Carmen Lucía Curcio1, Andrés Fernando Giraldo2, Fernando Gómez3. 1. Programa de Investigaciones en Gerontología y Geriatría, Facultad de Ciencias para la Salud, Universidad de Caldas, Manizales, Colombia. carmen.curcio@ucaldas.edu.co. 2. Programa de Investigaciones en Gerontología y Geriatría, Facultad de Ciencias para la Salud, Universidad de Caldas, Manizales, Colombia. afhaiku@hotmail.com. 3. Programa de Investigaciones en Gerontología y Geriatría, Facultad de Ciencias para la Salud, Universidad de Caldas, Manizales, Colombia. gomez.montes@ucaldas.edu.co.
Abstract
Introduction: The healthy aging phenotype is present in those individuals that age with low morbidity, no functional or cognitive deterioration, and retain an acceptable level of wellness and social participation. Objective: To establish the frequency of the healthy aging phenotype in older people in the community using a multidimensional, a biomedical, and a psychosocial model and to identify the predicting factors in each model. Materials and methods: We conducted a cross-sectional, observational and descriptive study. We assessed individuals (n= 402; 50.1% female) aged 65 years or older (69.2 years average) from the urban area of Manizales. The healthy aging phenotype included five domains: biomarkers of physiological and metabolic health, physical capability, cognitive function, and social and psychological wellbeing. We also analyzed sociodemographic- and health-related factors. Results: In the multidimensional model the prevalence of the healthy aging phenotype was 15.5% while in the biomedical model it was 12.3% and in the psychosocial one it was 63.3%. Good self-perceived health was an independent predictor of healthy aging in all the models assessed. Having enough income was a predictor in the biomedical and psychosocial models while being married was the only significant predictor in the psychosocial model. Conclusions: The prevalence of the healthy aging phenotype was low in the biological and multidimensional models (1 of every 10 individuals) and higher in the psychosocial one (6 of every 10 persons). However, independent predictor factors were the same in all models: Self-perceived good or very good health, having enough income and being married.
Introduction: The healthy aging phenotype is present in those individuals that age with low morbidity, no functional or cognitive deterioration, and retain an acceptable level of wellness and social participation. Objective: To establish the frequency of the healthy aging phenotype in older people in the community using a multidimensional, a biomedical, and a psychosocial model and to identify the predicting factors in each model. Materials and methods: We conducted a cross-sectional, observational and descriptive study. We assessed individuals (n= 402; 50.1% female) aged 65 years or older (69.2 years average) from the urban area of Manizales. The healthy aging phenotype included five domains: biomarkers of physiological and metabolic health, physical capability, cognitive function, and social and psychological wellbeing. We also analyzed sociodemographic- and health-related factors. Results: In the multidimensional model the prevalence of the healthy aging phenotype was 15.5% while in the biomedical model it was 12.3% and in the psychosocial one it was 63.3%. Good self-perceived health was an independent predictor of healthy aging in all the models assessed. Having enough income was a predictor in the biomedical and psychosocial models while being married was the only significant predictor in the psychosocial model. Conclusions: The prevalence of the healthy aging phenotype was low in the biological and multidimensional models (1 of every 10 individuals) and higher in the psychosocial one (6 of every 10 persons). However, independent predictor factors were the same in all models: Self-perceived good or very good health, having enough income and being married.
Entities:
Keywords:
Healthy aging; phenotype; biomarkers; social determinants of health
Authors: Tamer Ahmed; Emmanuelle Belanger; Afshin Vafaei; Georges K Koné; Beatriz Alvarado; François Béland; Maria Victoria Zunzunegui Journal: J Cross Cult Gerontol Date: 2018-03
Authors: Loes Jaspers; Josje D Schoufour; Nicole S Erler; Sirwan K L Darweesh; Marileen L P Portegies; Sanaz Sedaghat; Lies Lahousse; Guy G Brusselle; Bruno H Stricker; Henning Tiemeier; M Arfan Ikram; Joop S E Laven; Oscar H Franco; Maryam Kavousi Journal: J Am Med Dir Assoc Date: 2017-01-18 Impact factor: 4.669
Authors: Ana Carolina Patrício Albuquerque Sousa; Maria-Victoria Zunzunegui; Annie Li; Susan P Phillips; Jack M Guralnik; Ricardo Oliveira Guerra Journal: Age Ageing Date: 2016-01-28 Impact factor: 10.668
Authors: Paulo Ruiz-Grosso; Christian Loret de Mola; Johann M Vega-Dienstmaier; Jorge M Arevalo; Kristhy Chavez; Ana Vilela; Maria Lazo; Julio Huapaya Journal: PLoS One Date: 2012-10-08 Impact factor: 3.240
Authors: Jose Lara; Rachel Cooper; Jack Nissan; Annie T Ginty; Kay-Tee Khaw; Ian J Deary; Janet M Lord; Diana Kuh; John C Mathers Journal: BMC Med Date: 2015-09-15 Impact factor: 8.775