Literature DB >> 30993509

Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach.

Reem Waziry1,2, Luuk Gras3, Sanaz Sedaghat4,5, Henning Tiemeier6,4,7, Gerrit J Weverling3, Mohsen Ghanbari4, Jaco Klap3, Frank de Wolf3,8, Albert Hofman6,4, M Arfan Ikram4,9, Jaap Goudsmit6,10.   

Abstract

Chronological age alone is not a sufficient measure of the true physiological state of the body. The aims of the present study were to: (1) quantify biological age based on a physiological biomarker composite model; (2) and evaluate its association with death and age-related disease onset in the setting of an elderly population. Using structural equation modeling we computed biological age for 1699 individuals recruited from the first and second waves of the Rotterdam study. The algorithm included nine physiological parameters (c-reactive protein, creatinine, albumin, total cholesterol, cytomegalovirus optical density, urea nitrogen, alkaline phosphatase, forced expiratory volume and systolic blood pressure). We assessed the association between biological age, all-cause mortality, all-cause morbidity and specific age-related diseases over a median follow-up of 11 years. Biological age, compared to chronological age or the traditional biomarkers of age-related diseases, showed a stronger association with all-cause mortality (HR 1.15 vs. 1.13 and 1.10), all-cause morbidity (HR 1.06 vs. 1.05 and 1.03), stroke (HR 1.17 vs. 1.08 and 1.04), cancer (HR 1.07 vs. 1.04 and 1.02) and diabetes mellitus (HR 1.12 vs. 1.01 and 0.98). Individuals who were biologically younger exhibited a healthier life-style as reflected in their lower BMI (P < 0.001) and lower incidence of stroke (P < 0.001), cancer (P < 0.01) and diabetes mellitus (P = 0.02). Collectively, our findings suggest that biological age based on the biomarker composite model of nine physiological parameters is a useful construct to assess individuals 65 years and older at increased risk for specific age-related diseases.

Entities:  

Keywords:  Biological age; Biologically young; Elderly; Morbidity; Mortality

Mesh:

Substances:

Year:  2019        PMID: 30993509     DOI: 10.1007/s10654-019-00497-3

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  11 in total

1.  Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging.

Authors:  Chris P Verschoor; Daniel W Belsky; Jinhui Ma; Alan A Cohen; Lauren E Griffith; Parminder Raina
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2021-01-18       Impact factor: 6.053

2.  Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study.

Authors:  Ramya Ramasubramanian; Helen C S Meier; Sithara Vivek; Eric Klopack; Eileen M Crimmins; Jessica Faul; Janko Nikolich-Žugich; Bharat Thyagarajan
Journal:  Immun Ageing       Date:  2022-07-20       Impact factor: 9.701

3.  A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

Authors:  Karina Louise Skov Husted; Andreas Brink-Kjær; Mathilde Fogelstrøm; Pernille Hulst; Akita Bleibach; Kaj-Åge Henneberg; Helge Bjarup Dissing Sørensen; Flemming Dela; Jens Christian Brings Jacobsen; Jørn Wulff Helge
Journal:  JMIR Aging       Date:  2022-05-10

4.  Objectives, design and main findings until 2020 from the Rotterdam Study.

Authors:  M Arfan Ikram; Guy Brusselle; Mohsen Ghanbari; André Goedegebure; M Kamran Ikram; Maryam Kavousi; Brenda C T Kieboom; Caroline C W Klaver; Robert J de Knegt; Annemarie I Luik; Tamar E C Nijsten; Robin P Peeters; Frank J A van Rooij; Bruno H Stricker; André G Uitterlinden; Meike W Vernooij; Trudy Voortman
Journal:  Eur J Epidemiol       Date:  2020-05-04       Impact factor: 8.082

5.  Data-driven identification of ageing-related diseases from electronic health records.

Authors:  Valerie Kuan; Helen C Fraser; Melanie Hingorani; Spiros Denaxas; Arturo Gonzalez-Izquierdo; Kenan Direk; Dorothea Nitsch; Rohini Mathur; Constantinos A Parisinos; R Thomas Lumbers; Reecha Sofat; Ian C K Wong; Juan P Casas; Janet M Thornton; Harry Hemingway; Linda Partridge; Aroon D Hingorani
Journal:  Sci Rep       Date:  2021-02-03       Impact factor: 4.379

6.  Biological age in healthy elderly predicts aging-related diseases including dementia.

Authors:  Mohsen Ghanbari; Jaap Goudsmit; Julia W Wu; Amber Yaqub; Yuan Ma; Wouter Koudstaal; Albert Hofman; M Arfan Ikram
Journal:  Sci Rep       Date:  2021-08-05       Impact factor: 4.379

7.  Ageing-related markers and risks of cancer and cardiovascular disease: a prospective study in the EPIC-Heidelberg cohort.

Authors:  Bernard Srour; Rudolf Kaaks; Theron Johnson; Lucas Cory Hynes; Tilman Kühn; Verena A Katzke
Journal:  Eur J Epidemiol       Date:  2021-12-22       Impact factor: 8.082

8.  The association of proBNPage with manifestations of age-related cardiovascular, physical, and psychological impairment in community-dwelling older adults.

Authors:  Antonio Muscari; Giampaolo Bianchi; Paola Forti; Donatella Magalotti; Paolo Pandolfi; Marco Zoli
Journal:  Geroscience       Date:  2021-05-13       Impact factor: 7.713

9.  N-terminal pro B-type natriuretic peptide (NT-proBNP): a possible surrogate of biological age in the elderly people.

Authors:  Antonio Muscari; Giampaolo Bianchi; Paola Forti; Donatella Magalotti; Paolo Pandolfi; Marco Zoli
Journal:  Geroscience       Date:  2020-08-11       Impact factor: 7.713

10.  Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease.

Authors:  Qiong Ma; Bo-Lin Li; Lei Yang; Miao Zhang; Xin-Xin Feng; Qian Li; Hui Liu; Ya-Jie Gao; Wen-Zhuo Ma; Rui-Juan Shi; Yan-Bo Xue; Xiao-Pu Zheng; Ke Gao; Jian-Jun Mu
Journal:  Dis Markers       Date:  2022-01-13       Impact factor: 3.434

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