02524nas a2200409 4500008004100000022001400041245012800055210006900183260001300252300001100265490000700276520137500283653001001658653001601668653000901684653002201693653002101715653001101736653001101747653000901758653001901767653001601786653001501802653003001817653001601847100002401863700002301887700001601910700002401926700002601950700002301976700001901999700002102018700001802039700002102057856003602078 2010 eng d a1524-462800aPrevalence of asymptomatic carotid artery stenosis in the general population: an individual participant data meta-analysis.0 aPrevalence of asymptomatic carotid artery stenosis in the genera c2010 Jun a1294-70 v413 a
BACKGROUND AND PURPOSE: In the discussion on the cost-effectiveness of screening, precise estimates of severe asymptomatic carotid stenosis are vital. Accordingly, we assessed the prevalence of moderate and severe asymptomatic carotid stenosis by age and sex using pooled cohort data.
METHODS: We performed an individual participant data meta-analysis (23 706 participants) of 4 population-based studies (Malmö Diet and Cancer Study, Tromsø, Carotid Atherosclerosis Progression Study, and Cardiovascular Health Study). Outcomes of interest were asymptomatic moderate (> or =50%) and severe carotid stenosis (> or =70%).
RESULTS: Prevalence of moderate asymptomatic carotid stenosis ranged from 0.2% (95% CI, 0.0% to 0.4%) in men aged <50 years to 7.5% (5.2% to 10.5%) in men aged > or =80 years. For women, this prevalence increased from 0% (0% to 0.2%) to 5.0% (3.1% to 7.5%). Prevalence of severe asymptomatic carotid stenosis ranged from 0.1% (0.0% to 0.3%) in men aged <50 years to 3.1% (1.7% to 5.3%) in men aged > or =80. For women, this prevalence increased from 0% (0.0% to 0.2%) to 0.9% (0.3% to 2.4%).
CONCLUSIONS: The prevalence of severe asymptomatic carotid stenosis in the general population ranges from 0% to 3.1%, which is useful information in the discussion on the cost-effectiveness of screening.
10aAdult10aAge Factors10aAged10aAged, 80 and over10aCarotid Stenosis10aFemale10aHumans10aMale10aMass Screening10aMiddle Aged10aPrevalence10aSeverity of Illness Index10aSex Factors1 ade Weerd, Marjolein1 aGreving, Jacoba, P1 aHedblad, Bo1 aLorenz, Matthias, W1 aMathiesen, Ellisiv, B1 aO'Leary, Daniel, H1 aRosvall, Maria1 aSitzer, Matthias1 aBuskens, Erik1 aBots, Michiel, L uhttps://chs-nhlbi.org/node/119513785nas a2204513 4500008004100000022001400041245009700055210006900152260001600221300001000237490000800247520127300255653001101528653000901539653001901548653002801567653002801595653001101623653003801634653003401672653001101706653001701717653002001734653003601754653001101790110008001801700002001881700002401901700002101925700002101946700002301967700002301990700002102013700002102034700002602055700002002081700001702101700002402118700002202142700001602164700003102180700002202211700002202233700001902255700001902274700002102293700001702314700001702331700001802348700001802366700001802384700001902402700001602421700001702437700002102454700002102475700001902496700002002515700001902535700001602554700001602570700002102586700002002607700002002627700002202647700001802669700001802687700001602705700001502721700002402736700002202760700001502782700002202797700002102819700002202840700002202862700001702884700001402901700002202915700001902937700002002956700001702976700002902993700002203022700002203044700001303066700001803079700003103097700002303128700002203151700001403173700001903187700002003206700001803226700002203244700002103266700002403287700002703311700002303338700001903361700002003380700002003400700002403420700002103444700002603465700001903491700002003510700002203530700002603552700002403578700002603602700001903628700002203647700002403669700002003693700001803713700002203731700001803753700002103771700002103792700002403813700001803837700002203855700002003877700002403897700001903921700002303940700001703963700002503980700001304005700002104018700001904039700002004058700001904078700002004097700002004117700002004137700002004157700002204177700003004199700001904229700001904248700002004267700002104287700002504308700002004333700002104353700002204374700002304396700002504419700002304444700001604467700002404483700002304507700001904530700001904549700002404568700002004592700002004612700001804632700001904650700001804669700001904687700002004706700001604726700002404742700002404766700002504790700002004815700002604835700002004861700001904881700002204900700002504922700002104947700002004968700002004988700002605008700001705034700002005051700001905071700001705090700002005107700002105127700002305148700002305171700002205194700001805216700002005234700001905254700002105273700001805294700002605312700002005338700002305358700002205381700001905403700002205422700002205444700002405466700001705490700002305507700001805530700001805548700002005566700002005586700002205606700002205628700002305650700002105673700002405694700002205718700002205740700001905762700001905781700002005800700001705820700002005837700001805857700001605875700001905891700002405910700001705934700001905951700002505970700002205995700001306017700002206030700003006052700002506082700002006107700002006127700002206147700002006169700002206189700002006211700002106231700001806252700002006270700002106290700001906311700002206330700002406352700001706376700001806393700001506411700002006426700001806446700002506464700001906489700002606508700001906534700002806553700001906581700003306600700001906633700001906652700001806671700002606689700002306715700001706738700002006755700002206775700002106797700002806818700001906846700002006865700001806885700002306903700001806926700002106944700001706965700001906982700001707001700002007018700001807038700002007056700002007076700002107096700002007117700001807137700002207155700002207177700001907199700001807218700002407236700002107260700003107281700002607312700002407338700002207362700002407384700002207408700001707430700002007447700002107467700001507488700002207503700001907525700002407544700002007568700001507588700001607603700002007619700002007639700002207659700001907681700002107700700001807721700002007739700002207759700001807781700001907799700002307818700001907841700002007860700002207880700002307902700002407925700001907949700002307968700001807991700002108009700002408030700001808054700001808072700002008090700001808110700001808128700002208146700001908168700002008187700002508207700002308232700002008255700002108275700002008296700002008316700002308336700002608359700002008385700002008405700002508425700002108450700002008471700002308491700002208514700002008536700002808556700002508584700002408609700002208633700001908655700001708674700002308691700003008714700002808744700002508772700002108797700002708818700002108845700002208866700002708888700002008915700002508935700002608960700001808986700002309004700002909027700001709056700002309073700001809096710002609114710002209140710002509162710002309187710002509210856003609235 2011 eng d a1476-468700aGenetic variants in novel pathways influence blood pressure and cardiovascular disease risk.0 aGenetic variants in novel pathways influence blood pressure and c2011 Sep 11 a103-90 v4783 aBlood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.
10aAfrica10aAsia10aBlood Pressure10aCardiovascular Diseases10aCoronary Artery Disease10aEurope10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aHypertension10aKidney Diseases10aPolymorphism, Single Nucleotide10aStroke1 aInternational Consortium for Blood Pressure Genome-Wide Association Studies1 aEhret, Georg, B1 aMunroe, Patricia, B1 aRice, Kenneth, M1 aBochud, Murielle1 aJohnson, Andrew, D1 aChasman, Daniel, I1 aSmith, Albert, V1 aTobin, Martin, D1 aVerwoert, Germaine, C1 aHwang, Shih-Jen1 aPihur, Vasyl1 aVollenweider, Peter1 aO'Reilly, Paul, F1 aAmin, Najaf1 aBragg-Gresham, Jennifer, L1 aTeumer, Alexander1 aGlazer, Nicole, L1 aLauner, Lenore1 aZhao, Jing Hua1 aAulchenko, Yurii1 aHeath, Simon1 aSõber, Siim1 aParsa, Afshin1 aLuan, Jian'an1 aArora, Pankaj1 aDehghan, Abbas1 aZhang, Feng1 aLucas, Gavin1 aHicks, Andrew, A1 aJackson, Anne, U1 aPeden, John, F1 aTanaka, Toshiko1 aWild, Sarah, H1 aRudan, Igor1 aIgl, Wilmar1 aMilaneschi, Yuri1 aParker, Alex, N1 aFava, Cristiano1 aChambers, John, C1 aFox, Ervin, R1 aKumari, Meena1 aGo, Min Jin1 aHarst, Pim1 aKao, Wen Hong Linda1 aSjögren, Marketa1 aVinay, D G1 aAlexander, Myriam1 aTabara, Yasuharu1 aShaw-Hawkins, Sue1 aWhincup, Peter, H1 aLiu, Yongmei1 aShi, Gang1 aKuusisto, Johanna1 aTayo, Bamidele1 aSeielstad, Mark1 aSim, Xueling1 aNguyen, Khanh-Dung Hoang1 aLehtimäki, Terho1 aMatullo, Giuseppe1 aWu, Ying1 aGaunt, Tom, R1 aOnland-Moret, Charlotte, N1 aCooper, Matthew, N1 aPlatou, Carl, G P1 aOrg, Elin1 aHardy, Rebecca1 aDahgam, Santosh1 aPalmen, Jutta1 aVitart, Veronique1 aBraund, Peter, S1 aKuznetsova, Tatiana1 aUiterwaal, Cuno, S P M1 aAdeyemo, Adebowale1 aPalmas, Walter1 aCampbell, Harry1 aLudwig, Barbara1 aTomaszewski, Maciej1 aTzoulaki, Ioanna1 aPalmer, Nicholette, D1 aAspelund, Thor1 aGarcia, Melissa1 aChang, Yen-Pei, C1 aO'Connell, Jeffrey, R1 aSteinle, Nanette, I1 aGrobbee, Diederick, E1 aArking, Dan, E1 aKardia, Sharon, L1 aMorrison, Alanna, C1 aHernandez, Dena1 aNajjar, Samer1 aMcArdle, Wendy, L1 aHadley, David1 aBrown, Morris, J1 aConnell, John, M1 aHingorani, Aroon, D1 aDay, Ian, N M1 aLawlor, Debbie, A1 aBeilby, John, P1 aLawrence, Robert, W1 aClarke, Robert1 aHopewell, Jemma, C1 aOngen, Halit1 aDreisbach, Albert, W1 aLi, Yali1 aYoung, Hunter, J1 aBis, Joshua, C1 aKähönen, Mika1 aViikari, Jorma1 aAdair, Linda, S1 aLee, Nanette, R1 aChen, Ming-Huei1 aOlden, Matthias1 aPattaro, Cristian1 aBolton, Judith Hoffman, A1 aKöttgen, Anna1 aBergmann, Sven1 aMooser, Vincent1 aChaturvedi, Nish1 aFrayling, Timothy, M1 aIslam, Muhammad1 aJafar, Tazeen, H1 aErdmann, Jeanette1 aKulkarni, Smita, R1 aBornstein, Stefan, R1 aGrässler, Jürgen1 aGroop, Leif1 aVoight, Benjamin, F1 aKettunen, Johannes1 aHoward, Philip1 aTaylor, Andrew1 aGuarrera, Simonetta1 aRicceri, Fulvio1 aEmilsson, Valur1 aPlump, Andrew1 aBarroso, Inês1 aKhaw, Kay-Tee1 aWeder, Alan, B1 aHunt, Steven, C1 aSun, Yan, V1 aBergman, Richard, N1 aCollins, Francis, S1 aBonnycastle, Lori, L1 aScott, Laura, J1 aStringham, Heather, M1 aPeltonen, Leena1 aPerola, Markus1 aVartiainen, Erkki1 aBrand, Stefan-Martin1 aStaessen, Jan, A1 aWang, Thomas, J1 aBurton, Paul, R1 aArtigas, Maria, Soler1 aDong, Yanbin1 aSnieder, Harold1 aWang, Xiaoling1 aZhu, Haidong1 aLohman, Kurt, K1 aRudock, Megan, E1 aHeckbert, Susan, R1 aSmith, Nicholas, L1 aWiggins, Kerri, L1 aDoumatey, Ayo1 aShriner, Daniel1 aVeldre, Gudrun1 aViigimaa, Margus1 aKinra, Sanjay1 aPrabhakaran, Dorairaj1 aTripathy, Vikal1 aLangefeld, Carl, D1 aRosengren, Annika1 aThelle, Dag, S1 aCorsi, Anna Maria1 aSingleton, Andrew1 aForrester, Terrence1 aHilton, Gina1 aMcKenzie, Colin, A1 aSalako, Tunde1 aIwai, Naoharu1 aKita, Yoshikuni1 aOgihara, Toshio1 aOhkubo, Takayoshi1 aOkamura, Tomonori1 aUeshima, Hirotsugu1 aUmemura, Satoshi1 aEyheramendy, Susana1 aMeitinger, Thomas1 aWichmann, H-Erich1 aCho, Yoon Shin1 aKim, Hyung-Lae1 aLee, Jong-Young1 aScott, James1 aSehmi, Joban, S1 aZhang, Weihua1 aHedblad, Bo1 aNilsson, Peter1 aSmith, George Davey1 aWong, Andrew1 aNarisu, Narisu1 aStančáková, Alena1 aRaffel, Leslie, J1 aYao, Jie1 aKathiresan, Sekar1 aO'Donnell, Christopher, J1 aSchwartz, Stephen, M1 aIkram, Arfan, M1 aLongstreth, W T1 aMosley, Thomas, H1 aSeshadri, Sudha1 aShrine, Nick, R G1 aWain, Louise, V1 aMorken, Mario, A1 aSwift, Amy, J1 aLaitinen, Jaana1 aProkopenko, Inga1 aZitting, Paavo1 aCooper, Jackie, A1 aHumphries, Steve, E1 aDanesh, John1 aRasheed, Asif1 aGoel, Anuj1 aHamsten, Anders1 aWatkins, Hugh1 aBakker, Stephan, J L1 aGilst, Wiek, H1 aJanipalli, Charles, S1 aMani, Radha, K1 aYajnik, Chittaranjan, S1 aHofman, Albert1 aMattace-Raso, Francesco, U S1 aOostra, Ben, A1 aDemirkan, Ayse1 aIsaacs, Aaron1 aRivadeneira, Fernando1 aLakatta, Edward, G1 aOrrù, Marco1 aScuteri, Angelo1 aAla-Korpela, Mika1 aKangas, Antti, J1 aLyytikäinen, Leo-Pekka1 aSoininen, Pasi1 aTukiainen, Taru1 aWürtz, Peter1 aOng, Rick Twee-Hee1 aDörr, Marcus1 aKroemer, Heyo, K1 aVölker, Uwe1 aVölzke, Henry1 aGalan, Pilar1 aHercberg, Serge1 aLathrop, Mark1 aZelenika, Diana1 aDeloukas, Panos1 aMangino, Massimo1 aSpector, Tim, D1 aZhai, Guangju1 aMeschia, James, F1 aNalls, Michael, A1 aSharma, Pankaj1 aTerzic, Janos1 aKumar, Kranthi, M V1 aDenniff, Matthew1 aZukowska-Szczechowska, Ewa1 aWagenknecht, Lynne, E1 aFowkes, Gerald, F R1 aCharchar, Fadi, J1 aSchwarz, Peter, E H1 aHayward, Caroline1 aGuo, Xiuqing1 aRotimi, Charles1 aBots, Michiel, L1 aBrand, Eva1 aSamani, Nilesh, J1 aPolasek, Ozren1 aTalmud, Philippa, J1 aNyberg, Fredrik1 aKuh, Diana1 aLaan, Maris1 aHveem, Kristian1 aPalmer, Lyle, J1 aSchouw, Yvonne, T1 aCasas, Juan, P1 aMohlke, Karen, L1 aVineis, Paolo1 aRaitakari, Olli1 aGanesh, Santhi, K1 aWong, Tien, Y1 aTai, Shyong, E1 aCooper, Richard, S1 aLaakso, Markku1 aRao, Dabeeru, C1 aHarris, Tamara, B1 aMorris, Richard, W1 aDominiczak, Anna, F1 aKivimaki, Mika1 aMarmot, Michael, G1 aMiki, Tetsuro1 aSaleheen, Danish1 aChandak, Giriraj, R1 aCoresh, Josef1 aNavis, Gerjan1 aSalomaa, Veikko1 aHan, Bok-Ghee1 aZhu, Xiaofeng1 aKooner, Jaspal, S1 aMelander, Olle1 aRidker, Paul, M1 aBandinelli, Stefania1 aGyllensten, Ulf, B1 aWright, Alan, F1 aWilson, James, F1 aFerrucci, Luigi1 aFarrall, Martin1 aTuomilehto, Jaakko1 aPramstaller, Peter, P1 aElosua, Roberto1 aSoranzo, Nicole1 aSijbrands, Eric, J G1 aAltshuler, David1 aLoos, Ruth, J F1 aShuldiner, Alan, R1 aGieger, Christian1 aMeneton, Pierre1 aUitterlinden, André, G1 aWareham, Nicholas, J1 aGudnason, Vilmundur1 aRotter, Jerome, I1 aRettig, Rainer1 aUda, Manuela1 aStrachan, David, P1 aWitteman, Jacqueline, C M1 aHartikainen, Anna-Liisa1 aBeckmann, Jacques, S1 aBoerwinkle, Eric1 aVasan, Ramachandran, S1 aBoehnke, Michael1 aLarson, Martin, G1 aJarvelin, Marjo-Riitta1 aPsaty, Bruce, M1 aAbecasis, Goncalo, R1 aChakravarti, Aravinda1 aElliott, Paul1 aDuijn, Cornelia, M1 aNewton-Cheh, Christopher1 aLevy, Daniel1 aCaulfield, Mark, J1 aJohnson, Toby1 aCARDIoGRAM consortium1 aCKDGen Consortium1 aKidneyGen Consortium1 aEchoGen consortium1 aCHARGE-HF consortium uhttps://chs-nhlbi.org/node/132503965nas a2200493 4500008004100000022001400041245019200055210006900247260001600316300001200332490000800344520243000352653002802782653003502810653002402845653002202869653001102891653002602902653001402928653002002942653001102962100002402973700002102997700002003018700002603038700001903064700002603083700001703109700002303126700002603149700002803175700001903203700002003222700002303242700001503265700002103280700002803301700001203329700002503341700002103366700002303387710002503410856003603435 2012 eng d a1474-547X00aCarotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data.0 aCarotid intimamedia thickness progression to predict cardiovascu c2012 Jun 02 a2053-620 v3793 aBACKGROUND: Carotid intima-media thickness (cIMT) is related to the risk of cardiovascular events in the general population. An association between changes in cIMT and cardiovascular risk is frequently assumed but has rarely been reported. Our aim was to test this association.
METHODS: We identified general population studies that assessed cIMT at least twice and followed up participants for myocardial infarction, stroke, or death. The study teams collaborated in an individual participant data meta-analysis. Excluding individuals with previous myocardial infarction or stroke, we assessed the association between cIMT progression and the risk of cardiovascular events (myocardial infarction, stroke, vascular death, or a combination of these) for each study with Cox regression. The log hazard ratios (HRs) per SD difference were pooled by random effects meta-analysis.
FINDINGS: Of 21 eligible studies, 16 with 36,984 participants were included. During a mean follow-up of 7·0 years, 1519 myocardial infarctions, 1339 strokes, and 2028 combined endpoints (myocardial infarction, stroke, vascular death) occurred. Yearly cIMT progression was derived from two ultrasound visits 2-7 years (median 4 years) apart. For mean common carotid artery intima-media thickness progression, the overall HR of the combined endpoint was 0·97 (95% CI 0·94-1·00) when adjusted for age, sex, and mean common carotid artery intima-media thickness, and 0·98 (0·95-1·01) when also adjusted for vascular risk factors. Although we detected no associations with cIMT progression in sensitivity analyses, the mean cIMT of the two ultrasound scans was positively and robustly associated with cardiovascular risk (HR for the combined endpoint 1·16, 95% CI 1·10-1·22, adjusted for age, sex, mean common carotid artery intima-media thickness progression, and vascular risk factors). In three studies including 3439 participants who had four ultrasound scans, cIMT progression did not correlate between occassions (reproducibility correlations between r=-0·06 and r=-0·02).
INTERPRETATION: The association between cIMT progression assessed from two ultrasound scans and cardiovascular risk in the general population remains unproven. No conclusion can be derived for the use of cIMT progression as a surrogate in clinical trials.
FUNDING: Deutsche Forschungsgemeinschaft.
10aCardiovascular Diseases10aCarotid Intima-Media Thickness10aDisease Progression10aFollow-Up Studies10aHumans10aMyocardial Infarction10aPrognosis10aRisk Assessment10aStroke1 aLorenz, Matthias, W1 aPolak, Joseph, F1 aKavousi, Maryam1 aMathiesen, Ellisiv, B1 aVölzke, Henry1 aTuomainen, Tomi-Pekka1 aSander, Dirk1 aPlichart, Matthieu1 aCatapano, Alberico, L1 aRobertson, Christine, M1 aKiechl, Stefan1 aRundek, Tatjana1 aDesvarieux, Moïse1 aLind, Lars1 aSchmid, Caroline1 aDasMahapatra, Pronabesh1 aGao, Lu1 aZiegelbauer, Kathrin1 aBots, Michiel, L1 aThompson, Simon, G1 aPROG-IMT Study Group uhttps://chs-nhlbi.org/node/138203905nas a2200745 4500008004100000022001400041245017300055210006900228260001300297300001200310490000700322520172200329653001002051653000902061653002802070653001902098653002802117653002702145653003502172653001902207653001102226653001102237653001702248653000902265653002702274653001602301653002002317653001702337100002102354700002802375700002202403700002202425700002602447700002202473700001902495700002502514700002602539700001602565700001902581700002202600700001402622700002002636700002002656700002202676700002002698700001702718700002402735700002602759700001802785700002002803700002302823700002102846700002502867700002502892700002802917700001902945700002002964700002202984700002103006700002503027700002103052700002303073700002703096856003603123 2014 eng d a1524-456300aCommon carotid intima-media thickness measurements do not improve cardiovascular risk prediction in individuals with elevated blood pressure: the USE-IMT collaboration.0 aCommon carotid intimamedia thickness measurements do not improve c2014 Jun a1173-810 v633 aCarotid intima-media thickness (CIMT) is a marker of cardiovascular risk. It is unclear whether measurement of mean common CIMT improves 10-year risk prediction of first-time myocardial infarction or stroke in individuals with elevated blood pressure. We performed an analysis among individuals with elevated blood pressure (i.e., a systolic blood pressure ≥140 mm Hg and a diastolic blood pressure ≥ 90 mm Hg) in USE-IMT, a large ongoing individual participant data meta-analysis. We refitted the risk factors of the Framingham Risk Score on asymptomatic individuals (baseline model) and expanded this model with mean common CIMT (CIMT model) measurements. From both models, 10-year risks to develop a myocardial infarction or stroke were estimated. In individuals with elevated blood pressure, we compared discrimination and calibration of the 2 models and calculated the net reclassification improvement (NRI). We included 17 254 individuals with elevated blood pressure from 16 studies. During a median follow-up of 9.9 years, 2014 first-time myocardial infarctions or strokes occurred. The C-statistics of the baseline and CIMT models were similar (0.73). NRI with the addition of mean common CIMT was small and not significant (1.4%; 95% confidence intervals, -1.1 to 3.7). In those at intermediate risk (n=5008, 10-year absolute risk of 10% to 20%), the NRI was 5.6% (95% confidence intervals, 1.6-10.4). There is no added value of measurement of mean common CIMT in individuals with elevated blood pressure for improving cardiovascular risk prediction. For those at intermediate risk, the addition of mean common CIMT to an existing cardiovascular risk score is small but statistically significant.
10aAdult10aAged10aAntihypertensive Agents10aBlood Pressure10aCardiovascular Diseases10aCarotid Artery, Common10aCarotid Intima-Media Thickness10aCohort Studies10aFemale10aHumans10aHypertension10aMale10aMeta-Analysis as Topic10aMiddle Aged10aRisk Assessment10aRisk Factors1 aBots, Michiel, L1 aGroenewegen, Karlijn, A1 aAnderson, Todd, J1 aBritton, Annie, R1 aDekker, Jacqueline, M1 aEngström, Gunnar1 aEvans, Greg, W1 ade Graaf, Jacqueline1 aGrobbee, Diederick, E1 aHedblad, Bo1 aHofman, Albert1 aHolewijn, Suzanne1 aIkeda, Ai1 aKavousi, Maryam1 aKitagawa, Kazuo1 aKitamura, Akihiko1 aIkram, Arfan, M1 aLonn, Eva, M1 aLorenz, Matthias, W1 aMathiesen, Ellisiv, B1 aNijpels, Giel1 aOkazaki, Shuhei1 aO'Leary, Daniel, H1 aPolak, Joseph, F1 aPrice, Jacqueline, F1 aRobertson, Christine1 aRembold, Christopher, M1 aRosvall, Maria1 aRundek, Tatjana1 aSalonen, Jukka, T1 aSitzer, Matthias1 aStehouwer, Coen, D A1 aFranco, Oscar, H1 aPeters, Sanne, A E1 aRuijter, Hester, M den uhttps://chs-nhlbi.org/node/654903198nas a2200457 4500008004100000022001400041245011800055210006900173260001300242300001200255490000700267520196200274653001002236653001602246653000902262653002202271653001902293653002102312653001102333653001102344653001502355653000902370653001902379653001602398653001502414653001502429653000902444653001202453100002402465700002302489700001602512700002402528700002602552700002302578700001902601700002102620700002402641700001802665700002102683856003602704 2014 eng d a1524-462800aPrediction of asymptomatic carotid artery stenosis in the general population: identification of high-risk groups.0 aPrediction of asymptomatic carotid artery stenosis in the genera c2014 Aug a2366-710 v453 aBACKGROUND AND PURPOSE: Because of a low prevalence of severe carotid stenosis in the general population, screening for presence of asymptomatic carotid artery stenosis (ACAS) is not warranted. Possibly, for certain subgroups, screening is worthwhile. The present study aims to develop prediction rules for the presence of ACAS (>50% and >70%).
METHODS: Individual participant data from 4 population-based cohort studies (Malmö Diet and Cancer Study, Tromsø Study, Carotid Atherosclerosis Progression Study, and Cardiovascular Health Study; totaling 23 706 participants) were pooled. Multivariable logistic regression was performed to determine which variables predict presence of ACAS (>50% and >70%). Calibration and discrimination of the models were assessed, and bootstrapping was used to correct for overfitting.
RESULTS: Age, sex, history of vascular disease, systolic and diastolic blood pressure, total cholesterol/high-density lipoprotein ratio, diabetes mellitus, and current smoking were predictors of stenosis (>50% and >70%). The calibration of the model was good confirmed by a nonsignificant Hosmer and Lemeshow test for moderate (P=0.59) and severe stenosis (P=0.07). The models discriminated well between participants with and without stenosis, with an area under the receiver operating characteristic curve corrected for over optimism of 0.82 (95% confidence interval, 0.80-0.84) for moderate stenosis and of 0.87 (95% confidence interval, 0.85-0.90) for severe stenosis. The regression coefficients of the predictors were converted into a score chart to facilitate practical application.
CONCLUSIONS: A clinical prediction rule was developed that allows identification of subgroups with high prevalence of moderate (>50%) and severe (>70%) ACAS. When confirmed in comparable cohorts, application of the prediction rule may lead to a reduction in the number needed to screen for ACAS.
10aAdult10aAge Factors10aAged10aAged, 80 and over10aBlood Pressure10aCarotid Stenosis10aFemale10aHumans10aLife Style10aMale10aMass Screening10aMiddle Aged10aPrevalence10aRegistries10aRisk10aSmoking1 ade Weerd, Marjolein1 aGreving, Jacoba, P1 aHedblad, Bo1 aLorenz, Matthias, W1 aMathiesen, Ellisiv, B1 aO'Leary, Daniel, H1 aRosvall, Maria1 aSitzer, Matthias1 ade Borst, Gert, Jan1 aBuskens, Erik1 aBots, Michiel, L uhttps://chs-nhlbi.org/node/655705080nas a2201129 4500008004100000022001400041245007200055210006900127260001600196300001100212490000700223520191200230653002502142653002102167653002802188653001102216653001902227653001302246653002602259653001102285653000902296653003702305653001602342653003602358653002002394653001802414100002302432700002702455700001902482700001902501700002502520700002702545700002202572700002502594700001702619700002402636700002302660700002602683700002802709700001602737700002102753700001502774700002002789700002802809700001502837700002102852700001802873700002102891700002602912700002602938700002202964700001902986700002103005700002203026700001803048700002003066700002103086700001903107700002403126700002103150700002603171700002203197700002403219700002103243700002203264700001903286700002403305700002003329700002103349700001803370700001803388700001803406700002003424700002103444700002103465700001803486700002403504700001903528700001803547700002003565700002103585700002103606700002503627700002103652700002503673700002003698700002403718700001903742700002203761700002103783700002203804700002403826700002403850700001903874710002103893856003603914 2015 eng d a1522-964500aMendelian randomization of blood lipids for coronary heart disease.0 aMendelian randomization of blood lipids for coronary heart disea c2015 Mar 01 a539-500 v363 aAIMS: To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.
METHODS AND RESULTS: We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10(-6)); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).
CONCLUSION: The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.
10aCase-Control Studies10aCholesterol, HDL10aCoronary Artery Disease10aFemale10aGene Frequency10aGenotype10aGenotyping Techniques10aHumans10aMale10aMendelian Randomization Analysis10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Assessment10aTriglycerides1 aHolmes, Michael, V1 aAsselbergs, Folkert, W1 aPalmer, Tom, M1 aDrenos, Fotios1 aLanktree, Matthew, B1 aNelson, Christopher, P1 aDale, Caroline, E1 aPadmanabhan, Sandosh1 aFinan, Chris1 aSwerdlow, Daniel, I1 aTragante, Vinicius1 avan Iperen, Erik, P A1 aSivapalaratnam, Suthesh1 aShah, Sonia1 aElbers, Clara, C1 aShah, Tina1 aEngmann, Jorgen1 aGiambartolomei, Claudia1 aWhite, Jon1 aZabaneh, Delilah1 aSofat, Reecha1 aMcLachlan, Stela1 aDoevendans, Pieter, A1 aBalmforth, Anthony, J1 aHall, Alistair, S1 aNorth, Kari, E1 aAlmoguera, Berta1 aHoogeveen, Ron, C1 aCushman, Mary1 aFornage, Myriam1 aPatel, Sanjay, R1 aRedline, Susan1 aSiscovick, David, S1 aTsai, Michael, Y1 aKarczewski, Konrad, J1 aHofker, Marten, H1 aVerschuren, Monique1 aBots, Michiel, L1 aSchouw, Yvonne, T1 aMelander, Olle1 aDominiczak, Anna, F1 aMorris, Richard1 aBen-Shlomo, Yoav1 aPrice, Jackie1 aKumari, Meena1 aBaumert, Jens1 aPeters, Annette1 aThorand, Barbara1 aKoenig, Wolfgang1 aGaunt, Tom, R1 aHumphries, Steve, E1 aClarke, Robert1 aWatkins, Hugh1 aFarrall, Martin1 aWilson, James, G1 aRich, Stephen, S1 ade Bakker, Paul, I W1 aLange, Leslie, A1 aSmith, George, Davey1 aReiner, Alex, P1 aTalmud, Philippa, J1 aKivimaki, Mika1 aLawlor, Debbie, A1 aDudbridge, Frank1 aSamani, Nilesh, J1 aKeating, Brendan, J1 aHingorani, Aroon, D1 aCasas, Juan, P1 aUCLEB consortium uhttps://chs-nhlbi.org/node/656804529nas a2200901 4500008004100000022001400041245012300055210006900178260000900247300001300256490000700269520197100276653001002247653002102257653000902278653002802287653003502315653002102350653002102371653001602392653003402408653002202442653001802464653001802482653001102500653002202511653001802533653001102551653001702562653001402579653001802593653000902611653001602620653002602636653001502662653003202677653001702709653001202726653001102738100002602749700002802775700001902803700002802822700002702850700002202877700002202899700002602921700002202947700001902969700002502988700002603013700001603039700002203055700001403077700002003091700002203111700003003133700001703163700002403180700002603204700001803230700002003248700002303268700002303291700002303314700002103337700002503358700002503383700002803408700001903436700002003455700002203475700002103497700002503518700002103543700002703564856003603591 2015 eng d a1932-620300aRace/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events.0 aRaceEthnic Differences in the Associations of the Framingham Ris c2015 ae01323210 v103 aBACKGROUND: Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events.
METHODS: We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.
RESULTS: Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.
CONCLUSION: The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.
10aAdult10aAge Distribution10aAged10aCarotid Artery Diseases10aCarotid Intima-Media Thickness10aCholesterol, HDL10aCholesterol, LDL10aComorbidity10aContinental Population Groups10aDiabetes Mellitus10aDyslipidemias10aEthnic Groups10aFemale10aFollow-Up Studies10aGlobal Health10aHumans10aHypertension10aIncidence10aLinear Models10aMale10aMiddle Aged10aMyocardial Infarction10aPrevalence10aProportional Hazards Models10aRisk Factors10aSmoking10aStroke1 aGijsberts, Crystel, M1 aGroenewegen, Karlijn, A1 aHoefer, Imo, E1 aEijkemans, Marinus, J C1 aAsselbergs, Folkert, W1 aAnderson, Todd, J1 aBritton, Annie, R1 aDekker, Jacqueline, M1 aEngström, Gunnar1 aEvans, Greg, W1 ade Graaf, Jacqueline1 aGrobbee, Diederick, E1 aHedblad, Bo1 aHolewijn, Suzanne1 aIkeda, Ai1 aKitagawa, Kazuo1 aKitamura, Akihiko1 ade Kleijn, Dominique, P V1 aLonn, Eva, M1 aLorenz, Matthias, W1 aMathiesen, Ellisiv, B1 aNijpels, Giel1 aOkazaki, Shuhei1 aO'Leary, Daniel, H1 aPasterkamp, Gerard1 aPeters, Sanne, A E1 aPolak, Joseph, F1 aPrice, Jacqueline, F1 aRobertson, Christine1 aRembold, Christopher, M1 aRosvall, Maria1 aRundek, Tatjana1 aSalonen, Jukka, T1 aSitzer, Matthias1 aStehouwer, Coen, D A1 aBots, Michiel, L1 aRuijter, Hester, M den uhttps://chs-nhlbi.org/node/687602626nas a2200277 4500008004100000022001400041245009400055210006900149260001600218300000700234490000700241520180500248100002402053700002602077700002402103700001602127700002302143700002602166700002002192700001702212700002102229700001902250700001802269710002502287856003602312 2017 eng d a1472-694700aAutomatic identification of variables in epidemiological datasets using logic regression.0 aAutomatic identification of variables in epidemiological dataset c2017 Apr 13 a400 v173 aBACKGROUND: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.
METHODS: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.
RESULTS: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables.
CONCLUSIONS: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.
1 aLorenz, Matthias, W1 aAbdi, Negin, Ashtiani1 aScheckenbach, Frank1 aPflug, Anja1 aBülbül, Alpaslan1 aCatapano, Alberico, L1 aAgewall, Stefan1 aEzhov, Marat1 aBots, Michiel, L1 aKiechl, Stefan1 aOrth, Andreas1 aPROG-IMT Study Group uhttps://chs-nhlbi.org/node/757405196nas a2201345 4500008004100000022001400041245009400055210006900149260001300218300001200231490000700243520143500250100001901685700002401704700002101728700002501749700002101774700002301795700002101818700001701839700001801856700003001874700002001904700001801924700001801942700002001960700002801980700002102008700001802029700001902047700002202066700002802088700001302116700002202129700002202151700001202173700001902185700002402204700002102228700001902249700001802268700002402286700002002310700001702330700002202347700002202369700001902391700002102410700002402431700001702455700001602472700001902488700002202507700002702529700002402556700002202580700002402602700002002626700002602646700002202672700002302694700001902717700002102736700001602757700002302773700002402796700001302820700001702833700002002850700002202870700002202892700002902914700002002943700002602963700002402989700002203013700002503035700002003060700001903080700002203099700001803121700001703139700002103156700002403177700002103201700001703222700002003239700002303259700002403282700001903306700002403325700002003349700002303369700002403392700002103416700002203437700001903459700001903478700001903497700001503516700002303531700001903554700002303573700002203596700001803618700002003636700002703656700002403683700002203707700002103729700002403750700002203774700001803796856003603814 2018 eng d a2574-830000aCommon and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.0 aCommon and Rare Coding Genetic Variation Underlying the Electroc c2018 May ae0020370 v113 aBACKGROUND: Electrical conduction from the cardiac sinoatrial node to the ventricles is critical for normal heart function. Genome-wide association studies have identified more than a dozen common genetic loci that are associated with PR interval. However, it is unclear whether rare and low-frequency variants also contribute to PR interval heritability.
METHODS: We performed large-scale meta-analyses of the PR interval that included 83 367 participants of European ancestry and 9436 of African ancestry. We examined both common and rare variants associated with the PR interval.
RESULTS: We identified 31 genetic loci that were significantly associated with PR interval after Bonferroni correction (<1.2×10), including 11 novel loci that have not been reported previously. Many of these loci are involved in heart morphogenesis. In gene-based analysis, we found that multiple rare variants at (=5.9×10) and (=1.1×10) were associated with PR interval. locus also was implicated in the common variant analysis, whereas was a novel locus.
CONCLUSIONS: We identified common variants at 11 novel loci and rare variants within 2 gene regions that were significantly associated with PR interval. Our findings provide novel insights to the current understanding of atrioventricular conduction, which is critical for cardiac activity and an important determinant of health.
1 aLin, Honghuang1 avan Setten, Jessica1 aSmith, Albert, V1 aBihlmeyer, Nathan, A1 aWarren, Helen, R1 aBrody, Jennifer, A1 aRadmanesh, Farid1 aHall, Leanne1 aGrarup, Niels1 aMüller-Nurasyid, Martina1 aBoutin, Thibaud1 aVerweij, Niek1 aLin, Henry, J1 aLi-Gao, Ruifang1 avan den Berg, Marten, E1 aMarten, Jonathan1 aWeiss, Stefan1 aPrins, Bram, P1 aHaessler, Jeffrey1 aLyytikäinen, Leo-Pekka1 aMei, Hao1 aHarris, Tamara, B1 aLauner, Lenore, J1 aLi, Man1 aAlonso, Alvaro1 aSoliman, Elsayed, Z1 aConnell, John, M1 aHuang, Paul, L1 aWeng, Lu-Chen1 aJameson, Heather, S1 aHucker, William1 aHanley, Alan1 aTucker, Nathan, R1 aChen, Yii-Der Ida1 aBis, Joshua, C1 aRice, Kenneth, M1 aSitlani, Colleen, M1 aKors, Jan, A1 aXie, Zhijun1 aWen, Chengping1 aMagnani, Jared, W1 aNelson, Christopher, P1 aKanters, Jørgen, K1 aSinner, Moritz, F1 aStrauch, Konstantin1 aPeters, Annette1 aWaldenberger, Melanie1 aMeitinger, Thomas1 aBork-Jensen, Jette1 aPedersen, Oluf1 aLinneberg, Allan1 aRudan, Igor1 ade Boer, Rudolf, A1 avan der Meer, Peter1 aYao, Jie1 aGuo, Xiuqing1 aTaylor, Kent, D1 aSotoodehnia, Nona1 aRotter, Jerome, I1 aMook-Kanamori, Dennis, O1 aTrompet, Stella1 aRivadeneira, Fernando1 aUitterlinden, Andre1 aEijgelsheim, Mark1 aPadmanabhan, Sandosh1 aSmith, Blair, H1 aVölzke, Henry1 aFelix, Stephan, B1 aHomuth, Georg1 aVölker, Uwe1 aMangino, Massimo1 aSpector, Timothy, D1 aBots, Michiel, L1 aPerez, Marco1 aKähönen, Mika1 aRaitakari, Olli, T1 aGudnason, Vilmundur1 aArking, Dan, E1 aMunroe, Patricia, B1 aPsaty, Bruce, M1 aDuijn, Cornelia, M1 aBenjamin, Emelia, J1 aRosand, Jonathan1 aSamani, Nilesh, J1 aHansen, Torben1 aKääb, Stefan1 aPolasek, Ozren1 aHarst, Pim1 aHeckbert, Susan, R1 aJukema, Wouter1 aStricker, Bruno, H1 aHayward, Caroline1 aDörr, Marcus1 aJamshidi, Yalda1 aAsselbergs, Folkert, W1 aKooperberg, Charles1 aLehtimäki, Terho1 aWilson, James, G1 aEllinor, Patrick, T1 aLubitz, Steven, A1 aIsaacs, Aaron uhttps://chs-nhlbi.org/node/780105658nas a2201489 4500008004100000022001400041245010600055210006900161260001500230300000700245490000700252520146500259100001901724700002101743700002301764700002701787700001901814700002501833700002501858700002301883700002501906700002901931700002201960700002301982700001702005700001702022700001802039700002202057700002002079700002102099700001802120700002002138700001902158700001802177700002802195700002102223700001302244700003002257700001902287700002502306700002102331700001902352700002302371700002002394700002402414700002102438700001802459700002102477700001802498700001902516700002002535700002102555700002302576700002402599700002202623700002402645700001702669700002202686700002202708700002202730700002302752700001902775700001702794700002002811700001702831700002202848700002202870700001202892700002102904700002702925700001902952700001702971700002002988700001903008700002003027700002303047700002103070700002203091700002203113700002403135700002003159700002403179700002803203700001903231700002003250700002403270700002103294700002403315700001703339700001903356700002603375700002103401700001603422700002703438700001803465700002303483700002103506700002803527700002403555700001903579700001903598700002403617700002403641700002203665700001803687700002203705700002903727700002403756700003203780700002103812700001603833700002203849700002403871700002003895700001603915700002303931700001803954700002203972700002003994700001504014700002104029700002404050700001904074700001904093700002004112856003604132 2018 eng d a1474-760X00aExome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6.0 aExomechip metaanalysis identifies novel loci associated with car c2018 07 17 a870 v193 aBACKGROUND: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.
RESULTS: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.
CONCLUSIONS: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.
1 aPrins, Bram, P1 aMead, Timothy, J1 aBrody, Jennifer, A1 aSveinbjornsson, Gardar1 aNtalla, Ioanna1 aBihlmeyer, Nathan, A1 avan den Berg, Marten1 aBork-Jensen, Jette1 aCappellani, Stefania1 aVan Duijvenboden, Stefan1 aKlena, Nikolai, T1 aGabriel, George, C1 aLiu, Xiaoqin1 aGulec, Cagri1 aGrarup, Niels1 aHaessler, Jeffrey1 aHall, Leanne, M1 aIorio, Annamaria1 aIsaacs, Aaron1 aLi-Gao, Ruifang1 aLin, Honghuang1 aLiu, Ching-Ti1 aLyytikäinen, Leo-Pekka1 aMarten, Jonathan1 aMei, Hao1 aMüller-Nurasyid, Martina1 aOrini, Michele1 aPadmanabhan, Sandosh1 aRadmanesh, Farid1 aRamirez, Julia1 aRobino, Antonietta1 aSchwartz, Molly1 avan Setten, Jessica1 aSmith, Albert, V1 aVerweij, Niek1 aWarren, Helen, R1 aWeiss, Stefan1 aAlonso, Alvaro1 aArnar, David, O1 aBots, Michiel, L1 ade Boer, Rudolf, A1 aDominiczak, Anna, F1 aEijgelsheim, Mark1 aEllinor, Patrick, T1 aGuo, Xiuqing1 aFelix, Stephan, B1 aHarris, Tamara, B1 aHayward, Caroline1 aHeckbert, Susan, R1 aHuang, Paul, L1 aJukema, J, W1 aKähönen, Mika1 aKors, Jan, A1 aLambiase, Pier, D1 aLauner, Lenore, J1 aLi, Man1 aLinneberg, Allan1 aNelson, Christopher, P1 aPedersen, Oluf1 aPerez, Marco1 aPeters, Annette1 aPolasek, Ozren1 aPsaty, Bruce, M1 aRaitakari, Olli, T1 aRice, Kenneth, M1 aRotter, Jerome, I1 aSinner, Moritz, F1 aSoliman, Elsayed, Z1 aSpector, Tim, D1 aStrauch, Konstantin1 aThorsteinsdottir, Unnur1 aTinker, Andrew1 aTrompet, Stella1 aUitterlinden, Andre1 aVaartjes, Ilonca1 avan der Meer, Peter1 aVölker, Uwe1 aVölzke, Henry1 aWaldenberger, Melanie1 aWilson, James, G1 aXie, Zhijun1 aAsselbergs, Folkert, W1 aDörr, Marcus1 aDuijn, Cornelia, M1 aGasparini, Paolo1 aGudbjartsson, Daniel, F1 aGudnason, Vilmundur1 aHansen, Torben1 aKääb, Stefan1 aKanters, Jørgen, K1 aKooperberg, Charles1 aLehtimäki, Terho1 aLin, Henry, J1 aLubitz, Steven, A1 aMook-Kanamori, Dennis, O1 aConti, Francesco, J1 aNewton-Cheh, Christopher, H1 aRosand, Jonathan1 aRudan, Igor1 aSamani, Nilesh, J1 aSinagra, Gianfranco1 aSmith, Blair, H1 aHolm, Hilma1 aStricker, Bruno, H1 aUlivi, Sheila1 aSotoodehnia, Nona1 aApte, Suneel, S1 aHarst, Pim1 aStefansson, Kari1 aMunroe, Patricia, B1 aArking, Dan, E1 aLo, Cecilia, W1 aJamshidi, Yalda uhttps://chs-nhlbi.org/node/780905298nas a2201333 4500008004100000022001400041245011200055210006900167260001300236300001200249490000700261520154600268100002501814700002301839700002601862700002101888700001901909700001801928700001801946700002101964700002101985700002002006700001802026700001302044700003002057700002502087700001802112700001702130700001302147700002002160700002502180700001802205700001902223700002402242700002202266700002802288700001202316700001902328700002402347700001902371700001602390700002202406700002002428700002302448700002202471700002502493700002102518700001902539700001602558700002402574700001702598700002102615700002202636700002702658700002102685700002302706700001902729700001902748700002102767700002202788700002002810700002602830700002202856700002002878700001802898700001602916700002302932700002402955700001802979700002002997700002303017700002003040700001903060700001803079700002503097700002603122700002403148700001703172700001803189700001903207700002203226700002103248700002403269700002103293700001703314700002303331700002003354700001803374700002403392700002403416700002203440700002303462700003203485700002203517700002103539700002203560700002403582700002103606700001903627700001903646700001503665700002303680700002203703700002903725700002203754700001803776700002003794700002703814700002403841700002203865700001903887700002203906856003603928 2018 eng d a2574-830000aExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals.0 aExomeChipWide Analysis of 95 626 Individuals Identifies 10 Novel c2018 Jan ae0017580 v113 aBACKGROUND: QT interval, measured through a standard ECG, captures the time it takes for the cardiac ventricles to depolarize and repolarize. JT interval is the component of the QT interval that reflects ventricular repolarization alone. Prolonged QT interval has been linked to higher risk of sudden cardiac arrest.
METHODS AND RESULTS: We performed an ExomeChip-wide analysis for both QT and JT intervals, including 209 449 variants, both common and rare, in 17 341 genes from the Illumina Infinium HumanExome BeadChip. We identified 10 loci that modulate QT and JT interval duration that have not been previously reported in the literature using single-variant statistical models in a meta-analysis of 95 626 individuals from 23 cohorts (comprised 83 884 European ancestry individuals, 9610 blacks, 1382 Hispanics, and 750 Asians). This brings the total number of ventricular repolarization associated loci to 45. In addition, our approach of using coding variants has highlighted the role of 17 specific genes for involvement in ventricular repolarization, 7 of which are in novel loci.
CONCLUSIONS: Our analyses show a role for myocyte internal structure and interconnections in modulating QT interval duration, adding to previous known roles of potassium, sodium, and calcium ion regulation, as well as autonomic control. We anticipate that these discoveries will open new paths to the goal of making novel remedies for the prevention of lethal ventricular arrhythmias and sudden cardiac arrest.
1 aBihlmeyer, Nathan, A1 aBrody, Jennifer, A1 aSmith, Albert, Vernon1 aWarren, Helen, R1 aLin, Honghuang1 aIsaacs, Aaron1 aLiu, Ching-Ti1 aMarten, Jonathan1 aRadmanesh, Farid1 aHall, Leanne, M1 aGrarup, Niels1 aMei, Hao1 aMüller-Nurasyid, Martina1 aHuffman, Jennifer, E1 aVerweij, Niek1 aGuo, Xiuqing1 aYao, Jie1 aLi-Gao, Ruifang1 avan den Berg, Marten1 aWeiss, Stefan1 aPrins, Bram, P1 avan Setten, Jessica1 aHaessler, Jeffrey1 aLyytikäinen, Leo-Pekka1 aLi, Man1 aAlonso, Alvaro1 aSoliman, Elsayed, Z1 aBis, Joshua, C1 aAustin, Tom1 aChen, Yii-Der Ida1 aPsaty, Bruce, M1 aHarrris, Tamara, B1 aLauner, Lenore, J1 aPadmanabhan, Sandosh1 aDominiczak, Anna1 aHuang, Paul, L1 aXie, Zhijun1 aEllinor, Patrick, T1 aKors, Jan, A1 aCampbell, Archie1 aMurray, Alison, D1 aNelson, Christopher, P1 aTobin, Martin, D1 aBork-Jensen, Jette1 aHansen, Torben1 aPedersen, Oluf1 aLinneberg, Allan1 aSinner, Moritz, F1 aPeters, Annette1 aWaldenberger, Melanie1 aMeitinger, Thomas1 aPerz, Siegfried1 aKolcic, Ivana1 aRudan, Igor1 ade Boer, Rudolf, A1 avan der Meer, Peter1 aLin, Henry, J1 aTaylor, Kent, D1 ade Mutsert, Renée1 aTrompet, Stella1 aJukema, Wouter1 aMaan, Arie, C1 aStricker, Bruno, H C1 aRivadeneira, Fernando1 aUitterlinden, Andre1 aVölker, Uwe1 aHomuth, Georg1 aVölzke, Henry1 aFelix, Stephan, B1 aMangino, Massimo1 aSpector, Timothy, D1 aBots, Michiel, L1 aPerez, Marco1 aRaitakari, Olli, T1 aKähönen, Mika1 aMononen, Nina1 aGudnason, Vilmundur1 aMunroe, Patricia, B1 aLubitz, Steven, A1 aDuijn, Cornelia, M1 aNewton-Cheh, Christopher, H1 aHayward, Caroline1 aRosand, Jonathan1 aSamani, Nilesh, J1 aKanters, Jørgen, K1 aWilson, James, G1 aKääb, Stefan1 aPolasek, Ozren1 aHarst, Pim1 aHeckbert, Susan, R1 aRotter, Jerome, I1 aMook-Kanamori, Dennis, O1 aEijgelsheim, Mark1 aDörr, Marcus1 aJamshidi, Yalda1 aAsselbergs, Folkert, W1 aKooperberg, Charles1 aLehtimäki, Terho1 aArking, Dan, E1 aSotoodehnia, Nona uhttps://chs-nhlbi.org/node/778405160nas a2201237 4500008004100000022001400041245019700055210006900252260000900321300001300330490000700343520163700350653000901987653002801996653003502024653001102059653001102070653003202081653000902113653001602122653001402138653001702152100002402169700001202193700002502205700002902230700002702259700002302286700001702309700002102326700001902347700002102366700001702387700001802404700002802422700001502450700002002465700002502485700001902510700002102529700002302550700001702573700002002590700002302610700002002633700002702653700001802680700002302698700002302721700001902744700002202763700002002785700002102805700001702826700001902843700002102862700002302883700002402906700001602930700002202946700002602968700002102994700001903015700001803034700002003052700002003072700002103092700001803113700001603131700001903147700001903166700001503185700002203200700002303222700002403245700003003269700002103299700002103320700002303341700002003364700001903384700001803403700002103421700002003442700002603462700001903488700002103507700001803528700002003546700001903566700002603585700002503611700002403636700001903660700002003679700002003699700001403719700002503733700002003758700001903778700002103797700002003818700002303838710002503861856003603886 2018 eng d a1932-620300aPredictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration.0 aPredictive value for cardiovascular events of common carotid int c2018 ae01911720 v133 aAIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk.
METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C.
CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.
10aAged10aCardiovascular Diseases10aCarotid Intima-Media Thickness10aFemale10aHumans10aIntersectoral Collaboration10aMale10aMiddle Aged10aPrognosis10aRisk Factors1 aLorenz, Matthias, W1 aGao, Lu1 aZiegelbauer, Kathrin1 aNorata, Giuseppe, Danilo1 aEmpana, Jean, Philippe1 aSchmidtmann, Irene1 aLin, Hung-Ju1 aMcLachlan, Stela1 aBokemark, Lena1 aRonkainen, Kimmo1 aAmato, Mauro1 aSchminke, Ulf1 aSrinivasan, Sathanur, R1 aLind, Lars1 aOkazaki, Shuhei1 aStehouwer, Coen, D A1 aWilleit, Peter1 aPolak, Joseph, F1 aSteinmetz, Helmuth1 aSander, Dirk1 aPoppert, Holger1 aDesvarieux, Moïse1 aIkram, Arfan, M1 aJohnsen, Stein, Harald1 aStaub, Daniel1 aSirtori, Cesare, R1 aIglseder, Bernhard1 aBeloqui, Oscar1 aEngström, Gunnar1 aFriera, Alfonso1 aRozza, Francesco1 aXie, Wuxiang1 aParraga, Grace1 aGrigore, Liliana1 aPlichart, Matthieu1 aBlankenberg, Stefan1 aSu, Ta-Chen1 aSchmidt, Caroline1 aTuomainen, Tomi-Pekka1 aVeglia, Fabrizio1 aVölzke, Henry1 aNijpels, Giel1 aWilleit, Johann1 aSacco, Ralph, L1 aFranco, Oscar, H1 aUthoff, Heiko1 aHedblad, Bo1 aSuarez, Carmen1 aIzzo, Raffaele1 aZhao, Dong1 aWannarong, Thapat1 aCatapano, Alberico1 aDucimetiere, Pierre1 aEspinola-Klein, Christine1 aChien, Kuo-Liong1 aPrice, Jackie, F1 aBergström, Göran1 aKauhanen, Jussi1 aTremoli, Elena1 aDörr, Marcus1 aBerenson, Gerald1 aKitagawa, Kazuo1 aDekker, Jacqueline, M1 aKiechl, Stefan1 aSitzer, Matthias1 aBickel, Horst1 aRundek, Tatjana1 aHofman, Albert1 aMathiesen, Ellisiv, B1 aCastelnuovo, Samuela1 aLandecho, Manuel, F1 aRosvall, Maria1 aGabriel, Rafael1 ade Luca, Nicola1 aLiu, Jing1 aBaldassarre, Damiano1 aKavousi, Maryam1 ade Groot, Eric1 aBots, Michiel, L1 aYanez, David, N1 aThompson, Simon, G1 aPROG-IMT Study Group uhttps://chs-nhlbi.org/node/7846