Early life socioeconomic position and immune response to persistent infections among elderly Latinos

Abstract

Persistent infections, such as cytomegalovirus (CMV), herpes simplex virus-1 (HSV-1), Helicobacter pylori (H. pylori), and Toxoplasma gondii (T. gondii), are common in the U.S. but their prevalence varies by socioeconomic status. It is unclear if early or later life socioeconomic position (SEP) is a more salient driver of disparities in immune control of these infections. Using data from the Sacramento Area Latino Study on Aging, we examined whether early or later life SEP was the strongest predictor of immune control later in life by contrasting two life course models, the critical period model and the chain of risk model. Early life SEP was measured as a latent variable, derived from parental education and occupation, and food availability. Indicators for SEP in later life included education level and occupation. Individuals were categorized by immune response to each pathogen (seronegative, low, medium and high) with increasing immune response representing poorer immune control. Cumulative immune response was estimated using a latent profile analysis with higher total immune response representing poorer immune control. Structural equation models were used to examine direct, indirect and total effects of early life SEP on each infection and cumulative immune response, controlling for age and gender. The direct effect of early life SEP on immune response was not statistically significant for the infections or cumulative immune response. Higher early life SEP was associated with lower immune response for T. gondii, H. pylori and cumulative immune response through pathways mediated by later life SEP. For CMV, higher early life SEP was both directly associated and partially mediated by later life SEP. No association was found between SEP and HSV-1. Findings from this study support a chain of risk model, whereby early life SEP acts through later life SEP to affect immune response to persistent infections in older age.

Seroprevalence of Cytomegalovirus Infection Among HIV-Infected and HIV-Uninfected Pregnant Women Attending Antenatal Clinic in Harare, Zimbabwe

“…However, our study did not find any significant association between the risk of being either anti-CMV IgG or anti-CMV IgM positive with either income or education. Education and income as markers of socioeconomic status have been linked to CMV infection, with lower income and lower level of education being associated with a higher risk of being anti-CMV seropositive (30,32). Zimbabwe has a literacy rate of *87%, ranked position 99 in the world (40), suggesting a fairly uniform and high access of written public health information.…”

“…The median age of HIV-infected women was significantly higher ( p < 0.001) (30 years, 25th-75th percentile: 25-34) compared with that of HIV-uninfected women (26 years, 25th-75th percentile: [21][22][23][24][25][26][27][28][29][30][31][32]. The median age of gestation among the pregnant women was 33 weeks (25th-75th percentile: [29][30][31][32][33][34][35][36] and was significantly higher ( p < 0.001) in the HIV-uninfected group (33 weeks, 25th-75th percentile: 30-36) compared with the HIV infected (32 weeks, 25th-75th percentile: [28][29][30][31][32][33][34][35]. The earlier presentation of HIV-infected women to clinic (measured by lower gestation age) could be due to improved healthseeking behavior induced by HIV treatment programs.…”

Epidemiology of Cytomegalovirus among pregnant women in Africa

“…Since CMV infection is transmitted through contact with infected body fluids, hygiene and hence socioeconomic status are important risk factors for CMV acquisition. The influence of education/literacy on CMV acquisition could be explained by the direct relationship between education and income which determines living conditions [59][60]. A study in Tanzania found urban residence to be a risk factor for CMV seropositivity when compared to rural residence [38].…”

How does cytomegalovirus factor into diseases of aging and vaccine responses, and by what mechanisms?

“…For example, in 2009 Simanek et al showed that educational attainment was significantly associated with seropositivity for CMVand that CMV infection was associated with CVD using data from the NHANES for individuals ages 45 years and older (Simanek et al 2009 reported that CMV seropositivity was higher among adults and children with lower household income and educational levels (using parental data on socioeconomic indicators for the children) (Dowd et al , 2012. More recently, using data from the Sacramento Area Latino study of Aging, Meier et al found that early-life socioeconomic status influences adult probability of CMV infection and that this association was mediated by measures of midlife socioeconomic status (Meier et al 2016). In addition, using data from NHANES, Feinstein et al observed that socioeconomic disparities in all-cause mortality in the US were associated with CMV infection and that this pathway was strongest among older individuals (Feinstein et al 2016).…”