)[11,12]。最近，關(guān)于LTL與T2D的meta分析顯示縮短的端粒長度與T2D顯著相關(guān)[13,14]。然而，端粒長度縮短可能是受到疾病或治療影響并發(fā)生在疾病診斷之后，共同的環(huán)境因素也可能既影響端粒長度又影響糖尿病風(fēng)險，導(dǎo)致偏倚的效應(yīng)估計。

近年，隨著全基因組關(guān)聯(lián)研究(genome-wide association study, GWAS)的大量應(yīng)用，孟德爾隨機化(Mendelian randomization, MR)和多基因風(fēng)險評分(polygenic risk score, PRS)等方法被日益廣泛用于發(fā)現(xiàn)疾病病因以及因果推斷[15~19]。相比傳統(tǒng)的觀察性流行病學(xué)研究，MR研究和PRS分析不會受到常見混雜因素的影響，且因果時序合理。本研究旨在通過MR和PRS方法在中國漢族人群中檢驗端粒長度與T2D的因果關(guān)系。

1 材料與方法

1.1 研究對象

研究對象來自中國漢族人群T2D GWAS的2632名上海居民，包括1318例T2D患者和1314例正常對照。T2D患者均符合WHO糖尿病診斷標(biāo)準(zhǔn)，選取同一地區(qū)空腹血糖(fasting plasma glucose, FPG)< 6.1 mmol/L人群作為正常對照[20]。所有2632名研究對象均應(yīng)用定量PCR測量外周血LTL并進行中國漢族人群LTL GWAS[21]。以上研究已獲中國科學(xué)院上海生命科學(xué)研究院倫理委員會批準(zhǔn)(批準(zhǔn)號：ER- SIBS-250701)，研究對象均已簽署知情同意書。

1.2 孟德爾隨機化研究

采用MR方法評估遺傳預(yù)測的端粒長度與T2D的關(guān)系。MR是將與暴露相關(guān)聯(lián)的遺傳變異作為工具變量以推斷暴露與結(jié)局因果關(guān)聯(lián)的一種方法[22]。本研究采用以下標(biāo)準(zhǔn)篩選與端粒長度相關(guān)的遺傳變異：(1)在已發(fā)表的端粒長度GWAS研究中達到全基因組顯著性水平(<5×10?8)；(2)在中國人群中的最小等位基因頻率(minor allele frequency, MAF)>1%；(3)被選擇的遺傳變異間不存在明顯的連鎖不平衡(2<0.01)。符合標(biāo)準(zhǔn)(1)的遺傳變異共16個。同時符合標(biāo)準(zhǔn)(1)和標(biāo)準(zhǔn)(2)的遺傳變異共12個。本研究最終篩選到8個遺傳變異作為工具變量，并獲取相關(guān)的信息，包括與較長端粒長度相關(guān)的等位基因、MAF、效應(yīng)估計值()、標(biāo)準(zhǔn)誤和值。使用已發(fā)表端粒長度GWAS中工具變量與端粒長度的效應(yīng)估計值()和標(biāo)準(zhǔn)誤以及2632名中國漢族人群T2D GWAS中工具變量與T2D的效應(yīng)估計值()和標(biāo)準(zhǔn)誤計算因果效應(yīng)。本研究采用4種MR方法：逆方差加權(quán)(inverse-variance weighted, IVW)、MR-Egger回歸、簡單中位數(shù)(simple median estimator, SME)和加權(quán)中位數(shù)(weighted median estimator, WME)法。此外，通過MR-Egger的截距項評估工具變量是否存在多效性。所有的分析均采用R (version 3.4.0, R Foundation)的軟件包‘MendelianRandomization’進行。

1.3 多基因風(fēng)險評分分析

采用PRS分析檢驗遺傳預(yù)測的端粒長度與T2D的關(guān)系。PRS分析利用GWAS匯總數(shù)據(jù)在人群中構(gòu)建個體遺傳評分[23,24]。本研究將2632名研究對象隨機分為兩組，1316名T2D患者或者正常對照進行T2D GWAS，1316名研究對象進行LTL GWAS。LTL GWAS的研究對象與T2D GWAS的研究對象沒有重疊。本研究中端粒長度PRS的構(gòu)建基于1316名中國人群LTL GWAS的匯總數(shù)據(jù)。采用PRSice軟件[25](http://prsice.info/)進行數(shù)據(jù)處理和分析，在T2D GWAS研究的1316個個體中計算多個值閾值(P= 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)的端粒長度PRS。PRS分析采用Bonferroni法進行多重檢驗校正，校正后顯著性閾值設(shè)為0.05/7 = 0.007。

2 結(jié)果與分析

2.1 端粒長度與T2D的孟德爾隨機化研究

2.1.1 工具變量信息

根據(jù)本研究工具變量篩選標(biāo)準(zhǔn)，最終篩選到8個獨立的遺傳變異作為工具變量[26~28]。表1列出了8個遺傳變異的相關(guān)信息，包括所在染色體、臨近基因、效應(yīng)等位基因、MAF、與端粒長度關(guān)聯(lián)的系數(shù)、與T2D關(guān)聯(lián)的系數(shù)等。其中，6個遺傳變異與端粒長度和T2D具有相反的效應(yīng)方向，1個遺傳變異與T2D關(guān)聯(lián)的值小于0.05。

2.1.2 孟德爾隨機化研究結(jié)果

IVW、MR-Egger回歸、SME和WME法的OR值分別為0.78 (95%: 0.36~1.68,= 0.522)、0.23 (95%: 0.01~7.64,= 0.412)、0.60 (95%: 0.28~ 1.28,= 0.185)、0.64 (95%: 0.31~1.33，= 0.233)，表明遺傳預(yù)測的較長端粒長度與T2D之間不存在關(guān)聯(lián)。此外，MR-Egger回歸的截距為0.110 (95%: –0.198~0.417,= 0.485)，表明工具變量不存在多效性(圖1)。

進一步根據(jù)年齡將研究對象分為≤60歲和>60歲兩層。在≤60歲的研究對象中，IVW法的OR值為0.60 (95%: 0.27~1.33,= 0.211)。在>60歲的研究對象中，IVW法的OR值為1.22 (95%: 0.36~ 4.08,= 0.751)。在各層均未發(fā)現(xiàn)遺傳預(yù)測的較長端粒長度與T2D具有關(guān)聯(lián)。

表1 與端粒長度相關(guān)的遺傳變異

SNP：single-nucleotide polymorphism，單核苷酸多態(tài)性；Chr：染色體；效應(yīng)等位基因：與較長端粒長度相關(guān)的等位基因；MAF：最小等位基因頻率，來自既往GWAS研究；T2D：2型糖尿?。唬盒?yīng)估計值；“*”表示增加一個效應(yīng)等位基因時端粒長度的增加量(kb)。

圖1 不同孟德爾隨機化方法分析結(jié)果

T2D：2型糖尿??；IVW：逆方差加權(quán)法；SME：簡單中位數(shù)法；WME：加權(quán)中位數(shù)法。

2.2 端粒長度與T2D的多基因風(fēng)險評分分析

在1316名T2D或健康對照人群中構(gòu)建端粒長度PRS以檢驗端粒長度PRS與T2D的關(guān)系。僅有一個值閾值的端粒長度PRS與T2D存在關(guān)聯(lián)(= 0.015)，但經(jīng)過Bonferroni校正后，此關(guān)聯(lián)無統(tǒng)計學(xué)意義(圖2)。

3 討論

到目前為止，多項觀察性研究表明端粒長度縮短與T2D之間存在關(guān)聯(lián)。本課題組前期在4016例中國漢族人群中進行的一項病例對照研究也發(fā)現(xiàn)較短的LTL與T2D相關(guān)(OR = 1.52, 95%: 1.23~1.88,= 0.0001)[29]。最近，一項關(guān)于端粒長度與T2D的meta分析顯示縮短的端粒長度與T2D的關(guān)聯(lián)有統(tǒng)計學(xué)意義(OR = 1.117, 95%: 1.002~1.246,= 0.045)[13]。D’Mello等[14]進行的meta分析也顯示縮短的LTL與T2D有關(guān)聯(lián)關(guān)系(OR = 1.37, 95%: 1.10~1.72)。端粒孟德爾隨機化合作組織[30]于2017年發(fā)表的MR研究未發(fā)現(xiàn)遺傳預(yù)測的較長端粒長度與T2D存在關(guān)聯(lián)，但卻發(fā)現(xiàn)遺傳預(yù)測的較長端粒長度降低1型糖尿病的風(fēng)險(OR = 0.71, 95%: 0.51~0.98,= 0.04)。本研究采用MR和PRS方法，在中國漢族人群中評估端粒長度和T2D的因果關(guān)系，沒有發(fā)現(xiàn)遺傳預(yù)測的較長端粒長度和T2D存在任何顯著關(guān)聯(lián)。

圖2 端粒長度PRS與T2D的關(guān)聯(lián)

本研究中MR分析選取的工具變量均為歐洲人群發(fā)現(xiàn)的與端粒長度相關(guān)的遺傳變異。本課題組在前期的研究中驗證了歐洲人群發(fā)現(xiàn)的附近位點rs12696304和rs16847897在中國漢族人群中與LTL相關(guān)(= 4.5×10–3和9.5×10–5)[31]。此外，在中國漢族人群GWAS研究中發(fā)現(xiàn)上的位點rs2736100與端粒長度相關(guān)(= 1.93×10–5)[21]，該發(fā)現(xiàn)與歐洲人群研究結(jié)果一致[26]。一項在亞洲人群進行的MR研究也表明歐洲人群發(fā)現(xiàn)的端粒長度相關(guān)遺傳變異可以有效應(yīng)用于亞洲人群[32]。

在傳統(tǒng)的病例對照研究中，端粒長度縮短可能發(fā)生在疾病診斷之后并由疾病或治療導(dǎo)致，故其結(jié)果常受反向因果關(guān)聯(lián)的干擾，影響其論證因果關(guān)系的能力。本研究中遺傳預(yù)測的端粒長度與抽血、疾病診斷時間無關(guān)，遺傳變異先于疾病的發(fā)生，符合因果推斷中“先因后果”的時序性要求。此外，本研究運用遺傳預(yù)測的端粒長度，有利于將影響端粒長度的遺傳因素與非遺傳因素進行區(qū)分。常見影響端粒長度的非遺傳因素包括衰老、氧化損傷等。

與其他研究相比，本研究具有以下優(yōu)勢：(1)選取與端粒長度相關(guān)的8個獨立的遺傳變異作為工具變量，避免連鎖不平衡對因果估計結(jié)果的影響；(2)采用了多種MR方法。本研究也存在局限性：LTL GWAS和T2D GWAS的樣本量較小，PRS分析的把握度較低。

綜上所述，本研究在中國漢族人群中采用MR和PRS方法未發(fā)現(xiàn)端粒長度與T2D具有因果關(guān)聯(lián)。后續(xù)研究中發(fā)現(xiàn)更多新的端粒長度相關(guān)遺傳變異并增大樣本量有助于得出更可靠的結(jié)論。

[1] NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants., 2016, 387(10027): 1513–1530.

[2] Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045., 2018, 138: 271–281.

[3] Bragg F, Holmes MV, Iona A, Guo Y, Du HD, Chen YP, Bian Z, Yang L, Herrington W, Bennett D, Turnbull I, Liu YM, Feng SX, Chen JS, Clarke R, Collins R, Peto R, Li LM, Chen ZM, China Kadoorie Biobank Collaborative Group. Association between diabetes and cause-specific mortality in rural and urban areas of China., 2017, 317(3): 280–289.

[4] Gao KP, Ren YC, Wang JJ, Liu ZC, Li JN, Li LL, Wang BY, Li H, Wang YX, Cao YK, Ohno K, Zhai RH, Liang Z. Interactions between genetic polymorphisms of glucose metabolizing genes and smoking and alcohol consumption in the risk of type 2 diabetes mellitus., 2017, 42(12): 1316–1321.

[5] Huang X, Chen YQ, Xu GL, Peng SH. DNA methylation in adipose tissue and the development of diabetes and obesity., 2019, 41(2): 98–110.黃鑫, 陳永強, 徐國良, 彭淑紅. 脂肪組織DNA甲基化與糖尿病和肥胖的發(fā)生發(fā)展. 遺傳, 2019, 41(2): 98–110.

[6] Wang YZ, Zhang YM, Dong XL, Wang XC, Zhu JF, Wang N, Jiang F, Chen Y, Jiang QW, Fu CW. Modification effects of T2DM-susceptible SNPs on the reduction of blood glucose in response to lifestyle interventions., 2020, 42(5): 483–493.王玉琢, 張一鳴, 董曉蓮, 王學(xué)才, 朱建福, 王娜, 江峰, 陳躍, 姜慶五, 付朝偉. 2型糖尿病易感基因SNP位點對生活方式干預(yù)降低血糖應(yīng)答效果的修飾效應(yīng). 遺傳, 2020, 42(5): 483–493.

[7] Han CY, Zhang M, Luo XP, Wang CJ, Yin L, Pang C, Feng TP, Ren YC, Wang BY, Zhang L, Li LL, Yang XY, Zhang HY, Zhao Y, Zhou JM, Xie ZH, Zhao JZ, Hu DS. Secular trends in the prevalence of type 2 diabetes in adults in China from 1995 to 2014: A meta-analysis., 2017, 9(5): 450–461.

[8] Moyzis RK, Buckingham JM, Cram LS, Dani M, Deaven LL, Jones MD, Meyne J, Ratliff RL, Wu JR. A highly conserved repetitive DNA sequence, (TTAGGG)n, present at the telomeres of human chromosomes., 1988, 85(18): 6622–6626.

[9] Wolkowitz OM, Reus VI, Mellon SH. Of sound mind and body: depression, disease, and accelerated aging., 2011, 13(1): 25–39.

[10] Aviv A. Genetics of leukocyte telomere length and its role in atherosclerosis., 2012, 730(1–2): 68–74.

[11] Sampson MJ, Winterbone MS, Hughes JC, Dozio N, Hughes DA. Monocyte telomere shortening and oxidative DNA damage in type 2 diabetes., 2006, 29(2): 283–289.

[12] Zee RY, Castonguay AJ, Barton NS, Germer S, Martin M. Mean leukocyte telomere length shortening and type 2 diabetes mellitus: a case-control study., 2010, 155(4): 166–169.

[13] Zhao JZ, Miao K, Wang HR, Ding H, Wang DW. Association between telomere length and type 2 diabetes mellitus: a meta-analysis., 2013, 8(11): e79993.

[14] D'Mello MJJ, Ross SA, Briel M, Anand SS, Gerstein H, Paré G. Association between shortened leukocyte telomere length and cardiometabolic outcomes: systematic review and meta-analysis., 2015, 8(1): 82–90.

[15] Dudbridge F. Polygenic epidemiology., 2016, 40(4): 268–272.

[16] Meng LX, Que XM, Gao X, Wang T. Childhood obesity and coronary artery disease: a Mendelian randomization study., 2019, 40(7): 839–843.孟玲先, 闕喜妹, 高雪, 王彤. 兒童肥胖與冠狀動脈疾病的孟德爾隨機化研究. 中華流行病學(xué)雜志, 2019, 40(7): 839–843.

[17] Polimanti R, Ratanatharathorn A, Maihofer AX, Choi KW, Stein MB, Morey RA, Logue MW, Nievergelt C M, Stein DJ, Koenen KC, Gelernter J, Psychiatric Genomics Consortium Posttraumatic Stress Disorder Working Group. Association of economic status and educational attainment with posttraumatic stress disorder: a Mendelian randomi-zation study., 2019, 2(5): e193447.

[18] Colodro-Conde L, Couvy-Duchesne B, Whitfield JB, Streit F, Gordon S, Kemper KE, Yengo L, Zheng ZL, Trzaskowski M, de Zeeuw EL, Nivard MG, Das M, Neale RE, MacGregor S, Olsen CM, Whiteman DC, Boomsma DI, Yang J, Rietschel M, McGrath JJ, Medland SE, Martin NG. Association between population density and genetic risk for schizophrenia., 2018, 75(9): 901–910.

[19] Chen X, Yazdani S, Piehl F, Magnusson PKE, Fang F. Polygenic link between blood lipids and amyotrophic lateral sclerosis., 2018, 67: 202.e1–202.e6.

[20] Liu Y, Zhou DZ, Zhang D, Chen Z, Zhao T, Zhang Z, Ning M, Hu X, Yang YF, Zhang ZF, Yu L, He L, Xu H. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes in the population of mainland China., 2009, 52(7): 1315–1321.

[21] Liu Y, Cao L, Li ZQ, Zhou DZ, Liu WQ, Shen Q, Wu YT, Zhang D, Hu X, Wang T, Ye JY, Weng XL, Zhang H, Zhang D, Zhang Z, Liu FT, He L, Shi YY. A genome-wide association study identifies a locus on TERT for mean telomere length in Han Chinese., 2014, 9(1): e85043.

[22] Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference., 2007, 16(4): 309–330.

[23] International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sul-livan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder., 2009, 460(7256): 748–752.

[24] So HC, Chau KL, Ao FK, Mo CH, Sham PC. Exploring shared genetic bases and causal relationships of schizo-phrenia and bipolar disorder with 28 cardiovascular and metabolic traits., 2019, 49(8): 1286–1298.

[25] Euesden J, Lewis CM, O'Reilly PF. PRSice: Polygenic Risk Score software., 2015, 31(9): 1466– 1468.

[26] Codd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL, Hottenga JJ, Fischer K, Esko T, Surakka I, Broer L, Nyholt DR, Leach IM, Salo P, H?gg S, Matthews MK, Palmen J, Norata GD, O'Reilly PF, Saleheen D, Amin N, Balmforth AJ, Beekman M, de Boer RA, B?hringer S, Braund PS, Burton PR, de Craen AJ, Denniff M, Dong Y, Douroudis K, Dubinina E, Eriksson JG, Garlaschelli K, Guo DH, Hartikainen AL, Henders AK, Houwing- Duistermaat JJ, Kananen L, Karssen LC, Kettunen J, Klopp N, Lagou V, van Leeuwen EM, Madden PA, M?gi R, Magnusson PK, M?nnist? S, McCarthy MI, Medland SE, Mihailov E, Montgomery GW, Oostra BA, Palotie A, Peters A, Pollard H, Pouta A, Prokopenko I, Ripatti S, Salomaa V, Suchiman HE, Valdes AM, Verweij N, Vi?uela A, Wang XL, Wichmann HE, Widen E, Willemsen G, Wright MJ, Xia K, Xiao XJ, van Veldhuisen DJ, Catapano AL, Tobin MD, Hall AS, Blakemore AI, van Gilst WH, Zhu HD, Erdmann J, Reilly MP, Kathiresan S, Schunkert H, Talmud PJ, Pedersen NL, Perola M, Ouwehand W, Kaprio J, Martin NG, van Duijn CM, Hovatta I, Gieger C, Metspalu A, Boomsma DI, Jarvelin MR, Slagboom PE, Thompson JR, Spector TD, van der Harst P, Samani NJ. Identification of seven loci affecting mean telomere length and their association with disease., 2013, 45(4): 422–7, 427e1–2.

[27] Mangino M, Hwang SJ, Spector TD, Hunt SC, Kimura M, Fitzpatrick AL, Christiansen L, Petersen I, Elbers CC, Harris T, Chen W, Srinivasan SR, Kark JD, Benetos A, El Shamieh S, Visvikis-Siest S, Christensen K, Berenson GS, Valdes AM, Vi?uela A, Garcia M, Arnett DK, Broeckel U, Province MA, Pankow JS, Kammerer C, Liu Y, Nalls M, Tishkoff S, Thomas F, Ziv E, Psaty BM, Bis JC, Rotter JI, Taylor KD, Smith E, Schork NJ, Levy D, Aviv A. Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans., 2012, 21(24): 5385–5394.

[28] Levy D, Neuhausen SL, Hunt SC, Kimura M, Hwang SJ, Chen W, Bis JC, Fitzpatrick AL, Smith E, Johnson AD, Gardner JP, Srinivasan SR, Schork N, Rotter JI, Herbig U, Psaty BM, Sastrasinh M, Murray SS, Vasan RS, Province MA, Glazer NL, Lu XB, Cao XJ, Kronmal R, Mangino M, Soranzo N, Spector TD, Berenson GS, Aviv A. Genome- wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology., 2010, 107(20): 9293–9298.

[29] Shen Q, Zhao XZ, Yu L, Zhang Z, Zhou DZ, Kan MY, Zhang D, Cao L, Xing QH, Yang YF, Xu H, He L, Liu Y. Association of leukocyte telomere length with type 2 diabetes in mainland Chinese populations., 2012, 97(4): 1371–1374.

[30] Telomeres Mendelian Randomization Collaboration, Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, Bowden J, Wade KH, Timpson NJ, Evans DM, Willeit P, Aviv A, Gaunt TR, Hemani G, Mangino M, Ellis HP, Kurian KM, Pooley KA, Eeles RA, Lee JE, Fang SY, Chen WV, Law MH, Bowdler LM, Iles MM, Yang Q, Worrall BB, Markus HS, Hung RJ, Amos CI, Spurdle AB, Thompson DJ, O'Mara TA, Wolpin B, Amundadottir L, Stolzenberg-Solomon R, Trichopoulou A, Onland-Moret NC, Lund E, Duell EJ, Canzian F, Severi G, Overvad K, Gunter MJ, Tumino R, Svenson U, van Rij A, Baas AF, Bown MJ, Samani NJ, van t'Hof FNG, Tromp G, Jones GT, Kuivaniemi H, Elmore JR, Johansson M, Mckay J, Scelo G, Carreras-Torres R, Gaborieau V, Brennan P, Bracci PM, Neale RE, Olson SH, Gallinger S, Li D, Petersen GM, Risch HA, Klein AP, Han J, Abnet CC, Freedman ND, Taylor PR, Maris JM, Aben KK, Kiemeney LA, Vermeulen SH, Wiencke JK, Walsh KM, Wrensch M, Rice T, Turnbull C, Litchfield K, Paternoster L, Standl M, Abecasis GR, SanGiovanni JP, Li Y, Mijatovic V, Sapkota Y, Low SK, Zondervan KT, Montgomery GW, Nyholt DR, van Heel DA, Hunt K, Arking DE, Ashar FN, Sotoodehnia N, Woo D, Rosand J, Comeau ME, Brown WM, Silverman EK, Hokanson JE, Cho MH, Hui J, Ferreira MA, Thompson PJ, Morrison AC, Felix JF, Smith NL, Christiano AM, Petukhova L, Betz RC, Fan X, Zhang XJ, Zhu CH, Langefeld CD, Thompson SD, Wang FJ, Lin X, Schwartz DA, Fingerlin T, Rotter JI, Cotch MF, Jensen RA, Munz M, Dommisch H, Schaefer AS, Han F, Ollila HM, Hillary RP, Albagha O, Ralston SH, Zeng CJ, Zheng W, Shu XO, Reis A, Uebe S, Hüffmeier U, Kawamura Y, Otowa T, Sasaki T, Hibberd ML, Davila S, Xie G, Siminovitch K, Bei JX, Zeng YX, F?rsti A, Chen BW, Landi S, Franke A, Fischer A, Ellinghaus D, Flores C, Noth I, Ma SF, Foo JN, Liu J, Kim JW, Cox DG, Delattre O, Mirabeau O, Skibola CF, Tang CS, Garcia-Barcelo M, Chang KP, Su WH, Chang YS, Martin NG, Gordon S, Wade TD, Lee C, Kubo M, Cha PC, Nakamura Y, Levy D, Kimura M, Hwang SJ, Hunt S, Spector T, Soranzo N, Manichaikul AW, Barr RG, Kahali B, Speliotes E, Yerges-Armstrong LM, Cheng CY, Jonas JB, Wong TY, Fogh I, Lin K, Powell JF, Rice K, Relton CL, Martin RM, Davey Smith G. Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study., 2017, 3(5): 636–651.

[31] Shen Q, Zhang Z, Yu L, Cao L, Zhou DZ, Kan MY, Li BJ, Zhang D, He L, Liu Y. Common variants near TERC are associated with leukocyte telomere length in the Chinese Han population., 2011, 19(6): 721–723.

[32] Machiela MJ, Hsiung CA, Shu XO, Seow WJ, Wang ZM, Matsuo K, Hong YC, Seow A, Wu C, Hosgood HD, Chen KX, Wang JC, Wen WQ, Cawthon R, Chatterjee N, Hu W, Caporaso NE, Park JY, Chen CJ, Kim YH, Kim YT, Landi MT, Shen HB, Lawrence C, Burdett L, Yeager M, Chang IS, Mitsudomi T, Kim HN, Chang GC, Bassig BA, Tucker M, Wei FS, Yin ZH, An SJ, Qian BY, Lee VH, Lu DR, Liu JJ, Jeon HS, Hsiao CF, Sung JS, Kim JH, Gao YT, Tsai YH, Jung YJ, Guo H, Hu ZB, Hutchinson A, Wang WC, Klein RJ, Chung CC, Oh IJ, Chen KY, Berndt SI, Wu W, Chang J, Zhang XC, Huang MS, Zheng H, Wang JW, Zhao XY, Li YQ, Choi JE, Su WC, Park KH, Sung SW, Chen YM, Liu L, Kang CH, Hu LM, Chen CH, Pao W, Kim YC, Yang TY, Xu J, Guan P, Tan W, Su J, Wang CL, Li HX, Sihoe AD, Zhao ZH, Chen Y, Choi YY, Hung JY, Kim JS, Yoon HI, Cai QY, Lin CC, Park IK, Xu P, Dong J, Kim C, He Q, Perng RP, Kohno T, Kweon SS, Chen CY, Vermeulen RC, Wu JJ, Lim WY, Chen KC, Chow WH, Ji BT, Chan JK, Chu MJ, Li YJ, Yokota J, Li J, Chen H, Xiang YB, Yu CJ, Kunitoh H, Wu G, Jin L, Lo YL, Shiraishi K, Chen YH, Lin HC, Wu TC, Wong MP, Wu YL, Yang PC, Zhou BS, Shin MH, Fraumeni JF Jr, Zheng W, Lin DX, Chanock SJ, Rothman N, Lan Q. Genetic variants associated with longer telomere length are associated with increased lung cancer risk among never-smoking women in Asia: a report from the female lung cancer consortium in Asia., 2015, 137(2): 311–319.

Telomere length and type 2 diabetes: Mendelian randomization study and polygenic risk score analysis

Lan Cao1,3, Zhiqiang Li2,3, Yongyong Shi3, Yun Liu4

Recent epidemiological studies suggest an association between shorter telomere length and higher risk for type 2 diabetes (T2D). However, results from observational studies are susceptible to confounding and reverse causation, and it is not clear whether there is a causal association between telomere length and T2D. Using Mendelian randomization (MR) and polygenic risk score (PRS) approaches, we had evaluated the causal effect of telomere length on T2D in the Chinese Han population. Using 8 telomere-length associated genetic variants as instrumental variables, an analysis of genetically predicted telomere length and T2D risk was performed in the MR study based on data from a T2D genome-wide association study (GWAS) in 2632 individuals (1318 cases and 1314 controls). We also applied a PRS approach to investigate the causal relationship using Chinese GWAS data. The inverse-variance weighted, MR-Egger regression, simple median, and weighted median methods yielded no evidence of association between genetically predicted longer telomere length and risk of T2D (OR = 0.78, 95%: 0.36 ~ 1.68,= 0.522; OR = 0.23, 95%: 0.01 ~ 7.64,= 0.412; OR = 0.60, 95%: 0.28 ~ 1.28,= 0.185; OR = 0.64, 95%: 0.31 ~ 1.33，= 0.233; respectively). Further, PRS analysis did not produce consistent genetic overlap between telomere length and T2D. Accordingly, this study found no evidence supporting a causal association between telomere length and T2D. Further studies with larger cohorts could yield more reliable results and conclusions.

Mendelian randomization; polygenic risk score; telomere length; type 2 diabetes

2020-03-18;

2020-05-22

上海市衛(wèi)生和計劃生育委員會科研課題項目(編號：20164Y0163)資助[Supported by Foundation of Shanghai Municipal Health Commission (No. 20164Y0163)]

曹嵐，博士，研究方向：復(fù)雜疾病的遺傳學(xué)。E-mail: caolan@sjtu.edu.cn

曹嵐。

10.16288/j.yczz.20-077

2020/9/2 11:40:03

URI: https://kns.cnki.net/kcms/detail/11.1913.R.20200901.1436.001.html

(責(zé)任編委: 陳雁)