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van de Vegte YJ, Eppinga RN, van der Ende MY, Hagemeijer YP, Mahendran Y, Salfati E, Smith AV, Tan VY, Arking DE, Ntalla I, Appel EV, Schurmann C, Brody JA, Rueedi R, Polasek O, Sveinbjornsson G, Lecoeur C, Ladenvall C, Zhao JH, Isaacs A, Wang L, Luan J, Hwang SJ, Mononen N, Auro K, Jackson AU, Bielak LF, Zeng L, Shah N, Nethander M, Campbell A, Rankinen T, Pechlivanis S, Qi L, Zhao W, Rizzi F, Tanaka T, Robino A, Cocca M, Lange L, Müller-Nurasyid M, Roselli C, Zhang W, Kleber ME, Guo X, Lin HJ, Pavani F, Galesloot TE, Noordam R, Milaneschi Y, Schraut KE, den Hoed M, Degenhardt F, Trompet S, van den Berg ME, Pistis G, Tham YC, Weiss S, Sim XS, Li HL, van der Most PJ, Nolte IM, Lyytikäinen LP, Said MA, Witte DR, Iribarren C, Launer L, Ring SM, de Vries PS, Sever P, Linneberg A, Bottinger EP, Padmanabhan S, Psaty BM, Sotoodehnia N, Kolcic I, Arnar DO, Gudbjartsson DF, Holm H, Balkau B, Silva CT, Newton-Cheh CH, Nikus K, Salo P, Mohlke KL, Peyser PA, Schunkert H, Lorentzon M, Lahti J, Rao DC, Cornelis MC, Faul JD, Smith JA, Stolarz-Skrzypek K, Bandinelli S, Concas MP, Sinagra G, Meitinger T, Waldenberger M, Sinner MF, Strauch K, Delgado GE, Taylor KD, Yao J, Foco L, Melander O, de Graaf J, de Mutsert R, de Geus EJC, Johansson Å, Joshi PK, Lind L, Franke A, Macfarlane PW, Tarasov KV, Tan N, Felix SB, Tai ES, Quek DQ, Snieder H, Ormel J, Ingelsson M, Lindgren C, Morris AP, Raitakari OT, Hansen T, Assimes T, Gudnason V, Timpson NJ, Morrison AC, Munroe PB, Strachan DP, Grarup N, Loos RJF, Heckbert SR, Vollenweider P, Hayward C, Stefansson K, Froguel P, Groop L, Wareham NJ, van Duijn CM, Feitosa MF, O'Donnell CJ, Kähönen M, Perola M, Boehnke M, Kardia SLR, Erdmann J, Palmer CNA, Ohlsson C, Porteous DJ, Eriksson JG, Bouchard C, Moebus S, Kraft P, Weir DR, Cusi D, Ferrucci L, Ulivi S, Girotto G, Correa A, Kääb S, Peters A, Chambers JC, Kooner JS, März W, Rotter JI, Hicks AA, Smith JG, Kiemeney LALM, Mook-Kanamori DO, Penninx BWJH, Gyllensten U, Wilson JF, Burgess S, Sundström J, Lieb W, Jukema JW, Eijgelsheim M, Lakatta ELM, Cheng CY, Dörr M, Wong TY, Sabanayagam C, Oldehinkel AJ, Riese H, Lehtimäki T, Verweij N, van der Harst P. Genetic insights into resting heart rate and its role in cardiovascular disease. Nat Commun 2023; 14:4646. [PMID: 37532724 PMCID: PMC10397318 DOI: 10.1038/s41467-023-39521-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/16/2023] [Indexed: 08/04/2023] Open
Abstract
Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.
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Graham SE, Clarke SL, Wu KHH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung YJ, Chen S, Liu F, Yang J, Kentistou KA, Gorski M, Brumat M, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Xue C, Zhang J, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, Lee J, van der Laan SW, Chitrala KN, Weiss S, Zimmermann ME, Lee JY, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hildalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Schwander K, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Highland HM, Young KL, Kawaguchi T, Thiery J, Bis JC, Nadkarni GN, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, Bhatti KF, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YDI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Chen YE, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Kato N, Schulze MB, Girotto G, Jung B, Böger CA, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Yajnik CS, Mercader JM, Tusié-Luna T, Aguilar-Salinas CA, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Lu F, Qu J, Hishigaki H, Lin X, März W, Parra EJ, Cruz M, Gudnason V, Tardif JC, Lettre G, 't Hart LM, Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Psaty BM, Brandslund I, Pramstaller PP, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Cucca F, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Justice AE, Baras A, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Thorsteinsdottir U, Stefansson K, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Rotimi CN, Hazelhurst S, Ramsay M, Trembath RC, van Heel DA, Tamiya G, Yamamoto M, Kim BJ, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Natarajan P, Peloso GM, Brown CD, Morris AP, Assimes TL, Deloukas P, Sun YV, Willer CJ. Author Correction: The power of genetic diversity in genome-wide association studies of lipids. Nature 2023; 618:E19-E20. [PMID: 37237109 PMCID: PMC10355188 DOI: 10.1038/s41586-023-06194-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
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Mark PB, Carrero JJ, Matsushita K, Sang Y, Ballew SH, Grams ME, Coresh J, Surapaneni A, Brunskill NJ, Chalmers J, Chan L, Chang AR, Chinnadurai R, Chodick G, Cirillo M, de Zeeuw D, Evans M, Garg AX, Gutierrez OM, Heerspink HJL, Heine GH, Herrington WG, Ishigami J, Kronenberg F, Lee JY, Levin A, Major RW, Marks A, Nadkarni GN, Naimark DMJ, Nowak C, Rahman M, Sabanayagam C, Sarnak M, Sawhney S, Schneider MP, Shalev V, Shin JI, Siddiqui MK, Stempniewicz N, Sumida K, Valdivielso JM, van den Brand J, Yee-Moon Wang A, Wheeler DC, Zhang L, Visseren FLJ, Stengel B. Major cardiovascular events and subsequent risk of kidney failure with replacement therapy: a CKD Prognosis Consortium study. Eur Heart J 2023; 44:1157-1166. [PMID: 36691956 PMCID: PMC10319959 DOI: 10.1093/eurheartj/ehac825] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/14/2022] [Accepted: 12/23/2022] [Indexed: 01/25/2023] Open
Abstract
AIMS Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). METHODS AND RESULTS The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence. CONCLUSION Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.
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Chan YK, Cheng CY, Sabanayagam C. Eyes as the windows into cardiovascular disease in the era of big data. Taiwan J Ophthalmol 2023; 13:151-167. [PMID: 37484607 PMCID: PMC10361436 DOI: 10.4103/tjo.tjo-d-23-00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
Cardiovascular disease (CVD) is a major cause of mortality and morbidity worldwide and imposes significant socioeconomic burdens, especially with late diagnoses. There is growing evidence of strong correlations between ocular images, which are information-dense, and CVD progression. The accelerating development of deep learning algorithms (DLAs) is a promising avenue for research into CVD biomarker discovery, early CVD diagnosis, and CVD prognostication. We review a selection of 17 recent DLAs on the less-explored realm of DL as applied to ocular images to produce CVD outcomes, potential challenges in their clinical deployment, and the path forward. The evidence for CVD manifestations in ocular images is well documented. Most of the reviewed DLAs analyze retinal fundus photographs to predict CV risk factors, in particular hypertension. DLAs can predict age, sex, smoking status, alcohol status, body mass index, mortality, myocardial infarction, stroke, chronic kidney disease, and hematological disease with significant accuracy. While the cardio-oculomics intersection is now burgeoning, very much remain to be explored. The increasing availability of big data, computational power, technological literacy, and acceptance all prime this subfield for rapid growth. We pinpoint the specific areas of improvement toward ubiquitous clinical deployment: increased generalizability, external validation, and universal benchmarking. DLAs capable of predicting CVD outcomes from ocular inputs are of great interest and promise to individualized precision medicine and efficiency in the provision of health care with yet undetermined real-world efficacy with impactful initial results.
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, Arruda ALDSV, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Lithgart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Metspalu A, Mo H, Morris AD, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.31.23287839. [PMID: 37034649 PMCID: PMC10081410 DOI: 10.1101/2023.03.31.23287839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
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Li C, Yu H, Zhu Z, Shang X, Huang Y, Sabanayagam C, Yang X, Liu L. Association of blood pressure with incident diabetic microvascular complications among diabetic patients: Longitudinal findings from the UK Biobank. J Glob Health 2023; 13:04027. [PMID: 36960684 PMCID: PMC10039372 DOI: 10.7189/jogh.13.04027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023] Open
Abstract
Background Evidence suggests a correlation of blood pressure (BP) level with presence of diabetic microvascular complications (DMCs), but the effect of BP on DMCs incidence is not well-established. We aimed to explore the associations between BP and DMCs (diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy) risk in participants with diabetes. Methods This study included 23 030 participants, free of any DMCs at baseline, from the UK Biobank. We applied multivariable-adjusted Cox regression models to estimate BP-DMCs association and constructed BP genetic risk scores (GRSs) to test their association with DMCs phenotypes. Differences in incidences of DMCs were also compared between the 2017 ACC/AHA and JNC 7 guidelines (traditional criteria) of hypertension. Results Compared to systolic blood pressure (SBP)<120 mm Hg, participants with SBP≥160 mm Hg had a hazard ratio (HR) of 1.50 (95% confidence interval (CI) = 1.09, 2.06) for DMCs. Similarly, DMCs risk increased by 9% for every 10 mm Hg of higher SBP at baseline (95% CI = 1.04, 1.13). The highest tercile SBP GRS was associated with 32% higher DMCs risk (95% CI = 1.11, 1.56) compared to the lowest tercile. We found no significant differences in DMCs incidence between JNC 7 and 2017 ACC/AHA guidelines. Conclusions Genetic and epidemiological evidence suggests participants with higher SBP had an increased risk of DMCs, but hypertension defined by 2017 ACC/AHA guidelines may not impact DMCs incidence compared with JNC 7 criteria, contributing to the care and prevention of DMCs.
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EP, Jérome CS, Soto-Rojas VE, Soumaré A, Sousa-Poza A, Sovic S, Sparboe-Nilsen B, Sparrenberger K, Spencer PR, Spinelli A, Spiroski I, Staessen JA, Stamm H, Staub K, Stavreski B, Steene-Johannessen J, Stehle P, Stein AD, Stergiou GS, Stessman J, Stevanović R, Stieber J, Stöckl D, Stokwiszewski J, Stoyanova E, Stratton G, Stronks K, Strufaldi MW, Sturua L, Suárez-Medina R, Suka M, Sun CA, Sun L, Sundström J, Sung YT, Sunyer J, Suriyawongpaisal P, Sweis NWG, Swinburn BA, Sy RG, Sylva RC, Szklo M, Szponar L, Tabone L, Tai ES, Tambalis KD, Tammesoo ML, Tamosiunas A, Tan EJ, Tang X, Tanrygulyyeva M, Tanser F, Tao Y, Tarawneh MR, Tarp J, Tarqui-Mamani CB, Braunerová RT, Taylor A, Taylor J, Tchibindat F, Te Velde S, Tebar WR, Tell GS, Tello T, Tham YC, Thankappan KR, Theobald H, Theodoridis X, Thomas N, Thorand B, Thuesen BH, Tichá Ľ, Timmermans EJ, Tjandrarini DH, Tjonneland A, Tolonen HK, Tolstrup JS, Topbas M, Topór-Mądry R, Torheim LE, Tormo MJ, Tornaritis MJ, Torrent M, Torres-Collado L, Toselli S, Touloumi G, Traissac P, Tran TTH, Tremblay MS, Triantafyllou A, Trichopoulos D, Trichopoulou A, Trinh OTH, Trivedi A, Tsao YH, Tshepo L, Tsigga M, Tsintavis P, Tsugane S, Tuitele J, Tuliakova AM, Tulloch-Reid MK, Tullu F, Tuomainen TP, Tuomilehto J, Turley ML, Twig G, Tynelius P, Tzala E, Tzotzas T, Tzourio C, Ueda P, Ugel E, Ukoli FAM, Ulmer H, Unal B, Usupova Z, Uusitalo HMT, Uysal N, Vaitkeviciute J, Valdivia G, Vale S, Valvi D, van Dam RM, van den Born BJ, Van der Heyden J, van der Schouw YT, Van Herck K, Van Lippevelde W, Van Minh H, Van Schoor NM, van Valkengoed IGM, Vanderschueren D, Vanuzzo D, Varbo A, Varela-Moreiras G, Vargas LN, Varona-Pérez P, Vasan SK, Vasques DG, Vega T, Veidebaum T, Velasquez-Melendez G, Velika B, Verloigne M, Veronesi G, Verschuren WMM, Victora CG, Viegi G, Viet L, Vik FN, Vilar M, Villalpando S, Vioque J, Virtanen JK, Visvikis-Siest S, Viswanathan B, Vladulescu M, Vlasoff T, Vocanec D, Vollenweider P, Völzke H, Voutilainen A, Vrijheid M, Vrijkotte TGM, Wade AN, Waldhör T, Walton J, Wambiya EOA, Bebakar WMW, Mohamud WNW, de Souza Wanderley Júnior R, Wang MD, Wang N, Wang Q, Wang X, Wang YX, Wang YW, Wannamethee SG, Wareham N, Weber A, Webster-Kerr K, Wedderkopp N, Weghuber D, Wei W, Weres A, Werner B, Westbury LD, Whincup PH, Wickramasinghe K, Widhalm K, Widyahening IS, Więcek A, Wild PS, Wilks RJ, Willeit J, Willeit P, Williams J, Wilsgaard T, Wojciech R, Wojtyniak B, Wolf K, Wong-McClure RA, Wong A, Wong EB, Wong JE, Wong TY, Woo J, Woodward M, Wu FC, Wu HY, Wu J, Wu LJ, Wu S, Wyszyńska J, Xu H, Xu L, Yaacob NA, Yamborisut U, Yan W, Yang L, Yang X, Yang Y, Yardim N, Yasuharu T, García MY, Yiallouros PK, Yngve A, Yoosefi M, Yoshihara A, You QS, You SL, Younger-Coleman NO, Yu YL, Yu Y, Yusof SM, Yusoff AF, Zaccagni L, Zafiropulos V, Zainuddin AA, Zakavi SR, Zamani F, Zambon S, Zampelas A, Zamrazilová H, Zapata ME, Zargar AH, Zaw KK, Zayed AA, Zdrojewski T, Żegleń M, Zejglicova K, Vrkic TZ, Zeng Y, Zhang L, Zhang ZY, Zhao D, Zhao MH, Zhao W, Zhecheva YV, Zhen S, Zheng W, Zheng Y, Zholdin B, Zhou M, Zhu D, Zins M, Zitt E, Zocalo Y, Zoghlami N, Cisneros JZ, Zuziak M, Bhutta ZA, Black RE, Ezzati M. Diminishing benefits of urban living for children and adolescents' growth and development. Nature 2023; 615:874-883. [PMID: 36991188 PMCID: PMC10060164 DOI: 10.1038/s41586-023-05772-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 01/30/2023] [Indexed: 03/31/2023]
Abstract
Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1-6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5-19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m-2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified.
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Li M, Lanca C, Tan CS, Foo LL, Sun CH, Yap F, Najjar RP, Sabanayagam C, Saw SM. Association of time outdoors and patterns of light exposure with myopia in children. Br J Ophthalmol 2023; 107:133-139. [PMID: 33858839 DOI: 10.1136/bjophthalmol-2021-318918] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/06/2021] [Accepted: 03/28/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND/AIMS To evaluate the association of reported time outdoors and light exposure patterns with myopia among children aged 9 years from the Growing Up in Singapore Towards Healthy Outcomes birth cohort. METHODS We assessed reported time outdoors (min/day), light exposure patterns and outdoor activities of children aged 9 years (n=483) with a questionnaire, the FitSight watch and a 7-day activity diary. Light levels, the duration, timing and frequency of light exposure were assessed. Cycloplegic spherical equivalent (SE), myopia (SE≤-0.5 D) and axial length (AL) of paired eyes were analysed using generalised estimating equations. RESULTS In this study, 483 (966 eyes) multiethnic children (50.0% boys, 59.8% Chinese, 42.2% myopic) were included. Reported time outdoors (mean±SD) was 100±93 min/day, and average light levels were 458±228 lux. Of the total duration children spent at light levels of ≥1000 lux (37±19 min/day), 76% were spent below 5000 lux. Peak light exposure occurred at mid-day. Children had 1.7±1.0 light exposure episodes/day. Common outdoor activities were walks, neighbourhood play and swimming. Greater reported time outdoors was associated with lower odds of myopia (OR=0.82, 95% CI 0.70 to 0.95/hour increase daily; p=0.009). Light levels, timing and frequency of light exposures were not associated with myopia, SE or AL (p>0.05). CONCLUSION Reported time outdoors, light levels and number of light exposure episodes were low among Singaporean children aged 9 years. Reported time outdoors was protective against myopia but not light levels or specific light measures. A multipronged approach to increase time outdoors is recommended in the combat against the myopia epidemic.
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Kanoni S, Graham SE, Wang Y, Surakka I, Ramdas S, Zhu X, Clarke SL, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu KHH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Yousri NA, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Hwu CM, Hung YJ, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Vazquez-Moreno M, Feitosa MF, Wojczynski MK, Wang Z, Preuss MH, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Tsao NL, Verma A, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Frank M, Schmidt B, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Bayyana S, Stringham HM, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Chen S, Liu F, Yang J, Kentistou KA, Banas B, Nardone GG, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, van der Laan SW, Chitrala KN, Weiss S, Bentley AR, Doumatey AP, Adeyemo AA, Lee JY, Petersen ERB, Nielsen AA, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hidalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Liang J, Tang H, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Kawaguchi T, Thiery J, Bis JC, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YDI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Zimmermann ME, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Peyser PA, Kato N, Schulze MB, Girotto G, Böger CA, Jung B, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Tusié-Luna T, Aguilar-Salinas CA, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Mercader JM, Costanzo MC, Jang D, Burtt NP, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Jin ZB, Qu J, Hishigaki H, Lin X, März W, Gudnason V, Tardif JC, Lettre G, Hart LM', Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Parra EJ, Cruz M, Psaty BM, Brandslund I, Pramstaller PP, Rotimi CN, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Francesco C, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Chuang LM, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Li H, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, Willemsen AHM, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, Hazelhurst S, Ramsay M, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Baras A, Justice AE, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Tamiya G, Yamamoto M, van Heel DA, Trembath RC, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Kim BJ, Thorsteinsdottir U, Stefansson K, Zhang J, Chen YE, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson PWF, Frayling TM, Hirschhorn JN, Kathiresan S, Mohlke KL, Sun YV, Morris AP, Boehnke M, Brown CD, Natarajan P, Deloukas P, Willer CJ, Assimes TL, Peloso GM. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis. Genome Biol 2022; 23:268. [PMID: 36575460 PMCID: PMC9793579 DOI: 10.1186/s13059-022-02837-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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Sim RZH, Tham YC, Betzler BK, Zhou L, Wang X, Sabanayagam C, Cheung GCM, Wong TY, Cheng CY, Nusinovici S. Relationships between Lipid-Related Metabolites and Age-Related Macular Degeneration Vary with Complement Genotype. OPHTHALMOLOGY SCIENCE 2022; 2:100211. [PMID: 36531576 PMCID: PMC9755028 DOI: 10.1016/j.xops.2022.100211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE Lipid dysregulation and complement system (CS) activation are 2 important pathophysiology pathways for age-related macular degeneration (AMD). We hypothesized that the relationship between lipids and AMD may also differ according to CS genotype profile. Thus, the objective was to investigate the relationships between lipid-related metabolites and AMD according to CS genotypes. DESIGN Population-based cross-sectional study. PARTICIPANTS A total of 6947 participants from Singapore Epidemiology of Eye Diseases study with complete relevant data were included. METHODS We investigated a total of 32 blood lipid-related metabolites from nuclear magnetic resonance metabolomics data including lipoproteins and their subclasses, cholesterols, glycerides, and phospholipids, as well as 4 CS single nucleotide polymorphisms (SNPs): rs10922109 (complement factor H), rs10033900 (complement factor I), rs116503776 (C2-CFB-SKIV2L), and rs2230199 (C3). We first investigated the associations between AMD and the 32 lipid-related metabolites using multivariable logistic regression models. Then, to investigate whether the effect of lipid-related metabolites on AMD differ according to the CS SNPs, we tested the possible interactions between the CS SNPs and the lipid-related metabolites. MAIN OUTCOME MEASURES Age-related macular degeneration was defined using the Wisconsin grading system. RESULTS Among the 6947 participants, the prevalence of AMD was 6.1%, and the mean age was 58.3 years. First, higher levels of cholesterol in high-density lipoprotein (HDL) and medium and large HDL particles were associated with an increased risk of AMD, and higher levels of serum total triglycerides (TG) and several very-low-density lipoprotein subclass particles were associated with a decreased risk of AMD. Second, these lipids had significant interaction effects on AMD with 2 CS SNPs: rs2230199 and rs116503776 (after correction for multiple testing). For rs2230199, in individuals without risk allele, higher total cholesterol in HDL2 was associated with an increased AMD risk (odds ratio [OR] per standard deviation increase, 1.20; 95% confidence interval (CI), 1.06-1.37; P = 0.005), whereas, in individuals with at least 1 risk allele, higher levels of these particles were associated with a decreased AMD risk (OR, 0.69; 95% CI, 0.45-1.05; P = 0.079). Conversely, for rs116503776, in individuals without risk allele, higher serum total TG were associated with a decreased AMD risk (OR, 0.84; 95% CI, 0.74-0.95; P = 0.005), whereas, in individuals with 2 risk alleles, higher levels of these particles were associated with an increased risk of AMD (OR, 2.3, 95% CI, 0.99-5.39, P = 0.054). CONCLUSIONS Lipid-related metabolites exhibit opposite directions of effects on AMD according to CS genotypes. This indicates that lipid metabolism and CS may have synergistic interplay in the AMD pathogenesis.
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Key Words
- AMD, age-related macular degeneration
- Age-related macular degeneration
- CFH, complement factor H
- CS, complement system
- Complement system
- HDL, high-density lipoprotein
- Lipids
- Metabolites
- NMR, nuclear magnetic resonance
- OR, odds ratio
- RPE, retinal pigment epithelium
- SNP, single nucleotide polymorphism
- TG, triglycerides
- VLDL, very-low–density lipoprotein
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Chan SH, Bylstra Y, Teo JX, Kuan JL, Bertin N, Gonzalez-Porta M, Hebrard M, Tirado-Magallanes R, Tan JHJ, Jeyakani J, Li Z, Chai JF, Chong YS, Davila S, Goh LL, Lee ES, Wong E, Wong TY, Prabhakar S, Liu J, Cheng CY, Eisenhaber B, Karnani N, Leong KP, Sim X, Yeo KK, Chambers JC, Tai ES, Tan P, Jamuar SS, Ngeow J, Lim WK, Gluckman PD, Goh DLM, Jain K, Kam S, Kassam I, Lakshmanan LN, Lee CG, Lee J, Lee SC, Lee YS, Li H, Lim CW, Lim TH, Loh M, Maurer-Stroh S, Mina TH, Mok SQ, Ng HK, Pua CJ, Riboli E, Rim TH, Sabanayagam C, Sim WC, Subramaniam T, Tan ES, Tan EK, Tantoso E, Tay D, Teo YY, Tham YC, Toh LXG, Tsai PK, van Dam RM, Veeravalli L, Khin-lin GW, Wilm A, Yang C, Yap F, Yew YW, Prabhakar S, Liu J, Cheng CY, Eisenhaber B, Karnani N, Leong KP, Sim X, Yeo KK, Chambers JC, Tai ES, Tan P, Jamuar SS, Ngeow J, Lim WK. Analysis of clinically relevant variants from ancestrally diverse Asian genomes. Nat Commun 2022; 13:6694. [PMID: 36335097 PMCID: PMC9637116 DOI: 10.1038/s41467-022-34116-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Asian populations are under-represented in human genomics research. Here, we characterize clinically significant genetic variation in 9051 genomes representing East Asian, South Asian, and severely under-represented Austronesian-speaking Southeast Asian ancestries. We observe disparate genetic risk burden attributable to ancestry-specific recurrent variants and identify individuals with variants specific to ancestries discordant to their self-reported ethnicity, mostly due to cryptic admixture. About 27% of severe recessive disorder genes with appreciable carrier frequencies in Asians are missed by carrier screening panels, and we estimate 0.5% Asian couples at-risk of having an affected child. Prevalence of medically-actionable variant carriers is 3.4% and a further 1.6% harbour variants with potential for pathogenic classification upon additional clinical/experimental evidence. We profile 23 pharmacogenes with high-confidence gene-drug associations and find 22.4% of Asians at-risk of Centers for Disease Control and Prevention Tier 1 genetic conditions concurrently harbour pharmacogenetic variants with actionable phenotypes, highlighting the benefits of pre-emptive pharmacogenomics. Our findings illuminate the diversity in genetic disease epidemiology and opportunities for precision medicine for a large, diverse Asian population.
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Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Hilal S, Venketasubramanian N, Cheng CY, Sabanayagam C, Tham YC, Schmetterer L, McKay GJ, Williams MA, Wong A, Au LWC, Lu Z, Yam JC, Tham CC, Chen JJ, Dumitrascu OM, Heng PA, Kwok TCY, Mok VCT, Milea D, Chen CLH, Wong TY. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 2022; 4:e806-e815. [PMID: 36192349 DOI: 10.1016/s2589-7500(22)00169-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative). FINDINGS 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid β positive and those who were amyloid β negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING BrightFocus Foundation.
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Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S, Graff M, Eliasen AU, Jiang Y, Raghavan S, Miao J, Arias JD, Graham SE, Mukamel RE, Spracklen CN, Yin X, Chen SH, Ferreira T, Highland HH, Ji Y, Karaderi T, Lin K, Lüll K, Malden DE, Medina-Gomez C, Machado M, Moore A, Rüeger S, Sim X, Vrieze S, Ahluwalia TS, Akiyama M, Allison MA, Alvarez M, Andersen MK, Ani A, Appadurai V, Arbeeva L, Bhaskar S, Bielak LF, Bollepalli S, Bonnycastle LL, Bork-Jensen J, Bradfield JP, Bradford Y, Braund PS, Brody JA, Burgdorf KS, Cade BE, Cai H, Cai Q, Campbell A, Cañadas-Garre M, Catamo E, Chai JF, Chai X, Chang LC, Chang YC, Chen CH, Chesi A, Choi SH, Chung RH, Cocca M, Concas MP, Couture C, Cuellar-Partida G, Danning R, Daw EW, Degenhard F, Delgado GE, Delitala A, Demirkan A, Deng X, Devineni P, Dietl A, Dimitriou M, Dimitrov L, Dorajoo R, Ekici AB, Engmann JE, Fairhurst-Hunter Z, Farmaki AE, Faul JD, Fernandez-Lopez JC, Forer L, Francescatto M, Freitag-Wolf S, Fuchsberger C, Galesloot TE, Gao Y, Gao Z, Geller F, Giannakopoulou O, Giulianini F, Gjesing AP, Goel A, Gordon SD, Gorski M, Grove J, Guo X, Gustafsson S, Haessler J, Hansen TF, Havulinna AS, Haworth SJ, He J, Heard-Costa N, Hebbar P, Hindy G, Ho YLA, Hofer E, Holliday E, Horn K, Hornsby WE, Hottenga JJ, Huang H, Huang J, Huerta-Chagoya A, Huffman JE, Hung YJ, Huo S, Hwang MY, Iha H, Ikeda DD, Isono M, Jackson AU, Jäger S, Jansen IE, Johansson I, Jonas JB, Jonsson A, Jørgensen T, Kalafati IP, Kanai M, Kanoni S, Kårhus LL, Kasturiratne A, Katsuya T, Kawaguchi T, Kember RL, Kentistou KA, Kim HN, Kim YJ, Kleber ME, Knol MJ, Kurbasic A, Lauzon M, Le P, Lea R, Lee JY, Leonard HL, Li SA, Li X, Li X, Liang J, Lin H, Lin SY, Liu J, Liu X, Lo KS, Long J, Lores-Motta L, Luan J, Lyssenko V, Lyytikäinen LP, Mahajan A, Mamakou V, Mangino M, Manichaikul A, Marten J, Mattheisen M, Mavarani L, McDaid AF, Meidtner K, Melendez TL, Mercader JM, Milaneschi Y, Miller JE, Millwood IY, Mishra PP, Mitchell RE, Møllehave LT, Morgan A, Mucha S, Munz M, Nakatochi M, Nelson CP, Nethander M, Nho CW, Nielsen AA, Nolte IM, Nongmaithem SS, Noordam R, Ntalla I, Nutile T, Pandit A, Christofidou P, Pärna K, Pauper M, Petersen ERB, Petersen LV, Pitkänen N, Polašek O, Poveda A, Preuss MH, Pyarajan S, Raffield LM, Rakugi H, Ramirez J, Rasheed A, Raven D, Rayner NW, Riveros C, Rohde R, Ruggiero D, Ruotsalainen SE, Ryan KA, Sabater-Lleal M, Saxena R, Scholz M, Sendamarai A, Shen B, Shi J, Shin JH, Sidore C, Sitlani CM, Slieker RC, Smit RAJ, Smith AV, Smith JA, Smyth LJ, Southam L, Steinthorsdottir V, Sun L, Takeuchi F, Tallapragada DSP, Taylor KD, Tayo BO, Tcheandjieu C, Terzikhan N, Tesolin P, Teumer A, Theusch E, Thompson DJ, Thorleifsson G, Timmers PRHJ, Trompet S, Turman C, Vaccargiu S, van der Laan SW, van der Most PJ, van Klinken JB, van Setten J, Verma SS, Verweij N, Veturi Y, Wang CA, Wang C, Wang L, Wang Z, Warren HR, Bin Wei W, Wickremasinghe AR, Wielscher M, Wiggins KL, Winsvold BS, Wong A, Wu Y, Wuttke M, Xia R, Xie T, Yamamoto K, Yang J, Yao J, Young H, Yousri NA, Yu L, Zeng L, Zhang W, Zhang X, Zhao JH, Zhao W, Zhou W, Zimmermann ME, Zoledziewska M, Adair LS, Adams HHH, Aguilar-Salinas CA, Al-Mulla F, Arnett DK, Asselbergs FW, Åsvold BO, Attia J, Banas B, Bandinelli S, Bennett DA, Bergler T, Bharadwaj D, Biino G, Bisgaard H, Boerwinkle E, Böger CA, Bønnelykke K, Boomsma DI, Børglum AD, Borja JB, Bouchard C, Bowden DW, Brandslund I, Brumpton B, Buring JE, Caulfield MJ, Chambers JC, Chandak GR, Chanock SJ, Chaturvedi N, Chen YDI, Chen Z, Cheng CY, Christophersen IE, Ciullo M, Cole JW, Collins FS, Cooper RS, Cruz M, Cucca F, Cupples LA, Cutler MJ, Damrauer SM, Dantoft TM, de Borst GJ, de Groot LCPGM, De Jager PL, de Kleijn DPV, Janaka de Silva H, Dedoussis GV, den Hollander AI, Du S, Easton DF, Elders PJM, Eliassen AH, Ellinor PT, Elmståhl S, Erdmann J, Evans MK, Fatkin D, Feenstra B, Feitosa MF, Ferrucci L, Ford I, Fornage M, Franke A, Franks PW, Freedman BI, Gasparini P, Gieger C, Girotto G, Goddard ME, Golightly YM, Gonzalez-Villalpando C, Gordon-Larsen P, Grallert H, Grant SFA, Grarup N, Griffiths L, Gudnason V, Haiman C, Hakonarson H, Hansen T, Hartman CA, Hattersley AT, Hayward C, Heckbert SR, Heng CK, Hengstenberg C, Hewitt AW, Hishigaki H, Hoyng CB, Huang PL, Huang W, Hunt SC, Hveem K, Hyppönen E, Iacono WG, Ichihara S, Ikram MA, Isasi CR, Jackson RD, Jarvelin MR, Jin ZB, Jöckel KH, Joshi PK, Jousilahti P, Jukema JW, Kähönen M, Kamatani Y, Kang KD, Kaprio J, Kardia SLR, Karpe F, Kato N, Kee F, Kessler T, Khera AV, Khor CC, Kiemeney LALM, Kim BJ, Kim EK, Kim HL, Kirchhof P, Kivimaki M, Koh WP, Koistinen HA, Kolovou GD, Kooner JS, Kooperberg C, Köttgen A, Kovacs P, Kraaijeveld A, Kraft P, Krauss RM, Kumari M, Kutalik Z, Laakso M, Lange LA, Langenberg C, Launer LJ, Le Marchand L, Lee H, Lee NR, Lehtimäki T, Li H, Li L, Lieb W, Lin X, Lind L, Linneberg A, Liu CT, Liu J, Loeffler M, London B, Lubitz SA, Lye SJ, Mackey DA, Mägi R, Magnusson PKE, Marcus GM, Vidal PM, Martin NG, März W, Matsuda F, McGarrah RW, McGue M, McKnight AJ, Medland SE, Mellström D, Metspalu A, Mitchell BD, Mitchell P, Mook-Kanamori DO, Morris AD, Mucci LA, Munroe PB, Nalls MA, Nazarian S, Nelson AE, Neville MJ, Newton-Cheh C, Nielsen CS, Nöthen MM, Ohlsson C, Oldehinkel AJ, Orozco L, Pahkala K, Pajukanta P, Palmer CNA, Parra EJ, Pattaro C, Pedersen O, Pennell CE, Penninx BWJH, Perusse L, Peters A, Peyser PA, Porteous DJ, Posthuma D, Power C, Pramstaller PP, Province MA, Qi Q, Qu J, Rader DJ, Raitakari OT, Ralhan S, Rallidis LS, Rao DC, Redline S, Reilly DF, Reiner AP, Rhee SY, Ridker PM, Rienstra M, Ripatti S, Ritchie MD, Roden DM, Rosendaal FR, Rotter JI, Rudan I, Rutters F, Sabanayagam C, Saleheen D, Salomaa V, Samani NJ, Sanghera DK, Sattar N, Schmidt B, Schmidt H, Schmidt R, Schulze MB, Schunkert H, Scott LJ, Scott RJ, Sever P, Shiroma EJ, Shoemaker MB, Shu XO, Simonsick EM, Sims M, Singh JR, Singleton AB, Sinner MF, Smith JG, Snieder H, Spector TD, Stampfer MJ, Stark KJ, Strachan DP, 't Hart LM, Tabara Y, Tang H, Tardif JC, Thanaraj TA, Timpson NJ, Tönjes A, Tremblay A, Tuomi T, Tuomilehto J, Tusié-Luna MT, Uitterlinden AG, van Dam RM, van der Harst P, Van der Velde N, van Duijn CM, van Schoor NM, Vitart V, Völker U, Vollenweider P, Völzke H, Wacher-Rodarte NH, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Weir DR, Werge TM, Widen E, Wilkens LR, Willemsen G, Willett WC, Wilson JF, Wong TY, Woo JT, Wright AF, Wu JY, Xu H, Yajnik CS, Yokota M, Yuan JM, Zeggini E, Zemel BS, Zheng W, Zhu X, Zmuda JM, Zonderman AB, Zwart JA, Chasman DI, Cho YS, Heid IM, McCarthy MI, Ng MCY, O'Donnell CJ, Rivadeneira F, Thorsteinsdottir U, Sun YV, Tai ES, Boehnke M, Deloukas P, Justice AE, Lindgren CM, Loos RJF, Mohlke KL, North KE, Stefansson K, Walters RG, Winkler TW, Young KL, Loh PR, Yang J, Esko T, Assimes TL, Auton A, Abecasis GR, Willer CJ, Locke AE, Berndt SI, Lettre G, Frayling TM, Okada Y, Wood AR, Visscher PM, Hirschhorn JN. A saturated map of common genetic variants associated with human height. Nature 2022; 610:704-712. [PMID: 36224396 PMCID: PMC9605867 DOI: 10.1038/s41586-022-05275-y] [Citation(s) in RCA: 154] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 08/24/2022] [Indexed: 02/08/2023]
Abstract
Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
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Gorski M, Rasheed H, Teumer A, Thomas LF, Graham SE, Sveinbjornsson G, Winkler TW, Günther F, Stark KJ, Chai JF, Tayo BO, Wuttke M, Li Y, Tin A, Ahluwalia TS, Ärnlöv J, Åsvold BO, Bakker SJL, Banas B, Bansal N, Biggs ML, Biino G, Böhnke M, Boerwinkle E, Bottinger EP, Brenner H, Brumpton B, Carroll RJ, Chaker L, Chalmers J, Chee ML, Chee ML, Cheng CY, Chu AY, Ciullo M, Cocca M, Cook JP, Coresh J, Cusi D, de Borst MH, Degenhardt F, Eckardt KU, Endlich K, Evans MK, Feitosa MF, Franke A, Freitag-Wolf S, Fuchsberger C, Gampawar P, Gansevoort RT, Ghanbari M, Ghasemi S, Giedraitis V, Gieger C, Gudbjartsson DF, Hallan S, Hamet P, Hishida A, Ho K, Hofer E, Holleczek B, Holm H, Hoppmann A, Horn K, Hutri-Kähönen N, Hveem K, Hwang SJ, Ikram MA, Josyula NS, Jung B, Kähönen M, Karabegović I, Khor CC, Koenig W, Kramer H, Krämer BK, Kühnel B, Kuusisto J, Laakso M, Lange LA, Lehtimäki T, Li M, Lieb W, Lind L, Lindgren CM, Loos RJF, Lukas MA, Lyytikäinen LP, Mahajan A, Matias-Garcia PR, Meisinger C, Meitinger T, Melander O, Milaneschi Y, Mishra PP, Mononen N, Morris AP, Mychaleckyj JC, Nadkarni GN, Naito M, Nakatochi M, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Nutile T, O'Donoghue ML, O'Connell J, Olafsson I, Orho-Melander M, Parsa A, Pendergrass SA, Penninx BWJH, Pirastu M, Preuss MH, Psaty BM, Raffield LM, Raitakari OT, Rheinberger M, Rice KM, Rizzi F, Rosenkranz AR, Rossing P, Rotter JI, Ruggiero D, Ryan KA, Sabanayagam C, Salvi E, Schmidt H, Schmidt R, Scholz M, Schöttker B, Schulz CA, Sedaghat S, Shaffer CM, Sieber KB, Sim X, Sims M, Snieder H, Stanzick KJ, Thorsteinsdottir U, Stocker H, Strauch K, Stringham HM, Sulem P, Szymczak S, Taylor KD, Thio CHL, Tremblay J, Vaccargiu S, van der Harst P, van der Most PJ, Verweij N, Völker U, Wakai K, Waldenberger M, Wallentin L, Wallner S, Wang J, Waterworth DM, White HD, Willer CJ, Wong TY, Woodward M, Yang Q, Yerges-Armstrong LM, Zimmermann M, Zonderman AB, Bergler T, Stefansson K, Böger CA, Pattaro C, Köttgen A, Kronenberg F, Heid IM. Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies. Kidney Int 2022; 102:624-639. [PMID: 35716955 PMCID: PMC10034922 DOI: 10.1016/j.kint.2022.05.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/19/2022] [Accepted: 05/11/2022] [Indexed: 12/15/2022]
Abstract
Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.
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Li M, Xu L, Tan CS, Lanca C, Foo LL, Sabanayagam C, Saw SM. Systematic Review and Meta-Analysis on the Impact of COVID-19 Pandemic-Related Lifestyle on Myopia. Asia Pac J Ophthalmol (Phila) 2022; 11:470-480. [PMID: 36179338 DOI: 10.1097/apo.0000000000000559] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To conduct a systematic review and meta-analysis to assess the effects of coronavirus disease 2019 (COVID-19) pandemic-related lifestyle on myopia outcomes in children to young adults. METHODS A systematic search was conducted on PubMed, Embase, and the Cochrane Central Register of Controlled Trials databases (with manual searching of reference lists of reviews). Studies included assessed changes in myopia-related outcomes (cycloplegic refraction) during COVID and pre-COVID. Of 367 articles identified, 7 (6 prospective cohorts; 1 repeated cross-sectional study) comprising 6327 participants aged 6 to 17 were included. Quality appraisals were performed with Joanna Briggs Institute Critical Appraisal Checklists. Pooled differences in annualized myopic shifts or mean spherical equivalent (SE) during COVID and pre-COVID were obtained from random-effects models. RESULTS In all 7 studies, SE moved toward a myopic direction during COVID (vs pre-COVID), where 5 reported significantly faster myopic shifts [difference in means of changes: -1.20 to -0.35 diopters per year, [D/y]; pooled estimate: -0.73 D/y; 95% confidence interval (CI): -0.96, -0.50; P<0.001], and 2 reported significantly more myopic SE (difference in means: -0.72 to -0.44 D/y; pooled estimate: -0.54 D/y; 95% CI: -0.80, -0.28; P<0.001). Three studies reported higher myopia (SE ≤-0.50 D) incidence (2.0- to 2.6-fold increase) during COVID versus pre-COVID. Of studies assessing lifestyle changes, all 4 reported lower time outdoors (pre-COVID vs during COVID: 1.1-1.8 vs 0.4-1.0 hours per day, [h/d]), and 3 reported higher screen time (pre-COVID vs during COVID: 0.7-2.8 vs 2.4-6.9 h/d). CONCLUSIONS This review suggests more myopic SE shifts during COVID (vs pre-COVID) in participants aged 6 to 17. COVID-19 restrictions may have worsened SE shifts, and lifting of restrictions may lessen this effect. Evaluations of the long-term effects of the pandemic lifestyle on myopia onset and progression in large studies are warranted to confirm these findings.
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Ramdas S, Judd J, Graham SE, Kanoni S, Wang Y, Surakka I, Wenz B, Clarke SL, Chesi A, Wells A, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu KHH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Lee WJ, Hsiung CA, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Schönherr S, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Le P, Feitosa MF, Wojczynski MK, Hemerich D, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Noah TL, Verma A, Slieker RC, Lo KS, Zilhao NR, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Emmel C, Schmidt B, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Sankareswaran A, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Chen S, Liu F, Yang J, Kentistou KA, Banas B, Morgan A, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, van der Laan SW, Chitrala KN, Weiss S, Bentley AR, Doumatey AP, Adeyemo AA, Lee JY, Petersen ERB, Nielsen AA, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hidalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, He KY, Tang H, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Kawaguchi T, Thiery J, Bis JC, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Ida Chen YD, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Zimmermann ME, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Bandinelli S, Dedoussis G, Thanaraj TA, Peyser PA, Kato N, Schulze MB, Girotto G, Böger CA, Jung B, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Tusié-Luna T, Aguilar-Salinas CA, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Walker M, Scott LJ, Koistinen HA, Chandak GR, Mercader JM, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Jin ZB, Lu F, Hishigaki H, Lin X, März W, Gudnason V, Tardif JC, Lettre G, T Hart LM, Elders PJM, Rader DJ, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Parra EJ, Cruz M, Psaty BM, Brandslund I, Pramstaller PP, Rotimi CN, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney L, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Francesco C, Mook-Kanamori DO, Willems van Dijk K, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Huey-Herng Sheu W, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Li H, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ikram A, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Tuomilehto J, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, Hazelhurst S, Ramsay M, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Baras A, Justice AE, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Tamiya G, Yamamoto M, van Heel DA, Trembath RC, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Kim BJ, Thorsteinsdottir U, Stefansson K, Zhang J, Chen YE, Ho YL, Lynch JA, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Struan Grant, Natarajan P, Sun YV, Morris AP, Deloukas P, Peloso G, Assimes TL, Willer CJ, Zhu X, Brown CD. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids. Am J Hum Genet 2022; 109:1366-1387. [PMID: 35931049 PMCID: PMC9388392 DOI: 10.1016/j.ajhg.2022.06.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/23/2022] [Indexed: 02/06/2023] Open
Abstract
A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
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Betzler BK, Sultana R, He F, Tham YC, Lim CC, Wang YX, Nangia V, Tai ES, Rim TH, Bikbov MM, Jonas JB, Kang SW, Park KH, Cheng CY, Sabanayagam C. Impact of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) GFR Estimating Equations on CKD Prevalence and Classification Among Asians. Front Med (Lausanne) 2022; 9:957437. [PMID: 35911392 PMCID: PMC9329617 DOI: 10.3389/fmed.2022.957437] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022] Open
Abstract
Background In 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts. Methods Eight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data (n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (G3-G5). Prevalence of eGFR categories was compared within each study and within subgroups of age, sex, body mass index (BMI), diabetes, and hypertension status. The extent of reclassification was examined using net reclassification improvement (NRI). Findings Of 67,233 adults, CKD prevalence was 8.6% (n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% (n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from −7.0% (95% CI −8.5% to −5.4%) to −0.4% (−1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at −2.33% (p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%−4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used. Interpretation eGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted. Funding National Medical Research Council, Singapore.
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Ang M, He F, Lang S, Sabanayagam C, Cheng CY, Arundhati A, Mehta JS. Machine Learning to Analyze Factors Associated With Ten-Year Graft Survival of Keratoplasty for Cornea Endothelial Disease. Front Med (Lausanne) 2022; 9:831352. [PMID: 35721073 PMCID: PMC9200960 DOI: 10.3389/fmed.2022.831352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/28/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Machine learning analysis of factors associated with 10-year graft survival of Descemet stripping automated endothelial keratoplasty (DSAEK) and penetrating keratoplasty (PK) in Asian eyes. Methods Prospective study of donor characteristics, clinical outcomes and complications from consecutive patients (n = 1,335) who underwent DSAEK (946 eyes) or PK (389 eyes) for Fuchs’ endothelial dystrophy (FED) or bullous keratopathy (BK) were analyzed. Random survival forests (RSF) analysis using the highest variable importance (VIMP) factors were determined to develop the optimal Cox proportional hazards regression model. Main outcome measure was 10-year graft survival with RSF analysis of factors associated with graft failure. Results Mean age was 68 ± 11 years, 47.6% male, in our predominantly Chinese (76.6%) Asian cohort, with more BK compared to FED (62.2 vs. 37.8%, P < 0.001). Overall 10-year survival for DSAEK was superior to PK (73.6 vs. 50.9%, log-rank P < 0.001). RSF based on VIMP (best Harrell C statistic: 0.701) with multivariable modeling revealed that BK (HR:2.84, 95%CI:1.89–4.26; P < 0.001), PK (HR: 1.64, 95%CI:1.19–2.27; P = 0.002), male recipients (HR:1.75, 95%CI:1.31–2.34; P < 0.001) and poor pre-operative visual acuity (HR: 1.60, 95%CI:1.15–2.22, P = 0.005) were associated with graft failure. Ten-year cumulative incidence of complications such as immune-mediated graft rejection (P < 0.001), epitheliopathy (P < 0.001), and wound dehiscence (P = 0.002) were greater in the PK compared to the DSAEK group. Conclusion In our study, RSF combined with Cox regression was superior to traditional regression techniques alone in analyzing a large number of high-dimensional factors associated with 10-year corneal graft survival in Asian eyes with cornea endothelial disease.
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Seah JYH, Hong Y, Cichońska A, Sabanayagam C, Nusinovici S, Wong TY, Cheng CY, Jousilahti P, Lundqvist A, Perola M, Salomaa V, Tai ES, Würtz P, van Dam RM, Sim X. Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations. J Clin Endocrinol Metab 2022; 107:e2751-e2761. [PMID: 35390150 DOI: 10.1210/clinem/dgac212] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT While Asians have a higher risk of type 2 diabetes (T2D) than Europeans for a given body mass index (BMI), it remains unclear whether the same markers of metabolic pathways are associated with diabetes. OBJECTIVE We evaluated associations between metabolic biomarkers and incidence of T2D in 3 major Asian ethnic groups (Chinese, Malay, and Indian) and a European population. METHODS We analyzed data from adult males and females of 2 cohorts from Singapore (n = 6393) consisting of Chinese, Malays, and Indians and 3 cohorts of European-origin participants from Finland (n = 14 558). We used nuclear magnetic resonance to quantify 154 circulating metabolic biomarkers at baseline and performed logistic regression to assess associations with T2D risk adjusted for age, sex, BMI and glycemic markers. RESULTS Of the 154 metabolic biomarkers, 59 were associated with higher risk of T2D in both Asians and Europeans (P < 0.0003, Bonferroni-corrected). These included branched chain and aromatic amino acids, the inflammatory marker glycoprotein acetyls, total fatty acids, monounsaturated fatty acids, apolipoprotein B, larger very low-density lipoprotein particle sizes, and triglycerides. In addition, 13 metabolites were associated with a lower T2D risk in both populations, including omega-6 polyunsaturated fatty acids and larger high-density lipoprotein particle sizes. Associations were consistent within the Asian ethnic groups (all Phet ≥ 0.05) and largely consistent for the Asian and European populations (Phet ≥ 0.05 for 128 of 154 metabolic biomarkers). CONCLUSION Metabolic biomarkers across several biological pathways were consistently associated with T2D risk in Asians and Europeans.
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Winkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, Tayo B, Thio CHL, Cusi D, Chai JF, Sieber KB, Horn K, Li M, Scholz M, Cocca M, Wuttke M, van der Most PJ, Yang Q, Ghasemi S, Nutile T, Li Y, Pontali G, Günther F, Dehghan A, Correa A, Parsa A, Feresin A, de Vries APJ, Zonderman AB, Smith AV, Oldehinkel AJ, De Grandi A, Rosenkranz AR, Franke A, Teren A, Metspalu A, Hicks AA, Morris AP, Tönjes A, Morgan A, Podgornaia AI, Peters A, Körner A, Mahajan A, Campbell A, Freedman BI, Spedicati B, Ponte B, Schöttker B, Brumpton B, Banas B, Krämer BK, Jung B, Åsvold BO, Smith BH, Ning B, Penninx BWJH, Vanderwerff BR, Psaty BM, Kammerer CM, Langefeld CD, Hayward C, Spracklen CN, Robinson-Cohen C, Hartman CA, Lindgren CM, Wang C, Sabanayagam C, Heng CK, Lanzani C, Khor CC, Cheng CY, Fuchsberger C, Gieger C, Shaffer CM, Schulz CA, Willer CJ, Chasman DI, Gudbjartsson DF, Ruggiero D, Toniolo D, Czamara D, Porteous DJ, Waterworth DM, Mascalzoni D, Mook-Kanamori DO, Reilly DF, Daw EW, Hofer E, Boerwinkle E, Salvi E, Bottinger EP, Tai ES, Catamo E, Rizzi F, Guo F, Rivadeneira F, Guilianini F, Sveinbjornsson G, Ehret G, Waeber G, Biino G, Girotto G, Pistis G, Nadkarni GN, Delgado GE, Montgomery GW, Snieder H, Campbell H, White HD, Gao H, Stringham HM, Schmidt H, Li H, Brenner H, Holm H, Kirsten H, Kramer H, Rudan I, Nolte IM, Tzoulaki I, Olafsson I, Martins J, Cook JP, Wilson JF, Halbritter J, Felix JF, Divers J, Kooner JS, Lee JJM, O'Connell J, Rotter JI, Liu J, Xu J, Thiery J, Ärnlöv J, Kuusisto J, Jakobsdottir J, Tremblay J, Chambers JC, Whitfield JB, Gaziano JM, Marten J, Coresh J, Jonas JB, Mychaleckyj JC, Christensen K, Eckardt KU, Mohlke KL, Endlich K, Dittrich K, Ryan KA, Rice KM, Taylor KD, Ho K, Nikus K, Matsuda K, Strauch K, Miliku K, Hveem K, Lind L, Wallentin L, Yerges-Armstrong LM, Raffield LM, Phillips LS, Launer LJ, Lyytikäinen LP, Lange LA, Citterio L, Klaric L, Ikram MA, Ising M, Kleber ME, Francescatto M, Concas MP, Ciullo M, Piratsu M, Orho-Melander M, Laakso M, Loeffler M, Perola M, de Borst MH, Gögele M, Bianca ML, Lukas MA, Feitosa MF, Biggs ML, Wojczynski MK, Kavousi M, Kanai M, Akiyama M, Yasuda M, Nauck M, Waldenberger M, Chee ML, Chee ML, Boehnke M, Preuss MH, Stumvoll M, Province MA, Evans MK, O'Donoghue ML, Kubo M, Kähönen M, Kastarinen M, Nalls MA, Kuokkanen M, Ghanbari M, Bochud M, Josyula NS, Martin NG, Tan NYQ, Palmer ND, Pirastu N, Schupf N, Verweij N, Hutri-Kähönen N, Mononen N, Bansal N, Devuyst O, Melander O, Raitakari OT, Polasek O, Manunta P, Gasparini P, Mishra PP, Sulem P, Magnusson PKE, Elliott P, Ridker PM, Hamet P, Svensson PO, Joshi PK, Kovacs P, Pramstaller PP, Rossing P, Vollenweider P, van der Harst P, Dorajoo R, Sim RZH, Burkhardt R, Tao R, Noordam R, Mägi R, Schmidt R, de Mutsert R, Rueedi R, van Dam RM, Carroll RJ, Gansevoort RT, Loos RJF, Felicita SC, Sedaghat S, Padmanabhan S, Freitag-Wolf S, Pendergrass SA, Graham SE, Gordon SD, Hwang SJ, Kerr SM, Vaccargiu S, Patil SB, Hallan S, Bakker SJL, Lim SC, Lucae S, Vogelezang S, Bergmann S, Corre T, Ahluwalia TS, Lehtimäki T, Boutin TS, Meitinger T, Wong TY, Bergler T, Rabelink TJ, Esko T, Haller T, Thorsteinsdottir U, Völker U, Foo VHX, Salomaa V, Vitart V, Giedraitis V, Gudnason V, Jaddoe VWV, Huang W, Zhang W, Wei WB, Kiess W, März W, Koenig W, Lieb W, Gao X, Sim X, Wang YX, Friedlander Y, Tham YC, Kamatani Y, Okada Y, Milaneschi Y, Yu Z, Stark KJ, Stefansson K, Böger CA, Hung AM, Kronenberg F, Köttgen A, Pattaro C, Heid IM. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun Biol 2022; 5:580. [PMID: 35697829 PMCID: PMC9192715 DOI: 10.1038/s42003-022-03448-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/04/2022] [Indexed: 01/14/2023] Open
Abstract
Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.
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Betzler BK, Sabanayagam C, Tham YC, Cheung CY, Cheng CY, Wong TY, Nusinovici S. Retinal Vascular Profile in Predicting Incident Cardiometabolic Diseases among Individuals with Diabetes. Microcirculation 2022; 29:e12772. [PMID: 35652745 DOI: 10.1111/micc.12772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/12/2022] [Accepted: 05/25/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To determine the longitudinal associations between retinal vascular profile (RVP) and four major cardiometabolic diseases; and to quantify the predictive improvements when adding RVP beyond traditional risk factors in individuals with diabetes. METHODS Subjects were enrolled from the Singapore Epidemiology of Eye Disease (SEED) study, a multi-ethnic population-based cohort. Four incident cardiometabolic diseases, calculated over a ~6-year period, were considered: cardiovascular disease (CVD), hypertension (HTN), diabetic kidney disease (DKD) and hyperlipidaemia (HLD). The RVP - vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel calibre, and DR status - was characterized at baseline using a computer-assisted program. Traditional risk factors at baseline included age, gender, ethnicity, smoking, blood pressure (BP), HbA1c, estimated glomerular filtration rate (eGFR) or cholesterol. The improvements in predictive performance when adding RVP (compared to only traditional risk factors) was calculated using several metrics including area under the receiver operating characteristics curve (AUC) and Net Reclassification Improvement (NRI). RESULTS Among 1,770 individuals with diabetes, incidences were 6.3% (n=79/1259) for CVD, 48.7% (n=166/341) for HTN, 14.6% (n=175/1199) for DKD, and 59.4% (n=336/566) for HLD. DR preceded the onset of CVD (RR 1.85[1.14;3.00]) and DKD (1.44 [1.06;1.96]). Narrower arteriolar calibre preceding the onset of HTN (0.84 [0.72;0.99]), and changes in arteriolar branching angle preceded the onset of CVD (0.78 [0.62;0.98]) and HTN (1.15 [1.03;1.29]). The largest predictive improvement was found for HTN with AUC increment of 3.4% (p=0.027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p=0.008). CONCLUSION We found that RVPs improved the prediction of HTN in individuals with diabetes, but add limited information for CVD, DKD and HLD predictions.
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Tham YC, Goh JHL, Anees A, Lei X, Rim TH, Chee ML, Wang YX, Jonas JB, Thakur S, Teo ZL, Cheung N, Hamzah H, Tan GSW, Husain R, Sabanayagam C, Wang JJ, Chen Q, Lu Z, Keenan TD, Chew EY, Tan AG, Mitchell P, Goh RSM, Xu X, Liu Y, Wong TY, Cheng CY. Author Correction: Detecting visually significant cataract using retinal photograph-based deep learning. NATURE AGING 2022; 2:562. [PMID: 37118457 PMCID: PMC10154230 DOI: 10.1038/s43587-022-00245-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
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Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front Digit Health 2022; 4:889445. [PMID: 35706971 PMCID: PMC9190759 DOI: 10.3389/fdgth.2022.889445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
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Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee JJ, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Paré G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng CY, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers JC. Author Correction: Identification of genetic effects underlying type 2 diabetes in South Asian and European populations. Commun Biol 2022; 5:441. [PMID: 35513483 PMCID: PMC9072318 DOI: 10.1038/s42003-022-03404-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak SH, Robertson NR, Rayner NW, Loh M, Kim BJ, Chiou J, Miguel-Escalada I, Della Briotta Parolo P, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee JJ, Huerta-Chagoya A, Graff M, Chai JF, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen WM, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Lecoeur C, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe EK, An P, Xiang AH, Choi HS, Cade BE, Tan J, Flanagan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman BI, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen ME, Jørgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KR, Meitinger T, Metspalu A, Morris AD, Nadkarni GN, Nadler JL, Nalls MA, Nayak U, Nongmaithem SS, Ntalla I, Okada Y, Orozco L, Patel SR, Pereira MA, Peters A, Pirie FJ, Porneala B, Prasad G, Preissl S, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sander M, Sandow K, Sattar N, Schönherr S, Schurmann C, Shahriar M, Shi J, Shin DM, Shriner D, Smith JA, So WY, Stančáková A, Stilp AM, Strauch K, Suzuki K, Takahashi A, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tomlinson B, Torres JM, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Vujkovic M, Wacher-Rodarte N, Wheeler E, Whitsel EA, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamauchi T, Yengo L, Yoon K, Yu C, Yuan JM, Yusuf S, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Hanis CL, Peyser PA, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Zeggini E, Yokota M, Rich SS, Kooperberg C, Pankow JS, Engert JC, Chen YDI, Froguel P, Wilson JG, Sheu WHH, Kardia SLR, Wu JY, Hayes MG, Ma RCW, Wong TY, Groop L, Mook-Kanamori DO, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, McKean-Cowdin R, Grallert H, Cheng CY, Bottinger EP, Dehghan A, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Palmer CNA, Liu S, Abecasis G, Kooner JS, Loos RJF, North KE, Haiman CA, Florez JC, Saleheen D, Hansen T, Pedersen O, Mägi R, Langenberg C, Wareham NJ, Maeda S, Kadowaki T, Lee J, Millwood IY, Walters RG, Stefansson K, Myers SR, Ferrer J, Gaulton KJ, Meigs JB, Mohlke KL, Gloyn AL, Bowden DW, Below JE, Chambers JC, Sim X, Boehnke M, Rotter JI, McCarthy MI, Morris AP. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 2022; 54:560-572. [PMID: 35551307 PMCID: PMC9179018 DOI: 10.1038/s41588-022-01058-3] [Citation(s) in RCA: 208] [Impact Index Per Article: 104.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/23/2022] [Indexed: 02/02/2023]
Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
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