Background
In long term industrialized western populations poor childhood conditions are associated with cardiovascular disease [
1], although the underlying biological pathway is unclear. Cardiovascular disease may have an inflammatory component [
2,
3]. Poor childhood conditions are also usually associated with a pro-inflammatory state [
4‐
9]. It is increasingly clear that the social patterning of cardiovascular disease or its risk factors, both traditional and non-traditional, is not universal but epidemiologic stage specific [
9‐
14]. There is often a more marked and longer lasting reversal of the usual social patterning among men than women [
9‐
11] for reasons which have tended to be interpreted mainly in terms of contemporaneous risk factors that differ by sex [
11].
A factor that countered the generally protective effect of social advantage among men at the early stages of economic development would explain the observed patterns. Inter-generationally and nutritionally driven increases in the amount, tempo and intensity of pubertal development with macro-economic improvement provide a potential explanation [
10,
15], because pubertal sex-steroids may have long-term sex-specific effects on fat patterning [
16], some lipids [
17,
18] and immune responsiveness [
19‐
21], which are detrimental among men but sometimes protective among women [
16‐
22]. There is increasing evidence from populations with a recent history of economic development, that growing up in more affluent conditions is positively associated among men, but not women, with central obesity [
10], ischemic heart disease [
23‐
26] and inflammation [
9]. However, the positive association with inflammation among men may be due to obesity [
9]. In a large sample from the developing country setting of southern China, we examined whether a marker of childhood nutrition, positively associated with cognition [
27], but sex-specifically associated with central obesity and some lipids [
28], also had sex-specific associations with inflammatory markers.
Results
Of the 10,088 participants, 9,867 (99%) had complete data on inflammatory markers, as well as age, body mass index and waist-hip ratio and were included. There were more women (7,369) than men (2,498), and the women were younger (mean age 59.2 (standard deviation 7.6)) than the men (mean age 63.2 (standard deviation 7.6)). Age ranged from 50 to 96 years, but few participants were older than 75 years.
Table
1 shows that recalled childhood meat eating was positively associated with height and socio-economic position. Table
2 shows that the associations of childhood meat eating with total white blood cell count or its differentials did not differ with age, but did differ by sex for total white blood cell count and lymphocyte count.
Table 1
Participant Characteristics by Recalled Childhood Meat Eating in 9,867 Older Chinese Men (2,498) and Women (7,369) in Phase 3 Of The Guangzhou Biobank Cohort Study (2006-8).
Age (yrs): mean (standard deviation (SD)) | 64.3 (7.6) | 61.6 (7.3) | 62.5 (7.9) | 60.7 (7.9) | 57.5 (6.8) | 57.9 (7.4) |
Height (cm): mean (SD) | 163.9 (6.0) | 164.8 (5.9) | 165.2 (6.3) | 153.4 (5.5) | 154.5 (5.4) | 154.5 (5.4) |
Body mass index: mean (SD) | 23.4 (3.2) | 23.5 (3.0) | 23.9 (3.3) | 24.1 (3.5) | 23.7 (3.3) | 24.0 (3.2) |
Waist-hip ratio: mean (SD) | 0.90 (0.07) | 0.90 (0.06) | 0.91 (0.07) | 0.86 (0.07) | 0.84 (0.07) | 0.85 (0.06) |
Childhood socio-economic position (parental possessions) #
| Low | 81.2 | 53.0 | 35.0 | 73.6 | 41.2 | 23.3 |
| Medium | 10.0 | 21.6 | 16.6 | 13.9 | 23.6 | 20.6 |
| High | 7.1 | 23.6 | 46.6 | 9.9 | 32.3 | 53.2 |
| Unknown | 1.7 | 1.8 | 1.8 | 2.6 | 3.0 | 2.9 |
Highest educational attainment | < primary | 4.0 | 0.5 | 0.7 | 17.9 | 4.1 | 3.5 |
| Primary | 32.3 | 20.3 | 17.3 | 38.6 | 22.8 | 18.6 |
| Junior middle | 32.0 | 33.0 | 24.7 | 23.0 | 30.3 | 29.0 |
| Senior middle | 20.4 | 29.1 | 33.2 | 17.0 | 34.6 | 38.5 |
| Junior college | 7.0 | 11.2 | 12.0 | 2.7 | 6.8 | 8.0 |
| College | 4.4 | 5.9 | 12.0 | 0.8 | 1.4 | 2.6 |
Smoking status | Never | 34.0 | 41.5 | 38.5 | 96.1 | 97.4 | 96.7 |
| Ex-smoker | 29.1 | 22.3 | 26.5 | 1.8 | 0.6 | 1.0 |
| Current | 35.9 | 33.9 | 34.3 | 1.5 | 1.4 | 0.9 |
| Unknown | 1.0 | 0.3 | 0.7 | 0.5 | 0.6 | 1.3 |
Table 2
Akaike Information Criterion values‡ for Models with and without Interactions by Sex and Age in 9,867 Older Chinese Men (2,498) and Women (7,369) in Phase 3 of the Guangzhou Biobank Cohort Study (2006-8).
White blood cell count | 1 | 36632 | 36634 | 9662 | 9656 | 26958 | 26946 |
| 2 | 35981 | 35980 | 9565 | 9558 | 26417 | 26404 |
Lymphocyte count | 1 | 17899 | 17901 | 4779 | 4774 | 13130 | 13111 |
| 2 | 17360 | 17365 | 4700 | 4692 | 12681 | 12663 |
Granulocyte count | 1 | 31907 | 31905 | 8610 | 8603 | 23251 | 23241 |
| 2 | 31545 | 31542 | 8568 | 8561 | 22947 | 22936 |
Table
3 shows that among men, childhood meat eating was positively associated with total white blood cell count and lymphocyte count in model 1. The associations were attenuated slightly by additional adjustment for body mass index and waist-hip ratio in model 2. In contrast, among women childhood meat eating was not clearly associated with total white blood cell count, lymphocyte count or granulocyte count.
Table 3
Adjusted Associations of Recalled Childhood Meat Eating with Inflammatory Markers in 9,867 Older Chinese Men (2,498) and Women (7,369) in Phase 3 of the Guangzhou Biobank Cohort Study (2006-8).
White blood cell count (109/L) | men | 6.8 (1.72) | 1 | reference | 0.02 | -0.13 to 0.17 | 0.33 | 0.10 to 0.56 |
| | | 2 | reference | 0.02 | -0.13 to 0.16 | 0.27 | 0.04 to 0.49 |
| women | 6.3 (1.52) | 1 | reference | -0.03 | -0.11 to 0.05 | 0.09 | -0.02 to 0.21 |
| | | 2 | reference | 0.01 | -0.07 to 0.09 | 0.09 | -0.02 to 0.20 |
Lymphocyte count (109/L) | men | 2.17 (0.65) | 1 | reference | 0.05 | -0.01 to 0.10 | 0.16 | 0.07 to 0.25 |
| | | 2 | reference | 0.04 | -0.01 to 0.10 | 0.15 | 0.06 to 0.23 |
| women | 2.18 (0.59) | 1 | reference | 0.003 | -0.03 to 0.03 | 0.04 | -0.004 to 0.09 |
| | | 2 | reference | 0.02 | -0.01 to 0.05 | 0.04 | -0.004 to 0.08 |
Granulocyte count (109/L) | men | 4.18 (1.38) | 1 | reference | -0.01 | -0.13 to 0.11 | 0.18 | -0.01 to 0.36 |
| | | 2 | reference | -0.01 | -0.13 to 0.11 | 0.14 | -0.04 to 0.32 |
| women | 3.72 (1.18) | 1 | reference | -0.02 | -0.09 to 0.04 | 0.06 | -0.04 to 0.15 |
| | | 2 | reference | 0.002 | -0.06 to 0.06 | 0.05 | -0.04 to 0.14 |
Discussion
In a large study from an understudied non-western developing population, we found that a marker of early life conditions, i.e., recalled childhood meat eating, had sex-specific associations with some inflammatory markers. More frequent childhood meat eating, adjusted for childhood socio-economic position, was positively associated with white blood cell count and lymphocyte count among men but not women.
Despite using a large study, there are caveats. First, we used self-report of childhood meat eating as a proxy for childhood nutrition, which is undoubtedly subject to measurement error. Random misclassification most likely makes the results conservative, for which our large sample size compensates. Recall bias is also possible, although the participants were unlikely to have been aware of their inflammatory status, nor is it obvious why such status should affect their recall of childhood events. As would be expected, recalled childhood meat eating was strongly positively associated with childhood socio-economic position, education and height. Second, our participants were not a randomly selected, population representative sample; however that should not affect internal associations, unless we missed people with specific combinations of childhood meat eating and inflammatory markers. Third, survivor bias is possible, in which case we would have expected differences in associations by age, of which there was no evidence. Fourth, a single measurement of total white blood cell count and its differentials might not reflect long-term immune function. However, white blood cell count is used as a marker of immune status in clinical settings. Within the normal range, white blood cell count is associated with cardiovascular mortality [
3]. Fifth, white blood cell count or its differentials could be affected by acute infection or trauma, however people in such a condition would have been unlikely to participate in our study. Finally, we did not adjust for potential confounding by diet, because of the difficulty of obtaining reliable and accurate dietary data in large-scale studies of free-living participants, particularly amongst the Chinese who share several dishes during a meal making individual intake difficult to gauge.
Childhood conditions, in long-term developed countries, are usually consistently negatively associated with markers of a pro-inflammatory state [
4‐
8]. However, a study from a similarly middle income country [
9] also found that childhood living conditions had sex-specific associations with an inflammatory marker that were less favourable among men. In the United States, early life socio-economic position has been observed to have a negative association with infectious pathogens among women but not men [
34]. Our findings, of childhood meat eating positively associated with some potential markers of a pro-inflammatory state among men but mot women, has some commonality with these later two studies [
9,
34]. Our study went further by specifically examining a measure of childhood nutrition, recalled childhood meat eating, adjusted for childhood socio-economic position.
There are several possible explanations for our observations. Childhood meat eating could be a marker of a generally unhealthy childhood diet, including fast food, trans fats and a low fruit and vegetable intake, with corresponding consequences. However, our participants spent their lives in China. Their childhood in China spanned the mid-20
th century when living standards were low, and diet was very limited with no fast food and little trans fat [
35,
36]. More frequent meat eating in our population almost certainly represents a less limited diet, which is consistent with the taller height and earlier age of menarche amongst those with more frequent recalled childhood meat eating [
27,
28].
Alternatively, we could place these observations within a theoretical hypothesis driven framework [
15,
37]. Specifically, pubertal sex-steroids are nutritionally driven [
38‐
40], either by meat eating or some other aspect of a less limited diet, with potentially long-term effects. Testosterone suppresses immune responsiveness, while estrogen may promote it [
19‐
21]. Higher sex-steroids might be expected to result in a more pro-inflammatory state among men but not women. Whether a greater impact would be expected on lymphocytes than granulocytes remains to be determined, as do any other possible consequences for immunity or auto-immunity. As such, this hypothesis potentially provides an explanation for several previous observations [
9,
34] as well as ours; nevertheless it remains speculative because pubertal testosterone is not available in this study. Moreover, our study cannot establish causality; merely provide evidence consistent with a hypothesis.
Acknowledgements
The Guangzhou Biobank 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.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
THL, CQJ and KKC initiated and oversee the Guangzhou Biobank Cohort Study, WSZ and GML assisted in the planning and co-ordination of the study. CMS designed this analysis. GML contributed to the interpretation of this analysis. CMS drafted the manuscript. All authors critically reviewed the manuscript for intellectual content. All authors read and approved the final manuscript.