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01.12.2012 | Research article | Ausgabe 1/2012 Open Access

BMC Public Health 1/2012

Life-course origins of social inequalities in adult immune cell markers of inflammation in a developing southern Chinese population: the Guangzhou Biobank Cohort Study

Zeitschrift:
BMC Public Health > Ausgabe 1/2012
Autoren:
Douglas A West, Gabriel M Leung, Chao Q Jiang, Timothy M Elwell-Sutton, Wei S Zhang, Tai H Lam, Kar K Cheng, C Mary Schooling
Wichtige Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DA West carried out the statistical analyses and wrote the article. TM Elwell-Sutton performed additional analysis and reviewed drafts of the manuscript. CM Schooling and GM Leung helped conceptualize ideas, interpret findings and review drafts of the manuscript. TH Lam, CQ Jiang and KK Cheng initiated and oversee the Guangzhou Biobank Cohort Study and WS Zhang assisted in the planning and co-ordination of the study. All authors read and approved the final manuscript.

Background

Socioeconomic position (SEP) throughout life is usually inversely associated with morbidity and mortality from cardiovascular disease, although the underlying biological pathway is not entirely clear [1, 2]. Cardiovascular disease has been associated with higher levels of inflammatory molecules, perhaps as a consequence of exposure to pathogenic organisms [3], although it is unclear whether pathogen burden mediates SEP differences in cardiovascular risk [3, 4]. Poor early life conditions are usually associated with higher levels of inflammatory markers [511] and poorer adult immune function [12, 13]. These associations are less clear, however, amongst men from middle income countries [10]. Furthermore, little is known about the association of SEP across the life course with immune function. The duration or number of exposures across the life course may be most important (the accumulation hypothesis) [14]. Alternatively, the timing of exposure to poor socioeconomic conditions may be crucial as a number of sensitive periods or simply as a single critical period (the critical period hypothesis). It is also possible that either inter- or intra-generational social mobility plays a part.
Developmental trade-offs between growth, maintenance, and reproduction may occur when there are competing demands for energy resources between biological systems [13, 15, 16], potentially at the expense of immune function in resource-poor environments. Alternatively, intergenerationally and environmentally driven up-regulation of the gonadotropic axis with economic development may obscure some of the normally protective effects of social advantage in the first few generations of men to experience better living conditions [17, 18], thus generating epidemiologically stage specific associations between SEP and immune-related functions, such as pro-inflammatory states, among men [18, 19].
Rapidly developing mega-cities of China may provide a sentinel for the changes in non-communicable diseases expected with economic development and inform effective interventions to reduce the disease burden. In a large sample of older residents from one of the most developed mega-cities in China, Guangzhou in southern China, we assessed the association of SEP at four life stages with proxies of inflammation (total white blood cell, granulocyte, and lymphocyte counts) and compared models representing the accumulation, sensitive periods and critical period hypotheses. Additionally, we hypothesise that 1) higher life course SEP is protective for adult inflammation, 2) the normal protective effect of social advantage is obscured in men experiencing rapid socioeconomic development.

Methods

Sources of data

The Guangzhou Biobank Cohort Study is a collaboration between the Guangzhou No. 12 Hospital (Guangzhou, China) and the universities of Hong Kong (Hong Kong, China) and Birmingham (Birmingham, United Kingdom). The study has been described previously in detail [20]. Participants were drawn from the Guangzhou Health and Happiness Association for the Respectable Elders (GHHARE), a community social and welfare association unofficially aligned with the municipal government, where membership is open to anyone aged 50 years or older for a nominal monthly fee of 4 yuan (US $0.50). Approximately 7 percent of permanent Guangzhou residents aged 50 years or more are members of the GHHARE. Eleven percent of the members were included in this study, who were capable of consenting, were ambulatory, and were not receiving treatments which if discontinued might have resulted in immediate, life-threatening risk, such as chemotherapy, radiotherapy or dialysis. Those with less serious chronic illnesses or with acute illnesses were not specifically excluded from the study though they may have been less likely to attend. Participants were recruited in three phases and this study includes participants recruited in phase 3 only (recruited between 2006 and 2008), because only phase 3 has detailed information on childhood socioeconomic position and inflammatory markers.
Participants underwent a detailed half-day medical interview, as well as a physical examination with fasting blood being sampled. Quantitative haematological analysis was performed using a SYSMEX KX-21 haematology analyser. The Guangzhou Medical Ethics Committee of the Chinese Medical Association approved the study and all participants gave written, informed, consent prior to participation.

Socioeconomic position across the life course

We used indicators of SEP at four life stages: childhood, early adult, late adult and current SEP. Childhood SEP was measured by an index of notable parental possessions that were appropriate to China in the mid-20th century, based on sociologic accounts of life in southern China at that time [17]. The items selected were a watch, a sewing machine, and a bicycle. These items were categorized, as previously, as none or at least one [21]. As in other similar studies, we used education and longest-held occupation as proxies for early and late adult SEP [22]. Early adult SEP was assessed from the highest level of education (primary or less versus secondary or more). Occupation was categorised as manual (agricultural work, factory work, or sales and service) or non-manual (administrative/managerial, professional/technical, or military/police). Current SEP was assessed from household income per head. Household income was recorded in six categories (<5,000 Yuan, 5000-9,999 Yuan, 10,000-19,999 Yuan, 20,000-29,999 Yuan, 30,000-49,999 Yuan and ≥50,000 Yuan). Household income per head was estimated using the mid-point of each income category and assuming that those in the highest category had an annual income of 75,000 Yuan. The median household income per head was used as the cut-off point between low and high SEP.

Outcome measures

The primary outcome was total white blood cell count used, as in other studies, as a marker of a pro-inflammatory state [5], and less well functioning immune system. As we do not have a detailed breakdown of different white blood cell types, such as macrophages, we also considered granulocyte and lymphocyte counts as outcomes because these immune cell sub-populations largely relate to innate and adaptive immunity respectively. They have previously been used as markers of inflammation [23, 24]. Other measures of inflammation (e.g. C-reactive protein) were not available.

Statistical analysis

Multivariable linear regression was used to assess the adjusted associations of SEP with the outcomes. Following Mishra et al. [25] we determined the most parsimonious representation of life course SEP by comparing models for three different life course hypotheses (the accumulation, sensitive periods and critical period hypotheses) to a 'fully saturated' model which represents all possible life course SEP trajectories. As in previous work [26], the accumulation hypothesis was represented by a model representing the number of life stages with high socioeconomic position, and the sensitive periods hypothesis by a model in which all four measures of SEP were considered as separate items in one model adjusted for all four measures of SEP. The critical period hypothesis was represented by models in which only one SEP exposure (the critical period) was included [25]. We used the Akaike Information Criterion (AIC) to compare models [27]. A smaller AIC indicates a better model.
We examined whether the outcomes had different associations with SEP by sex or age, from the heterogeneity across subgroups and the significance of an interaction term obtained from a model including all interaction terms with age or sex. All models were adjusted for age (in 5 year age groups) and sex. A second set of models was additionally adjusted for lifestyle factors (smoking, alcohol use, and physical activity categorized as in Table 1) as potential mediators and a third set of models additionally adjusted for body mass index (BMI) as a potential mediator
Table 1
Characteristics (mean value or percentage) of 9,981 Chinese adults (≥ 50 years) by socioeconomic position at four life stages in women and men from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
  
Socio-economic position in:
 
  
Childhood(Parental possession of a watch, sewing machine, and bicycle)
Early Adulthood (Education*)
Late Adulthood (Occupation)
Current (Household Income per Head)
 
No.
0 items
1+ items
Unknown
p
Low
High
p
Manual
Non-manual
Unknown
p
Low
High
Unknown
p
Women
                
Number of participants
7445
4084
3123
238
 
3057
4386
 
5225
1211
1009
 
3025
3141
1279
 
Mean (SD)
                
White blood cells (109/L)
7445
6.38(1.5)
6.13(1.5)
6.36(1.4)
<0.001
6.52(1.6)
6.10(1.5)
<0.001
6.33(1.5)
6.04(1.4)
6.25(1.5)
<0.001
6.38(1.6)
6.12(1.5)
6.41(1.6)
<0.001
Granulocytes (109/L)
7445
3.79(1.2)
3.62(1.2)
3.71(1.1)
<0.001
3.89(1.2)
3.60(1.1)
<0.001
3.76(1.2)
3.53(1.1)
3.71(1.2)
<0.001
3.79(1.2)
3.60(1.1)
3.84(1.2)
<0.001
Lymphocytes (109/L)
7445
2.19(0.6)
2.15(0.6)
2.26(0.6)
<0.001
2.22(0.6)
2.14(0.6)
<0.001
2.18(0.6)
2.15(0.6)
2.18(0.6)
0.227
2.20(0.6)
2.16(0.6)
2.16(0.6)
0.008
Leg length (cm)
7430
69.7(3.8)
69.8(3.6)
69.8(4.3)
0.393
69.6(3.8)
69.8(3.7)
0.008
69.7(3.7)
69.9(3.6)
69.6(3.8)
0.202
69.6(3.8)
69.8(3.6)
69.6(3.8)
0.099
Sitting height (cm)
7431
83.7(3.6)
85.1(3.1)
84.1(3.7)
<0.001
83.1(3.6)
85.1(3.1)
<0.001
84.0(3.5)
85.0(3.3)
84.6(3.3)
<0.001
83.9(3.5)
85.0(3.2)
83.5(3.7)
<0.001
Row%
                
Age group (years)
                
50-54
2644
36.0
60.7
3.3
 
17.9
82.1
 
65.0
18.5
16.5
 
36.0
53.0
11.1
 
55-59
1892
52.7
44.5
2.7
 
39.1
60.9
 
69.8
17.2
12.9
 
39.0
44.2
16.8
 
60-64
1133
64.9
32.7
2.5
 
51.5
48.5
 
73.8
14.7
11.6
 
46.5
34.6
18.9
 
65-69
833
74.5
21.5
4.0
 
63.5
36.4
 
76.1
13.8
10.1
 
46.2
29.7
24.1
 
70-74
621
82.8
13.4
3.9
 
75.7
24.3
 
75.0
13.7
11.3
 
44.8
28.3
26.9
 
75-79
228
82.0
14.5
3.5
 
81.1
18.4
 
75.9
10.5
13.6
 
49.1
25.0
25.9
 
80+
94
80.9
12.8
6.4
<0.001
81.9
18.1
<0.001
81.9
5.3
12.8
<0.001
37.2
34.0
28.7
<0.001
Alcohol use
                
Never
3981
57.9
39.1
3.1
 
45.9
54.1
 
71.7
15.2
13.1
 
43.8
37.8
18.4
 
<1/week
2811
49.8
47.1
3.2
 
33.8
66.2
 
67.4
18.1
14.5
 
36.7
48.4
14.9
 
1-4/week
162
53.1
40.7
6.2
 
35.2
64.8
 
72.8
16.7
10.5
 
35.8
47.5
16.7
 
5+/week
126
63.5
33.3
3.2
 
43.7
56.3
 
70.6
13.5
15.9
 
34.1
46.8
19.0
 
Ex-drinker
196
62.8
34.7
2.6
<0.001
50.5
49.5
<0.001
78.1
11.2
10.7
0.001
44.4
35.2
20.4
<0.001
Unknown
169
54.4
40.8
4.7
 
41.4
57.4
 
68.6
17.8
13.6
 
36.1
43.2
20.7
 
Smoking status
                
Never
7192
54.5
42.4
3.1
 
40.1
59.9
 
70.0
16.4
13.5
 
40.4
42.5
17.1
 
Ex-smoker
95
76.8
18.9
4.2
 
87.4
12.6
 
77.9
6.3
15.8
 
46.3
27.4
26.3
 
Current
106
63.2
28.3
8.5
<0.001
65.1
34.9
<0.001
79.2
12.3
8.5
0.013
55.7
27.4
17.0
<0.001
Unknown
52
48.1
46.2
5.8
 
38.5
57.7
 
61.5
17.3
21.2
 
34.6
51.9
13.5
 
Physical activity
                
Inactive
626
55.4
40.7
3.8
 
42.8
56.9
 
71.4
15.8
12.8
 
40.3
38.0
21.7
 
Minimally active
1848
54.9
42.0
3.1
 
41.5
58.5
 
70.1
17.4
12.6
 
41.7
41.3
16.9
 
HEPA active $
4971
54.8
42.1
3.2
0.877
40.7
59.3
0.520
70.1
15.9
14.0
0.450
40.3
43.0
16.7
0.249
 
Socio-economic position in
  
Childhood(Parental possession of a watch, sewing machine, and bicycle
Early Adulthood (Education*)
Late Adulthood (Occupation)
Current (Household Income per Head)
 
No.
0 items
1+ items
Unknown
p
Low
High
p
Manual
Non-manual
Unknown
p
Low
High
Unknown
p
Men
                
Number of participants
2536
1664
812
60
 
730
1803
 
1581
791
164
 
1131
1035
370
 
Mean (SD)
                
White blood cells (109/L)
2536
6.85(1.7)
6.86(1.7)
6.74(1.5)
0.878
7.01(1.7)
6.79(1.7)
0.010
6.87(1.7)
6.75(1.7)
7.15(1.9)
0.021
6.93(1.7)
6.73(1.7)
6.95(1.7)
0.012
Granulocytes (109/L)
2536
4.21(1.4)
4.14(1.4)
4.17(1.1)
0.439
4.38(1.4)
4.11(1.3)
<0.001
4.21(1.4)
4.10(1.3)
4.40(1.6)
0.018
4.27(1.4)
4.07(1.3)
4.27(1.4)
0.002
Lymphocytes (109/L)
2536
2.14(0.6)
2.23(0.7)
2.05(0.5)
0.002
2.10(0.6)
2.19(0.7)
0.006
2.16(0.7)
2.17(0.6)
2.23(0.6)
0.389
2.15(0.7)
2.18(0.6)
2.18(0.7)
0.619
Leg length (cm)
2519
75.2(3.8)
75.7(4.2)
75.3(3.7)
0.005
74.6(3.7)
75.6(4.0)
<0.001
75.2(3.9)
75.6(4.0)
75.6(3.9)
0.020
75.1(4.0)
75.7(3.8)
75.1(4.0)
0.003
Sitting height (cm)
2520
88.6(3.6)
89.8(3.4)
88.7(3.7)
<0.001
87.6(3.5)
89.6(3.4)
<0.001
88.8(3.6)
89.3(3.4)
89.7(4.0)
<0.001
88.5(3.5)
89.7(3.4)
88.6(3.8)
<0.001
Row%
                
Age group (years)
                
50-54
346
41.3
56.6
2.0
 
11.3
88.7
 
68.8
21.4
9.8
 
43.9
42.8
13.3
 
55-59
568
57.0
41.0
1.9
 
23.6
76.4
 
67.3
25.4
7.4
 
44.9
43.0
12.1
 
60-64
599
65.4
32.2
2.3
 
25.2
74.6
 
61.3
32.9
5.8
 
37.4
47.9
14.7
 
65-69
470
76.0
22.3
1.7
 
30.6
69.1
 
56.8
38.5
4.7
 
48.5
37.0
14.5
 
70-74
339
82.3
15.3
2.4
 
44.5
55.5
 
58.7
36.3
5.0
 
48.7
32.2
19.2
 
75-79
156
78.2
16.0
5.8
 
51.3
48.1
 
60.9
32.1
7.1
 
48.1
35.9
16.0
 
80+
58
81.0
13.8
5.2
<0.001
53.4
46.6
<0.001
56.9
37.9
5.2
<0.001
55.2
29.3
15.5
<0.001
Alcohol use
                
Never
880
68.3
29.9
1.8
 
30.1
69.9
 
61.0
32.0
6.9
 
48.1
36.8
15.1
 
<1/week
1068
63.2
34.7
2.1
 
24.4
75.6
 
61.5
32.0
6.5
 
41.5
44.4
14.1
 
1-4/week
176
62.5
34.7
2.8
 
25.6
74.4
 
67.6
28.4
4.0
 
46.0
44.9
9.1
 
5+/week
271
65.7
32.5
1.8
 
39.9
60.1
 
66.4
28.8
4.8
 
43.9
39.9
16.2
 
Ex-drinker
93
71.0
21.5
7.5
0.046
37.6
62.4
<0.001
67.7
24.7
7.5
0.323
49.5
34.4
16.1
0.011
Unknown
48
70.8
18.8
10.4
 
33.3
60.4
 
52.1
33.3
14.6
 
39.6
37.5
22.9
 
Smoking status
                
Never
940
63.8
34.3
1.9
 
20.3
79.7
 
58.3
35.3
6.4
 
41.0
45.5
13.5
 
Ex-smoker
675
69.0
28.1
2.8
 
33.5
66.5
 
62.2
31.3
6.5
 
46.2
40.4
13.3
 
Current
900
65.1
32.9
2.0
0.039
34.3
65.7
<0.001
66.8
27.0
6.2
<0.001
47.6
36.2
16.2
<0.001
Unknown
21
57.1
19.0
23.8
 
19.0
66.7
 
57.1
23.8
19.0
 
28.6
38.1
33.3
 
Physical activity
                
Inactive
223
65.9
31.4
2.7
 
31.8
66.8
 
68.6
26.0
5.4
 
48.4
30.9
20.6
 
Minimally active
866
61.1
36.4
2.5
 
29.1
70.9
 
58.8
33.9
7.3
 
44.3
40.9
14.8
 
HEPA active $
1447
68.3
29.5
2.2
0.002
28.1
71.9
0.438
63.5
30.3
6.2
0.021
44.2
42.3
13.5
0.046
* Highest level of education was categorised as low (primary or less) and high (above primary)
# Manual is agricultural worker, factory worker or sales and service, non-manual is administrator/manager, professional/technical or military/disciplined
† p value for linear trend for continuous variables and χ 2 p value for categorical variables
$ Health enhancing physical activity: vigorous activity at least 3 days a week achieving at least 1500 metabolically equivalent tasks (MET) minutes per week or activity on 7 days of the week achieving at least 3000 MET minutes per week
Proxies of SEP were unavailable or unclassifiable for 28.7% of the participants, mainly because information on household income or the longest-held occupation was missing. Alcohol use or smoking was not available for 2% of participants. We used multiple imputation for missing data [28, 29]. Socioeconomic position at any stage, alcohol use and smoking were predicted based on a flexible additive regression model with predictive mean matching incorporating age, sex, leg length, seated height, alcohol use, smoking status, physical activity and SEP at the other three stages [28]. We imputed missing values 10 times and analysed each complete dataset separately, then summarized estimates with confidence intervals adjusted for missing data uncertainty [30]. As a sensitivity analysis, a complete case analysis without imputation was performed. We used STATA version 10.0 (STATA Corp., College Station, TX) and R version 2.12.2 for analysis, imputation and model estimation.

Results

Of the 10,088 phase 3 participants examined, 1.1% (n = 107) had missing data for total white blood cell, granulocyte or lymphocyte counts. Analysis was based on the remaining 9,981 participants. There were more women (n = 7,445) than men (n = 2,536) and the women were younger [mean age 59.2 years (S.D. 7.6)] than the men [mean age 63.1 years (S.D. 7.6)]. Overall the mean white blood cell and granulocyte counts were lower in women than men (Table 1).
The associations of SEP with white blood cell, granulocyte or lymphocyte counts did not vary with age (data not shown). However, associations of SEP with lymphocyte count varied with sex, so only sex stratified results have been presented for this outcome. For white blood cell count and granulocyte count, the sensitive periods model performed better than the fully saturated model, accumulation or critical period models (Table 2). The sensitive periods model shows that some life stages had stronger negative associations than others with white blood cell count and granulocyte count; the early adult stage had the strongest association for both outcomes.
Table 2
Associations of high socioeconomic position (compared to low) at four life stages with white cell count and its differentials, adjusted for agea and sex in 9,981 Chinese adults (≥ 50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
  
White Blood Cell Count (109/L)
  
Granulocyte Cell Count (109/L)
 
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Men and Women
      
Critical period Modelb
      
Childhood
-0.14
(-0.20, -0.07)
8995
-0.10
(-0.15, -0.04)
4095
Early Adult
-0.35
(-0.42, -0.29)
8909
-0.26
(-0.31, -0.21)
4018
Late Adult
-0.21
(-0.29, -0.13)
8980
-0.17
(-0.23, -0.11)
4074
Current
-0.22
(-0.29, -0.15)
8965
-0.16
(-0.22, -0.11)
4064
Accumulation Model c
  
8905
  
4012
0
0
  
0
  
1
-0.19
(-0.29, -0.09)
 
-0.14
(-0.22, -0.06)
 
2
-0.32
(-0.42, -0.22)
 
-0.23
(-0.31, -0.16)
 
3
-0.48
(-0.58, -0.38)
 
-0.35
(-0.43, -0.27)
 
4
-0.51
(-0.65, -0.38)
 
-0.40
(-0.50, -0.29)
 
Sensitive Periods Modeld
  
8892
  
4000
Childhood
-0.06
(-0.13, 0.01)
 
-0.03
(-0.09, 0.02)
 
Early Adult
-0.28
(-0.36, -0.20)
 
-0.20
(-0.26, -0.14)
 
Late Adult
-0.08
(-0.17, 0.002)
 
-0.08
(-0.14, -0.01)
 
Current
-0.12
(-0.19, -0.05)
 
-0.09
(-0.15, -0.04)
 
Fully Saturated Modele
  
8905
  
4015
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
* Test for interaction with sex significant at p < 0.05 level
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age and sex but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age and sex. dMutually adjusted for SEP at other life stages and adjusted for age and sex. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age and sex
The pattern for lymphocyte cell count was somewhat different. Associations varied by sex. Table 3 shows that for both sexes the accumulation and sensitive periods models did not perform as well as critical period models. The early adult life stage was a critical period for women, with a negative association between SEP and lymphocyte cell count. By contrast, for men, all estimates of association between SEP and lymphocyte cell count were positive, although all confidence intervals included zero.
Table 3
Associations of high socioeconomic position (compared to low) at four life stages with lymphocyte cell count (109/L) by sex, adjusted for agea in 9,981 Chinese adults (≥ 50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
 
Women (n = 7,445)
Men (n = 2,536)
 
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Critical period Modelb
      
   Childhood
-0.04
(-0.07, -0.01)
-7874
0.03
(-0.03, 0.08)
-2277
   Early Adult
-0.08
(-0.11, -0.05)
-7894
0.03
(-0.02, 0.09)
-2277
   Late Adult
-0.03
(-0.07, 0.01)
-7871
0.04
(-0.01, 0.10)
-2279
   Current
-0.04
(-0.07, -0.01)
-7874
0.01
(-0.05 0.07)
-2277
Accumulation Model c
  
-7885
  
-2274
   0
0
  
0
  
   1
-0.02
(-0.07, 0.02)
 
0.02
(-0.07, 0.11)
 
   2
-0.05
(-0.10, -0.01)
 
0.05
(-0.03, 0.13)
 
   3
-0.10
(-0.14, -0.05)
 
0.04
(-0.05, 0.12)
 
   4
-0.09
(-0.15, -0.03)
 
0.10
(-0.01, 0.21)
 
Sensitive Periods Modeld
  
-7891
  
-2274
   Childhood
-0.02
(-0.05, 0.01)
 
0.02
(-0.03, 0.08)
 
   Early Adult
-0.07
(-0.10, -0.04)
 
0.02
(-0.04, 0.08)
 
   Late Adult
0.00
(-0.04, 0.04)
 
0.04
(-0.02, 0.10)
 
   Current
-0.01
(-0.05, 0.02)
 
0.00
(-0.06, 0.06)
 
Fully Saturated Modele
  
-7882
  
-2262
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age. dMutually adjusted for SEP at other life stages and adjusted for age eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age
Additional adjustment for lifestyle factors (smoking, alcohol use, and physical exercise) attenuated estimates slightly (see Appendix) but the pattern of associations generally remained the same. Smoking among men is associated with both low SEP and higher lymphocyte count, hence adjustment for smoking strengthened the positive association of SEP with lymphocyte count (Appendix). Further adjustment for BMI (Appendix) produced very similar results; estimates of association were little changed. All results were similar in a complete case analysis (Appendix).

Discussion

Consistent with other studies in developed and developing settings examining the association between SEP and inflammation [511], we found that SEP was negatively associated with adult immune cell numbers, particularly among women. Consistent with the only other study from a developing country setting, the advantage of higher SEP for adult inflammation was less marked among men [10]. In general, considering SEP at all four life stages was better than considering individual life stages (critical periods) except for lymphocyte cell counts.
This study has a number of strengths. To our knowledge, it is the first study to investigate the role of life course SEP in later adulthood inflammation in a non-western, developing setting. Moreover, we explicitly determined the most parsimonious representation of life course SEP. The large sample size allowed sex-specific analysis. Nevertheless, there are limitations. First, it is a cross-sectional study with recalled SEP, which may be imprecise, although most likely non-differential. Second, in a cross-sectional design reverse causality must be considered although it is unlikely that inflammation has a causal effect on life course SEP. Third, there may have been gender bias in the allocation of resources within families, most likely favouring boys and men, which may have mitigated the disadvantages of low SEP. However it is unclear why this should have mitigated the effect of SEP for lymphocytes but not for white blood cells and granulocytes. Fourth, our cohort may not be fully population representative. However, prevalence of certain morbidities, such as diabetes, were similar to those in a representative sample of urban Chinese [31]. Fifth, survivor bias is possible, which may have limited participants' socioeconomic and health diversity, biasing results towards the null. If survivorship were an issue we would have expected differences in associations by age, of which there was no evidence. Sixth, we did not explicitly consider the life course effects of social mobility since these are particularly hard to define and test clearly. Inter- and intra-generational mobility, upward and downward mobility are all potential risk factors.
Seventh, a single measurement of white blood cells and differential cell counts may not accurately reflect long-term immune function or inflammation. However, white blood cell count is used as a marker of immune status in clinical settings and is a well-established and routinely-used marker of systemic inflammation [32]. White blood cell count is associated with disease risk and predicts disease outcome [33, 34]. Eighth, although we report associations between SEP and differential white blood cell counts, clinical significance remains to be determined. Within the normal range, elevated white blood cell counts are associated with risk factors for chronic diseases, such as cardiovascular disease [32, 35]. White blood cell counts can be conceptualised as a mixed marker of exposure and response, even a relatively small shift towards a healthier inflammation-immunological profile might have significant public health benefits at the population level [33, 34]. Ninth, acute infection, trauma and underlying chronic disease or medication could be mediators. There is no evidence to suggest that participants were experiencing infection during the assessment process, nor significant trauma. Although only those with life-threatening illness were specifically excluded, those experiencing significant acute infection or trauma were less likely to attend this study, which should have minimized any bias from this source. We also performed descriptive analysis of the data to detect and exclude outliers, which may have resulted from unknown underlying disease, medication, or recording error.
One possible explanation for the association of low SEP with inflammation is via current health behaviour linked to inflammation [5, 3638]. Although we did not perform formal tests of mediation, we did adjust for smoking, alcohol consumption, physical activity and BMI in separate models (Appendix), which had little effect among women, but among men, this attenuated the negative association of early adult SEP with white blood cell and granulocyte counts and strengthened the positive association of early adult SEP with lymphocyte counts. This suggests that any associations are unlikely to be driven by adult health behaviour in women, though these may obscure negative associations of early adult SEP with inflammatory markers in men.
Low SEP may increase exposure to pro-inflammatory agents, such as microbial pathogens, pollutants or adverse work conditions. Mechanisms for increased exposure or vulnerability to pathogens in low SEP groups include earlier and/or greater lifetime exposure due to adverse living conditions, such as overcrowding, and increased susceptibility to primary infection through nutritional deficiencies, or stress-related immune dysfunction [3]. A gender bias may have protected low SEP men from such exposures and adverse work conditions, although it is not clear why the effects should be most obvious for lymphocytes. Lower birth weight amongst those with low childhood SEP is another possible explanation, but birth weight is not available for our participants. Birth weight is inversely associated with inflammatory markers [6, 39]. However, birth weight appears to be less relevant in developing country settings such as ours [40], and there is no reason why birth weight should have sex-specific effects on some white cell sub-types.
An alternative explanation is that better early life conditions would be expected to promote development of the adaptive immune system, particularly of the thymus,[41] whose development takes place in early life [41] and which is sensitive to malnutrition, micro-nutrient deficiencies and infections during growth and development [4244]. Moreover, the same exposure would also allow upregulation of the gonadotropic axis resulting in sex-specific effects on some immune cell sub-populations [4547], particularly those relating to adaptive immunity. Consistent with this mechanism we have previously observed similar sex-specific associations, in the Guangzhou Biobank Cohort Study, of childhood stress with white cell count [48] and of childhood diet with lymphocytes but not granulocytes [19]. However, we do not have measurements that would allow proof of this mechanism.

Conclusions

Socioeconomic position was inversely associated with white blood cell differential counts, as a marker of inflammation, with a clearer and more consistent association among women than men. Environmentally and inter-generationally driven changes to the gonadotropic axis may obscure the normally protective effect of social advantage in the first few generations of men, but not women, to experience better living conditions. Given the links between the immune system, inflammation and chronic disease, this provides a biological mechanism between SEP and the pathophysiological genesis of chronic disease. Understanding such mechanisms for populations experiencing the epidemiological transition is of public health significance.

Appendix

Associations of socioeconomic position with markers of inflammation, additionally adjusted for lifestyles factors (smoking, alcohol use and physical exercise), are shown below: associations with white cell counts and granulocyte counts are shown in Table 4 and sex-specific associations with lymphocyte counts are shown in Table 5. Associations further adjusted for body mass index are shown in Table 6 (for white cell count and granulocyte count) and Table 7 (lymphocyte count). Results from the complete case analysis are shown in below in Table 8 (white cell count and granulocyte count) and Table 9 (lymphocyte count).
Table 4
Associations of high socioeconomic position compared to low at four life stages with white cell count and granulocyte count, adjusted for agea, sex and lifestyle factors in 9,981 Chinese adults (≥50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
 
White Blood Cell Count (109/L)
Granulocyte Cell Count (109/L)
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Men and Women
      
Critical Period Modelb
      
   Childhood
-0.13
(-0.19, -0.06)
8789
-0.09
(-0.14, -0.04)
3967
   Early Adult
-0.31*
(-0.38, -0.24)
8725
-0.23
(-0.29, -0.18)
3907
   Late Adult
-0.18*
(-0.27, -0.10)
8779
-0.16
(-0.22, -0.09)
3951
   Current
-0.18
(-0.25, -0.12)
8769
-0.15
(-0.20, -0.09)
3944
Accumulation Model c
  
8722
  
3901
   0
0
  
0
  
   1
-0.17
(-0.27, -0.07)
 
-0.13
(-0.21, -0.05)
 
   2
-0.28
(-0.37, -0.18)
 
-0.21
(-0.28, -0.13)
 
   3
-0.42
(-0.52, -0.32)
 
-0.32
(-0.40, -0.24)
 
   4
-0.46
(-0.59, -0.32)
 
-0.37
(-0.47, -0.26)
 
Sensitive Periods Modeld
  
8713
  
3892
   Childhood
-0.06
(-0.13, 0.01)
 
-0.04
(-0.09, 0.02)
 
   Early Adult
-0.24
(-0.32, -0.17)
 
-0.18
(-0.24, -0.12)
 
   Late Adult
-0.08
(-0.16, 0.01)
 
-0.07
(-0.14, -0.01)
 
   Current
-0.10
(-0.18, -0.03)
 
-0.08
(-0.14, -0.02)
 
Fully Saturated Modele
  
8725
  
3907
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs * Test for interaction with sex significant at p < 0.05 level
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age, sex, smoking, alcohol use, and physical activity but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age, sex, smoking, alcohol use, and physical activity. dMutually adjusted for SEP at other life stages and adjusted for age, sex, smoking, alcohol use, and physical activity. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age, sex, smoking, alcohol use, and physical activity
Table 5
Associations of high socioeconomic position (compared to low) at four life stages with lymphocyte cell count (109/L) by sex, adjusted for agea and lifestyle factors in 9,981 Chinese adults (≥50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
 
Women (n = 7,445)
 
Men (n = 2,536)
 
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Critical period Modelb
      
   Childhood
-0.03
(-0.06, -0.01)
-7876
0.03
(-0.02, 0.09)
-2354
   Early Adult
-0.08
(-0.11, -0.05)
-7894
0.07
(0.02, 0.13)
-2360
   Late Adult
-0.03
(-0.07, 0.01)
-7873
0.06
(0.003, 0.11)
-2358
   Current
-0.03
(-0.06, -0.001)
-7876
0.03
(-0.02, 0.09)
-2355
Accumulation Model c
  
-7885
  
-2357
   0
0
  
0
  
   1
-0.02
(-0.07, 0.02)
 
0.04
(-0.05, 0.12)
 
   2
-0.05
(-0.10, -0.01)
 
0.08
(0.004, 0.16)
 
   3
-0.09
(-0.13, -0.04)
 
0.08
(0.001, 0.17)
 
   4
-0.09
(-0.14, -0.03)
 
0.15
(0.03, 0.26)
 
Sensitive Periods Modeld
  
-7891
  
-2357
   Childhood
-0.02
(-0.05, 0.01)
 
0.02
(-0.03, 0.08)
 
   Early Adult
-0.07
(-0.10, -0.03)
 
0.06
(-0.01, 0.12)
 
   Late Adult
0.00
(-0.04, 0.04)
 
0.04
(-0.02, 0.10)
 
   Current
-0.01
(-0.04, 0.02)
 
0.01
(-0.05, 0.07)
 
Fully Saturated Modele
  
-7881
  
-2345
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age, smoking, alcohol use, and physical activity but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age, smoking, alcohol use, and physical activity. dMutually adjusted for SEP at other life stages and adjusted for age, smoking, alcohol use, and physical activity. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age, smoking, alcohol use, and physical activity
Table 6
Associations of high socioeconomic position (compared to low) at four life stages with white cell count and granulocyte count, adjusted for agea, sex and lifestyle factors and body mass index in 9,981 Chinese adults (≥50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008
  
White Blood Cell Count (109/L)
  
Granulocyte Cell Count (109/L)
 
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Men and Women
      
Critical period Modelb
      
   Childhood
-0.13
(-0.19, -0.06)
8248
-0.09
(-0.14, -0.04)
3675
   Early Adult
-0.31
(-0.38, -0.24)
8206
-0.24
(-0.29, -0.18)
3631
   Late Adult
-0.20
(-0.27, -0.12)
8239
-0.17
(-0.23, -0.10)
3659
   Current
-0.20
(-0.27, -0.14)
8223
-0.16
(-0.21, -0.11)
3646
Accumulation Model c
     
3611
   0
0
 
8186.0
0
  
   1
-0.19
(-0.29, -0.09)
 
-0.14
(-0.22, -0.06)
 
   2
-0.28
(-0.37, -0.18)
 
-0.21
(-0.29, -0.14)
 
   3
-0.40
(-0.50, -0.30)
 
-0.31
(-0.39, -0.23)
 
   4
-0.42
(-0.56, -0.29)
 
-0.35
(-0.45, -0.24)
 
Sensitive Periods Modeld
  
8185.9
  
3608
   Childhood
-0.06
(-0.13, 0.01)
 
-0.04
(-0.09, 0.02)
 
   Early Adult
-0.18
(-0.25, -0.10)
 
-0.14
(-0.20, -0.08)
 
   Late Adult
-0.08
(-0.16, 0.00)
 
-0.08
(-0.14, -0.02)
 
   Current
-0.12
(-0.20, -0.05)
 
-0.10
(-0.15, -0.05)
 
Fully Saturated Modele
  
8198
  
3624
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age, sex, smoking, alcohol use, and physical activity and body mass index but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age, sex, smoking, alcohol use, and physical activity and body mass index. dMutually adjusted for SEP at other life stages and adjusted for age, sex, smoking, alcohol use, and physical activity and body mass index. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age, sex, smoking, alcohol use, and physical activity
Table 7
Associations of high socioeconomic position (compared to low) at four life stages with lymphocyte cell count (109/L) by sex, adjusted for agea, lifestyle factors and body mass index in 9,981 Chinese adults(≥50 years) from the Guangzhou Biobank Cohort Study, phase3, 2006-2008
 
Women (n = 7,445)
 
Men (n = 2,536)
 
 
Δ
95% CI
AIC
Δ
95% CI
AIC
Critical period Modelb
      
   Childhood
-0.03
(-0.06, -0.01)
-8283
0.03
(-0.02, 0.09)
-2438
   Early Adult
-0.08
(-0.11, -0.05)
-8288
0.07
(0.02, 0.13)
-2443
   Late Adult
-0.03
(-0.06, 0.01)
-8278
0.05
(0.00, 0.11)
-2440
   Current
-0.03
(-0.06, -0.01)
-8281
0.03
(-0.02, 0.09)
-2438
Accumulation Model c
  
-8284
  
-2439
   0
0*
  
0*
  
   1
-0.03
(-0.08, 0.01)
 
0.02
(-0.06, 0.10)
 
   2
-0.04
(-0.08, 0.002)
 
0.07
(-0.01, 0.15)
 
   3
-0.07
(-0.12, -0.02)
 
0.07
(-0.02, 0.15)
 
   4
-0.06
(-0.12, -0.0003)
 
0.13
(0.02, 0.24)
 
Sensitive Periods Modeld
  
-8285
  
-2440
   Childhood
-0.02
(-0.05, 0.01)
 
0.02
(-0.04, 0.08)
 
   Early Adult
-0.04
(-0.07, -0.01)
 
0.06
(0.002, 0.12)
 
   Late Adult
0.01
(-0.03, 0.05)
 
0.03
(-0.03, 0.09)
 
   Current
-0.01
(-0.04, 0.02)
 
0.00
(-0.06, 0.06)
 
Fully Saturated Modele
  
-8274
  
-2428
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age, smoking, alcohol use, and physical activity and body mass index but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age, smoking, alcohol use, and physical activity and body mass index. dMutually adjusted for SEP at other life stages and adjusted for age, smoking, alcohol use, and physical activity and body mass index. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age, smoking, alcohol use, and physical activity
Table 8
Associations of high socioeconomic position (compared to low) at four life stages with white cell count and its differentials, adjusted for agea and sex Chinese adults (≥50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008 including complete cases only
  
White Blood Cell Count (109/L)
  
Granulocyte Cell Count (109/L)
 
 
N
Δ
95% CI
AIC
N
Δ
95% CI
AIC
Men and Women
        
Critical period Modelb
        
   Childhood
9683
-0.14
(-0.21, -0.07)
8769
9683
-0.10
(-0.15, -0.04)
4038
   Early Adult
9976
-0.35
(-0.42, -0.29)
8906
9976
-0.26
(-0.31, -0.21)
4018
   Late Adult
8808
-0.23
(-0.31, -0.15)
7901
8808
-0.19
(-0.25, -0.12)
3575
   Current
8332
-0.23
(-0.30, -0.16)
7446
8332
-0.17
(-0.23, -0.12)
3362
Accumulation Model c
   
6327
   
2872
   0
7117
0
  
7117
0.00
  
   1
7117
-0.19
(-0.31, -0.07)
 
7117
-0.18
(-0.28, -0.08)
 
   2
7117
-0.38
(-0.50, -0.26)
 
7117
-0.30
(-0.40, -0.21)
 
   3
7117
-0.54
(-0.66, -0.42)
 
7117
-0.43
(-0.52, -0.33)
 
   4
7117
-0.53
(-0.69, -0.38)
 
7117
-0.42
(-0.55, -0.30)
 
Sensitive Periods Modeld
   
6299
   
2851
   Childhood
7112
-0.05
(-0.13, 0.03)
 
7112
-0.03
(-0.09, 0.04)
 
   Early Adult
7112
-0.35
(-0.44, -0.26)
 
7112
-0.25
(-0.32, -0.18)
 
   Late Adult
7112
-0.07
(-0.16, 0.02)
 
7112
-0.07
(-0.14, -0.002)
 
   Current
7112
-0.12
(-0.20, -0.05)
 
7112
-0.10
(-0.16, -0.03)
 
Fully Saturated Model e
7112
  
6310
7112
  
2864
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
* Test for interaction with sex significant at p < 0.05 level
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age and sex but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age and sex. dMutually adjusted for SEP at other life stages and adjusted for age and sex. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age and sex
Table 9
Associations of high socioeconomic position (compared to low) at four life stages with lymphocyte cell count (109/L) by sex, adjusted for agea in 9,981 Chinese adults (≥50 years) from the Guangzhou Biobank Cohort Study, phase 3, 2006-2008 including complete cases only
   
Women (n = 7,445)
   
Men (n = 2,536)
 
 
N
Δ
95% CI
AIC
N
Δ
95% CI
AIC
Critical period Modelb
        
Childhood
7207
-0.04
(-0.07, -0.01)
-7619
2476
0.03
(-0.03, 0.08)
-2206
Early Adult
7443
-0.08
(-0.11, -0.05)
-7893
2533
0.03
(-0.02, 0.09)
-2274
Late Adult
6436
-0.03
(-0.07, 0.01)
-6835
2372
0.04
(-0.02, 0.09)
-2118
Current
6166
-0.04
(-0.07, -0.01)
-6589
2166
0.01
(-0.05, 0.06)
-1982
Accumulation Model c
   
-5520
   
-1783
0
 
0.00
  
1981
0.00
  
1
5136
0.02
(-0.04, 0.07)
 
1981
0.03
(-0.08, 0.13)
 
2
5136
-0.04
(-0.10, 0.01)
 
1981
0.07
(-0.03, 0.16)
 
3
5136
-0.10
(-0.15, -0.04)
 
1981
0.08
(-0.02, 0.17)
 
4
5136
-0.07
(-0.14, -0.002)
 
1981
0.07
(-0.05, 0.20)
 
Sensitive Periods Modeld
   
-5512
   
-1782
Childhood
5131
-0.03
(-0.06, 0.01)
 
1981
0.03
(-0.03, 0.09)
 
Early Adult
5131
-0.08
(-0.12, -0.04)
 
1981
-0.01
(-0.08, 0.06)
 
Late Adult
5131
0.02
(-0.03, 0.06)
 
1981
0.03
(-0.03, 0.09)
 
Current
5131
-0.01
(-0.05, 0.02)
 
1981
-0.01
(-0.06, 0.05)
 
Fully Saturated Modele
5131
  
-7222
1981
  
-7222
Abbreviations: CI Confidence Interval; AIC Akaike Information Criterion
Bold numbers indicate P < 0.05 for coefficients or best fitting model for AICs
aAge in five year age groups. bEstimates for SEP at each individual life stage adjusted for age but unadjusted for SEP at other life stages. cNumber of periods in high SEP adjusted for age. dMutually adjusted for SEP at other life stages and adjusted for age. eIncludes SEP at all four life stages and all possible interactions between all four measures of SEP. Also adjusted for age

Acknowledgements

The Guangzhou Cohort Study investigators include: Guangzhou No. 12 Hospital: WS Zhang, M Cao, T Zhu, B Liu, CQ Jiang (Co-PI); The University of Hong Kong: CM Schooling, SM McGhee, GM Leung, R Fielding, TH Lam (Co-PI); The University of Birmingham: P Adab, GN Thomas, KK Cheng (Co-PI). This work was supported by the University of Hong Kong Foundation for Development and Research, Hong Kong; The University of Hong Kong University Research Committee Strategic Research Theme Public Health, Hong Kong; Guangzhou Public Health Bureau, and Guangzhou Science and Technology Committee, Guangzhou, China; and The University of Birmingham, Birmingham, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. TM Elwell-Sutton was supported by a studentship from the Leverhulme Trust, UK.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DA West carried out the statistical analyses and wrote the article. TM Elwell-Sutton performed additional analysis and reviewed drafts of the manuscript. CM Schooling and GM Leung helped conceptualize ideas, interpret findings and review drafts of the manuscript. TH Lam, CQ Jiang and KK Cheng initiated and oversee the Guangzhou Biobank Cohort Study and WS Zhang assisted in the planning and co-ordination of the study. All authors read and approved the final manuscript.
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