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Human Genetics

, Volume 138, Issue 3, pp 271–285 | Cite as

Shared genetic architecture between metabolic traits and Alzheimer’s disease: a large-scale genome-wide cross-trait analysis

  • Zhaozhong Zhu
  • Yifei Lin
  • Xihao Li
  • Jane A. Driver
  • Liming LiangEmail author
Original investigation

Abstract

A growing number of studies clearly demonstrate a substantial link between metabolic dysfunction and the risk of Alzheimer’s disease (AD), especially glucose-related dysfunction; one hypothesis for this comorbidity is the presence of a common genetic etiology. We conducted a large-scale cross-trait GWAS to investigate the genetic overlap between AD and ten metabolic traits. Among all the metabolic traits, fasting glucose, fasting insulin and HDL were found to be genetically associated with AD. Local genetic covariance analysis found that 19q13 region had strong local genetic correlation between AD and T2D (P = 6.78 × 10− 22), LDL (P = 1.74 × 10− 253) and HDL (P = 7.94 × 10− 18). Cross-trait meta-analysis identified 4 loci that were associated with AD and fasting glucose, 3 loci that were associated with AD and fasting insulin, and 20 loci that were associated with AD and HDL (Pmeta < 1.6 × 10− 8, single trait P < 0.05). Functional analysis revealed that the shared genes are enriched in amyloid metabolic process, lipoprotein remodeling and other related biological pathways; also in pancreas, liver, blood and other tissues. Our work identifies common genetic architectures shared between AD and fasting glucose, fasting insulin and HDL, and sheds light on molecular mechanisms underlying the association between metabolic dysregulation and AD.

Notes

Acknowledgements

We thank IGAP consortium, GIANT consortium, DIAGRAM consortium, MAGIC Consortium and ENGAGE Consortium for providing GWAS summary statistic data. We also thank Dr. Huwenbo Shi for statistical advice. This study was supported by grants from National Institute of Environmental Health Sciences (NIEHS) P30ES000002 (Zhu), A Merit Review Award Clinical Science R&D I01CX000934-01A1 (Driver) and Dr. Liang is a collaborator funded by this award.

Author contributions

ZZ, JAD, and LL designed the study. ZZ and XL performed the statistical analysis. ZZ, YL, JAD, and LL wrote the manuscript. All authors helped interpret the data, reviewed and edited the final paper, and approved the submission.

Compliance with ethical standards

Conflict of interest

The authors declare no competing interests.

Supplementary material

439_2019_1988_MOESM1_ESM.png (153 kb)
Supplementary material 1 (PNG 152 KB)
439_2019_1988_MOESM2_ESM.xlsx (519 kb)
Supplementary material 2 (XLSX 518 KB)

References

  1. Alzheimers.net (2016) 2016 Alzheimer’s statisticsGoogle Scholar
  2. Arrais RF, Dib SA (2006) The hypothalamus-pituitary-ovary axis and type 1 diabetes mellitus: a mini review. Hum Reprod 21:327–337.  https://doi.org/10.1093/humrep/dei353 CrossRefGoogle Scholar
  3. Atzmon G, Gabriely I, Greiner W, Davidson D, Schechter C, Barzilai N (2002) Plasma HDL levels highly correlate with cognitive function in exceptional longevity. J Gerontol A Biol Sci Med Sci 57:M712–M715CrossRefGoogle Scholar
  4. Battle A, Brown CD, Engelhardt BE, Montgomery SB, Consortium GT, Laboratory DA, Coordinating Center-Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing G, Fund NIHC, N/Nci, N/Nhgri, N/Nimh, N/Nida, Biospecimen Collection Source Site N, Biospecimen Collection Source Site Biospecimen Core Resource R V, Brain Bank Repository-University of Miami Brain Endowment B, Leidos Biomedical-Project M, Study E, Genome Browser Data I, Visualization EBI, Genome Browser Data I, Visualization-Ucsc Genomics Institute UoCSC, Lead a, Laboratory DA, Coordinating C, management NIHp, Biospecimen c, Pathology, e QTLmwg (2017) Genetic effects on gene expression across human tissues. Nature 550: 204–213.  https://doi.org/10.1038/nature24277 CrossRefGoogle Scholar
  5. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3:186–191.  https://doi.org/10.1016/j.jalz.2007.04.381 CrossRefGoogle Scholar
  6. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Psychiatric Genomics C, Control C, Duncan L, Perry JR, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case (2015a) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241.  https://doi.org/10.1038/ng.3406 CrossRefGoogle Scholar
  7. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Genomics C, Patterson N, Daly MJ, Price AL, Neale BM, Schizophrenia Working Group of the Psychiatric (2015b) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295.  https://doi.org/10.1038/ng.3211 CrossRefGoogle Scholar
  8. Carantoni M, Zuliani G, Munari MR, D’Elia K, Palmieri E, Fellin R (2000) Alzheimer disease and vascular dementia: relationships with fasting glucose and insulin levels. Dement Geriatr Cogn Disord 11:176–180.  https://doi.org/10.1159/000017232 CrossRefGoogle Scholar
  9. Chan O, Inouye K, Riddell MC, Vranic M, Matthews SG (2003) Diabetes and the hypothalamo-pituitary-adrenal (HPA) axis. Minerva Endocrinol 28:87–102Google Scholar
  10. Cheng G, Huang C, Deng H, Wang H (2012) Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern Med J 42:484–491CrossRefGoogle Scholar
  11. Chung W, Chen J, Turman C, Lindstrom S, Zhu Z, Loh PR, Kraft P, Liang L (2019) Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun 10:569.  https://doi.org/10.1038/s41467-019-08535-0 CrossRefGoogle Scholar
  12. Coon KD, Myers AJ, Craig DW, Webster JA, Pearson JV, Lince DH, Zismann VL, Beach TG, Leung D, Bryden L (2007) A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J Clin Psychiatry 68:613–618CrossRefGoogle Scholar
  13. Cornes BK, Brody JA, Nikpoor N, Morrison AC, Dang HCP, Ahn BS, Wang S, Dauriz M, Barzilay JI, Dupuis J (2014) Association of levels of fasting glucose and insulin with rare variants at the chromosome 11p11. 2-MADD locus: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study. Circulation Genom Precis Med 7: 374–382Google Scholar
  14. Craft S (2009) The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged. Arch Neurol 66:300–305CrossRefGoogle Scholar
  15. Craft S, Asthana S, Newcomer JW, Wilkinson CW, Matos IT, Baker LD, Cherrier M, Lofgreen C, Latendresse S, Petrova A (1999) Enhancement of memory in Alzheimer disease with insulin and somatostatin, but not glucose. Arch General Psychiatry 56:1135–1140CrossRefGoogle Scholar
  16. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, Haneuse S, Craft S, Montine TJ, Kahn SE, McCormick W, McCurry SM, Bowen JD, Larson EB (2013) Glucose levels and risk of dementia. N Engl J Med 369:540–548.  https://doi.org/10.1056/NEJMoa1215740 CrossRefGoogle Scholar
  17. Demetrius LA, Driver J (2013) Alzheimer’s as a metabolic disease. Biogerontology 14:641–649.  https://doi.org/10.1007/s10522-013-9479-7 CrossRefGoogle Scholar
  18. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Magi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparso T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proenca C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Bottcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42:105–116.  https://doi.org/10.1038/ng.520 CrossRefGoogle Scholar
  19. Fabbri E, Zoli M, Gonzalez-Freire M, Salive ME, Studenski SA, Ferrucci L (2015) Aging and multimorbidity: new tasks, priorities, and frontiers for integrated gerontological and clinical research. J Am Med Dir Assoc 16:640–647.  https://doi.org/10.1016/j.jamda.2015.03.013 CrossRefGoogle Scholar
  20. Feng YA, Cho K, Lindstrom S, Kraft P, Cormack J, Igap Consortium CTS, Discovery B,, Liang L, Driver JA, Risk of Inherited Variants in Breast C, Elucidating Loci Involved in Prostate Cancer S, Transdisciplinary Research in Cancer of the L (2017) Investigating the genetic relationship between Alzheimer’s disease and cancer using GWAS summary statistics. Hum Genet 136:1341–1351.  https://doi.org/10.1007/s00439-017-1831-6 CrossRefGoogle Scholar
  21. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K, Ripke S, Day FR, ReproGen C, Purcell S, Stahl E, Lindstrom S, Perry JR, Okada Y, Raychaudhuri S, Daly MJ, Patterson N, Neale BM, Price AL, Schizophrenia Working Group of the Psychiatric Genomics C, Consortium R (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228–1235.  https://doi.org/10.1038/ng.3404 CrossRefGoogle Scholar
  22. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, Fiske A, Pedersen NL (2006) Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 63:168–174.  https://doi.org/10.1001/archpsyc.63.2.168 CrossRefGoogle Scholar
  23. Genin E, Hannequin D, Wallon D, Sleegers K, Hiltunen M, Combarros O, Bullido MJ, Engelborghs S, De Deyn P, Berr C, Pasquier F, Dubois B, Tognoni G, Fievet N, Brouwers N, Bettens K, Arosio B, Coto E, Del Zompo M, Mateo I, Epelbaum J, Frank-Garcia A, Helisalmi S, Porcellini E, Pilotto A, Forti P, Ferri R, Scarpini E, Siciliano G, Solfrizzi V, Sorbi S, Spalletta G, Valdivieso F, Vepsalainen S, Alvarez V, Bosco P, Mancuso M, Panza F, Nacmias B, Bossu P, Hanon O, Piccardi P, Annoni G, Seripa D, Galimberti D, Licastro F, Soininen H, Dartigues JF, Kamboh MI, Van Broeckhoven C, Lambert JC, Amouyel P, Campion D (2011) APOE and Alzheimer disease: a major gene with semi-dominant inheritance. Mol Psychiatry 16:903–907.  https://doi.org/10.1038/mp.2011.52 CrossRefGoogle Scholar
  24. Gloyn AL, Braun M, Rorsman P (2009) Type 2 diabetes susceptibility gene TCF7L2 and its role in beta-cell function. Diabetes 58:800–802.  https://doi.org/10.2337/db09-0099 CrossRefGoogle Scholar
  25. Green RC, Roberts JS, Cupples LA, Relkin NR, Whitehouse PJ, Brown T, Eckert SL, Butson M, Sadovnick AD, Quaid KA, Chen C, Cook-Deegan R, Farrer LA, Group RS (2009) Disclosure of APOE genotype for risk of Alzheimer’s disease. N Engl J Med 361:245–254.  https://doi.org/10.1056/NEJMoa0809578 CrossRefGoogle Scholar
  26. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimaki T, Raitoharju E, Kahonen M, Seppala I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252.  https://doi.org/10.1038/ng.3506 CrossRefGoogle Scholar
  27. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Love S, Kehoe PG, Hardy J, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Schurmann B, Heun R, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frolich L, Hampel H, Hull M, Rujescu D, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn PP, Van Broeckhoven C, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Muhleisen TW, Nothen MM, Moebus S, Jockel KH, Klopp N, Wichmann HE, Carrasquillo MM, Pankratz VS, Younkin SG, Holmans PA, O’Donovan M, Owen MJ, Williams J (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41:1088–1093.  https://doi.org/10.1038/ng.440 CrossRefGoogle Scholar
  28. Huyghe JR, Jackson AU, Fogarty MP, Buchkovich ML, Stancakova A, Stringham HM, Sim X, Yang L, Fuchsberger C, Cederberg H, Chines PS, Teslovich TM, Romm JM, Ling H, McMullen I, Ingersoll R, Pugh EW, Doheny KF, Neale BM, Daly MJ, Kuusisto J, Scott LJ, Kang HM, Collins FS, Abecasis GR, Watanabe RM, Boehnke M, Laakso M, Mohlke KL (2013) Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45:197–201.  https://doi.org/10.1038/ng.2507 CrossRefGoogle Scholar
  29. International HapMap C, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MM, Tsui SK, Xue H, Wong JT, Galver LM, Fan JB, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, Chagnon F, Ferretti V, Leboeuf M, Olivier JF, Phillips MS, Roumy S, Sallee C, Verner A, Hudson TJ, Kwok PY, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller P, Xiao M, Tsui LC, Mak W, Song YQ, Tam PK, Nakamura Y, Kawaguchi T, Kitamoto T, Morizono T, Nagashima A et al (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861.  https://doi.org/10.1038/nature06258 CrossRefGoogle Scholar
  30. Jayaraman A, Pike CJ (2014) Alzheimer’s disease and type 2 diabetes: multiple mechanisms contribute to interactions. Curr Diab Rep 14:476.  https://doi.org/10.1007/s11892-014-0476-2 CrossRefGoogle Scholar
  31. Karlsson IK, Ploner A, Song C, Gatz M, Pedersen NL, Hagg S (2017) Genetic susceptibility to cardiovascular disease and risk of dementia. Transl Psychiatry 7:e1142.  https://doi.org/10.1038/tp.2017.110 CrossRefGoogle Scholar
  32. Kim B, Kim S, Lee S, Shin Y, Min B, Bendayan M, Park I (2006) Clusterin induces differentiation of pancreatic duct cells into insulin-secreting cells. Diabetologia 49:311–320CrossRefGoogle Scholar
  33. Kivipelto M, Mangialasche F, Ngandu T (2018) Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol 14:653–666.  https://doi.org/10.1038/s41582-018-0070-3 CrossRefGoogle Scholar
  34. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Moron FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fievet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossu P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F,, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, European Alzheimer’s Disease I, Genetic, Environmental Risk in Alzheimer’s D, Alzheimer’s Disease Genetic C, Cohorts for H, Aging Research in Genomic E et al (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45:1452–1458.  https://doi.org/10.1038/ng.2802 CrossRefGoogle Scholar
  35. Lancaster TM, Brindley LM, Tansey KE, Sims RC, Mantripragada K, Owen MJ, Williams J, Linden DE (2015) Alzheimer’s disease risk variant in CLU is associated with neural inefficiency in healthy individuals. Alzheimer’s Dementia 11:1144–1152CrossRefGoogle Scholar
  36. Lee PH, Anttila V, Won H, Feng Y-CA, Rosenthal J, Zhu Z, Tucker-Drob EM, Nivard MG, Grotzinger AD, Posthuma D, Wang MM-J, Yu D, Stahl E, Walters RK, Anney RJL, Duncan LE, Belangero S, Luykx J, Kranzler H, Keski-Rahkonen A, Cook EH, Kirov G, Coppola G, Kaprio J, Zai CC, Hoekstra PJ, Banaschewski T, Rohde LA, Sullivan PF, Franke B, Daly MJ, Bulik CM, Lewis CM, McIntosh AM, O’Donovan MC, Zheutlin A, Andreassen OA, Borglum AD, Breen G, Edenberg HJ, Fanous AH, Faraone SV, Gelernter J, Mathews CA, Mattheisen M, Mitchell K, Neale MC, Nurnberger JI, Ripke S, Santangelo SL, Scharf JM, Stein MB, Thornton LM, Walters JTR, Wray NR, Geschwind DH, Neale B, Kendler KS, Smoller JW (2019) Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. J bioRxiv.  https://doi.org/10.1101/528117 Google Scholar
  37. Leoni V, Solomon A, Kivipelto M (2010) Links between ApoE, brain cholesterol metabolism, tau and amyloid beta-peptide in patients with cognitive impairment. Biochem Soc Trans 38:1021–1025.  https://doi.org/10.1042/BST0381021 CrossRefGoogle Scholar
  38. Li LC, Wang Y, Carr R, Haddad CS, Li Z, Qian L, Oberholzer J, Maker AV, Wang Q, Prabhakar BS (2014) IG20/MADD plays a critical role in glucose-induced insulin secretion. Diabetes 63:1612–1623.  https://doi.org/10.2337/db13-0707 CrossRefGoogle Scholar
  39. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T, Fall T, Ferreira T, Gustafsson S, Kutalik Z, Luan J, Magi R, Randall JC, Winkler TW, Wood AR, Workalemahu T, Faul JD, Smith JA, Zhao JH, Zhao W, Chen J, Fehrmann R, Hedman AK, Karjalainen J, Schmidt EM, Absher D, Amin N, Anderson D, Beekman M, Bolton JL, Bragg-Gresham JL, Buyske S, Demirkan A, Deng G, Ehret GB, Feenstra B, Feitosa MF, Fischer K, Goel A, Gong J, Jackson AU, Kanoni S, Kleber ME, Kristiansson K, Lim U, Lotay V, Mangino M, Leach IM, Medina-Gomez C, Medland SE, Nalls MA, Palmer CD, Pasko D, Pechlivanis S, Peters MJ, Prokopenko I, Shungin D, Stancakova A, Strawbridge RJ, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van Setten J, Van Vliet-Ostaptchouk JV, Wang Z, Yengo L, Zhang W, Isaacs A, Albrecht E, Arnlov J, Arscott GM, Attwood AP, Bandinelli S, Barrett A, Bas IN, Bellis C, Bennett AJ, Berne C, Blagieva R, Bluher M, Bohringer S, Bonnycastle LL, Bottcher Y, Boyd HA, Bruinenberg M, Caspersen IH, Chen YI, Clarke R, Daw EW, de Craen AJM, Delgado G, Dimitriou M et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206.  https://doi.org/10.1038/nature14177 CrossRefGoogle Scholar
  40. Marengoni A, Rizzuto D, Fratiglioni L, Antikainen R, Laatikainen T, Lehtisalo J, Peltonen M, Soininen H, Strandberg T, Tuomilehto J, Kivipelto M, Ngandu T (2017) The effect of a 2-year intervention consisting of diet, physical exercise, cognitive training, and monitoring of vascular risk on chronic morbidity-the FINGER randomized controlled trial. J Am Med Dir Assoc.  https://doi.org/10.1016/j.jamda.2017.09.020 Google Scholar
  41. Merched A, Xia Y, Visvikis S, Serot JM, Siest G (2000) Decreased high-density lipoprotein cholesterol and serum apolipoprotein AI concentrations are highly correlated with the severity of Alzheimer’s disease. Neurobiol Aging 21:27–30CrossRefGoogle Scholar
  42. Milionis HJ, Florentin M, Giannopoulos S (2008) Metabolic syndrome and Alzheimer’s disease: a link to a vascular hypothesis? CNS Spectr 13:606–613CrossRefGoogle Scholar
  43. Mrak RE, Griffin WS (2005) Potential inflammatory biomarkers in Alzheimer’s disease. J Alzheimers Dis 8:369–375CrossRefGoogle Scholar
  44. Østergaard SD, Mukherjee S, Sharp SJ, Proitsi P, Lotta LA, Day F, Perry JRB, Boehme KL, Walter S, Kauwe JS, Gibbons LE, Larson EB, Powell JF, Langenberg C, Crane PK, Wareham NJ, Scott RA, Alzheimer’s Disease Genetics C, The GC Consortium EP-I (2015) Associations between potentially modifiable risk factors and Alzheimer disease: a mendelian randomization study. PLOS Med 12:e1001841.  https://doi.org/10.1371/journal.pmed.1001841 CrossRefGoogle Scholar
  45. Pasinetti GM, Eberstein JA (2008) Metabolic syndrome and the role of dietary lifestyles in Alzheimer’s disease. J Neurochem 106:1503–1514.  https://doi.org/10.1111/j.1471-4159.2008.05454.x CrossRefGoogle Scholar
  46. Pedersen NL (2010) Reaching the limits of genome-wide significance in alzheimer disease: back to the environment. JAMA 303:1864–1865.  https://doi.org/10.1001/jama.2010.609 CrossRefGoogle Scholar
  47. Peila R, Rodriguez BL, Launer LJ, Honolulu-Asia Aging S (2002) Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: the Honolulu-Asia Aging study. Diabetes 51:1256–1262CrossRefGoogle Scholar
  48. Pomara N, Greenberg WM, Branford MD, Doraiswamy PM (2003) Therapeutic implications of HPA axis abnormalities in Alzheimer’s disease: review and update. Psychopharmacol Bull 37:120–134Google Scholar
  49. Rasmussen KL (2016) Plasma levels of apolipoprotein E, APOE genotype and risk of dementia and ischemic heart disease: a review. Atherosclerosis 255:145–155.  https://doi.org/10.1016/j.atherosclerosis.2016.10.037 CrossRefGoogle Scholar
  50. Scelsi MA, Khan RR, Lorenzi M, Christopher L, Greicius MD, Schott JM, Ourselin S, Altmann A (2018) Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci. Brain 141:2167–2180.  https://doi.org/10.1093/brain/awy141 CrossRefGoogle Scholar
  51. Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ, Thompson PM, Jack CR Jr, Weiner MW, Alzheimer’s Disease Neuroimaging I (2009) MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 132:1067–1077.  https://doi.org/10.1093/brain/awp007 CrossRefGoogle Scholar
  52. Schwarz NF, Nordstrom LK, Pagen LHG, Palombo DJ, Salat DH, Milberg WP, McGlinchey RE, Leritz EC (2018) Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome. Neuroimage Clin 17:98–108.  https://doi.org/10.1016/j.nicl.2017.09.022 CrossRefGoogle Scholar
  53. Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, Kaakinen M, Pervjakova N, Pers TH, Johnson AD, Eicher JD, Jackson AU, Ferreira T, Lee Y, Ma C, Steinthorsdottir V, Thorleifsson G, Qi L, Van Zuydam NR, Mahajan A, Chen H, Almgren P, Voight BF, Grallert H, Muller-Nurasyid M, Ried JS, Rayner NW, Robertson N, Karssen LC, van Leeuwen EM, Willems SM, Fuchsberger C, Kwan P, Teslovich TM, Chanda P, Li M, Lu Y, Dina C, Thuillier D, Yengo L, Jiang L, Sparso T, Kestler HA, Chheda H, Eisele L, Gustafsson S, Franberg M, Strawbridge RJ, Benediktsson R, Hreidarsson AB, Kong A, Sigurethsson G, Kerrison ND, Luan J, Liang L, Meitinger T, Roden M, Thorand B, Esko T, Mihailov E, Fox C, Liu CT, Rybin D, Isomaa B, Lyssenko V, Tuomi T, Couper DJ, Pankow JS, Grarup N, Have CT, Jorgensen ME, Jorgensen T, Linneberg A, Cornelis MC, van Dam RM, Hunter DJ, Kraft P, Sun Q, Edkins S, Owen KR, Perry JRB, Wood AR, Zeggini E, Tajes-Fernandes J, Abecasis GR, Bonnycastle LL, Chines PS, Stringham HM, Koistinen HA, Kinnunen L, Sennblad B, Muhleisen TW, Nothen MM, Pechlivanis S, Baldassarre D, Gertow K, Humphries SE, Tremoli E, Klopp N, Meyer J, Steinbach G et al (2017) An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66:2888–2902.  https://doi.org/10.2337/db16-1253 CrossRefGoogle Scholar
  54. Shi H, Mancuso N, Spendlove S, Pasaniuc B (2017) Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am J Hum Genet 101:737–751.  https://doi.org/10.1016/j.ajhg.2017.09.022 CrossRefGoogle Scholar
  55. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R, Strawbridge RJ, Pers TH, Fischer K, Justice AE, Workalemahu T, Wu JMW, Buchkovich ML, Heard-Costa NL, Roman TS, Drong AW, Song C, Gustafsson S, Day FR, Esko T, Fall T, Kutalik Z, Luan J, Randall JC, Scherag A, Vedantam S, Wood AR, Chen J, Fehrmann R, Karjalainen J, Kahali B, Liu CT, Schmidt EM, Absher D, Amin N, Anderson D, Beekman M, Bragg-Gresham JL, Buyske S, Demirkan A, Ehret GB, Feitosa MF, Goel A, Jackson AU, Johnson T, Kleber ME, Kristiansson K, Mangino M, Leach IM, Medina-Gomez C, Palmer CD, Pasko D, Pechlivanis S, Peters MJ, Prokopenko I, Stancakova A, Sung YJ, Tanaka T, Teumer A, Van Vliet-Ostaptchouk JV, Yengo L, Zhang W, Albrecht E, Arnlov J, Arscott GM, Bandinelli S, Barrett A, Bellis C, Bennett AJ, Berne C, Bluher M, Bohringer S, Bonnet F, Bottcher Y, Bruinenberg M, Carba DB, Caspersen IH, Clarke R, Daw EW, Deelen J, Deelman E, Delgado G, Doney AS, Eklund N, Erdos MR, Estrada K, Eury E, Friedrich N, Garcia ME, Giedraitis V, Gigante B, Go AS, Golay A, Grallert H, Grammer TB, Grassler J, Grewal J, Groves CJ, Haller T, Hallmans G et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518:187–196.  https://doi.org/10.1038/nature14132 CrossRefGoogle Scholar
  56. Sridhar GR, Lakshmi G, Nagamani G (2015) Emerging links between type 2 diabetes and Alzheimer’s disease. World J Diabetes 6:744–751.  https://doi.org/10.4239/wjd.v6.i5.744 CrossRefGoogle Scholar
  57. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, Oliver-Williams C, Kamat MA, Prins BP, Wilcox SK, Zimmerman ES, Chi A, Bansal N, Spain SL, Wood AM, Morrell NW, Bradley JR, Janjic N, Roberts DJ, Ouwehand WH, Todd JA, Soranzo N, Suhre K, Paul DS, Fox CS, Plenge RM, Danesh J, Runz H, Butterworth AS (2018) Genomic atlas of the human plasma proteome. Nature 558:73–79.  https://doi.org/10.1038/s41586-018-0175-2 CrossRefGoogle Scholar
  58. Surakka I, Horikoshi M, Magi R, Sarin AP, Mahajan A, Lagou V, Marullo L, Ferreira T, Miraglio B, Timonen S, Kettunen J, Pirinen M, Karjalainen J, Thorleifsson G, Hagg S, Hottenga JJ, Isaacs A, Ladenvall C, Beekman M, Esko T, Ried JS, Nelson CP, Willenborg C, Gustafsson S, Westra HJ, Blades M, de Craen AJ, de Geus EJ, Deelen J, Grallert H, Hamsten A, Havulinna AS, Hengstenberg C, Houwing-Duistermaat JJ, Hypponen E, Karssen LC, Lehtimaki T, Lyssenko V, Magnusson PK, Mihailov E, Muller-Nurasyid M, Mpindi JP, Pedersen NL, Penninx BW, Perola M, Pers TH, Peters A, Rung J, Smit JH, Steinthorsdottir V, Tobin MD, Tsernikova N, van Leeuwen EM, Viikari JS, Willems SM, Willemsen G, Schunkert H, Erdmann J, Samani NJ, Kaprio J, Lind L, Gieger C, Metspalu A, Slagboom PE, Groop L, van Duijn CM, Eriksson JG, Jula A, Salomaa V, Boomsma DI, Power C, Raitakari OT, Ingelsson E, Jarvelin MR, Thorsteinsdottir U, Franke L, Ikonen E, Kallioniemi O, Pietiainen V, Lindgren CM, Stefansson K, Palotie A, McCarthy MI, Morris AP, Prokopenko I, Ripatti S, Consortium E (2015) The impact of low-frequency and rare variants on lipid levels. Nat Genet 47:589–597.  https://doi.org/10.1038/ng.3300 CrossRefGoogle Scholar
  59. Takahashi Y, Ito Y, Wada N, Nagasaka A, Fujikawa M, Sakurai T, Shrestha R, Hui SP, Chiba H (2016) Development of homogeneous assay for simultaneous measurement of apoE-deficient, apoE-containing, and total HDL-cholesterol. Clin Chim Acta 454:135–142.  https://doi.org/10.1016/j.cca.2016.01.013 CrossRefGoogle Scholar
  60. van de Bunt M, Manning Fox JE, Dai X, Barrett A, Grey C, Li L, Bennett AJ, Johnson PR, Rajotte RV, Gaulton KJ, Dermitzakis ET, MacDonald PE, McCarthy MI, Gloyn AL (2015) Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet 11:e1005694.  https://doi.org/10.1371/journal.pgen.1005694 CrossRefGoogle Scholar
  61. Vemuri P, Lesnick TG, Przybelski SA, Knopman DS, Lowe VJ, Graff-Radford J, Roberts RO, Mielke MM, Machulda MM, Petersen RC, Jack CR Jr (2017) Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann Neurol 82:706–718.  https://doi.org/10.1002/ana.25071 CrossRefGoogle Scholar
  62. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698.  https://doi.org/10.1038/s41588-018-0099-7 CrossRefGoogle Scholar
  63. Wagner R, Dudziak K, Herzberg-Schäfer SA, Machicao F, Stefan N, Staiger H, Häring H-U, Fritsche A (2011) Glucose-raising genetic variants in MADD and ADCY5 impair conversion of proinsulin to insulin. PLoS One 6:e23639CrossRefGoogle Scholar
  64. Watanabe K, Taskesen E, van Bochoven A, Posthuma D (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8:1826.  https://doi.org/10.1038/s41467-017-01261-5 CrossRefGoogle Scholar
  65. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191.  https://doi.org/10.1093/bioinformatics/btq340 CrossRefGoogle Scholar
  66. Yi SS, Hwang IK, Shin JH, Choi JH, Lee CH, Kim IY, Kim YN, Won MH, Park IS, Seong JK, Yoon YS (2010) Regulatory mechanism of hypothalamo-pituitary-adrenal (HPA) axis and neuronal changes after adrenalectomy in type 2 diabetes. J Chem Neuroanat 40:130–139.  https://doi.org/10.1016/j.jchemneu.2010.05.003 CrossRefGoogle Scholar
  67. Zhang B, Kirov S, Snoddy J (2005) WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res 33:W741–W748.  https://doi.org/10.1093/nar/gki475 CrossRefGoogle Scholar
  68. Zhang H, Ren Y, Pang D, Liu C (2014) Clinical implications of AGBL2 expression and its inhibitor latexin in breast cancer. World J Surg Oncol 12:142.  https://doi.org/10.1186/1477-7819-12-142 CrossRefGoogle Scholar
  69. Zhu X, Feng T, Tayo BO, Liang J, Young JH, Franceschini N, Smith JA, Yanek LR, Sun YV, Edwards TL, Chen W, Nalls M, Fox E, Sale M, Bottinger E, Rotimi C, Consortium CB, Liu Y, McKnight B, Liu K, Arnett DK, Chakravati A, Cooper RS, Redline S (2015) Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet 96:21–36.  https://doi.org/10.1016/j.ajhg.2014.11.011 CrossRefGoogle Scholar
  70. Zhu Z, Anttila V, Smoller JW, Lee PH (2018a) Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies. PLoS One 13:e0193256.  https://doi.org/10.1371/journal.pone.0193256 CrossRefGoogle Scholar
  71. Zhu Z, Lee PH, Chaffin MD, Chung W, Loh PR, Lu Q, Christiani DC, Liang L (2018b) A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet doi.  https://doi.org/10.1038/s41588-018-0121-0 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhaozhong Zhu
    • 1
    • 2
  • Yifei Lin
    • 1
  • Xihao Li
    • 3
  • Jane A. Driver
    • 4
    • 5
  • Liming Liang
    • 1
    • 3
    Email author
  1. 1.Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  2. 2.Department of Environmental HealthHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Geriatric Research Education and Clinical Center and Massachusetts Veterans Epidemiology Research and Information CenterVA Medical CenterBostonUSA
  5. 5.Division of Aging, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

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