Home > Publications database > Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project. > print

001     178793
005     20240229143556.0
024 7 _ |a 10.1016/j.pmedr.2022.101700
|2 doi
024 7 _ |a pmid:35141116
|2 pmid
024 7 _ |a pmc:PMC8814644
|2 pmc
024 7 _ |a altmetric:122839834
|2 altmetric
037 _ _ |a DKFZ-2022-00272
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Rosberg, Victoria
|b 0
245 _ _ |a Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project.
260 _ _ |a Amsterdam [u.a.]
|c 2022
|b Elsevier
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1644839292_25510
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a To 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.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
650 _ 7 |a ACM, all-cause mortality
|2 Other
650 _ 7 |a ASCVD, atherosclerotic cardiovascular disease
|2 Other
650 _ 7 |a AUCROC, area under the receiver-operating-characteristic curve
|2 Other
650 _ 7 |a Adipose tissue
|2 Other
650 _ 7 |a Assessment, risk
|2 Other
650 _ 7 |a BMI, body mass index
|2 Other
650 _ 7 |a BP, blood pressure
|2 Other
650 _ 7 |a Body mass index
|2 Other
650 _ 7 |a CEP, composite cardiovascular endpoint
|2 Other
650 _ 7 |a CI, confidence interval
|2 Other
650 _ 7 |a CV, cardiovascular
|2 Other
650 _ 7 |a CVD, cardiovascular disease
|2 Other
650 _ 7 |a CVM, cardiovascular mortality
|2 Other
650 _ 7 |a Cardiovascular diseases
|2 Other
650 _ 7 |a Chol, serum total cholesterol
|2 Other
650 _ 7 |a Cholesterol
|2 Other
650 _ 7 |a DBP, diastolic blood pressure
|2 Other
650 _ 7 |a EFM, estimated fat mass
|2 Other
650 _ 7 |a HDL-cholesterol, high density lipoprotein cholesterol
|2 Other
650 _ 7 |a HR, hazard ratio
|2 Other
650 _ 7 |a IQR, interquartile range
|2 Other
650 _ 7 |a MACE, major adverse cardiovascular events
|2 Other
650 _ 7 |a MBP, mean blood pressure
|2 Other
650 _ 7 |a MONICA, Multi-national MONItoring of Trends and Determinants in CArdiovascular Disease
|2 Other
650 _ 7 |a MORGAM, MOnica, Risk, Genetics, Archiving and Monograph
|2 Other
650 _ 7 |a NRI, net reclassification improvement
|2 Other
650 _ 7 |a NS, non-significant
|2 Other
650 _ 7 |a PP, pulse pressure
|2 Other
650 _ 7 |a SBP, systolic blood pressure
|2 Other
650 _ 7 |a SCORE, Systematic COronary Risk Evaluation
|2 Other
650 _ 7 |a WHR, waist-hip ratio
|2 Other
650 _ 7 |a Waist-hip ratio
|2 Other
650 _ 7 |a cNRI, continuous net reclassification improvement
|2 Other
700 1 _ |a Vishram-Nielsen, Julie Kk
|b 1
700 1 _ |a Kristensen, Anna M Dyrvig
|b 2
700 1 _ |a Pareek, Manan
|b 3
700 1 _ |a Sehested, Thomas S G
|b 4
700 1 _ |a Nilsson, Peter M
|b 5
700 1 _ |a Linneberg, Allan
|b 6
700 1 _ |a Palmieri, Luigi
|b 7
700 1 _ |a Giampaoli, Simona
|b 8
700 1 _ |a Donfrancesco, Chiara
|b 9
700 1 _ |a Kee, Frank
|b 10
700 1 _ |a Mancia, Giuseppe
|b 11
700 1 _ |a Cesana, Giancarlo
|b 12
700 1 _ |a Veronesi, Giovanni
|b 13
700 1 _ |a Grassi, Guido
|b 14
700 1 _ |a Kuulasmaa, Kari
|b 15
700 1 _ |a Salomaa, Veikko
|b 16
700 1 _ |a Palosaari, Tarja
|b 17
700 1 _ |a Sans, Susana
|b 18
700 1 _ |a Ferrieres, Jean
|b 19
700 1 _ |a Dallongeville, Jean
|b 20
700 1 _ |a Söderberg, Stefan
|b 21
700 1 _ |a Moitry, Marie
|b 22
700 1 _ |a Drygas, Wojciech
|b 23
700 1 _ |a Tamosiunas, Abdonas
|b 24
700 1 _ |a Peters, Annette
|b 25
700 1 _ |a Brenner, Hermann
|0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
|b 26
|u dkfz
700 1 _ |a Schöttker, Ben
|0 P:(DE-He78)c67a12496b8aac150c0eef888d808d46
|b 27
|u dkfz
700 1 _ |a Grimsgaard, Sameline
|b 28
700 1 _ |a Biering-Sørensen, Tor
|b 29
700 1 _ |a Olsen, Michael H
|b 30
773 _ _ |a 10.1016/j.pmedr.2022.101700
|g Vol. 26, p. 101700 -
|0 PERI:(DE-600)2785569-7
|p 101700
|t Preventive Medicine Reports
|v 26
|y 2022
|x 2211-3355
909 C O |o oai:inrepo02.dkfz.de:178793
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 26
|6 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 27
|6 P:(DE-He78)c67a12496b8aac150c0eef888d808d46
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND (No Version)
|0 LIC:(DE-HGF)CCBYNCNDNV
|2 V:(DE-HGF)
|b DOAJ
|d 2020-09-12
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2020-09-12
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2020-09-12
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PREV MED REP : 2021
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2022-08-09T10:54:20Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2022-08-09T10:54:20Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2022-08-09T10:54:20Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2022-11-18
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2022-11-18
920 1 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C070-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21