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000178793 0247_ $$2doi$$a10.1016/j.pmedr.2022.101700
000178793 0247_ $$2pmid$$apmid:35141116
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000178793 037__ $$aDKFZ-2022-00272
000178793 041__ $$aEnglish
000178793 082__ $$a610
000178793 1001_ $$aRosberg, Victoria$$b0
000178793 245__ $$aSimple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project.
000178793 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2022
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000178793 520__ $$aTo assess whether anthropometric measures (body mass index [BMI], waist-hip ratio [WHR], and estimated fat mass [EFM]) are independently associated with major adverse cardiovascular events (MACE), and to assess their added prognostic value compared with serum total-cholesterol. The study population comprised 109,509 individuals (53% men) from the MORGAM-Project, aged 19-97 years, without established cardiovascular disease, and not on antihypertensive treatment. While BMI was reported in all, WHR and EFM were reported in ∼52,000 participants. Prognostic importance of anthropometric measurements and total-cholesterol was evaluated using adjusted Cox proportional-hazards regression, logistic regression, area under the receiver-operating-characteristic curve (AUCROC), and net reclassification improvement (NRI). The primary endpoint was MACE, a composite of stroke, myocardial infarction, or death from coronary heart disease. Age interacted significantly with anthropometric measures and total-cholesterol on MACE (P ≤ 0.003), and therefore age-stratified analyses (<50 versus ≥ 50 years) were performed. BMI, WHR, EFM, and total-cholesterol were independently associated with MACE (P ≤ 0.003) and resulted in significantly positive NRI when added to age, sex, smoking status, and systolic blood pressure. Only total-cholesterol increased discrimination ability (AUCROC difference; P < 0.001). In subjects < 50 years, the prediction model with total-cholesterol was superior to the model including BMI, but not superior to models containing WHR or EFM, while in those ≥ 50 years, the model with total-cholesterol was superior to all models containing anthropometric variables, whether assessed individually or combined. We found a potential role for replacing total-cholesterol with anthropometric measures for MACE-prediction among individuals < 50 years when laboratory measurements are unavailable, but not among those ≥ 50 years.
000178793 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
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000178793 650_7 $$2Other$$aACM, all-cause mortality
000178793 650_7 $$2Other$$aASCVD, atherosclerotic cardiovascular disease
000178793 650_7 $$2Other$$aAUCROC, area under the receiver-operating-characteristic curve
000178793 650_7 $$2Other$$aAdipose tissue
000178793 650_7 $$2Other$$aAssessment, risk
000178793 650_7 $$2Other$$aBMI, body mass index
000178793 650_7 $$2Other$$aBP, blood pressure
000178793 650_7 $$2Other$$aBody mass index
000178793 650_7 $$2Other$$aCEP, composite cardiovascular endpoint
000178793 650_7 $$2Other$$aCI, confidence interval
000178793 650_7 $$2Other$$aCV, cardiovascular
000178793 650_7 $$2Other$$aCVD, cardiovascular disease
000178793 650_7 $$2Other$$aCVM, cardiovascular mortality
000178793 650_7 $$2Other$$aCardiovascular diseases
000178793 650_7 $$2Other$$aChol, serum total cholesterol
000178793 650_7 $$2Other$$aCholesterol
000178793 650_7 $$2Other$$aDBP, diastolic blood pressure
000178793 650_7 $$2Other$$aEFM, estimated fat mass
000178793 650_7 $$2Other$$aHDL-cholesterol, high density lipoprotein cholesterol
000178793 650_7 $$2Other$$aHR, hazard ratio
000178793 650_7 $$2Other$$aIQR, interquartile range
000178793 650_7 $$2Other$$aMACE, major adverse cardiovascular events
000178793 650_7 $$2Other$$aMBP, mean blood pressure
000178793 650_7 $$2Other$$aMONICA, Multi-national MONItoring of Trends and Determinants in CArdiovascular Disease
000178793 650_7 $$2Other$$aMORGAM, MOnica, Risk, Genetics, Archiving and Monograph
000178793 650_7 $$2Other$$aNRI, net reclassification improvement
000178793 650_7 $$2Other$$aNS, non-significant
000178793 650_7 $$2Other$$aPP, pulse pressure
000178793 650_7 $$2Other$$aSBP, systolic blood pressure
000178793 650_7 $$2Other$$aSCORE, Systematic COronary Risk Evaluation
000178793 650_7 $$2Other$$aWHR, waist-hip ratio
000178793 650_7 $$2Other$$aWaist-hip ratio
000178793 650_7 $$2Other$$acNRI, continuous net reclassification improvement
000178793 7001_ $$aVishram-Nielsen, Julie Kk$$b1
000178793 7001_ $$aKristensen, Anna M Dyrvig$$b2
000178793 7001_ $$aPareek, Manan$$b3
000178793 7001_ $$aSehested, Thomas S G$$b4
000178793 7001_ $$aNilsson, Peter M$$b5
000178793 7001_ $$aLinneberg, Allan$$b6
000178793 7001_ $$aPalmieri, Luigi$$b7
000178793 7001_ $$aGiampaoli, Simona$$b8
000178793 7001_ $$aDonfrancesco, Chiara$$b9
000178793 7001_ $$aKee, Frank$$b10
000178793 7001_ $$aMancia, Giuseppe$$b11
000178793 7001_ $$aCesana, Giancarlo$$b12
000178793 7001_ $$aVeronesi, Giovanni$$b13
000178793 7001_ $$aGrassi, Guido$$b14
000178793 7001_ $$aKuulasmaa, Kari$$b15
000178793 7001_ $$aSalomaa, Veikko$$b16
000178793 7001_ $$aPalosaari, Tarja$$b17
000178793 7001_ $$aSans, Susana$$b18
000178793 7001_ $$aFerrieres, Jean$$b19
000178793 7001_ $$aDallongeville, Jean$$b20
000178793 7001_ $$aSöderberg, Stefan$$b21
000178793 7001_ $$aMoitry, Marie$$b22
000178793 7001_ $$aDrygas, Wojciech$$b23
000178793 7001_ $$aTamosiunas, Abdonas$$b24
000178793 7001_ $$aPeters, Annette$$b25
000178793 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b26$$udkfz
000178793 7001_ $$0P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aSchöttker, Ben$$b27$$udkfz
000178793 7001_ $$aGrimsgaard, Sameline$$b28
000178793 7001_ $$aBiering-Sørensen, Tor$$b29
000178793 7001_ $$aOlsen, Michael H$$b30
000178793 773__ $$0PERI:(DE-600)2785569-7$$a10.1016/j.pmedr.2022.101700$$gVol. 26, p. 101700 -$$p101700$$tPreventive Medicine Reports$$v26$$x2211-3355$$y2022
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