The Journal of Nutrition Nutritional Epidemiology
Neighborhood Socioeconomic Status Is Associated with Serum Carotenoid Concentrations in Older, Community-Dwelling Women1,2 Emily J. Nicklett,3 Sarah Szanton,4 Kai Sun,3 Luigi Ferrucci,5 Linda P. Fried,6 Jack M. Guralnik,7 and Richard D. Semba3* 3 Johns Hopkins University School of Medicine, Baltimore, MD 21287; 4Johns Hopkins University School of Nursing, Baltimore, MD 21205; 5Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, MD 21225; 6Mailman School of Public Health, Columbia University, New York, NY 10032; and 7Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD 20892
Abstract A high dietary intake of fruit and vegetables has been shown to be protective for health. Neighborhood socioeconomic differences may influence the consumption of carotenoid-rich foods, as indicated by serum carotenoid concentrations. To test this hypothesis, we examined the relationship between neighborhood socioeconomic status (SES) and serum carotenoid concentrations in a population-based sample of community-dwelling women, aged 70–79 y, who participated in the Women’s Health and Aging Study II in Baltimore, Maryland. Neighborhood socioeconomic Z-scores were derived from characteristics of the census block of the participants. Serum carotenoid concentrations were measured at baseline and at 18, 36, 72, 98, and 108 mo follow-up visits. Neighborhood Z-scores were positively associated with serum a-carotene (P = 0.0006), b-carotene (P = 0.07), b-cryptoxanthin (P = 0.03), and lutein+zeaxanthin (P = 0.004) after adjusting for age, race, BMI, smoking, inflammation, and season. There was no significant association between neighborhood Z-score and serum lycopene. Older, community-dwelling women from neighborhoods with lower SES have lower serum carotenoid concentrations, which reflect a lower consumption of carotenoid-rich fresh fruits and vegetables. J. Nutr. 141: 284–289, 2011.
Introduction Carotenoids are major dietary antioxidants found in foods that appear to be protective of health in older adults (1). Serum carotenoids are considered the best indicator of fruit and vegetable intake (2). The 6 major serum carotenoids and the food intake they generally reflect are a- and b-carotene (carrots, orange vegetables, green leafy vegetables), lutein and zeaxanthin (green leafy vegetables, corn), b-cryptoxanthin (citrus fruits), and lycopene (tomatoes) (3,4). In older, community-dwelling adults, low serum carotenoids are associated with poor muscle strength (5) and predict decline in muscle strength (6), walking disability (7), and mortality (8,9). The availability of fresh fruit and vegetables varies considerably by neighborhood, with less availability in low-income neighborhoods (10). In Baltimore, lower income neighborhoods have relatively lower availability of healthy foods such as fresh fruit and vegetables compared with higher income neighbor-
hoods (11). Neighborhood disparities in access to healthy food may be a factor underlying health disparities in the US (12). Among U.S. adults in cross-sectional analyses, neighborhood socioeconomic status (SES) is also a predictor of fruit and vegetable intake (13) and is associated with serum carotenoid concentrations (14). We hypothesized that differences in neighborhoods may influence the consumption of fruits and vegetables, as reflected by differences in carotenoid levels. To address this hypothesis, we examined the relationship between neighborhood SES and serum carotenoids concentrations over 9 y of follow-up in a longitudinal cohort study of older, community-dwelling women. Testing this hypothesis is important, because low carotenoids levels have been shown to adversely affect health in older adults. If this hypothesis is correct, increasing consumption through improved accessibility to fruit and vegetables in disadvantaged neighborhoods may ultimately ameliorate the health and function of older adults.
1
Supported by National Institute on Aging grants R01 AG027012 and R01 AG029148 and the Intramural Research Program, National Institute on Aging, NIH. 2 Author disclosures: E. J. Nicklett, S. Szanton, K. Sun, L. Ferrucci, L. P. Fried, J. M. Guralnik, R. D. Semba, no conflicts of interest. * To whom correspondence should be addressed. E-mail:
[email protected].
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Participants and Methods Participants in this study were women aged 70–79 y who participated in the Women’s Health and Aging Study II (WHAS II), a population-based
ã 2011 American Society for Nutrition. Manuscript received July 20, 2010. Initial review completed August 30, 2010. Revision accepted November 18, 2010. First published online December 22, 2010; doi:10.3945/jn.110.129684.
study designed to evaluate the causes and course of physical disability in older women living in the community. Details of the study methods and sampling design are summarized elsewhere (15). Participants in WHAS II were recruited from an age-stratified, random sample of women selected from Medicare enrollees residing in 12 contiguous zip code areas in Baltimore, MD. Women were screened to identify self-reported physical disability that was categorized into 4 domains by report of difficulty with tasks in the following areas: 1) mobility; 2) upper extremity function; 3) higher functioning household management; and 4) self-care. WHAS II enrolled the two-thirds least disabled women, 70– 79 y old, i.e. those with either no disability or disability in only one domain. In 1994, 880 women were eligible for WHAS II, of which 436 consented to participate. Those agreeing to participate were more highly educated and reported more diseases than those who refused, but they did not differ significantly from nonparticipants in disability characteristics. Demographic characteristics, self-rated health, and other information were measured using standardized questionnaires administered at the Johns Hopkins Functional Status Laboratory. Trained technicians conducted a standardized examination that included a physical examination, assessment of height and weight, and physical performance measures. Phlebotomy was performed in 93% of participants in WHAS II. Chronic diseases were adjudicated by WHAS coinvestigators based on the questionnaire, physical examination, physician contact, and diagnostic algorithms, as published elsewhere (16). The Institutional Review Board of the Johns Hopkins University School of Medicine approved the study protocol. Written informed consent was obtained from all participants. Participants were linked to their neighborhood of residence by their home address at the time of enrollment. Census block groups were used as proxies for neighborhoods. Block groups are subdivisions of census tracts with an average of 1000 residents (17). A summary neighborhood score was used as the main indicator of the SES of the neighborhood. The summary neighborhood score was calculated according to Diez Roux et al. (18) based on 6 variables in each census block group: 1) log median household income; 2) log median value of housing units; 3) percentage of households receiving interest, dividend, or net rental income; 4) the percentage of adults 25 y of age or older who had completed high school; 5) the percentage of adults 25 y or older who had completed college; and 6) occupation (the percentage of employed persons 16 y of age or older in executive, managerial, or professional specialty occupations). We computed a Z-score for each block group for each variable. We then summed the Z-scores for each of the 6 neighborhood variables to compute the neighborhood SES index (19), which is a continuous variable. In WHAS II, the neighborhood SES index ranged from 27.28 to 10.64. The tertile cutoffs for the neighborhood SES Z-scores were 21.430 and 0.855. Blood samples from nonfasting participants were obtained by venipuncture and the samples were processed, placed on ice, and sent the same day to the central laboratory of Quest Diagnostics (formerly Corning Clinical Laboratories and MedPath) for analysis. Serum acarotene, b-carotene, b-cryptoxanthin, lutein+zeaxanthin, and lycopene were measured in the laboratory of one of the coinvestigators (R.D.S.) using HPLC (20). Within-run and between-run CV were 10.7 and 23.9% for a-carotene, 7.0 and 19.1% for b-carotene, 4.7 and 8.5% for bcryptoxanthin, 4.1 and 4.6% for lutein+zeaxanthin, and 10.0 and 14.0% for lycopene. Serum IL-6 was included as a covariate, because it is predictive of inflammation among older adults and is negatively associated with total carotenoid levels (8). IL-6 was measured using a commercial ELISA (Quantikine Human IL-6, R & D Systems). The minimum detection limit for the IL-6 ELISA reported by the manufacturer is 39 mg/L. Intra-assay and interassay CV for IL-6 measurements were 4 and 6%, respectively. Serum carotenoid concentrations were measured at baseline in 408 of 436 women in WHAS II. Neighborhood SES Z-scores were available for 380 of the 408 women with serum carotenoid measurements at baseline. At the 18, 36, 72, 98, and 108 mo follow-up visits, there were 1, 4, 31, 15, and 15 women, respectively, who moved from their original address to other locations. Some women moved into institutional settings, such as nursing homes and retirement facilities. Carotenoid measurements were excluded from the analyses for any follow-up visits of participants
who moved from their original neighborhood, because the neighborhood SES Z-score would be less relevant to access to carotenoid-rich foods in institutional settings. Serum carotenoid measurements were available at baseline and follow-up at 18, 36, 72, 98, and 108 mo in 380, 328, 278, 299, 213, and 177 participants, respectively. Thus, in total, the study involved 1605 measurements of serum carotenoids. The intra-class correlation coefficients for a-carotene, b-carotene, b-cryptoxanthin, lutein+zeaxanthin, and lycopene measured at baseline and follow-up visits were 0.89, 0.85, 0.82, 0.78, and 0.82, respectively. Means and SD are reported for continuous variables. Log transformation was used to normalize variables that had a skewed distribution, such as individual serum carotenoids and IL-6 measurements. We used multivariable random effects linear mixed models to examine the relationship between neighborhood SES Z-score and individual serum carotenoids measured at baseline, 18, 36, 72, 98, and 108 mo (a total of 9 y of follow-up from baseline). No significant neighborhood Z-score and time interactions were found in the individual models. All analyses were performed using SAS (v. 9.1.3, SAS Institute) with a type I error of 0.05.
Results The sample characteristics and serum carotenoid concentrations of the study women are shown (Table 1). The geometric mean carotenoid concentrations at baseline and over 9 y of follow-up are shown according to tertile of neighborhood SES Z-score adjusted by age, race, BMI, current smoking, season, IL-6, and time (Fig. 1A). Serum a-carotene, lutein+zeaxanthin, and b-carotene had a significant trend across tertiles of neighborhood Z-score. Serum b-cryptoxanthin showed a trend across tertiles of marginal significance (P = 0.06). No significant relationship was found between serum lycopene and tertile of neighborhood Z-score. To examine the relationship between individual SES and serum carotenoid concentrations, we also characterized the relationship between geometric mean carotenoid concentrations by tertile of individual household income (Fig. 1B). The tertile TABLE 1
Characteristics of 408 participants, aged 70–79 y, in the WHAS II in Baltimore, MD1
Characteristic Age, y Race, % White Black Education, y Income, U.S.$/y Current smoker, % BMI, kg/m2 Neighborhood Z-score Log a-carotene, mmol/L Log b-carotene, mmol/L Log b-cryptoxanthin, mmol/L Log lutein+zeaxanthin, mmol/L Log lycopene, mmol/L Log IL-6, ng/L Total cholesterol, mmol/L Season, % Winter Spring Summer Autumn 1
73.8 (2.8) 81.1 18.9 12.5 (3.3) 24,974 (20,938) 10.0 26.7 (5.1) 0.16 (2.59) 22.39 (0.80) 20.87 (0.84) 22.01 (0.70) 20.92 (0.48) 20.51 (0.56) 1.12 (0.59) 6.07 (0.96) 23.8 27.2 22.8 26.2
Data are mean 6 SD or %, n = 408.
Neighborhoods and carotenoids in women
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for age (model 1), additionally for race, BMI, and current smoking (model 2), additionally for season (model 3), and finally, by adding baseline IL-6 (model 4). In the fully adjusted models for individual carotenoids, neighborhood Z-score was positively associated with a-carotene (P = 0.0006), b-carotene (P = 0.07), b-cryptoxanthin (P = 0.03), and lutein+zeaxanthin (P = 0.004). The neighborhood Z-score was negatively but not significantly associated with lycopene (P = 0.46). In each of the fully adjusted models (model 4), BMI was negatively and significantly associated with each of the respective carotenoids. Current smoking was also negatively and significantly associated with each of the respective carotenoids. No significant association was found between age and individual carotenoids or between IL-6 and individual carotenoids. In the final models (model 4), women who identified themselves as white had lower serum b-carotene (P = 0.0006), b-cryptoxanthin (P = 0.03), and lutein+zeaxanthin (P , 0.0001) and higher serum lycopene (P , 0.0001) compared with women who identified themselves as black. Race was not significantly associated with a-carotene. In an alternative model, we also adjusted for total cholesterol in addition to the variables in model 4. These results were similar: a-carotene, b = 0.043 (P = 0.0008); b-carotene, b = 0.023 (P = 0.09); b-cryptoxanthin, b = 0.024 (P = 0.03); lutein +zeaxanthin, b = 0.012 (P = 0.005); and lycopene, b = 20.008 (P = 0.41).
Discussion
FIGURE 1 Serum carotenoid concentrations by tertile of neighborhood SES Z score (A) or household income (B) in older, communitydwelling women. Values are geometric means, adjusted for age, race, BMI, current smoking, baseline serum IL-6, season for each study visit, and time of study visit. (A) P-values for linear trends across tertiles: a-carotene (0.0007), b-cryptoxanthin (0.04), lutein+zeaxanthin (0.008), b-carotene (0.02), and lycopene (0.46). (B) P-values for linear trend across tertiles: a-carotene (0.74), b-cryptoxanthin (0.22), lutein +zeaxanthin (0.24), b-carotene (0.88), and lycopene (0.11).
cutoffs for income levels were $12,000 and $27,000. After adjusting for age, race, BMI, current smoking, season, IL-6, and time, none of the individual carotenoids were significantly associated with tertile of income. Separate multivariable linear mixed models were used to examine the relationship between neighborhood Z-score and individual serum carotenoids measured at baseline and over 9 y of follow-up (Table 2). Three basic models were used adjusting 286
Nicklett et al.
The results of this study suggest that high neighborhood SES, as reflected by the neighborhood Z-score, is positively associated with serum a-carotene, b-carotene, b-cryptoxanthin, and lutein +zeaxanthin measured over a 9-y period. No significant association was found between neighborhood SES and serum lycopene. As mentioned previously, serum carotenoids are considered the best indicator of fruit and vegetable intake (2). Foods that are rich in a- and b-carotene, b-cryptoxanthin, lutein, and zeaxanthin, with various levels of overlapping, include oranges, kiwi fruit, papaya, cantaloupe, carrots, bell peppers, dark green leafy vegetables, corn, and pumpkin. To our knowledge, this is the first study to examine the relationship between neighborhood SES and longitudinal measurements of serum carotenoids. These findings are consistent with a previous cross-sectional study from the NHANES III, 1988–1994, in which neighborhood SES was significantly associated with serum a-carotene, b-carotene, b-cryptoxanthin, and lutein+zeaxanthin but not with serum lycopene (14). Lycopene may be found in high concentrations in canned foods (canned tomatoes, sauces, and paste) as well as fresh foods and is not as restricted to fresh food availability and consumption (21). The findings of this study are also consistent with an emerging field of research on geographic disparities in access to quality foods (12). These disparities are most strongly related to access to fruits and vegetables and other healthy foods (12,22–24). Neighborhoods with lower SES tend to have fewer large grocery stores available and instead have smaller stores with fewer healthy foods (25–28). In contrast to most of the previous studies on racial disparities in fruit and vegetable consumption and access (28–31), we found that being non-white was positively associated with higher levels of serum carotenoid levels. Women who self-identified as black generally had higher levels of serum carotenoids relative to white women, with the exception of lycopene. In NHANES III, black women had higher
TABLE 2
Separate multivariable linear regression models for neighborhood Z-score and other variables with individual serum carotenoids over a 9-y period1 Model 1, adjusted for age b
Log a-carotene Neighborhood Z-score 0.071 Age, y 0.019 Race (white) BMI, kg/m2 Current smoking Log IL-6, ng/L Log b-carotene Neighborhood Z-score 0.026 Age, y 0.008 Race (white) BMI, kg/m2 Current smoking Log IL-6, ng/L Log b-cryptoxanthin Neighborhood Z-score 0.031 Age, y 0.010 Race (white) BMI, kg/m2 Current smoking Log IL-6, ng/L Log lutein+zeaxanthin Neighborhood Z-score 0.019 Age, y 0.003 Race (white) BMI, kg/m2 Current smoking Log IL-6, ng/L Log lycopene Neighborhood Z-score 0.015 Age, y 20.005 Race (white) BMI, kg/m2 Current smoking Log IL-6, ng/L 1
Model 2, adjusted for age, race, BMI, current smoking
Model 3, adjusted for age, race, BMI, current smoking, season
Model 4, adjusted for age, race, BMI, current smoking, season, IL-6
Mean 6 SE
P
b
Mean 6 SE
P
b
Mean 6 SE
P
b
Mean 6 SE
P
0.012 0.011
,0.0001 0.08
0.044 0.007 0.081 20.048 20.462
0.012 0.011 0.085 0.006 0.111
0.0006 0.50 0.34 ,0.0001 ,0.0001
0.044 0.010 0.100 20.048 20.469
0.013 0.011 0.085 0.006 0.111
0.0005 0.36 0.24 ,0.0001 ,0.0001
0.043 0.008 0.088 20.048 20.485 20.011
0.013 0.011 0.085 0.007 0.113 0.054
0.0007 0.46 0.31 ,0.0001 ,0.0001 0.83
0.013 0.011
0.05 0.45
0.026 0.009 20.315 20.040 20.430
0.013 0.011 0.090 0.007 0.118
0.05 0.45 0.0005 ,0.001 0.0003
0.025 0.012 20.306 20.041 20.415
0.013 0.011 0.090 0.007 0.118
0.06 0.31 0.0007 ,0.0001 0.0004
0.025 0.011 20.312 20.040 20.410 20.081
0.013 0.011 0.090 0.007 0.119 0.057
0.07 0.34 0.0006 ,0.0001 0.0006 0.16
0.011 0.009
0.003 0.31
0.026 0.004 20.169 20.018 20.437
0.011 0.009 0.076 0.006 0.099
0.02 0.67 0.03 0.002 ,0.0001
0.025 0.007 20.157 20.018 20.422
0.011 0.010 0.076 0.006 0.099
0.03 0.49 0.04 0.003 ,0.0001
0.027 0.005 20.170 20.016 20.413 20.080
0.011 0.009 0.076 0.006 0.100 0.048
0.02 0.55 0.03 0.004 ,0.0001 0.10
0.007 0.006
0.009 0.58
0.023 0.001 20.302 20.018 20.276
0.007 0.006 0.048 0.004 0.063
0.001 0.88 ,0.0001 ,0.0001 ,0.0001
0.022 0.001 20.297 20.018 20.274
0.007 0.006 0.048 0.004 0.063
0.002 0.87 ,0.0001 ,0.0001 ,0.0001
0.021 0.002 20.302 20.017 20.259 20.051
0.007 0.006 0.048 0.004 0.064 0.031
0.003 0.71 ,0.0001 ,0.0001 ,0.0001 0.09
0.010 0.009
0.14 0.60
20.007 20.012 0.347 20.002 20.290
0.010 0.009 0.070 0.005 0.091
0.48 0.17 ,0.0001 0.63 0.001
20.008 20.011 0.352 20.002 20.284
0.010 0.009 0.069 0.005 0.091
0.42 -0.21 ,0.0001 0.65 0.002
20.007 20.009 0.343 20.002 20.276 20.052
0.010 0.008 0.069 0.005 0.092 0.044
0.46 0.28 ,0.0001 0.61 0.003 0.23
Data are linear regression coefficients, mean 6 SE, and P-values for models 1–4, n = 408 participants.
serum lutein+zeaxanthin concentrations compared with white women, and white women had higher serum lycopene concentrations than black women (32). In the present study, individual household income was not significantly associated with serum carotenoid concentrations, in contrast to neighborhood SES score. These findings suggest that the socioeconomic levels or resources available to neighborhoods are stronger determinants of serum carotenoid concentrations, i.e. fruit and vegetable intake, than household income alone. The strengths of this study are that it involved a populationbased sample of older, community-dwelling women who were followed for 9 y with high follow-up and with multiple measurements of serum carotenoids. The analyses were carefully adjusted for factors that are known to affect serum carotenoid concentrations, such as BMI (33), current smoking (34), season (35), and inflammation (36). Census block data, in contrast with census tract data, were used to calculate the summary neighborhood score, which captures multiple socioeconomic aspects of a more focused subdivision of a census tract (19).
This study is limited in that it involved only older women who were representative of the two-thirds least disabled women in the community. The women were all in their 70s. It is not known whether the findings could be extrapolated to men or more generally to other women in a wider age range. A limitation of the present study is that baseline measures of access to stores or vendors offering fresh fruit and vegetables were not available; however, previous studies have shown a strong relationship between low neighborhood SES and more limited availability of fresh fruits and vegetables. The census block is used as a proxy for neighborhood and this measure may not necessarily capture all features of a neighborhood community. Other features of neighborhoods with low SES levels could potentially limit access to fresh fruits and vegetables, such as higher levels of crime, unwalkable streets, or lack of public transportation rather than the availability of fresh fruits and vegetables alone. Further, changes in the SES of the neighborhood over the study period are not examined here. As with any epidemiological study, there may be unmeasured factors that Neighborhoods and carotenoids in women
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confound the relationship between neighborhood SES and serum carotenoids. There may be features of Baltimore City itself that may limit the generalizability of the findings; however, the relationship between neighborhood SES and the availability of fruits and vegetables has been described in other American cities (10). Private charities and other entities, such as churches and community organizations, have long recognized the benefit of providing locally available fruits and vegetables, particularly in socially disadvantaged communities (37). More recently, the effectiveness of these efforts and government-sponsored interventions has been evaluated in prospective research studies. Such interventions include the establishment of community gardens and promotion or subsidization of home gardening and/or local farmer’s markets (38,39). These interventions were found to be most effective when food affordability is addressed (in addition to availability) and when interventions take a more grass-roots, community-driven approach (39–42). In addition to providing more accessible fruits and vegetables to deprived communities, such programs also could have the benefit of greening, reducing crime, and increasing social interaction, which all have positive effects on health and are protective against chronic illness (38,42,43). Further research should address the linkages between changes in serum carotenoid levels and changes in neighborhood health outcomes over time through natural experiments or intervention research. In conclusion, among older women living in the community, low neighborhood SES is associated with lower serum carotenoid concentrations. These findings suggest that the consumption of carotenoid-rich fruit and vegetables is lower in poorer neighborhoods. Improving accessibility to healthy foods in these neighborhoods may beneficially affect the health and function of the older population.
7.
Acknowledgments E.J.N., S.S., and R.D.S. designed research; L.P.F., E.J.N., S.S., and R.D.S. conducted the research; L.P.F., J.M.G., and S.S. provided essential materials; K.S., E.J.N., S.S., and R.D.S. analyzed data; E.J.N., S.S., R.D.S., and L.F. wrote the paper; and E.J.N. and R.D.S. had primary responsibility for final content. All authors read and approved the final manuscript.
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