Longitudinal and cross-sectional changes with age in selected anthropometric and physiological traits in hospitalized adults: an insight from the Polish Longitudinal Study of Aging (PLSA)

Longitudinal studies of aging concerning individuals with comparable lifestyle, diet, health profile, socioeconomic status, and income remain extraordinarily rare. The purposes of our ongoing project are as follows: (i) to collect extensive data on biological and medical aspects of aging in the Polish population, (ii) to determine factors affecting the rate and course of aging, (iii) to understand how aging unfolds as a dynamic and malleable process in ontogeny, and (iv) to find novel predictors of longevity. Our investigation followed 142 physically healthy asylum inmates, including 68 males and 74 females, for at least 25 years from the age of 45 years onward. Cross-sectional assessment involved 225 inmates, including 113 males and 112 females. All the patients lived for a very long time under similar and good environmental conditions at the hospital in Cibórz, Lubuskie Province. They maintained virtually the same daily schedule and lifestyle. The rate and direction of changes with age in selected anthropometric and physiological traits were determined using ANOVA, t-test, and regression analysis. There were sex differences in the rate and pattern of age-related changes in certain characteristics such as relative weight, red blood cell count, monocyte count, thymol turbidity value, systolic blood pressure, and body temperature. Body weight, the body mass index (BMI), and total bilirubin level increased with advancing age, while body height decreased with age in both sexes. In conclusion, the aging process was associated with many regressive alterations in biological traits in both sexes but the rate and pattern of these changes depended on biological factors such as age and sex. There were only few characteristics which did not change significantly during the period under study. On the basis of comparison between the pattern of longitudinal changes with aging and the pattern of cross-sectional changes with age in the analyzed traits, we were able to predict which pattern of changes is associated with longer lifespan.