👤 Mette Nyegaard

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Gitte S Brix, Laust D Rasmussen, Palle D Rohde +10 more · 2026 · European heart journal. Cardiovascular Imaging · Oxford University Press · added 2026-04-24
Risk factor-weighted clinical likelihood (RF-CL) estimates the probability of obstructive coronary artery disease (CAD) in patients without known CAD. We examined whether adding lipoprotein(a) [Lp(a)] Show more
Risk factor-weighted clinical likelihood (RF-CL) estimates the probability of obstructive coronary artery disease (CAD) in patients without known CAD. We examined whether adding lipoprotein(a) [Lp(a)] measurements to the RF-CL model improves predictions of obstructive CAD. In a derivation cohort (N = 4262; 54% male; mean age 58 years), the prevalence of obstructive CAD at invasive angiography with fractional flow reserve was assessed by Lp(a)-strata. On the basis of initial results, an Lp(a)-adjusted model (RF-CLLp(a)) was developed: RF-CL was multiplied by 1.5 in patients with elevated Lp(a) (≥125 nmol/L) and otherwise unchanged. Discrimination, calibration, and reclassification were compared. Findings were validated in an external validation cohort (N = 1595; 49% male; mean age 60 years) using a comparative endpoint; significant stenosis at invasive angiography or coronary computed tomography.In the derivation cohort, 473 patients (11.1%) had obstructive CAD; in the validation cohort, 206 patients (12.9%) had significant stenosis. The relative risk in patients with elevated Lp(a) was 1.51 [95% confidence interval (CI) 1.23-1.86] and 1.19 (95% CI 0.88-1.60) in the derivation and validation cohort, respectively. In the derivation cohort, the RF-CLLp(a) model showed a higher area under the receiver operating curve than the RF-CL model [0.743 (standard error 0.011) vs. 0.740 (0.013)] and better calibration in patients with elevated Lp(a). Reclassification from RF-CL to RF-CLLp(a) improved likelihood stratification in the derivation cohort but not in the validation cohort. Adding elevated Lp(a) as a risk factor to the RF-CL model improves accuracy of obstructive CAD in patients with high Lp(a). Show less
no PDF DOI: 10.1093/ehjci/jeag021
LPA
Nilufer Rahmioglu, Sally Mortlock, Marzieh Ghiasi +135 more · 2023 · Nature genetics · Nature · added 2026-04-24
Nilufer Rahmioglu, Sally Mortlock, Marzieh Ghiasi, Peter L Møller, Lilja Stefansdottir, Geneviève Galarneau, Constance Turman, Rebecca Danning, Matthew H Law, Yadav Sapkota, Paraskevi Christofidou, Sini Skarp, Ayush Giri, Karina Banasik, Michal Krassowski, Maarja Lepamets, Błażej Marciniak, Margit Nõukas, Danielle Perro, Eeva Sliz, Marta Sobalska-Kwapis, Gudmar Thorleifsson, Nura F Topbas-Selcuki, Allison Vitonis, David Westergaard, Ragnheidur Arnadottir, Kristoffer S Burgdorf, Archie Campbell, Cecilia S K Cheuk, Caterina Clementi, James Cook, Immaculata De Vivo, Amy DiVasta, O Dorien, Jacqueline F Donoghue, Todd Edwards, Pierre Fontanillas, Jenny N Fung, Reynir T Geirsson, Jane E Girling, Paivi Harkki, Holly R Harris, Martin Healey, Oskari Heikinheimo, Sarah Holdsworth-Carson, Isabel C Hostettler, Henry Houlden, Sahar Houshdaran, Juan C Irwin, Marjo-Riitta Jarvelin, Yoichiro Kamatani, Stephen H Kennedy, Ewa Kepka, Johannes Kettunen, Michiaki Kubo, Bartosz Kulig, Venla Kurra, Hannele Laivuori, Marc R Laufer, Cecilia M Lindgren, Stuart MacGregor, Massimo Mangino, Nicholas G Martin, Charoula Matalliotaki, Michail Matalliotakis, Alison D Murray, Anne Ndungu, Camran Nezhat, Catherine M Olsen, Jessica Opoku-Anane, Sandosh Padmanabhan, Manish Paranjpe, Maire Peters, Grzegorz Polak, David J Porteous, Joseph Rabban, Kathyrn M Rexrode, Hanna Romanowicz, Merli Saare, Liisu Saavalainen, Andrew J Schork, Sushmita Sen, Amy L Shafrir, Anna Siewierska-Górska, Marcin Słomka, Blair H Smith, Beata Smolarz, Tomasz Szaflik, Krzysztof Szyłło, Atsushi Takahashi, Kathryn L Terry, Carla Tomassetti, Susan A Treloar, Arne Vanhie, Katy Vincent, Kim C Vo, David J Werring, Eleftheria Zeggini, Maria I Zervou, DBDS Genomic Consortium, FinnGen Study, FinnGen Endometriosis Taskforce, Celmatix Research Team, 23andMe Research Team, Sosuke Adachi, Julie E Buring, Paul M Ridker, Thomas D'Hooghe, George N Goulielmos, Dharani K Hapangama, Caroline Hayward, Andrew W Horne, Siew-Kee Low, Hannu Martikainen, Daniel I Chasman, Peter A W Rogers, Philippa T Saunders, Marina Sirota, Tim Spector, Dominik Strapagiel, Joyce Y Tung, David C Whiteman, Linda C Giudice, Digna R Velez-Edwards, Outi Uimari, Peter Kraft, Andres Salumets, Dale R Nyholt, Reedik Mägi, Kari Stefansson, Christian M Becker, Piraye Yurttas-Beim, Valgerdur Steinthorsdottir, Mette Nyegaard, Stacey A Missmer, Grant W Montgomery, Andrew P Morris, Krina T Zondervan Show less
Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and Show more
Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention. Show less
📄 PDF DOI: 10.1038/s41588-023-01323-z
MLLT10