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Campbell DJ, Magliano DJ, Shaw JE. Prediction of cardiovascular death and non-fatal cardiovascular events by the Kidney age-Chronological age Difference (KCD) score in men and women of different ages in a community-based cohort. BMJ Open 2023; 13:e068494. [PMID: 36882235 PMCID: PMC10008409 DOI: 10.1136/bmjopen-2022-068494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
Abstract
OBJECTIVE We examined the utility of the Kidney age-Chronological age Difference (KCD) score, an age-adapted measure of kidney function, to identify increased cardiovascular (CV) death or non-fatal CV event risk in participants of the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab), a community-based cohort aged 23-95 years. DESIGN Cohort study. SETTING Community. PARTICIPANTS 11205 randomly selected participants from urban and nonurban areas across Australia. OUTCOME MEASURES Mortality status and underlying and contributory causes of death obtained from the Australian National Death Index, and non-fatal CV events from adjudicated hospital records. The association of CV death or non-fatal CV event risk with KCD score was examined using penalised spline curve analysis. RESULTS Of 11 180 participants with serum creatinine measurement at baseline and 5-year outcome data, there were 308 CV deaths or non-fatal CV events after 5 years. Penalised spline curve analysis showed similar progressive increase in CV death or non-fatal CV event risk with increasing KCD score in men and women, and participants aged <50 years to ≥80 years. Receiver operating characteristic curve analysis showed optimal discrimination at a KCD score ≥20 years (KCD20) for all participants. Among 148 participants aged<70 years with CV death or non-fatal CV event, KCD20 identified 24 (16%) participants, whereas estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 identified 8 (5%) participants (p=0.0001), with specificities of 95% and 99%, respectively (p<0.0001). CONCLUSION KCD20 predicted CV death or non-fatal CV event risk similarly in men and women of different ages in this population-based cohort. The higher sensitivity for prediction of CV death or non-fatal CV event risk in participants aged <70 years by KCD20 than by eGFR <60 mL/min/1.73 m2 offers opportunity for earlier renoprotective therapy in individuals with eGFR-associated increased CV death or non-fatal CV event risk.
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Affiliation(s)
- Duncan J Campbell
- Department of Molecular Cardiology, St Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
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Bakshi A, Yan M, Riaz M, Polekhina G, Orchard SG, Tiller J, Wolfe R, Joshi A, Cao Y, McInerney-Leo AM, Yanes T, Janda M, Soyer HP, Cust AE, Law MH, Gibbs P, McLean C, Chan AT, McNeil JJ, Mar VJ, Lacaze P. Genomic Risk Score for Melanoma in a Prospective Study of Older Individuals. J Natl Cancer Inst 2021; 113:1379-1385. [PMID: 33837773 PMCID: PMC8921762 DOI: 10.1093/jnci/djab076] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/16/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Recent genome-wide association meta-analysis for melanoma doubled the number of previously identified variants. We assessed the performance of an updated polygenic risk score (PRS) in a population of older individuals, where melanoma incidence and cumulative ultraviolet radiation exposure is greatest. METHODS We assessed a PRS for cutaneous melanoma comprising 55 variants in a prospective study of 12 712 individuals in the ASPirin in Reducing Events in the Elderly Trial. We evaluated incident melanomas diagnosed during the trial and prevalent melanomas diagnosed preenrolment (self-reported). Multivariable models examined associations between PRS as a continuous variable (per SD) and categorical (low-risk [0%-20%], medium-risk [21%-80%], high-risk [81%-100%] groups) with incident melanoma. Logistic regression examined the association between PRS and prevalent melanoma. RESULTS At baseline, mean participant age was 75 years; 55.0% were female, and 528 (4.2%) had prevalent melanomas. During follow-up (median = 4.7 years), 120 (1.0%) incident cutaneous melanomas occurred, 98 of which were in participants with no history. PRS was associated with incident melanoma (hazard ratio = 1.46 per SD, 95% confidence interval [CI] = 1.20 to 1.77) and prevalent melanoma (odds ratio [OR] = 1.55 per SD, 95% CI = 1.42 to 1.69). Participants in the highest-risk PRS group had increased risk compared with the low-risk group for incident melanoma (OR = 2.51, 95% CI = 1.28 to 4.92) and prevalent melanoma (OR = 3.66, 95% CI = 2.69 to 5.05). When stratifying by sex, only males had an association between the PRS and incident melanoma, whereas both sexes had an association between the PRS and prevalent melanoma. CONCLUSIONS A genomic risk score is associated with melanoma risk in older individuals and may contribute to targeted surveillance.
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Affiliation(s)
- Andrew Bakshi
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Mabel Yan
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Galina Polekhina
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Suzanne G Orchard
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jane Tiller
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; MGH Cancer Center, Boston, MA, USA
| | - Yin Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA; Alvin J. Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
| | - Aideen M McInerney-Leo
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Tatiane Yanes
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Monika Janda
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - H Peter Soyer
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Anne E Cust
- Sydney School of Public Health and Melanoma Institute Australia, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia, Personalised Oncology Division, Walter and Eliza Hall Institute Medical Research and Faculty of Medicine University of Melbourne, Australia
| | - Peter Gibbs
- Department of Anatomical Pathology, Alfred Hospital, Melbourne, Victoria, Australia
| | - Catriona McLean
- Department of Anatomical Pathology, Alfred Hospital, Melbourne, Victoria, Australia
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; MGH Cancer Center, Boston, MA, USA
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Victoria J Mar
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Victorian Melanoma Service, Alfred Health, Melbourne, Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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