HIV-Infected Patients as a Model of Aging

Abstract

We appraised the relationship between the biological and the chronological age and estimated the rate of biological aging in HIV-infected patients. Two independent biomarkers, the relative telomere length and iron metabolism parameters, were analyzed in younger (<35) and older (>50) HIV-infected and uninfected patients (control group). In our control group, telomeres of younger patients were significantly longer than telomeres of older ones. However, in HIV-infected participants, the difference in the length of telomeres was lost. By combining the length of telomeres with serum iron, ferritin, and transferrin iron-binding capacity, a new formula for determination of the aging process was developed. The life expectancy of the healthy population was related to their biological age, and HIV-infected patients were biologically older. The effect of antiretroviral HIV drug therapies varied with respect to the biological aging process. IMPORTANCE This article is focused on the dynamics of human aging. Moreover, its interdisciplinary approach is applicable to various systems that are aging.

Keywords: HIV; aging; iron; pharmacotherapy; telomere.

FIG 1

(A) Comparison of iron metabolism parameters levels in blood serum of uninfected and HIV-infected patients. (B) Comparison of iron metabolism parameters levels in blood serum of uninfected and HIV-infected patients younger than 35 years of age. (C) Comparison of iron metabolism parameters levels in blood serum of uninfected and HIV-infected patients older than 50 years of age. All data values, as well as the corresponding means ± standard deviations, are depicted for each parameter and patient group. Asterisks designate P values lower than 0.05 (*) and 0.001 (***). (D) Dependence of serum ferritin concentration on chronological age in uninfected and HIV-infected subjects. All data values as well as the second order polynomial fits are shown. (E) Dependence of serum ferritin concentration on chronological age in HIV-infected subjects treated with two different drug combinations. All data values, as well as the second-order polynomial fits, are shown.

FIG 2

Scatter diagrams showing the relation of HIV pVL to RTL. To enable taking into account number of copies smaller than 20 it was assumed that they are all equal to 10 as a value in the middle of the range. (A) Relation between pVL and RTL; (B) relation between RTL and therapy duration.

FIG 3

(A) Biological age dependence on chronological age. An approximate illustration of the biological age was calculated using equation 1 and Table 3. The subjects above the line a = a_bio are older biologically than chronologically, whereas those below this line are biologically younger than chronologically. (B) Biological age dependence on chronological age for treated patients. The patients treated with 2NRTI+NNRTI and patients that were subject to 2NRTI+PI treatment were considered separately. Note that the graph scaling is equivalent to the one in panel A, which enables comparison of the data presented in the two graphs.