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Immunological markers contributing to successful aging in Bulgarians
Experimental Gerontology 39 (2004) 637–644 www.elsevier.com/locate/expgero
Immunological markers contributing to successful aging in Bulgarians Elissaveta Naumova*, Anastasia Mihaylova, Milena Ivanova, Snejina Michailova, Kalina Penkova, Daniela Baltadjieva Central Laboratory of Clinical Immunology, University Hospital Alexandrovska, 1 Georgy Sofiisky Str., 1431 Sofia, Bulgaria Received 18 July 2003; received in revised form 4 August 2003; accepted 13 August 2003
Abstract In order to clarify immunogenetic markers contributing to successful aging, HLA and cytokine gene profiles were analyzed in healthy elderly Bulgarians. Family segregation analysis was performed to define combined effect of haplotypes and immunophenotype profiles. The results of this study did not reveal any statisticaly significant allele and haplotype frequency differences between elderly and control group. In families with two generations longevity members we did not observed HLA alleles and haplotypes associated with autoimmunity. IL-10 genotype 21082G/A, 2 819 C/C, 2 592 C/C, related to the intermediate production, was positively associated, while genotype 21082A/A, 2 819 C/T, 2 592 C/A, related to the low level of production, was negatively associated with longevity in Bulgarians. This effect was modulated by IL-6 and IFNg genotypes associated with the low level of these pro-inflammatory cytokines. Immunophenotypic studies indicated lower relative and absolute numbers of CD3 þ 8 þ , CD8 þ 28 þ and CD8 þ 57 þ cells in elderly people. Analysis in familes showed that although most pronounced in the elderly group, lower numbers of CD8 þ T cells were also found in middle aged and young members of the families compared to the age matched controls. A progressive CD8 þ 28 þ cell subsets decline was seen with aging. In addition, we did not observed the ‘immune risk phenotype’ which is a marker of an increased inflammatory activity. Based on the results of this study, it seems reasonable to suggest that a combination of specific immunogenetic and immunophenotype profiles could contribute to the successful aging and to maintaining healthy status in elderly. q 2004 Published by Elsevier Inc. Keywords: Aging; HLA; Cytokine gene polymorphism; Lymphocyte subsets; Bulgarians
1. Introduction The human mean life span has been enhanced greatly since antiquity as a result of better health and living conditions. The average life span of the Bulgarian population is 71.8 years (68.5 years for male and 75.2 years for female). The majority of the population is between 25 –64 years old (541%), 16.9 percent is between 64 and 95 years old, and a very small proportion (, 0.05%) is 95 and 99 years old and centenarians. Aging is a very complex process and longevity is a multifactorial trait, which is determined by genetic and environmental factors. Since it is too difficult to study aging as a whole, investigations try to focus on a specific physiological system that exhibits age-dependent functional changes. Immune remodeling and numerous changes in T-lymphocyte subset profiles were observed in elderly * Corresponding author. Tel.: þ 3592-9230-690; fax: þ 3592-9230-496. E-mail address: [email protected] (E. Naumova). 0531-5565/$ - see front matter q 2004 Published by Elsevier Inc. doi:10.1016/j.exger.2003.08.014
individuals (Pawelec et al., 1997). The absolute number of CD4 þ and CD8 þ T cells decreases with age from 20 to 100 years (Doria and Frasca, 1997). In the elderly, the increase in the relative proportion of CD8 lymphocytes lacking CD28 has been recognized (Fagnoni et al., 1996; Nociari et al., 1999). An ‘immunological risk phenotype’ (low CD4 count, high CD8 count, decreased IL-2 production and mitogen responssivness together with CD28 negativity and CD57 positivity) has been found as predictive of shorter remaining life span in the very old population (Ferguson, 1995; Wikby et al., 1998; Olsson et al., 2000). Family segregation analysis has shown that CD4, CD8 and CD4/CD8 ratio are under genetic control (Amadori et al., 1995; Clementi et al., 1999). The level of immune responses as well as possibly longevity could be associated with genes regulating immune function (Modica et al., 1990). Although case-control data are still controversial (Caruso et al., 2000), it was suggested that longevity could be associated with MHC and cytokine gene polymorphisms. HLA alleles and haplotypes relevant
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to susceptibility or resistance to infectious and autoimmune diseases (Dawkins et al., 1999; Thorsby, 1997) have been disscused. A positive association with longevity was found for HLA alleles DRB1*07, *11, *13 (Lagaay et al., 1991; Ivanova et al., 1998) and for the ancestral haplotype HLAB8-DR3 (Proust et al., 1982; Rea and Middleton, 1994). Genetic diversity of pro- and anti inflammatory cytokines, that affects gene transcription causing variations in cytokine production might influence successful aging and longevity by modulating an individual’s response to life-threatening disorders (Bruunsgaard et al., 2001; Ershler and Keller, 2000; Volpato et al., 2001). IL-10 2 1082G involved in high-production of this anti-inflammatory cytokine is positively associated (Lio et al., 2002a,b), while IFN-g þ 874 T (Lio et al., 2002a,b) and IL-6 2 174G (Bonafe et al., 2001) allele involved in high production of these proinflammatory cytokines show negative gender specific association with longevity. In order to clarify immunogenetic markers contributing to successful aging, HLA and cytokine gene profiles were analyzed in healthy elderly Bulgarians. Family segregation analysis was performed to define combined effect of haplotypes and immunophenotype profiles.
2. Material and methods 2.1. Subjects Ten families with longevity members from the Bulgarian population were analyzed in the present study. The following criteria for family selection were applied: two generation longevity members (octogenarians and nonagenarians) and no family history of inherited diseases. A total of 40 individuals: 17 unrelated elderly (age 65 – 90
years, mean age: 78.9 ^ 8.9 years)—6 male and 11 female, and 23 family members (age 18 – 57 years)—9 male and 14 female were included. The control group consisted of 105 randomly selected, matched for geographical distribution healthy controls aged 25– 53 years (40 male and 65 female). For the T-cell subset study 47 individuals were randomly selected among the total control population. Twenty one of them were young (mean age: 27.3 ^ 2.4 years) and 26middle-aged (mean age: 40.4 ^ 5.8 years) subjects with a male/female ratio 1:2 in the two groups. All individuals included in the analysis did not suffer from any chronic physical or mental disease, and did not experience any acute infection in the last 4 months before the enrolment. Their hematological and biochemical parameters were within the normal ranges. The blood samples were obtained after an informed consent. 2.2. HLA and cytokine gene polymorphism Genomic DNA was extracted from whole venous blood using the standard proteinase K digestion method followed by salting-out extraction and ethanol precipitation (Miller et al., 1988). HLA class I (HLA-A, -B, and -C) and class II (-DRB, and -DQB1) genotyping was performed by PCRSSP method using GENOVISIONe kits. Gene polymorphism of TNFa, TGFb1, IL-10, IL-6, and IFNg was tested by PCR-SSP method with Cytokine Genotyping Tray OneLambda. Eight polymorphism, previously shown to be associated with the level of cytokine production were analyzed (Table 1). 2.3. Flow cytometric analysis of T-cell subsets Complete blood cell counts were assessed for each subject. Peripheral blood lymphocyte subsets were determined by
Table 1 Correspondence between analyzed cytokine gene polymorphism and level of production Cytokine polymorphism
Genotype
Level of production
Reference
TNF-a (2308)
G/G G/A, A/A
Low High
Kroeger et al. (2000) Haung et al. (1999)
TGF-b1 (codons 10,25)
T/T G/G, T/C G/C T/C G/C, C/C G/G, T/T G/C C/C G/C, C/C C/C, T/T C/C, T/C C/C
High Intermediate Low
Awad et al. (1998) Awad et al. (1998) Awad et al. (1998)
IL-10 (21082, 2 819, 2 592)
GCC/GCC GCC/ACC, GCC/ATA ACC/ACC, ACC/ATA, ATA/ATA
High Intermediate Low
Crawley et al. (1999) Maurer et al. (2000) Crawley et al. (1999)
IL-6 (2174)
G/G G/C C/C
High Low
Fishman et al. (1998) Fishman et al. (1998)
IFN-g (þ 874)
T/T T/A A/A
High Intermediate Low
Turner et al. (1997) Turner et al. (1997) Turner et al. (1997)
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
Flow cytometric analysis using a panel of monoclonal antibodies (Becton-Dickinson, USA). Direct four-color immunofluorescence in whole blood was performed according to standard techniques. FACSCalibur flow cytometer, CellQuest and Multitest softwares (Becton Dickinson, USA) were used for data acquisition and analysis. Results were documented as % positive cells and cells/mm3. 2.4. Statistical analysis HLA class I and class II allele frequencies were estimated by maximum-likelihood analysis using
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the Arlequin program v1.1 (Shneider et al., 1996). Standard deviations were calculated from 100 bootstrap iteractions. Hardy – Weinberg equilibrium was tested by two methods: a hidden Markov chain with 100,000 steps, implemented in the Arlequin program and a conventional x2 heterosigosity test. Arlequin software was also used to estimate through an expectation-maximization (EM) (Slatkin and Excoffier, 1996) algorithm maximum-likelihood frequencies of haplotypes defined in family studies. Comparisons of HLA allele and haplotype frequencies, and pro- and anti-inflammatory cytokine genotypes between elderly individuals and controls were performed by x2 test
Table 2 HLA class I and class II allele frequencies in elderly Bulgarians compared to the controls HLA allele
HLA-A *01 *02 *03 *11 *23 *24 *25 *26 *29 *30 *31 *32 *33 *36 *68 *69 HLA-B *07 *08 *13 *14 *15 *18 *27 *35 *37 *38 *39 *40 *41 *42 *44 *47 *49 *50 *51 *52 *53 *55 *56 *57 *58 *73
Allele frequency Elderly
Controls
0.118 0.412 0.088 0.088 0.029 0.059 0.029 0.029 0.000 0.029 0.059 0.029 0.000 0.000 0.000 0.029
0.099 0.333 0.057 0.056 0.040 0.131 0.020 0.048 0.016 0.016 0.036 0.044 0.032 0.004 0.040 0.008
0.000 0.059 0.029 0.029 0.043 0.147 0.000 0.265 0.000 0.029 0.029 0.029 0.029 0.000 0.088 0.000 0.029 0.000 0.147 0.000 0.000 0.059 0.000 0.000 0.000 0.000
0.048 0.032 0.016 0.036 0.016 0.083 0.048 0.131 0.004 0.036 0.024 0.075 0.012 0.004 0.087 0.008 0.028 0.020 0.194 0.036 0.008 0.020 0.008 0.012 0.016 0.004
P
,0.05
Pc
ns
HLA allele
Allele frequency Elderly
Controls
HLA-C *01 *02 *03 *04 *05 *06 *07 *08 *12 *14 *15 *16 *17
0.059 0.000 0.088 0.235 0.000 0.029 0.265 0.029 0.118 0.029 0.118 0.000 0.029
0.071 0.090 0.052 0.167 0.038 0.071 0.205 0.048 0.138 0.038 0.043 0.029 0.009
DRB1 *01 *03 *04 *07 *08 *1001 *11 *12 *13 *14 *15 *16
0.118 0.059 0.088 0.000 0.029 0.000 0.235 0.059 0.176 0.059 0.059 0.118
0.143 0.075 0.095 0.060 0.020 0.012 0.218 0.008 0.115 0.067 0.075 0.111
DQB1 *02 *03 *04 *05 *06
0.059 0.382 0.059 0.294 0.206
0.098 0.402 0.009 0.366 0.116
P
Pc
,0.05
ns
,0.05
ns
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or Fisher’s exact test when appropriate. Bonferoni correction for multiple comparisons was applied. For T cell subset analysis the statistical significance was evaluated using nonparametric Mann –Whitney U-test. P-value lower than 0.05 was considered to indicate a significant difference between groups.
3. Results 3.1. HLA alleles and haplotypes HLA-A, -B, -C, -DRB1 and -DQB1 allele frequencies in elderly Bulgarians compared to the control group are shown in Table 2. Seventy-three different alleles were found in the present study- 51 (12 HLA-A, 14 HLA-B, 10 HLA-C, 10 DRB1, and 5 DQB1) in elderly and 72 (16 HLA-A, 26 HLA-B, 13 HLA-C, 12 DRB1, 5 DQB1) in control group. No deviations from Hardy –Weinberg equilibrium were observed in both groups. The most frequent HLA alleles in elderly Bulgarians were as follow: A*02, *01; B*35, *18, *51; C*07, *04; DRB1*11, *13; DQB1*03, *05. All these alleles were observed with high frequency in the control group. HLA-B*35 and -DRB1*12 allele groups showed a higher frequency in elderly population, however
the differences were not statistically significant after correction of p-values. HLA-C*02 was not found in the elderly group. Two-loci (HLA-DRB1-DQB1), three-loci (HLA-A-B-C; HLA-A-B-DRB1) and extended (HLA-A-B-C-DRB1DQB1) haplotypes were defined based on allele inheritance in the families (Fig. 1). Fifty different haplotypes were found in families with longevity members. The majority of these haplotypes were observed also in the control group. Interestingly, haplotypes DRB1*0301-DQB1*02 and DRB1*04 (0401,0402,0404,0405)-DQB1*0302, shown to be predisposing for different autoimune diseases in the Bulgarian population (Ivanova et al., 2003) were not observed in elderly Bulgarians and their family members. The most frequent haplotypes in elderly individuals are presented in Table 3. Compared to the controls in the elderly group an increased frequency was observed for the following haplotypes: A*02-B*35-C*04, A*01-B*08C*12, A*01-B*35-C*04, A*02-B*35-DRB1*13, A*01-B*08-DRB1*03, A*03-B*35-DRB1*01, A*02B*35-C*04-DRB1*13-DQB1*06, and A*01-B*08-C*07DRB1*03-DQB1*02, but a statistical significance was not found after Bonferoni correction. Additionally, the haplotype DRB1*12-DQB1*03 was found only in the elderly individuals (elderly 0.058, controls 0.000).
Fig. 1. Inheritance of HLA and cytokine haplotypes in a family with longevity members.
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
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Table 3 The most frequent haplotypes in elderly Bulgarians compared to the controls Haplotype
DRB1*11–DQB1*03 DRB1*13–DQB1*06 A*02–B*35–C*04 A*01–B*08–C*12 A*01–B*35–C*04 A*02–B*35–DRB1*13 A*01–B*08–DRB1*03 A*03–B*35–DRB1*01 A*02–B*35–C*04–DRB1*13–DQB1*06 A*01–B*08–C*07–DRB1*03–DQB1*02
Haplotype frequency Elderly
Controls
0.235 0.147 0.117 0.059 0.059 0.088 0.058 0.058 0.088 0.058
0.256 0.071 0.033 0.000 0.000 0.024 0.016 0.000 0.000 0.038
3.2. Cytokine gene polymorphism Genotypes of pro- and anti-inflamatory cytokines observed in elderly Bulgarians in comparison to the control group are shown in Table 4. Genotype profile frequencies of pro-inflammatory cytokines TNFa, IL-6, and IFN-g did not show statistically significant differences between the two groups studied. Similar results were observed for genotype frequencies of anti-inflammatory cytokine TGFb. On the other hand, significant differences between elderly and control group were found for two genotypes of IL-10. The genotype GCC/ACC, that is associated with intermediate level of IL-10 production, was increased, while the genotype ACC/ATA, that is associated with low level of IL-10 production, was decreased in elderly Bulgarians. In order to assess the impact of combinations of genotypes, associated with different level of expression we analyzed cytokine gene profiles. IL-10 (intermediate)IL-6 (low) (0.118 vs. 0.061) and IL-10 (intermediate)-IFNg (low) (0.176 vs. 0.061) showed statistically significant increase in elderly Bulgarians compared to the controls. Similar result was observed for gene profile IL-6 (low) - IFNg (low) (0.118 vs. 0.020, p , 0:05). 3.3. T-cell subsets The values of various T-cell subsets, including CD28 þ , CD28 2 , and CD57 þ phenotypes were examined in elderly, middle-aged and young randomly selected healthy individuals (Table 5). Significantly lower relative and absolute numbers of CD3 þ 8 þ ðp , 0; 02Þ and CD8 þ 28 þ ðp , 0:01Þ cells were found in elderly people compared to both middle-aged and young ones. Decreased CD8 þ 57 þ cells ðp , 0:05Þ were observed in old individuals in comparison with middle-aged subjects. The lower percentage of CD8 þ T cells was more pronounced (, 12%) in individuals aged over 85 years. No significant changes in the values of CD8 þ 28 2 , CD4 þ 28 þ and CD4 þ 28 2 cell subsets in the peripheral blood
P
Pc
,0.05 ,0.05 ,0.05
ns ns ns
,0.05 ,0.05
ns ns
lymphocyte pool were detected in elderly people compared to both control groups. However, the CD28 2 cells represented a significantly ðp , 0:01Þ higher proportion (55.4 ^ 10.7%) within CD8 þ lymphocytes in elderly people compared to the controls (40.0 ^ 14.8%). Analysis of the numbers of CD8 þ and CD4 þ cell subsets were also performed in families. Family members
Table 4 Pro- and anti-inflammatory cytokine genotypes in elderly Bulgarians and the control group Genotype
Genotype frequency Elderly
Controls
TNF a 2 308 G/A (high) G/G (low)
0.294 0.706
0.240 0.760
IL-6 2 174 G/G (high) G/C (high) C/C (low)
0.353 0.471 0.176
0.540 0.220 0.240
IFN g þ 874 T/T (high) T/A (intermediate) A/A (low)
0.118 0.588 0.294
0.160 0.580 0.260
0.118 0.353 0.118 0.235 0.059 0.118
0.100 0.120 0.260 0.080 0.280 0.160
0.294 0.294 0.235 0.059 0.059 0.000 0.059
0.360 0.320 0.180 0.080 0.000 0.020 0.040
IL-10 2 1082, 2819, 2592 GCC/GCC (high) GCC/ACC (intermediate) GCC/ATA (intermediate) ACC/ACC (low) ACC/ATA (low) ATA/ATA (low) TGFb codons 10, 25 T/T G/G (high) T/C G/G (high) C/C G/G (intermediate) T/C G/C (intermediate) T/T G/C (intermediate) T/T C/C (low) C/C G/C (low)
P
,0.05
,0.05
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E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
Table 5 Phenotype distribution of T-cell subsets (%, cells/mm3) in elderly compared to middle-aged and young subjects CD3 þ T-cells
CD3 þ 4 þ
CD3 þ 8 þ
CD8 þ 57 þ
CD8 þ 28 þ
CD8 þ 28 2
CD4 þ 28 þ
CD4 þ 28 2
Elderly ðn ¼ 17Þ
70.4 ^ 6.45 1229 ^ 456
49.9 ^ 10.15 850 ^ 289
17.62 ^ 4.82**3 12 ^ 149**
7.5 ^ 2.61* 124 ^ 65*
10.1 ^ 3.33*** 177 ^ 96***
10.33 ^ 4.33 172 ^ 63
46.4 ^ 12.4 728 ^ 264
3.4 ^ 3.3 53 ^ 42
Middle-aged ðn ¼ 26Þ
74.7 ^ 7.1 1499 ^ 517
46.9 ^ 8.6 948 ^ 316
28.0 ^ 5.9 548 ^ 208
11.4 ^ 5.1 223 ^ 125
16.9 ^ 3.8 324 ^ 114
12.6 ^ 7.2 235 ^ 142
44.9 ^ 6.9 881 ^ 312
3.2 ^ 3.1 59 ^ 57
Young ðn ¼ 21Þ
74.18 ^ 7.37 1432 ^ 329
47.6 ^ 6.26 927 ^ 248
27.3 ^ 4.24 515 ^ 109
9.11 ^ 4.73 184 ^ 91
19.0 ^ 4.05 332 ^ 127
9.66 ^ 3.08 162 ^ 49
44.4 ^ 3.65 776 ^ 165
2.2 ^ 1.3 36 ^ 17
Values are given as mean ^ SD; *p , 0:05; **p , 0:02; ***p , 0:01:
were divided into 3 subgroups: old (proband, aged . 65 years), middle aged (first generation, age 37– 57 years) and young (second generation, aged 18 – 24 years) individuals. The immunophenotype analysis of lymphocyte subsets showed that although most pronounced in the elderly group, lower numbers of CD8 þ T cells were also found in middleaged and young members of the families compared to the age matched controls. A progressive CD8 þ 28 þ cell subsets decline was seen with aging, with significant difference between the old subjects on one hand and the middle-aged ðp , 0:02Þ and young ðp , 0:01Þ individuals on the other hand (Fig. 2).
4. Discussion In this pilot study we investigated how immunological factors, particularly HLA, cytokine gene polymorphism and immunophenotype profiles, contribute to the successful aging in the Bulgarian population. For the first time we also performed family analyses in order to assess the combine effect of these three biological markers. In the last years many studies have searched for the impact of HLA genes in human longevity. Due to the central role of HLA molecules in the immune response it has been suggested that this genetic complex could be associated with successful aging and that particular HLA alleles and haplotypes might have different distribution in longevity individuals (Caruso et al., 2000). However, the results were contradicting and no clear consensus has been reached. DRB1*07, *11, *13 alleles (Lagaay et al., 1991; Ivanova et al., 1998) as well as the ancestral haplotype HLA-B8DR3 (Proust et al., 1982; Rea and Middleton, 1994), that confers resistance to viral infections, were shown to be increased in centenarians. Additionally, it has been suggested that HLA/longevity associations are population specific. The Bulgarian population is relatively homogeneous and based on HLA class I and class II profile it is characterized by features of Southern European anthropological type with some influence of additional ethnic groups (Ivanova et al., 2001, 2002). Therefore Bulgarians
represent a suitable population to assess genetic associations in longevity. Our study in extended families with longevity members and matched for geographical distribution controls from the Bulgarian population showed a slightly increased frequency of HLA-B*35, DRB1*12 alleles and corresponding haplotypes. Positive association of DRB1*12 with longevity has been previously observed in the Japanese population (Akisaka and Suzuki, 1998). Further it has been shown that life span prolongation is associated with delay of immunodeficiency of normal aging or with possible amelioration of the autoimmunity that develops with age. In agreement with this in elderly and their family members we did not observed the common alleles and haplotypes, positively associated with autoimmunity in Bulgarians. Additionally, gene polymorphisms within known or putative regulatory regions have been demonstrated to be associated with different disorders, including age related (Haukim et al., 2002). Pro-inflammatory cytokines have been shown to play a pathogenic role (Bruunsgaard, 2001). Some of cytokine gene polymorphisms have been demonstrated to affect gene transcription causing variations in cytokine production. However, recent publications showed contradicting results regarding the association of cytokine gene/haplotypes and the level of expression (Bidwell et al.,
Fig. 2. Phenotype distribution of CD8 þ cells in families with longevity members.
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
1999, 2001). In the present study it was shown that IL-10 genotype 2 1082G/A, 2 819 C/C, 2 592 C/C related to intermediate production was positively associated, while genotype 2 1082A/A, 2 819 C/T, 2 592 C/A related to low level of production was negatively associated with longevity in Bulgarians. These results confirm data, showing that longevity is associated with genotypes coding for antiinflammatory profile (Lio et al., 2002; Bonafe et al., 2001). Recent data indicated that pro-inflammatory profile is associated with increased mortality in elderly individuals (Franceschi et al., 2000). Additionally, our study on cytokine gene profiles demonstrated that the effect of IL10 gene polymorphism is modulated by IL-6 and IFNg genotypes associated with low level of production of these pro-inflammatory cytokines. In order to evaluate a combined effect of different immune factors in longevity we also searched for changes in the immune phenotype profile with aging. The results of the present study demonstrated markedly decreased CD8 þ T cell subsets in elderly people. Our observation of lower CD8 cell levels in middle aged and young family members supports the concept that genetic factors influence the regulation of lymphocyte subpopulations as well (Hall et al., 2002). The lowest CD8 numbers were seen in the old individuals. It will be reasonable to expect that although initially low compared to the general age-matched population, CD8 cell values will decrease with aging in the younger individuals of these families. Loss of CD28 expression with increasing age resulting in decline of the proportion of CD28 positive T cells (Effros et al., 1994; Effros, 2000) and an increase of CD8 þ 28 2 T cells (Nociari et al., 1999) have been reported. The old individuals in our study group have low CD3 þ CD8 þ cell values associated with a major decrease of the CD8 þ 28 þ cell subset, but we did not found an increase of CD8 þ CD28 2 cells within the lymphocytes. Looking at the CD8 þ lymphocyte subpopulation, CD28 negative cells were predominant compared to the CD28 positive cells. Thus, an increase in proportion of CD28 negative cells in the elderly do not necessarily equate with the increased number of such cells. These changes in the T cell subset profile in old individuals were supported by our observation of progressive age related decline of CD8 þ CD28 þ cells and a redistribution of CD28 negative and CD28 positive cells within the CD8 þ lymphocytes in the different family generations. It has been demonstrated that CD28 2 T cells are frequently CD57 þ cells (Azuma et al., 1993). Our data showed decreased values of CD8 þ 57 þ cells in elderly compared to middle aged controls with no significant changes of their proportion (29.2 ^ 9.1% vs. 29.4 ^ 9.4%) within the CD8 þ cells. Therefore, in this study we did not observe the ‘immune risk profile’ characterized by elevated CD8 þ , CD8 þ 28 2 , CD8 þ 57 þ cells, decreased CD4 þ T cells, and inverted CD4/CD8 index (Wikby et al., 2002) in old people. Taking into account the above considerations, it could be speculated that our results are
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consistent with a healthy immune status of elderly people in our study group. It could be summarized that in families with two generations longevity members we did not observed HLA alleles and haplotypes associated with autoimmunity. Our data also indicate an association of longevity with genotypes coding for anti-inflammatory cytokine profile. In addition, we did not observed the ‘immune risk phenotype’ which is a marker of an increased inflammatory activity. Based on the results of this study, it seems reasonable to suggest that a combination of specific immunogenetic and immunophenotype profiles could contribute to successful aging and to maintaining healthy status in elderly.
Acknowledgements The authors are grateful to the volunteers for making this study possible. This investigation has been done within ImAginE and was supported in part by a research grant from T-CIA project N-QLK6-CT-2002-02283.
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Ivanova, M., Michailova, S., Michailova, A., Nedialkova, A., Naumova, E., 2003. Moecular model for HLA class II associations in common autoimmune diseases. Genes Immunity 4, 146. Kroeger, K.M., Steer, J.H., Joyce, D.A., Abraham, L.J., 2000. Effects of stimulus and cell type on the expression of the -308 tumor necrosis factor promoter polymorphism. Cytokine 12, 110– 119. Lagaay, A.M., D’Amaro, J., Ligthart, G.J., Schreuder, G.M., van Rood, J.J., Hijmans, W., 1991. Longevity and heredity in humans. Association with the human leucocyte antigen phenotype. Annu. NY Acad. Sci. 621, 78. Lio, D., Scola, L., Crivello, A., Bonafe, M., Franceschi, C., Olivieri, F., Colonna-Romano, G., Candore, G., Caruso, C., 2002a. Allele frequencies of þ 874T- . A single nucleotide polymorphism at the first intron of interferon-gamma gene in a group of Italian centenarians. Exp. Gerontol. 37, 315–319. Lio, D., Scola, L., Crivello, A., Colonna-Romano, G., Candore, G., Bonafe, M., Cavallone, L., Franceschi, C., Caruso, C., 2002b. Gender-specific association between 21082 IL-10 promoter polymorphism and longevity. Genes Immunity 3, 30 –33. Maurer, M., Kruse, N., Giess, R., Toyka, K.V., Rieckmann, P., 2000. Genetic variation at position 21082 of interleukin 10 (IL10) promotor and the outcome of multiple sclerosis. J. Neuroimmunol. 104, 98 –100. Miller, S.A., Dykes, D., Polesky, H.F., 1988. A simple sating-out procedure for extracting DNA form human nucleated cells. Nucl. Acids Res. 16, 1215. Modica, M.A., Cammarata, G., Caruso, C., 1990. HLA-B8, DR3 phenotype and lymphocyte responses to phytohaemagglutinin. J. Immunogenet. 17, 101–107. Nociari, M.M., Telford, W., Russo, C., 1999. Postthymic development of CD28-CD8 þ T cell subsets: age-associated expansion and shift from memory to naive phenotype. J. Immunol. 162, 3327–3335. Olsson, J., Wikby, A., Johansson, B., Lofgren, S., Nilsson, B.-O., Ferguson, F.G., 2000. Age-related change in peripheral blood T-lymphocyte subpopulations and cytomegalovirus infection in the very old: the Swedish longitudinal OCTO immune study. Mech. Ageing Dev. 121, 187 –201. Pawelec, G., Adibzadeh, M., Solana, R., Beckman, I., 1997. The T cell in the aging individual. Mech. Ageing Dev. 93, 35. Proust, J., Moulias, R., Fumeron, F., Bekkhoucha, F., Busson, M., Schmid, M., Hors, J., 1982. HLA and longevity. Tissue Antigens 19, 168. Rea, I.M., Middleton, D., 1994. Is the phenotyping combination A1B8Cw7DR3 a marker for male longevity? J. Am. Geriatr. Soc. 42, 978. Shneider, S., Kueffer, J.M., Roessli, D., Excoffier, L., 1996. Arlequin: A Software Environment for the Analysis of Population Genetics Data. Genetics and Biometry Lab, Geneva. Slatkin, M., Excoffier, L., 1996. Testing for linkage disequilibrium in genotypic data using the Expectation-Maximization algorithm. Heredity 76, 377–383. Thorsby, E., 1997. HLA associated diseases. Hum. Immunol. 53, 1. Turner, D.M., Grant, S.C.D., Yonan, N., Sheldon, S., Dyer, P.A., Sinnott, P.j., Hutchinson, I.V., 1997. Cytokine genotypes and heart transplant rejection. Transplantation 64, 776– 778. Volpato, S., Gyralink, J.M., Ferrucci, L., Balfour, J., Chaves, P., Fried, L.P., Harris, T.B., 2001. Cardiovascular disease, interleukin-6, and risk of mortality in older women: the women’s health and aging study. Circulation 103, 947–953. Wikby, A., Maxson, P., Olsson, J., Johansson, B., Ferguson, F.G., 1998. Changes in CD8 and CD4 lymphocyte subsets, T-cell proliferation responses and non-survival in the very old: the Swedish longitudinal OCTO immune study. Mech. Aging Dev. 102, 187– 198. Wikby, A., Johansson, B., Olsson, J., Lofgren, S., Nilsson, B., Ferguson, F., 2002. Expansion of peripheral blood CD8 T lymphocyte subpopulations and an association with cytomegalovirus seropositivity in the elderly: the Swedish NONA immune study. Exp. Gerontol. 37, 445–453.
Immunological markers contributing to successful aging in Bulgarians Elissaveta Naumova*, Anastasia Mihaylova, Milena Ivanova, Snejina Michailova, Kalina Penkova, Daniela Baltadjieva Central Laboratory of Clinical Immunology, University Hospital Alexandrovska, 1 Georgy Sofiisky Str., 1431 Sofia, Bulgaria Received 18 July 2003; received in revised form 4 August 2003; accepted 13 August 2003
Abstract In order to clarify immunogenetic markers contributing to successful aging, HLA and cytokine gene profiles were analyzed in healthy elderly Bulgarians. Family segregation analysis was performed to define combined effect of haplotypes and immunophenotype profiles. The results of this study did not reveal any statisticaly significant allele and haplotype frequency differences between elderly and control group. In families with two generations longevity members we did not observed HLA alleles and haplotypes associated with autoimmunity. IL-10 genotype 21082G/A, 2 819 C/C, 2 592 C/C, related to the intermediate production, was positively associated, while genotype 21082A/A, 2 819 C/T, 2 592 C/A, related to the low level of production, was negatively associated with longevity in Bulgarians. This effect was modulated by IL-6 and IFNg genotypes associated with the low level of these pro-inflammatory cytokines. Immunophenotypic studies indicated lower relative and absolute numbers of CD3 þ 8 þ , CD8 þ 28 þ and CD8 þ 57 þ cells in elderly people. Analysis in familes showed that although most pronounced in the elderly group, lower numbers of CD8 þ T cells were also found in middle aged and young members of the families compared to the age matched controls. A progressive CD8 þ 28 þ cell subsets decline was seen with aging. In addition, we did not observed the ‘immune risk phenotype’ which is a marker of an increased inflammatory activity. Based on the results of this study, it seems reasonable to suggest that a combination of specific immunogenetic and immunophenotype profiles could contribute to the successful aging and to maintaining healthy status in elderly. q 2004 Published by Elsevier Inc. Keywords: Aging; HLA; Cytokine gene polymorphism; Lymphocyte subsets; Bulgarians
1. Introduction The human mean life span has been enhanced greatly since antiquity as a result of better health and living conditions. The average life span of the Bulgarian population is 71.8 years (68.5 years for male and 75.2 years for female). The majority of the population is between 25 –64 years old (541%), 16.9 percent is between 64 and 95 years old, and a very small proportion (, 0.05%) is 95 and 99 years old and centenarians. Aging is a very complex process and longevity is a multifactorial trait, which is determined by genetic and environmental factors. Since it is too difficult to study aging as a whole, investigations try to focus on a specific physiological system that exhibits age-dependent functional changes. Immune remodeling and numerous changes in T-lymphocyte subset profiles were observed in elderly * Corresponding author. Tel.: þ 3592-9230-690; fax: þ 3592-9230-496. E-mail address: [email protected] (E. Naumova). 0531-5565/$ - see front matter q 2004 Published by Elsevier Inc. doi:10.1016/j.exger.2003.08.014
individuals (Pawelec et al., 1997). The absolute number of CD4 þ and CD8 þ T cells decreases with age from 20 to 100 years (Doria and Frasca, 1997). In the elderly, the increase in the relative proportion of CD8 lymphocytes lacking CD28 has been recognized (Fagnoni et al., 1996; Nociari et al., 1999). An ‘immunological risk phenotype’ (low CD4 count, high CD8 count, decreased IL-2 production and mitogen responssivness together with CD28 negativity and CD57 positivity) has been found as predictive of shorter remaining life span in the very old population (Ferguson, 1995; Wikby et al., 1998; Olsson et al., 2000). Family segregation analysis has shown that CD4, CD8 and CD4/CD8 ratio are under genetic control (Amadori et al., 1995; Clementi et al., 1999). The level of immune responses as well as possibly longevity could be associated with genes regulating immune function (Modica et al., 1990). Although case-control data are still controversial (Caruso et al., 2000), it was suggested that longevity could be associated with MHC and cytokine gene polymorphisms. HLA alleles and haplotypes relevant
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to susceptibility or resistance to infectious and autoimmune diseases (Dawkins et al., 1999; Thorsby, 1997) have been disscused. A positive association with longevity was found for HLA alleles DRB1*07, *11, *13 (Lagaay et al., 1991; Ivanova et al., 1998) and for the ancestral haplotype HLAB8-DR3 (Proust et al., 1982; Rea and Middleton, 1994). Genetic diversity of pro- and anti inflammatory cytokines, that affects gene transcription causing variations in cytokine production might influence successful aging and longevity by modulating an individual’s response to life-threatening disorders (Bruunsgaard et al., 2001; Ershler and Keller, 2000; Volpato et al., 2001). IL-10 2 1082G involved in high-production of this anti-inflammatory cytokine is positively associated (Lio et al., 2002a,b), while IFN-g þ 874 T (Lio et al., 2002a,b) and IL-6 2 174G (Bonafe et al., 2001) allele involved in high production of these proinflammatory cytokines show negative gender specific association with longevity. In order to clarify immunogenetic markers contributing to successful aging, HLA and cytokine gene profiles were analyzed in healthy elderly Bulgarians. Family segregation analysis was performed to define combined effect of haplotypes and immunophenotype profiles.
2. Material and methods 2.1. Subjects Ten families with longevity members from the Bulgarian population were analyzed in the present study. The following criteria for family selection were applied: two generation longevity members (octogenarians and nonagenarians) and no family history of inherited diseases. A total of 40 individuals: 17 unrelated elderly (age 65 – 90
years, mean age: 78.9 ^ 8.9 years)—6 male and 11 female, and 23 family members (age 18 – 57 years)—9 male and 14 female were included. The control group consisted of 105 randomly selected, matched for geographical distribution healthy controls aged 25– 53 years (40 male and 65 female). For the T-cell subset study 47 individuals were randomly selected among the total control population. Twenty one of them were young (mean age: 27.3 ^ 2.4 years) and 26middle-aged (mean age: 40.4 ^ 5.8 years) subjects with a male/female ratio 1:2 in the two groups. All individuals included in the analysis did not suffer from any chronic physical or mental disease, and did not experience any acute infection in the last 4 months before the enrolment. Their hematological and biochemical parameters were within the normal ranges. The blood samples were obtained after an informed consent. 2.2. HLA and cytokine gene polymorphism Genomic DNA was extracted from whole venous blood using the standard proteinase K digestion method followed by salting-out extraction and ethanol precipitation (Miller et al., 1988). HLA class I (HLA-A, -B, and -C) and class II (-DRB, and -DQB1) genotyping was performed by PCRSSP method using GENOVISIONe kits. Gene polymorphism of TNFa, TGFb1, IL-10, IL-6, and IFNg was tested by PCR-SSP method with Cytokine Genotyping Tray OneLambda. Eight polymorphism, previously shown to be associated with the level of cytokine production were analyzed (Table 1). 2.3. Flow cytometric analysis of T-cell subsets Complete blood cell counts were assessed for each subject. Peripheral blood lymphocyte subsets were determined by
Table 1 Correspondence between analyzed cytokine gene polymorphism and level of production Cytokine polymorphism
Genotype
Level of production
Reference
TNF-a (2308)
G/G G/A, A/A
Low High
Kroeger et al. (2000) Haung et al. (1999)
TGF-b1 (codons 10,25)
T/T G/G, T/C G/C T/C G/C, C/C G/G, T/T G/C C/C G/C, C/C C/C, T/T C/C, T/C C/C
High Intermediate Low
Awad et al. (1998) Awad et al. (1998) Awad et al. (1998)
IL-10 (21082, 2 819, 2 592)
GCC/GCC GCC/ACC, GCC/ATA ACC/ACC, ACC/ATA, ATA/ATA
High Intermediate Low
Crawley et al. (1999) Maurer et al. (2000) Crawley et al. (1999)
IL-6 (2174)
G/G G/C C/C
High Low
Fishman et al. (1998) Fishman et al. (1998)
IFN-g (þ 874)
T/T T/A A/A
High Intermediate Low
Turner et al. (1997) Turner et al. (1997) Turner et al. (1997)
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
Flow cytometric analysis using a panel of monoclonal antibodies (Becton-Dickinson, USA). Direct four-color immunofluorescence in whole blood was performed according to standard techniques. FACSCalibur flow cytometer, CellQuest and Multitest softwares (Becton Dickinson, USA) were used for data acquisition and analysis. Results were documented as % positive cells and cells/mm3. 2.4. Statistical analysis HLA class I and class II allele frequencies were estimated by maximum-likelihood analysis using
639
the Arlequin program v1.1 (Shneider et al., 1996). Standard deviations were calculated from 100 bootstrap iteractions. Hardy – Weinberg equilibrium was tested by two methods: a hidden Markov chain with 100,000 steps, implemented in the Arlequin program and a conventional x2 heterosigosity test. Arlequin software was also used to estimate through an expectation-maximization (EM) (Slatkin and Excoffier, 1996) algorithm maximum-likelihood frequencies of haplotypes defined in family studies. Comparisons of HLA allele and haplotype frequencies, and pro- and anti-inflammatory cytokine genotypes between elderly individuals and controls were performed by x2 test
Table 2 HLA class I and class II allele frequencies in elderly Bulgarians compared to the controls HLA allele
HLA-A *01 *02 *03 *11 *23 *24 *25 *26 *29 *30 *31 *32 *33 *36 *68 *69 HLA-B *07 *08 *13 *14 *15 *18 *27 *35 *37 *38 *39 *40 *41 *42 *44 *47 *49 *50 *51 *52 *53 *55 *56 *57 *58 *73
Allele frequency Elderly
Controls
0.118 0.412 0.088 0.088 0.029 0.059 0.029 0.029 0.000 0.029 0.059 0.029 0.000 0.000 0.000 0.029
0.099 0.333 0.057 0.056 0.040 0.131 0.020 0.048 0.016 0.016 0.036 0.044 0.032 0.004 0.040 0.008
0.000 0.059 0.029 0.029 0.043 0.147 0.000 0.265 0.000 0.029 0.029 0.029 0.029 0.000 0.088 0.000 0.029 0.000 0.147 0.000 0.000 0.059 0.000 0.000 0.000 0.000
0.048 0.032 0.016 0.036 0.016 0.083 0.048 0.131 0.004 0.036 0.024 0.075 0.012 0.004 0.087 0.008 0.028 0.020 0.194 0.036 0.008 0.020 0.008 0.012 0.016 0.004
P
,0.05
Pc
ns
HLA allele
Allele frequency Elderly
Controls
HLA-C *01 *02 *03 *04 *05 *06 *07 *08 *12 *14 *15 *16 *17
0.059 0.000 0.088 0.235 0.000 0.029 0.265 0.029 0.118 0.029 0.118 0.000 0.029
0.071 0.090 0.052 0.167 0.038 0.071 0.205 0.048 0.138 0.038 0.043 0.029 0.009
DRB1 *01 *03 *04 *07 *08 *1001 *11 *12 *13 *14 *15 *16
0.118 0.059 0.088 0.000 0.029 0.000 0.235 0.059 0.176 0.059 0.059 0.118
0.143 0.075 0.095 0.060 0.020 0.012 0.218 0.008 0.115 0.067 0.075 0.111
DQB1 *02 *03 *04 *05 *06
0.059 0.382 0.059 0.294 0.206
0.098 0.402 0.009 0.366 0.116
P
Pc
,0.05
ns
,0.05
ns
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E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
or Fisher’s exact test when appropriate. Bonferoni correction for multiple comparisons was applied. For T cell subset analysis the statistical significance was evaluated using nonparametric Mann –Whitney U-test. P-value lower than 0.05 was considered to indicate a significant difference between groups.
3. Results 3.1. HLA alleles and haplotypes HLA-A, -B, -C, -DRB1 and -DQB1 allele frequencies in elderly Bulgarians compared to the control group are shown in Table 2. Seventy-three different alleles were found in the present study- 51 (12 HLA-A, 14 HLA-B, 10 HLA-C, 10 DRB1, and 5 DQB1) in elderly and 72 (16 HLA-A, 26 HLA-B, 13 HLA-C, 12 DRB1, 5 DQB1) in control group. No deviations from Hardy –Weinberg equilibrium were observed in both groups. The most frequent HLA alleles in elderly Bulgarians were as follow: A*02, *01; B*35, *18, *51; C*07, *04; DRB1*11, *13; DQB1*03, *05. All these alleles were observed with high frequency in the control group. HLA-B*35 and -DRB1*12 allele groups showed a higher frequency in elderly population, however
the differences were not statistically significant after correction of p-values. HLA-C*02 was not found in the elderly group. Two-loci (HLA-DRB1-DQB1), three-loci (HLA-A-B-C; HLA-A-B-DRB1) and extended (HLA-A-B-C-DRB1DQB1) haplotypes were defined based on allele inheritance in the families (Fig. 1). Fifty different haplotypes were found in families with longevity members. The majority of these haplotypes were observed also in the control group. Interestingly, haplotypes DRB1*0301-DQB1*02 and DRB1*04 (0401,0402,0404,0405)-DQB1*0302, shown to be predisposing for different autoimune diseases in the Bulgarian population (Ivanova et al., 2003) were not observed in elderly Bulgarians and their family members. The most frequent haplotypes in elderly individuals are presented in Table 3. Compared to the controls in the elderly group an increased frequency was observed for the following haplotypes: A*02-B*35-C*04, A*01-B*08C*12, A*01-B*35-C*04, A*02-B*35-DRB1*13, A*01-B*08-DRB1*03, A*03-B*35-DRB1*01, A*02B*35-C*04-DRB1*13-DQB1*06, and A*01-B*08-C*07DRB1*03-DQB1*02, but a statistical significance was not found after Bonferoni correction. Additionally, the haplotype DRB1*12-DQB1*03 was found only in the elderly individuals (elderly 0.058, controls 0.000).
Fig. 1. Inheritance of HLA and cytokine haplotypes in a family with longevity members.
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
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Table 3 The most frequent haplotypes in elderly Bulgarians compared to the controls Haplotype
DRB1*11–DQB1*03 DRB1*13–DQB1*06 A*02–B*35–C*04 A*01–B*08–C*12 A*01–B*35–C*04 A*02–B*35–DRB1*13 A*01–B*08–DRB1*03 A*03–B*35–DRB1*01 A*02–B*35–C*04–DRB1*13–DQB1*06 A*01–B*08–C*07–DRB1*03–DQB1*02
Haplotype frequency Elderly
Controls
0.235 0.147 0.117 0.059 0.059 0.088 0.058 0.058 0.088 0.058
0.256 0.071 0.033 0.000 0.000 0.024 0.016 0.000 0.000 0.038
3.2. Cytokine gene polymorphism Genotypes of pro- and anti-inflamatory cytokines observed in elderly Bulgarians in comparison to the control group are shown in Table 4. Genotype profile frequencies of pro-inflammatory cytokines TNFa, IL-6, and IFN-g did not show statistically significant differences between the two groups studied. Similar results were observed for genotype frequencies of anti-inflammatory cytokine TGFb. On the other hand, significant differences between elderly and control group were found for two genotypes of IL-10. The genotype GCC/ACC, that is associated with intermediate level of IL-10 production, was increased, while the genotype ACC/ATA, that is associated with low level of IL-10 production, was decreased in elderly Bulgarians. In order to assess the impact of combinations of genotypes, associated with different level of expression we analyzed cytokine gene profiles. IL-10 (intermediate)IL-6 (low) (0.118 vs. 0.061) and IL-10 (intermediate)-IFNg (low) (0.176 vs. 0.061) showed statistically significant increase in elderly Bulgarians compared to the controls. Similar result was observed for gene profile IL-6 (low) - IFNg (low) (0.118 vs. 0.020, p , 0:05). 3.3. T-cell subsets The values of various T-cell subsets, including CD28 þ , CD28 2 , and CD57 þ phenotypes were examined in elderly, middle-aged and young randomly selected healthy individuals (Table 5). Significantly lower relative and absolute numbers of CD3 þ 8 þ ðp , 0; 02Þ and CD8 þ 28 þ ðp , 0:01Þ cells were found in elderly people compared to both middle-aged and young ones. Decreased CD8 þ 57 þ cells ðp , 0:05Þ were observed in old individuals in comparison with middle-aged subjects. The lower percentage of CD8 þ T cells was more pronounced (, 12%) in individuals aged over 85 years. No significant changes in the values of CD8 þ 28 2 , CD4 þ 28 þ and CD4 þ 28 2 cell subsets in the peripheral blood
P
Pc
,0.05 ,0.05 ,0.05
ns ns ns
,0.05 ,0.05
ns ns
lymphocyte pool were detected in elderly people compared to both control groups. However, the CD28 2 cells represented a significantly ðp , 0:01Þ higher proportion (55.4 ^ 10.7%) within CD8 þ lymphocytes in elderly people compared to the controls (40.0 ^ 14.8%). Analysis of the numbers of CD8 þ and CD4 þ cell subsets were also performed in families. Family members
Table 4 Pro- and anti-inflammatory cytokine genotypes in elderly Bulgarians and the control group Genotype
Genotype frequency Elderly
Controls
TNF a 2 308 G/A (high) G/G (low)
0.294 0.706
0.240 0.760
IL-6 2 174 G/G (high) G/C (high) C/C (low)
0.353 0.471 0.176
0.540 0.220 0.240
IFN g þ 874 T/T (high) T/A (intermediate) A/A (low)
0.118 0.588 0.294
0.160 0.580 0.260
0.118 0.353 0.118 0.235 0.059 0.118
0.100 0.120 0.260 0.080 0.280 0.160
0.294 0.294 0.235 0.059 0.059 0.000 0.059
0.360 0.320 0.180 0.080 0.000 0.020 0.040
IL-10 2 1082, 2819, 2592 GCC/GCC (high) GCC/ACC (intermediate) GCC/ATA (intermediate) ACC/ACC (low) ACC/ATA (low) ATA/ATA (low) TGFb codons 10, 25 T/T G/G (high) T/C G/G (high) C/C G/G (intermediate) T/C G/C (intermediate) T/T G/C (intermediate) T/T C/C (low) C/C G/C (low)
P
,0.05
,0.05
642
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
Table 5 Phenotype distribution of T-cell subsets (%, cells/mm3) in elderly compared to middle-aged and young subjects CD3 þ T-cells
CD3 þ 4 þ
CD3 þ 8 þ
CD8 þ 57 þ
CD8 þ 28 þ
CD8 þ 28 2
CD4 þ 28 þ
CD4 þ 28 2
Elderly ðn ¼ 17Þ
70.4 ^ 6.45 1229 ^ 456
49.9 ^ 10.15 850 ^ 289
17.62 ^ 4.82**3 12 ^ 149**
7.5 ^ 2.61* 124 ^ 65*
10.1 ^ 3.33*** 177 ^ 96***
10.33 ^ 4.33 172 ^ 63
46.4 ^ 12.4 728 ^ 264
3.4 ^ 3.3 53 ^ 42
Middle-aged ðn ¼ 26Þ
74.7 ^ 7.1 1499 ^ 517
46.9 ^ 8.6 948 ^ 316
28.0 ^ 5.9 548 ^ 208
11.4 ^ 5.1 223 ^ 125
16.9 ^ 3.8 324 ^ 114
12.6 ^ 7.2 235 ^ 142
44.9 ^ 6.9 881 ^ 312
3.2 ^ 3.1 59 ^ 57
Young ðn ¼ 21Þ
74.18 ^ 7.37 1432 ^ 329
47.6 ^ 6.26 927 ^ 248
27.3 ^ 4.24 515 ^ 109
9.11 ^ 4.73 184 ^ 91
19.0 ^ 4.05 332 ^ 127
9.66 ^ 3.08 162 ^ 49
44.4 ^ 3.65 776 ^ 165
2.2 ^ 1.3 36 ^ 17
Values are given as mean ^ SD; *p , 0:05; **p , 0:02; ***p , 0:01:
were divided into 3 subgroups: old (proband, aged . 65 years), middle aged (first generation, age 37– 57 years) and young (second generation, aged 18 – 24 years) individuals. The immunophenotype analysis of lymphocyte subsets showed that although most pronounced in the elderly group, lower numbers of CD8 þ T cells were also found in middleaged and young members of the families compared to the age matched controls. A progressive CD8 þ 28 þ cell subsets decline was seen with aging, with significant difference between the old subjects on one hand and the middle-aged ðp , 0:02Þ and young ðp , 0:01Þ individuals on the other hand (Fig. 2).
4. Discussion In this pilot study we investigated how immunological factors, particularly HLA, cytokine gene polymorphism and immunophenotype profiles, contribute to the successful aging in the Bulgarian population. For the first time we also performed family analyses in order to assess the combine effect of these three biological markers. In the last years many studies have searched for the impact of HLA genes in human longevity. Due to the central role of HLA molecules in the immune response it has been suggested that this genetic complex could be associated with successful aging and that particular HLA alleles and haplotypes might have different distribution in longevity individuals (Caruso et al., 2000). However, the results were contradicting and no clear consensus has been reached. DRB1*07, *11, *13 alleles (Lagaay et al., 1991; Ivanova et al., 1998) as well as the ancestral haplotype HLA-B8DR3 (Proust et al., 1982; Rea and Middleton, 1994), that confers resistance to viral infections, were shown to be increased in centenarians. Additionally, it has been suggested that HLA/longevity associations are population specific. The Bulgarian population is relatively homogeneous and based on HLA class I and class II profile it is characterized by features of Southern European anthropological type with some influence of additional ethnic groups (Ivanova et al., 2001, 2002). Therefore Bulgarians
represent a suitable population to assess genetic associations in longevity. Our study in extended families with longevity members and matched for geographical distribution controls from the Bulgarian population showed a slightly increased frequency of HLA-B*35, DRB1*12 alleles and corresponding haplotypes. Positive association of DRB1*12 with longevity has been previously observed in the Japanese population (Akisaka and Suzuki, 1998). Further it has been shown that life span prolongation is associated with delay of immunodeficiency of normal aging or with possible amelioration of the autoimmunity that develops with age. In agreement with this in elderly and their family members we did not observed the common alleles and haplotypes, positively associated with autoimmunity in Bulgarians. Additionally, gene polymorphisms within known or putative regulatory regions have been demonstrated to be associated with different disorders, including age related (Haukim et al., 2002). Pro-inflammatory cytokines have been shown to play a pathogenic role (Bruunsgaard, 2001). Some of cytokine gene polymorphisms have been demonstrated to affect gene transcription causing variations in cytokine production. However, recent publications showed contradicting results regarding the association of cytokine gene/haplotypes and the level of expression (Bidwell et al.,
Fig. 2. Phenotype distribution of CD8 þ cells in families with longevity members.
E. Naumova et al. / Experimental Gerontology 39 (2004) 637–644
1999, 2001). In the present study it was shown that IL-10 genotype 2 1082G/A, 2 819 C/C, 2 592 C/C related to intermediate production was positively associated, while genotype 2 1082A/A, 2 819 C/T, 2 592 C/A related to low level of production was negatively associated with longevity in Bulgarians. These results confirm data, showing that longevity is associated with genotypes coding for antiinflammatory profile (Lio et al., 2002; Bonafe et al., 2001). Recent data indicated that pro-inflammatory profile is associated with increased mortality in elderly individuals (Franceschi et al., 2000). Additionally, our study on cytokine gene profiles demonstrated that the effect of IL10 gene polymorphism is modulated by IL-6 and IFNg genotypes associated with low level of production of these pro-inflammatory cytokines. In order to evaluate a combined effect of different immune factors in longevity we also searched for changes in the immune phenotype profile with aging. The results of the present study demonstrated markedly decreased CD8 þ T cell subsets in elderly people. Our observation of lower CD8 cell levels in middle aged and young family members supports the concept that genetic factors influence the regulation of lymphocyte subpopulations as well (Hall et al., 2002). The lowest CD8 numbers were seen in the old individuals. It will be reasonable to expect that although initially low compared to the general age-matched population, CD8 cell values will decrease with aging in the younger individuals of these families. Loss of CD28 expression with increasing age resulting in decline of the proportion of CD28 positive T cells (Effros et al., 1994; Effros, 2000) and an increase of CD8 þ 28 2 T cells (Nociari et al., 1999) have been reported. The old individuals in our study group have low CD3 þ CD8 þ cell values associated with a major decrease of the CD8 þ 28 þ cell subset, but we did not found an increase of CD8 þ CD28 2 cells within the lymphocytes. Looking at the CD8 þ lymphocyte subpopulation, CD28 negative cells were predominant compared to the CD28 positive cells. Thus, an increase in proportion of CD28 negative cells in the elderly do not necessarily equate with the increased number of such cells. These changes in the T cell subset profile in old individuals were supported by our observation of progressive age related decline of CD8 þ CD28 þ cells and a redistribution of CD28 negative and CD28 positive cells within the CD8 þ lymphocytes in the different family generations. It has been demonstrated that CD28 2 T cells are frequently CD57 þ cells (Azuma et al., 1993). Our data showed decreased values of CD8 þ 57 þ cells in elderly compared to middle aged controls with no significant changes of their proportion (29.2 ^ 9.1% vs. 29.4 ^ 9.4%) within the CD8 þ cells. Therefore, in this study we did not observe the ‘immune risk profile’ characterized by elevated CD8 þ , CD8 þ 28 2 , CD8 þ 57 þ cells, decreased CD4 þ T cells, and inverted CD4/CD8 index (Wikby et al., 2002) in old people. Taking into account the above considerations, it could be speculated that our results are
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consistent with a healthy immune status of elderly people in our study group. It could be summarized that in families with two generations longevity members we did not observed HLA alleles and haplotypes associated with autoimmunity. Our data also indicate an association of longevity with genotypes coding for anti-inflammatory cytokine profile. In addition, we did not observed the ‘immune risk phenotype’ which is a marker of an increased inflammatory activity. Based on the results of this study, it seems reasonable to suggest that a combination of specific immunogenetic and immunophenotype profiles could contribute to successful aging and to maintaining healthy status in elderly.
Acknowledgements The authors are grateful to the volunteers for making this study possible. This investigation has been done within ImAginE and was supported in part by a research grant from T-CIA project N-QLK6-CT-2002-02283.
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