NUTRITION AND IMMUNE FUNCTION IN HEALTHY OLDER WOMEN
Open Access
- Author:
- Molls, Roshni Rajendra
- Graduate Program:
- Nutrition
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 09, 2003
- Committee Members:
- Namanjeet Ahluwalia, Committee Chair/Co-Chair
Gordon C Handte, Committee Member
Terryl Johnson Hartman, Committee Member
Andrea Marie Mastro, Committee Member
Helen Smiciklas Wright, Committee Member - Keywords:
- Nutrition
immune function
aging
variation - Abstract:
- Aging is often associated with a dysregulation in the immune system. A decline in immunocompetence with advancing age, particularly in T cell functions, has been reported, even in healthy older adults. Nutrition is important for maintaining optimal immune function, as both macronutrients (energy and protein) and micronutrients (Vitamins A, E, B6 and C, iron, and zinc) can influence immune function outcomes. Nutrient deficiencies can, therefore, contribute further to the age-related decline in immune function in older individuals, and can increase the risk of infections and disease. Few studies have comprehensively examined the impact of nutrition on immune function in the healthy older adults. Most of these studies focus on correlations between levels of certain nutrients and immune response or the effect of nutrient supplements on immune function. Thus, literature on whether certain nutrients, examined concurrently, can predict immune function in older adults is very limited. Because several nutrients can influence immune function, and there are interactions between several nutrients, determining the interactive effects of nutrients on immune function is important. Therefore, we examined the relationship between specific nutrients, known to be involved in maintaining immune response, protein, iron, zinc, vitamin B12, and folate, with tests of acquired immune function in healthy older women (Chapter 2). Older women (> 60y; n=130) were recruited with assistance of the Agencies on Aging from three counties and from local housing complexes for seniors. We used discriminant analysis to identify the predictive subset of nutrients, which can correctly classify women as low and high responders on tests of acquired immune response namely, T cells and subsets, lymphocyte proliferation response to phytohemagglutinin A (PHA) and concanavalin A (Con A), and production of interleukin (IL)-1b, IL-2 and IL-6. Protein and iron status variables were identified in the predictive subset for all immune function variables examined; zinc emerged in the final predictive subset for T cells and subsets, and lymphocyte proliferation response to Con A. Vitamin B12 and folate were identified in the final predictive subset for only cytokine variables. The probability of correctly classifying women into low or high responders of immune function tests by the predictive subset of nutrition variables was high and ranged from 62.8-83.5% for T cells and subsets, 79.3-89.7% for lymphocyte proliferation response, and 77.8-88.9% for cytokines. Thus, this study shows that several nutrients namely iron, protein and zinc are significant predictors of immune function in an older cohort and maintaining the status of these nutrients may help maintain immunity in older adults. The associations between the triad of nutrition, immune function and aging are influenced by a variety of factors in study design such as health status of participants, gender, and several methodological issues concerning assessment. To establish relationships between immune function and variables of interest, it is important to determine these variables accurately and precisely. Precision relates to the amount of variability in the laboratory test. The nature and magnitude of variation in immune function tests has not been described extensively. Therefore, we examined inter- and intra-individual variation in tests of cell-mediated immunity (CMI) in generally healthy and well-nourished young (20-40y; n=15) and old (60-80y; n=15) women (Chapter 3). Subjects provided blood samples on two days within a week to determine leukocyte subsets, T-cell proliferation response to PHA and Con A, and IL-1b, IL-2 and IL-6 production. Intra-individual variation was partitioned into day-to-day biological and analytical variation. Inter-individual variation was greater than intra-individual variability for all tests of CMI for both age groups. Furthermore, in both groups, all CMI tests exhibited large day-to-day intra-individual variation (CV~15% or greater), which was primarily due to biological rather than analytical sources. In conclusion, both age groups showed large between-person and considerable within-person variation in CMI tests. Therefore, repeated blood sampling to determine immune function tests can improve precision of these measurements.