Article in Annals of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 54% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors at 6 institutions in 2 countries.
Lindsay M MillerDivision of Nephrology-Hypertension, University of California San Diego, La Jolla, CA; School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR. Electronic address: lmmiller@health.ucsd.edu.
Chenkai WuGlobal Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China.
Calvin H HirschDivision of General Internal Medicine, UC Davis Health, Sacramento, CA.
Oscar L LopezDepartment of Neurology, University of Pittsburgh, Pittsburgh, PA.
Mary CushmanDepartment of Medicine and Pathology & Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT.
Michelle C OddenSchool of Biological and Population Health Sciences, Oregon State University, Corvallis, OR; Department of Epidemiology & Pop. Health, Stanford University School of Medicine, Stanford University, Stanford CA; School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR.
Duke Kunshan University · CNOregon State University · USStanford University · USUC Davis Health System · USUniversity of Pittsburgh · USUniversity of Vermont · US
Funding
Exceptional Survival: Trajectories to Functional Aging (CHS All Stars)R01AG023629 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NEWMAN, ANNE B. · 2004 to 2016
$9.5M
CARDIOVASCULAR HEALTH STUDY (CHS) - TASK AREA C, STUDY CLOSEOUT75N92021D00006 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE · 2021 to 2024
$8.4M
CHS research resources for the cardiovascular health of older adultsU01HL130114 · NHLBI · UNIVERSITY OF WASHINGTON · PI BURKE, GREGORY L, KRONMAL, RICHARD A · 2016 to 2019
$6.6M
CHS Events Follow-up StudyU01HL080295 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M · 2005 to 2008
$4.6M
Role of EIIIA fibronectin in pulmonary fibrosisR01HL085083 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WHITE, ERIC S · 2007 to 2011
$1.9M
CORONARY HEART DISEASE AND STROKE IN PEOPLE AGED 65-84N01HC085082 · HC · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KULLER, LEWIS H · 1988 to 2000
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CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65-84N01HC085083 · HC · UNIVERSITY OF CALIFORNIA DAVIS · PI ROBBINS, JOHN A · 1988 to 1999
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CENTRAL BLOOD ANALYSIS LABORATORY FOR CHSN01HC085086 · HC · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 1988 to 2000
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CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65 TO 84N01HC085079 · HC · UNIVERSITY OF WASHINGTON · 1988 to 2005
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CORONARY HEART DISEASE &STROKE IN THE ELDERLYN01HC085080 · HC · WAKE FOREST UNIVERSITY · PI BURKE, GREGORY L · 1988 to 2000
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CHS-Transition Phase -268055222-268055222N01HC055222 · HC · UNIVERSITY OF WASHINGTON · PI KRONMAL, RICHARD A · 2005 to 2006
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CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65-84N01HC085081 · HC · JOHNS HOPKINS UNIVERSITY · PI FRIED, LINDA P · 1988 to 2000
purposeThe association between CVD risk factors and mortality is well established, however, current tools for addressing subgroups have focused on the overall burden of disease. The identification of risky combinations of characteristics may lead to a better understanding of physiologic pathways that underlie morbidity and mortality in older adults.
methodsParticipants included 5067 older adults from the Cardiovascular Health Study, followed for up to 6 years. Using latent class analysis (LCA), we created CV damage phenotypes based on probabilities of abnormal brain infarctions, major echocardiogram abnormalities, N-terminal probrain natriuretic peptide, troponin T, interleukin-6, c reactive-protein, galectin-3, cystatin C. We assigned class descriptions based on the probability of having an abnormality among risk factors, such that a healthy phenotype would have low probabilities in all risk factors. Participants were assigned to phenotypes based on the maximum probability of membership. We used Cox-proportional hazards regression to evaluate the association between the categorical CV damage phenotype and all-cause and CVD-mortality.
resultsThe analysis yielded 5 CV damage phenotypes consistent with the following descriptions: healthy (59%), cardio-renal (11%), cardiac (15%), multisystem morbidity (6%), and inflammatory (9%). All four phenotypes were statistically associated with a greater risk of all-cause mortality when compared with the healthy phenotype. The multisystem morbidity phenotype had the greatest risk of all-cause death (HR: 4.02; 95% CI: 3.44, 4.70), and CVD-mortality (HR: 4.90, 95% CI: 3.95, 6.06).
conclusionsFive CV damage phenotypes emerged from CVD risk factor measures. CV damage across multiple systems confers a greater mortality risk compared to damage in any single domain.
Indexed as
Cardiovascular DiseasesAgedBiomarkersC-Reactive ProteinHumansPhenotypeRisk FactorsBiomarkersC-Reactive ProteinAAI, ankle arm indexADL, Activities of Daily LivingAIC, Akaike Information CriterionAPOE, Apolipoprotein e4BIC, Bayesian Information CriterionCHS, Cardiovascular Health StudyCRP, C-reactive proteinECG, major echocardiogram abnormalitiesGal3, galectin-3GOF, Goodness of FitHR, Hazard RatioIL-6, interleukin-6IMT, internal intima-media thicknessLCA, Latent Class AnalysisLDLcholesterol, Low-density Lipoprotein CholesterolNTproBNP, N-terminal probrain natriuretic peptideRisk factors, Cardiovascular disease, Latent Class Analysis. Abbreviations: CVD, Cardiovascular DiseaseSCVD, Subclinical Cardiovascular DiseaseWMG, white matter grade
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Cardiovascular damage phenotypes and all-cause and CVD mortality in older adults. · full record | OpenQuestion