2022
Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study
Xu J, Johnson JS, Signer R, Consortium E, Birgegård A, Jordan J, Kennedy MA, Landén M, Maguire SL, Martin NG, Mortensen PB, Petersen LV, Thornton LM, Bulik CM, Huckins LM. Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study. The Lancet Digital Health 2022, 4: e604-e614. PMID: 35780037, PMCID: PMC9612590, DOI: 10.1016/s2589-7500(22)00099-1.Peer-Reviewed Original ResearchConceptsElectronic health recordsPolygenic risk scoresWeight trajectoriesDepression polygenic risk scoresObesity polygenic risk scoresHealth recordsWeight changeUK BiobankIndividual health statusLower disease riskGenetic associationPatient populationUS National InstitutesWeight managementStable weightRisk scoreHealthy populationHealth statusAnorexia nervosaBioMe BiobankDisease riskDisorder diagnosisMental healthWeight lossPhenome-wide association study
2020
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City
Paranjpe I, Russak A, De Freitas J, Lala A, Miotto R, Vaid A, Johnson K, Danieletto M, Golden E, Meyer D, Singh M, Somani S, Kapoor A, O'Hagan R, Manna S, Nangia U, Jaladanki S, O’Reilly P, Huckins L, Glowe P, Kia A, Timsina P, Freeman R, Levin M, Jhang J, Firpo A, Kovatch P, Finkelstein J, Aberg J, Bagiella E, Horowitz C, Murphy B, Fayad Z, Narula J, Nestler E, Fuster V, Cordon-Cardo C, Charney D, Reich D, Just A, Bottinger E, Charney A, Glicksberg B, Nadkarni G, Center M. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City. BMJ Open 2020, 10: e040736. PMID: 33247020, PMCID: PMC7702220, DOI: 10.1136/bmjopen-2020-040736.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overComorbidityCOVID-19C-Reactive ProteinCritical CareFemaleFibrin Fibrinogen Degradation ProductsHospital MortalityHospitalizationHospitalsHumansLymphocytesMaleMiddle AgedNew York CityPandemicsProcalcitoninRetrospective StudiesRisk FactorsSARS-CoV-2Young AdultConceptsIn-hospital mortalityHospitalised patientsPre-existing conditionsInstitutional electronic health recordsElectronic health recordsHealth System hospitalsMount Sinai Health SystemUrban hospital systemMount Sinai Health System hospitalsSinai Health SystemStudy periodIntensive careHealth recordsInvestigate in-hospital mortalityCohort of hospitalised patientsPublic health crisisHealth systemRetrospective cohort studyHospital systemSystem hospitalsGlobal public health crisisClinical characteristicsCohort studyCOVID-19New York CityMachine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Vaid A, Somani S, Russak A, De Freitas J, Chaudhry F, Paranjpe I, Johnson K, Lee S, Miotto R, Richter F, Zhao S, Beckmann N, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly P, Huckins L, Kovatch P, Finkelstein J, Freeman R, Argulian E, Kasarskis A, Percha B, Aberg J, Bagiella E, Horowitz C, Murphy B, Nestler E, Schadt E, Cho J, Cordon-Cardo C, Fuster V, Charney D, Reich D, Bottinger E, Levin M, Narula J, Fayad Z, Just A, Charney A, Nadkarni G, Glicksberg B. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal Of Medical Internet Research 2020, 22: e24018. PMID: 33027032, PMCID: PMC7652593, DOI: 10.2196/24018.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAdolescentAdultAgedAged, 80 and overBetacoronavirusCohort StudiesCoronavirus InfectionsCOVID-19Electronic Health RecordsFemaleHospital MortalityHospitalizationHospitalsHumansMachine LearningMaleMiddle AgedNew York CityPandemicsPneumonia, ViralPrognosisRisk AssessmentROC CurveSARS-CoV-2Young AdultConceptsElectronic health recordsNew York CityYork CityMount Sinai Health SystemSinai Health SystemMortality predictionAdmitted to hospitalAt-risk patientsHealth recordsHealth systemEHR dataIn-hospital mortalityEarly identification of high-risk patientsCOVID-19Identification of high-risk patientsMultiple hospitalsStudy populationPatient characteristicsSingle hospitalHospitalArea under the receiver operating characteristic curveEarly identificationPredicting mortalityCohort of patientsCOVID-19 pandemic
2019
Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa
Watson H, Yilmaz Z, Thornton L, Hübel C, Coleman J, Gaspar H, Bryois J, Hinney A, Leppä V, Mattheisen M, Medland S, Ripke S, Yao S, Giusti-Rodríguez P, Hanscombe K, Purves K, Adan R, Alfredsson L, Ando T, Andreassen O, Baker J, Berrettini W, Boehm I, Boni C, Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone R, Courtet P, Crow S, Crowley J, Danner U, Davis O, de Zwaan M, Dedoussis G, Degortes D, DeSocio J, Dick D, Dikeos D, Dina C, Dmitrzak-Weglarz M, Docampo E, Duncan L, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández-Aranda F, Fichter M, Fischer K, Föcker M, Foretova L, Forstner A, Forzan M, Franklin C, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder S, Herms S, Herpertz-Dahlmann B, Herzog W, Huckins L, Hudson J, Imgart H, Inoko H, Janout V, Jiménez-Murcia S, Julià A, Kalsi G, Kaminská D, Kaprio J, Karhunen L, Karwautz A, Kas M, Kennedy J, Keski-Rahkonen A, Kiezebrink K, Kim Y, Klareskog L, Klump K, Knudsen G, La Via M, Le Hellard S, Levitan R, Li D, Lilenfeld L, Lin B, Lissowska J, Luykx J, Magistretti P, Maj M, Mannik K, Marsal S, Marshall C, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone A, Monteleone P, Munn-Chernoff M, Nacmias B, Navratilova M, Ntalla I, O’Toole J, Ophoff R, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn-Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer S, Schmidt U, Schork N, Schosser A, Seitz J, Slachtova L, Slagboom P, Slof-Op ‘t Landt M, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz J, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz-Nwafor M, Tziouvas K, van Elburg A, van Furth E, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen A, Boden J, Brandt H, Crawford S, Halmi K, Horwood L, Johnson C, Kaplan A, Kaye W, Mitchell J, Olsen C, Pearson J, Pedersen N, Strober M, Werge T, Whiteman D, Woodside D, Stuber G, Gordon S, Grove J, Henders A, Juréus A, Kirk K, Larsen J, Parker R, Petersen L, Jordan J, Kennedy M, Montgomery G, Wade T, Birgegård A, Lichtenstein P, Norring C, Landén M, Martin N, Mortensen P, Sullivan P, Breen G, Bulik C. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nature Genetics 2019, 51: 1207-1214. PMID: 31308545, PMCID: PMC6779477, DOI: 10.1038/s41588-019-0439-2.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesTwin-based heritability estimatesEating Disorders Working GroupPsychiatric Genomics ConsortiumAnorexia nervosaBody-mass indexSignificant lociGenetic architectureRisk lociGenetics InitiativeGenomics ConsortiumLow body-mass indexMetabo-psychiatric disorderGenetic correlationsMetabolic componentsLociCases of anorexia nervosaPhysical activityAnthropometric traitsPsychiatric disordersHeritability estimatesAnorexia Nervosa Genetics InitiativeNervosaImprove outcomesAssociations Between Attention-Deficit/Hyperactivity Disorder and Various Eating Disorders: A Swedish Nationwide Population Study Using Multiple Genetically Informative Approaches
Yao S, Kuja-Halkola R, Martin J, Lu Y, Lichtenstein P, Norring C, Birgegård A, Yilmaz Z, Hübel C, Watson H, Baker J, Almqvist C, Consortium E, Adan R, Ando T, Baker J, Bergen A, Berrettini W, Birgegård A, Boni C, Perica V, Brandt H, Burghardt R, Cassina M, Cesta C, Clementi M, Coleman J, Cone R, Courtet P, Crawford S, Crow S, Crowley J, Danner U, Davis O, de Zwaan M, Dedoussis G, Degortes D, DeSocio J, Dick D, Dikeos D, Dmitrzak-Weglarz M, Docampo E, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Favaro A, Fernández-Aranda F, Fichter M, Finan C, Fischer K, Föcker M, Foretova L, Forzan M, Franklin C, Gaspar H, Gonidakis F, Gorwood P, Gratacos M, Guillaume S, Guo Y, Hakonarson H, Halmi K, Hatzikotoulas K, Hauser J, Hebebrand J, Helder S, Hendriks J, Herpertz-Dahlmann B, Herzog W, Hilliard C, Hinney A, Huckins L, Hudson J, Huemer J, Imgart H, Inoko H, Jiménez-Murcia S, Johnson C, Jordan J, Juréus A, Kalsi G, Kaminska D, Kaplan A, Kaprio J, Karhunen L, Karwautz A, Kas M, Kaye W, Kennedy J, Kennedy M, Keski-Rahkonen A, Kiezebrink K, Kim Y, Klump K, Knudsen G, Koeleman B, Koubek D, La Via M, Landén M, Levitan R, Li D, Lichtenstein P, Lilenfeld L, Lissowska J, Magistretti P, Maj M, Mannik K, Martin N, McDevitt S, McGuffin P, Merl E, Metspalu A, Meulenbelt I, Micali N, Mitchell J, Mitchell K, Monteleone P, Monteleone A, Mortensen P, Munn-Chernoff M, Nacmias B, Nilsson I, Norring C, Ntalla I, O’Toole J, Pantel J, Papezova H, Parker R, Rabionet R, Raevuori A, Rajewski A, Ramoz N, Rayner N, Reichborn-Kjennerud T, Ricca V, Ripke S, Ritschel F, Roberts M, Rotondo A, Rybakowski F, Santonastaso P, Scherag A, Schmidt U, Schork N, Schosser A, Seitz J, Slachtova L, Slagboom P, Landt M, Slopien A, Smith T, Sorbi S, Strengman E, Strober M, Sullivan P, Szatkiewicz J, Szeszenia-Dabrowska N, Tachmazidou I, Tenconi E, Thornton L, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tziouvas K, van Elburg A, van Furth E, Wade T, Wagner G, Walton E, Watson H, Woodside D, Yao S, Yilmaz Z, Zeggini E, Zerwas S, Zipfel S, Alfredsson L, Andreassen O, Aschauer H, Barrett J, Bencko V, Carlberg L, Cichon S, Cohen-Woods S, Dina C, Ding B, Espeseth T, Floyd J, Gallinger S, Gambaro G, Giegling I, Herms S, Janout V, Julià A, Klareskog L, Le Hellard S, Leboyer M, Lundervold A, Marsal S, Mattingsdal M, Navratilova M, Ophoff R, Palotie A, Pinto D, Ripatti S, Rujescu D, Scherer S, Scott L, Sladek R, Soranzo N, Southam L, Steen V, Wichmann H, Widen E, Breen G, Bulik C, Thornton L, Magnusson P, Bulik C, Larsson H. Associations Between Attention-Deficit/Hyperactivity Disorder and Various Eating Disorders: A Swedish Nationwide Population Study Using Multiple Genetically Informative Approaches. Biological Psychiatry 2019, 86: 577-586. PMID: 31301758, PMCID: PMC6776821, DOI: 10.1016/j.biopsych.2019.04.036.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAttention-deficit/hyperactivity disorder polygenic risk scoresAttention-deficit/hyperactivity disorderQuantitative genetic modelRisk scoreGenetic associationGenetic correlationsEating disordersRegister-based informationAnorexia nervosaPopulation-based sampleGenetic modelsDegree of relatednessGenetically informed approachesAttention-deficit/hyperactivityNationwide population studyMaternal half-sistersCorrelates of attention-deficit/hyperactivity disorderFull-sistersAttention-deficit/hyperactivity disorder symptomsFamilial coaggregationNationwide populationGenetically informative designsShared etiologySubscales Drive