Abstract
Background
Chronic inflammation is associated with AIDS-defining and non-AIDS–defining conditions. Limited research has considered how food insecurity influences chronic inflammation among people living with human immunodeficiency virus (HIV). We examined whether food insecurity was associated with higher levels of inflammation among women living with HIV (WWH) in the United States.
Methods
We analyzed cross-sectional data collected in 2015 from 421 participants on antiretroviral therapy from the Women’s Interagency HIV Study. The exposure was any food insecurity. The outcome was inflammation, measured by proinflammatory cytokine interleukin-6 (IL-6) and tumor necroses factor receptor 1 (TNFR1) levels. We conducted multivariable linear regressions, adjusting for sociodemographic, clinical, and nutritional factors.
Results
Nearly one-third of participants (31%) were food insecure and 79% were virally suppressed (<20 copies/mL). In adjusted analyses, food insecurity was associated with 1.23 times the level of IL-6 (95% confidence interval [CI], 1.06–1.44) and 1.13 times the level of TNFR1 (95% CI, 1.05–1.21). Findings did not differ by HIV control (virally suppressed with CD4 counts ≥500 cells/mm3 or not) in adjusted stratified analyses.
Conclusion
Food insecurity was associated with elevated inflammation among WWH regardless of HIV control. Findings support the need for programs that address food insecurity among WWH.
Keywords: food insecurity, chronic inflammation, HIV, inflammatory cytokines, women living with HIV
Among a sample of women living with HIV in the United States, food insecurity was associated with elevated levels of proinflammatory cytokines interleukin-6 and tumor necrosis factor receptor 1, regardless of HIV control.
Chronic inflammation among people living with human immunodeficiency virus (PWH) is associated with AIDS-defining conditions, including opportunistic infections and human immunodeficiency virus (HIV)-related morbidity and mortality, as well as non-AIDS–defining conditions such as cardiovascular disease [1–3]. In PWH, persistent immune activation and inflammation leads to a vicious cycle where proinflammatory cytokines promote further T-cell activation, which, in turn, leads to further HIV replication. Eventually this cycle contributes to increased T-cell apoptosis and the eventual exhaustion of the immune system in PWH [1]. Proinflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor receptor 1 (TNFR1) have been shown to be intricately linked to HIV infection [1, 4–6]. Both IL-6 and TNFR1 are secreted as a result of immune activation in HIV infection, and PWH have significantly higher plasma levels of these inflammatory markers compared to uninfected individuals, even in the context of viral suppression [1, 4–6]. High levels of IL-6 and TNFR1 have been associated with HIV progression and non-AIDS–defining conditions among PWH [5–7].
Limited research has considered how social and structural factors may influence chronic inflammation among PWH. Food insecurity may be one such factor. Food insecurity refers to having limited or uncertain availability of nutritionally adequate and safe food, or the inability to procure food in socially acceptable ways [8], and is associated with increased morbidity [9, 10] and mortality [11, 12] among PWH. Evidence from the general population in the United States suggests that food insecurity is associated with elevated levels of C-reactive protein [13, 14], an inflammatory marker, which has been linked to chronic diseases such as cardiovascular disease [15]. To our knowledge, no study has examined the relationship between food insecurity and inflammation among PWH. This warrants particular attention because PWH are disproportionately affected by food insecurity in the United States; an estimated 50%–60% of PWH are considered food insecure [16, 17]. Women living with HIV (WWH) may be at even greater risk for food insecurity given that nationally, female-headed households experience significantly higher rates of food insecurity than households in general (30% vs 13%) [18]. This study aimed to examine whether food insecurity was associated with higher levels of proinflammatory cytokines IL-6 and TNFR1 among a sample of WWH in the United States.
METHODS
Study Design and Population
The Women’s Interagency HIV Study (WIHS) is a large, multisite, prospective cohort study of WWH and demographically similar controls in the United States, established in 1993. Biological, clinical, demographic, and behavioral data are collected semiannually through interviews, physical exams, and laboratory tests. Participants provide signed informed consent at each visit and are compensated for participation in the study.
Beginning in 2013, the Food Insecurity Substudy added measures of food security and dietary intake to WIHS biannual interviews. The present study was a cross-sectional investigation of HIV-positive participants enrolled in the substudy from 10 sites across the United States (Bronx, NY; Brooklyn, NY; Washington, DC; San Francisco, CA; Chicago, IL; Chapel Hill, NC; Atlanta, GA; Miami, FL; Birmingham, AL; and Jackson, MI). Data were collected from April through September 2015. We identified 896 women who were eligible for inclusion in the analysis because they were HIV seropositive, on antiretroviral therapy (ART), had stored peripheral blood mononuclear cell and fasting plasma samples, as well as food security data available at the visit. To improve the homogeneity of the study sample, we excluded 475 women with: (1) confirmed cancer (n = 48); (2) self-reported autoimmune diseases (n = 60); or (3) active or inactive hepatitis B or C (n = 377) to keep the study sample limited to those with HIV monoinfection. The final study sample size was 421. This analytic sample is similar to the rest of the cohort in terms of age, race, and income, but they were less likely to use illicit drugs since the last visit (4% vs 11%) and less likely to be current cigarette smokers (31% vs 44%).
Laboratory Methods
Standard commercially available enzyme-linked immunosorbent assay (ELISA) kits were used to assess inflammation via proinflammatory cytokines IL-6 (R&D Systems, HS600B) and TNFR1 (R&D Systems, DRT100) at Columbia University’s Irving Institute for Clinical and Translational Research, Biomarkers Core lab. All samples were stored in a −80°C freezer and assays were performed in triplicate with a positive and negative control on each ELISA plate.
Measures
Outcome variables were 2 measures of immune inflammation: proinflammatory cytokines, IL-6 and TNFR1. These variables were transformed using the natural logarithm to fulfill the assumption of a normal distribution necessary for linear regressions.
The exposure was household food insecurity over the past 6 months, assessed using the validated 18-item United States Department of Agriculture Household Food Security Survey Module (HFSSM) [8]. The HFSSM measures uncertainty about food supplies, insufficient diet quality, and insufficient food quantity. For households with 1 or more children, the raw scores for high, marginal, low, and very low food security are zero, 1–2, 3–7, and 8–18, respectively [8]. For households with no children the raw scores for high, marginal, low, and very low food security are zero, 1–2, 3–5, and 6–10, respectively [8]. A binary variable was created to capture any food insecurity (defined as marginal, low, and very low food security), compared to no food insecurity (ie, high food security). Cronbach alpha for the HFSSM in this sample was 0.91, indicating high internal consistency.
Control variables were selected based on prior research on factors associated with food insecurity and IL-6 and TNFR1. Control variables were age at visit (per 10 years), average annual household income (<$12000, $12001–$24000, or ≥$24001), race/ethnicity (non-Hispanic white, Hispanic, Black/African American, or other), education (≥ high school education or equivalent, compared to less than high school education), recent illicit substance use, and being a current smoker (vs not). Substance use was defined as self-reported cocaine, crack, heroin, methamphetamine, hallucinogens, club drugs, nonprescribed narcotics, or any other illicit recreational drugs, excluding any form of marijuana, in the last 6 months or since their last visit. Substance use is associated with increased inflammation [19] and food insecurity [20].
HIV-related clinical factors (viral load and CD4 count) and nutritional factors (body mass index [BMI], fat intake, sugar intake, red and processed meat intake, and fruit and vegetable intake) may mediate the relationship between food insecurity and inflammation, or they could confound this relationship. For example, viral load has been associated with both food insecurity [21] and inflammatory markers IL-6 and TNFR1 [5, 22], as has CD4 cell count [5, 11]. Nutritional factors such as BMI, fat, sugar, red and processed meat, and fruit and vegetable intake have also been associated with both food insecurity and clinical markers of inflammation [5, 23–26]. Because neither the HIV-related clinical factors nor the nutritional factors were significantly associated with both the exposure and outcomes in this sample, they were included in our models as control variables. Viral load was a continuous measure (copies/mL), which was transformed using the logarithm base 10. CD4 count was also a continuous variable (cells/mm3). BMI was a continuous measure in kg/m2 (analyzed per every 3 kg/m2 to facilitate interpretation). An adapted version of the multifactor screener from the 2000 National Health Interview Survey was used to measure intake of fat, sugar, red and processed meats, and fruits and vegetables [27]. All intake measures were continuous (analyzed per every 3 servings a day to facilitate interpretation). Daily servings of fat comprised the number of servings of red meat, processed meat (eg, hot dogs/cold cuts), dessert (eg, donuts/cakes/pastries), butter/margarine/full-fat salad dressing or mayonnaise, whole milk, and French fries. Daily servings of sugar measured sugar intake from 3 sources: sugar-sweetened beverages, desserts, and fruit juices. Daily servings of meat was a combined measure of red meat and processed meats. Daily servings of fruits and vegetables comprised the number of servings of fruit (not including juice) and vegetables or green salad.
Analysis
Summary statistics were analyzed on all study participants for all control variables. Separate bivariate linear regression models assessed the associations of food insecurity and control variables with the 2 outcome variables. Control variables were included in the multivariable model based on a priori knowledge of their potentially confounding relationship with food insecurity and clinical markers of inflammation, and if they were significant at the P < .1 level in the bivariate models. To assess multicollinearity, variance inflation factors were assessed for each model using a postestimation command in Stata. For the adjusted model, variance inflation factors were low, ranging from 1.3 to 1.4, indicating little to no multicollinearity. Given that the outcome variables were log transformed, the regression coefficients were exponentiated and interpreted as the relative difference in the outcome compared to the referent group. As an additional sensitivity analysis, we conducted the adjusted analysis stratified by HIV control; HIV control was defined as virally suppressed (<20 copies/mL) and CD4 ≥ 500 cells/mm3.
This research was conducted as part of the WIHS and was approved by the Institutional Review Boards of all the WIHS study sites. All studies were conducted in accordance with the principles outlined in the Declaration of Helsinki.
RESULTS
Sample Characteristics
The median age of the study sample was 47 years (interquartile range [IQR], 40–52; Table 1). The majority of the study sample (n = 331, 79%) was Black/African American, and 71% (n = 298) had obtained a high school education or higher. Over half (n = 211, 52%) had an average annual household income of $12000 or less and 31% (n = 129) reported being food insecure. The median BMI of the study sample was 31 (IQR, 27–39), which is in the obese range, and 31% (n = 130) were current smokers. Over three-quarters of the study sample (n = 327, 79%) was virally suppressed, and 70% (n = 292) had a CD4 count of greater than or equal to 500 cells/mm3. Median IL-6 and TNFR1 values were 1.57 pg/mL (IQR, 1.02–2.64) and 730.99 pg/mL (IQR, 601.99–912.11), respectively.
Table 1.
Background Characteristics of Women Living With HIV in the Women’s Interagency HIV Study (N = 421)
| Characteristic | Value |
|---|---|
| Any food insecurity, n (%) | 129 (31) |
| Age, y, median (IQR) | 47 (40–52) |
| Race, n (%) | |
| White | 31 (7) |
| Hispanic | 46 (11) |
| Black/African American | 331 (79) |
| Other | 12 (3) |
| Annual household income, n (%) | |
| ≤$12000 | 211 (52) |
| $12001–$24000 | 97 (24) |
| ≥$24001 | 102 (25) |
| High school education or more, n (%) | 298 (71) |
| Any illicit substance use since last visit, n (%) | 18 (4) |
| Current smoker, n (%) | 130 (31) |
| Virally suppressed, n (%) | 327 (79) |
| CD4 ≥500 cells/mm3, n (%) | 292 (70) |
| BMI kg/m2, median (IQR) | 31 (27–39) |
| Intake of high-fat foods, servings, median (IQR) | 2 (1–3) |
| Intake of sugar, servings, median (IQR) | 1 (0.4–2) |
| Intake of red and processed meats, servings, median (IQR) | 3 (2–5) |
| Intake of fruit and vegetables, servings, median (IQR) | 1 (0.8–2) |
Abbreviation: IQR, interquartile range.
Association Between Food Insecurity and Inflammation
In the unadjusted analyses (Table 2 and Table 3, column 2), any food insecurity was significantly associated with 1.33 times the level of IL-6 (95% confidence interval [CI], 1.14–1.55) and 1.14 times the level of TNFR1 (95% CI, 1.06–1.23). Among women reporting food insecurity, median IL-6 and TNFR1 values were 1.83 pg/mL (IQR, 1.26–3.05) and 765.52 pg/mL (IQR, 655.84–982.21), respectively. The median values of IL6 and TNFR1 among women who were food secure were 1.39 pg/mL (IQR, 0.93–2.48) and 718.16 pg/mL (IQR, 585.60–879.44), respectively. Women of Black/African American race/ethnicity (compared to non-Hispanic white) had significantly higher levels of IL-6, while women of Black, Hispanic, and other races/ethnicities had lower levels of TNFR1 compared to non-Hispanic whites. Compared to those who earned an average annual household income of $12000 or less, those who earned between $12001–$24000 and $24001 or more had lower IL-6. Earning an average household income of $24001 or more was significantly associated with lower levels of TNFR1 (relative difference 0.92, 95% CI, 0.84–0.99). Greater viral load and BMI were also associated with significantly higher IL-6 and TNFR1. Intakes of sugar, red and processed meat, and fruit and vegetables were not significantly associated with either inflammatory marker.
Table 2.
Unadjusted and Adjusted Associations Between Food Insecurity and IL-6 Among Women Living with HIV in the Women’s Interagency HIV Study (N = 421)
| Characteristic | Unadjusted Relative Difference (95% CI)a | Adjusted Relative Difference (95% CI)b |
|---|---|---|
| Any food insecurity | 1.33 (1.14–1.55)e | 1.23 (1.06–1.44)e |
| Age at visit, per 10 years | 1.08 (0.99–1.17) | 1.15 (1.05–1.25)d |
| High school education or more | 1.01 (0.86–1.18) | 1.08 (0.92–1.26) |
| Race/ethnicity | ||
| White | (reference) | (reference) |
| Hispanic | 1.26 (0.89–1.79) | 1.26 (0.91–1.75) |
| Black/African American | 1.40 (1.06–1.85)c | 1.22 (0.94–1.59) |
| Other | 1.10 (0.67–1.83) | 1.02 (0.62–1.68) |
| Annual household income | ||
| ≤$12000 | (reference) | (reference) |
| $12001–$24000 | 0.77 (0.64–0.92)d | 0.79 (0.67–0.95)c |
| ≥$24001 | 0.74 (0.62–0.88)d | 0.83 (0.69–0.99)c |
| Illicit substance use | 0.93 (0.65–1.34) | 0.99 (0.69–1.42) |
| Current smoker | 1.06 (0.91–1.24) | 0.05 (0.89–1.23) |
| Viral load, log10 | 1.12 (1.03–1.21)d | 1.11 (1.02–1.19)c |
| CD4 cells/mm3 | 1.00 (1.00–1.00) | … |
| BMI, per 3 kg/m2 | 1.09 (1.07–1.11)e | 1.09 (1.06–1.11)e |
| Intake of high-fat foods, servings per day, per 3 | 1.16 (1.03–1.30)c | 1.14 (0.99–1.32) |
| Intake of sugar, servings per day, per 3 | 1.13 (0.98–1.31) | 1.08 (0.89–1.29) |
| Intake of red and processed meat, servings per day, per 3 | 0.96 (0.89–1.03) | … |
| Intake of fruits and vegetables, servings per day, per 3 | 0.96 (0.77–1.17) | … |
Abbreviations: BMI, body mass index; CI, confidence interval; HIV, human immunodeficiency virus; IL-6, interleukin-6.
aThe natural logarithm of IL-6 was used to satisfy the assumption of a normal distribution. The relative differences are natural exponential e of the regression coefficients, and are interpreted as multiplicative factors.
bThis multivariable linear regression model examined the relationship between food insecurity and IL-6 adjusting for age, education, race/ethnicity, annual household income, illicit substance use, current smoker, viral load, BMI, intake of high fat food, and intake of sugar.
c P < .05.
d P < .01.
e P < .001.
Table 3.
Unadjusted and Adjusted Associations Between Food Insecurity and TNFR1 Among Women Living With HIV in the Women’s Interagency HIV Study (N = 421)
| Characteristic | Unadjusted Relative Difference (95% CI)a | Adjusted Relative Difference (95% CI)b |
|---|---|---|
| Any food insecurity | 1.14 (1.06–1.23)e | 1.13 (1.05–1.21)d |
| Age at visit, per 10 years | 1.03 (0.99–1.07) | 1.05 (1.01–1.09)c |
| High school education or more | 1.04 (0.96–1.12) | 1.07 (0.99–1.15) |
| Race/ethnicity | ||
| White | (reference) | (reference) |
| Hispanic | 0.80 (0.68–0.94)d | 0.78 (0.67–0.91)d |
| Black/African American | 0.85 (0.75–0.98)c | 0.81 (0.71–0.92)e |
| Other | 0.78 (0.61–0.99)c | 0.76 (0.59–0.97)c |
| Annual household income | ||
| ≤$12000 | (reference) | (reference) |
| $12001–$24000 | 1.05 (0.96–1.14) | 1.08 (0.99–1.18) |
| ≥$24001 | 0.92 (0.84–0.99)c | 0.94 (0.87–1.03) |
| Illicit substance use | 1.03 (0.86–1.22) | 0.99 (0.83–1.17) |
| Current smoker | 1.07 (0.99–1.16) | 1.10(1.02–1.19)c |
| Viral load, log10 | 1.05 (1.01–1.10)d | 1.06 (1.02–1.10)d |
| CD4 cells/mm3 | 1.00 (1.00–1.00) | … |
| BMI, per 3 kg/m2 | 1.04 (1.03–1.05)e | 1.04 (1.03–1.05)e |
| Intake of high-fat foods, servings per day, per 3 | 1.03 (0.97–1.09) | … |
| Intake of sugar, servings per day, per 3 | 1.02 (0.09–1.09) | … |
| Intake of red and processed meat, servings per day, per 3 | 1.02 (0.98–1.06) | … |
| Intake of fruits and vegetables, servings per day, per 3 | 0.98 (0.88–1.08) | … |
Abbreviations: BMI, body mass index; CI, confidence interval; HIV, human immunodeficiency virus; TNFR1, tumor necroses factor receptor 1.
aThe natural logarithm of TNFR1 was used to satisfy the assumption of a normal distribution. The relative differences are natural exponential e of the regression coefficients, and are interpreted as multiplicative factors.
bThis multivariable linear regression model examined the relationship between food insecurity and TNFR1 adjusting for age, education, race/ethnicity, annual household income, illicit substance use, current smoker viral load, and BMI.
c P < .05.
d P < .01.
e P < .001.
In the multivariable models (Table 2 and Table 3, column 3), food insecurity was significantly associated with 1.23 times the level of IL-6 (95% CI, 1.06–1.44) and 1.13 times the level of TNFR1 (95% CI, 1.05–1.21).
Age was significantly associated with higher levels of IL-6 (relative difference 1.15; 95% CI, 1.05–1.25) and TNFR1 (relative difference 1.05; 95% CI, 1.01–1.09). Women of Hispanic, Black/African-American, and other race/ethnicity all had significantly lower TNFR1 levels compared to non-Hispanic whites. Earning an average annual household income of $12001–$24000 and $24001 or more remained associated with lower IL-6. Being a current smoker was significantly associated with 1.10 times the level of TNFR1 (95% CI, 1.02–1.19). Viral load was significantly associated with higher IL-6 and TNFR1, as was BMI. In the adjusted sensitivity analysis (Table 4), food insecurity remained associated with higher IL-6 and TNFR1, regardless of HIV control, and measures of association did not change substantively.
Table 4.
Adjusted Associations Between Food Insecurity and IL-6 and TNFR1, by HIV Control
| Characteristic |
Relative Difference (95% CI)a | |||
|---|---|---|---|---|
| IL-6 | TNFR1 | |||
| HIV controlledb (n = 233) |
Not HIV controlled (n = 167) |
HIV controlled (n = 233) |
Not HIV controlled (n = 167) |
|
| Any food insecurity | 1.27c (1.01–1.58) |
1.30c (1.03–1.63) |
1.10 (0.99–1.21) |
1.20d (1.60–1.37) |
| Age at visit, per 10 years | 1.12 (1.00–1.26) |
1.00 (0.87–1.14) |
1.03 (0.98–1.08) |
1.02 (0.95–1.10) |
| High school education or more | 1.06 (0.84–1.35) |
1.13 (0.89–1.43) |
1.05 (0.95–1.17) |
1.09 (0.96–1.25) |
| Race/ethnicity | ||||
| White | (Ref) | (Ref) | (Ref) | (Ref) |
| Hispanic | 1.11 (0.68–1.81) |
1.24 (0.75–2.05) |
0.77c (0.62–0.95) |
0.79 (0.60–1.04) |
| Black/African American | 1.38 (0.94–2.04) |
1.26 (0.83–1.91) |
0.85 (0.72–1.01) |
0.84 (0.66–1.05) |
| Other | 1.78 (0.87–3.63) |
0.63 (0.30–1.33) |
0.89 (0.66–1.21) |
0.67 (0.45–1.01) |
| Annual household income | ||||
| ≤ $12000 | (Ref) | (Ref) | (Ref) | (Ref) |
| $12001–$24000 | 0.86 (0.67–1.09) |
0.66d (0.49–0.88) |
0.98 (0.89–1.09) |
1.20c (1.02–1.42) |
| ≥$24001 | 0.71d (0.55–0.92) |
0.92 (0.70–1.21) |
0.89c (0.80–1.00) |
0.96 (0.83–1.12) |
| Illicit substance use | 0.92 (0.53–1.61) |
0.95 (0.58–1.58) |
0.90 (0.71–1.15) |
1.07 (0.81–1.41) |
| Current smoker | 0.94 (0.74–1.18) |
1.14 (0.89–1.45) |
1.05 (0.95–1.16) |
1.11 (0.97–1.27) |
Both multivariable linear regression models adjusted for all the variables included in the tables
Abbreviations: CI, confidence interval; HIV, human immunodeficiency virus; IL-6, interleukin-6; Ref, reference;TNFR1, tumor necroses factor receptor 1.
aThe natural logarithm of IL-6 and TNFR1 was used to satisfy the assumption of a normal distribution. The relative differences are natural exponential e of the regression coefficients, and are interpreted as multiplicative factors.
bHIV control was defined as undetectable viral load and CD4 ≥ 500 cells/mm3.
c P < .05.
d P < .01.
e P < .001.
DISCUSSION
In this diverse sample of WWH on ART in the United States, food insecurity was associated with elevations in inflammatory markers IL-6 and TNFR1. Given that inflammation is associated with a number of negative health outcomes including HIV-related morbidity and mortality [1], our findings bolster the rationale for addressing food insecurity among PWH.
Food insecurity may influence inflammation in PWH through several paths. Food insecurity could lead to increased inflammation through poor HIV viral control. Food insecurity has been identified as a barrier to antiretroviral adherence [28] and ultimately HIV control [10, 21, 29]. PWH who are food insecure may avoid taking ART when they do not have enough food to eat [28]. This is due to fears or actual experiences of acute hunger while on ART without access to a nutritious diet, or increased side effects such as nausea and vomiting from taking ART on an empty stomach [28]. Additionally, in the context of strained resources, the need for food might compete with resources needed to obtain ART [28]. Likely as a consequence of limited ART adherence, food insecurity has been shown to be associated with poor virologic control [21]. Other research has demonstrated an association between poor HIV control and elevated inflammatory markers among PWH [5].
The association between food insecurity and inflammatory markers persisted even after adjusting for viral suppression. Furthermore, in our adjusted sensitivity analysis, food insecurity remained associated with higher levels of inflammation among both HIV controlled, and noncontrolled participants. This suggests that ART adherence and poor viral suppression did not explain the relationship between food insecurity and inflammation in this sample. Longitudinal research, including longitudinal mediation analyses, is needed to further explore the role HIV control plays in the relationship between food insecurity and inflammation among WWH.
Food insecurity might also lead to increased inflammation through a nutritional path. Food insecurity has been associated with obesity [30] and diabetes [31], likely as a result of the need to consume cheaper, energy-dense foods on a limited budget [32], and binge-eating when food is available [33]. Higher BMI [5] and higher intake of fat, sugar, and red and processed meat are associated with higher levels of inflammation [23–26]. In contrast, consumption of fruits and vegetables is associated with lower inflammation [23–25]. The association between food insecurity persisted after adjusting for nutritional factors. Our measures of nutritional factors were limited by the use of the multifactor screener, which does not capture total energy intake by each food type, and BMI which does not capture micronutrient deficiencies. Further research using comprehensive nutritional assessments and longitudinal data could more fully assess the role that nutritional factors play in the relationship between food insecurity and inflammation.
Food insecurity is a powerful stressor [34, 35], and prior research suggests that stress is associated with elevated levels of inflammation [36]. Thus, it is possible that stress lies on the causal path between food insecurity and inflammation. We did not have available measures of perceived stress or biomarkers related to stress in this sample. Future studies should examine this potential path. Tryptophan catabolism may be another mechanism linking food insecurity to inflammation. The breakdown products of tryptophan, one of the essential amino acids mainly obtained from consumption of protein-rich foods, have been previously noted to contribute to immunosuppression and disease progression in chronic viral infections such as HIV [37]. We were unable to examine this potential path because our data lacked measures of tryptophan catabolism. Finally, evidence suggests that diet can affect the gut microbiota, and the gut microbiome in turn is an important determinant of systemic inflammation [38]. Future studies should assess whether food insecurity and diet intake affect the induction of the tryptophan catabolism pathway and the gut microbiota, and ultimately inflammation.
Findings presented in this study were based on cross-sectional associations, making us unable to specify the temporal relationship between food insecurity and inflammation. There is a need for longitudinal research to assess the relationship between food insecurity and inflammation over time, as well as potential mediators of this relationship. Research is also needed to explore the relationship between food insecurity and inflammation among men living with HIV. The generalizability of these findings are limited to women living with HIV in the United States without hepatitis B or C, cancer, or autoimmune diseases.
CONCLUSION
This study advances our knowledge about the role of food insecurity in inflammation among women living with HIV in the US context. Food insecurity was associated with elevated levels of inflammatory biomarkers IL-6 and TNFR1 in this sample of WWH on ART in the United States. Findings did not differ significantly by level HIV control, suggesting that ART adherence is not driving the relationship between food insecurity and inflammation, and that a biological path may explain our results. These findings reinforce the need for HIV care and treatment services to incorporate programming that addresses food insecurity. Findings from such research could help inform interventions seeking to address chronic inflammation among PWH, and ultimately the negative health outcomes associated with inflammation, including morbidity and mortality.
Notes
Disclaimer. The contents of this publication are solely the responsibility of the authors and do not represent the official views of the NIH.
Financial support. This work was supported by a Women’s Interagency HIV Study (WIHS) substudy grant from the National Institute of Mental Health (grant numbers R01MH095683 PI: Sheri Weiser and 5U01AI103401), and the National Institute of Allergy and Infectious Diseases (grant number K24AI134326 PI: Sheri Weiser).
Data in this manuscript were collected by the WIHS. WIHS sites (Principle Investigator) and National Institutes of Health (NIH) grant numbers were: UAB-MS WIHS (Mirjam-Colette Kempf and Deborah Konkle-Parker), U01-AI-103401; Atlanta WIHS (Ighovwerha Ofotokun and Gina Wingood), U01-AI-103408; Bronx WIHS (Kathryn Anastos and Anjali Sharma), U01-AI-035004; Brooklyn WIHS (Howard Minkoff and Deborah Gustafson), U01-AI-031834; Chicago WIHS (Mardge Cohen and Audrey French), U01-AI-034993; Metropolitan Washington WIHS (Seble Kassaye), U01-AI-034994; Miami WIHS (Margaret Fischl and Lisa Metsch), U01-AI-103397; UNC WIHS (Adaora Adimora), U01-AI-103390; Connie Wofsy Women’s HIV Study, Northern California (Ruth Greenblatt, Bradley Aouizerat, and Phyllis Tien), U01-AI-034989; WIHS Data Management and Analysis Center (Stephen Gange and Elizabeth Golub), U01-AI-042590; Southern California WIHS (Joel Milam), U01-HD-032632 (WIHS I–WIHS IV).
Additional support was from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Cancer Institute, National Institute on Drug Abuse, National Institute of Dental and Craniofacial Research, National Institute on Alcohol Abuse and Alcoholism, National Institute on Deafness and other Communication Disorders, and NIH Office of Research on Women’s Health. WIHS data collection is also supported by University of California San Francisco Clinical and Translational Science Award (grant number UL1-TR000004), Atlanta Clinical and Translational Science Award (grant number UL1-TR000454), and University of North Carolina Centre for AIDS Research (grant number P30-AI-050410). Additional investigator support was provided by NIMH grant number R01MH104114
Potential conflicts of interest. A. A. A. reports grants from Gilead and personal fees from Merck outside the submitted work. P. C. T. reports grants from Merck and Theratechnologies outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
Presented in part: 8th International Workshop on HIV and Women, 2 March 2018, Boston, MA; and Conference on Retroviruses and Opportunistic Infections, 7 March 2018, Boston, MA (abstract number 744).
References
- 1. Appay V, Sauce D. Immune activation and inflammation in HIV-1 infection: causes and consequences. J Pathol 2008; 214:231–41. [DOI] [PubMed] [Google Scholar]
- 2. Grund B, Baker JV, Deeks SG, et al. ; INSIGHT SMART/ESPRIT/SILCAAT Study Group Relevance of interleukin-6 and D-dimer for serious non-AIDS morbidity and death among HIV-positive adults on suppressive antiretroviral therapy. PLoS One 2016; 11:e0155100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kuller LH, Tracy R, Belloso W, et al. ; INSIGHT SMART Study Group Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 2008; 5:e203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Pasquereau S, Kumar A, Herbein G. Targeting TNF and TNF receptor pathway in HIV-1 infection: from immune activation to viral reservoirs. Viruses 2017; 9:pii: E64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Borges AH, O’Connor JL, Phillips AN, et al. . Factors associated with plasma IL-6 levels during HIV infection. J Infect Dis 2015; 212:585–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Tenorio AR, Zheng Y, Bosch RJ, et al. . Soluble markers of inflammation and coagulation but not T-cell activation predict non-AIDS-defining morbid events during suppressive antiretroviral treatment. J Infect Dis 2014; 210:1248–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Wada NI, Jacobson LP, Margolick JB, et al. . The effect of HAART-induced HIV suppression on circulating markers of inflammation and immune activation. AIDS 2015; 29:463–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. National Research Council. Food insecurity and hunger in the United States: An assessment of the measure. Washington, DC:National Academies Press, 2006. [Google Scholar]
- 9. Weiser SD, Tsai AC, Gupta R, et al. . Food insecurity is associated with morbidity and patterns of healthcare utilization among HIV-infected individuals in a resource-poor setting. AIDS 2012; 26:67–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Spinelli MA, Frongillo EA, Sheira LA, et al. . Food insecurity is associated with poor HIV outcomes among women in the United States. AIDS Behav 2017; 21:3473–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Weiser SD, Fernandes KA, Brandson EK, et al. . The association between food insecurity and mortality among HIV-infected individuals on HAART. J Acquir Immune Defic Syndr 2009; 52:342–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Anema A, Chan K, Chen Y, Weiser S, Montaner JS, Hogg RS. Relationship between food insecurity and mortality among HIV-positive injection drug users receiving antiretroviral therapy in British Columbia, Canada. PLoS One 2013; 8:e61277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Gowda C, Hadley C, Aiello AE. The association between food insecurity and inflammation in the US adult population. Am J Public Health 2012; 102:1579–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Ford ES. Food security and cardiovascular disease risk among adults in the United States: findings from the National Health and Nutrition Examination Survey, 2003–2008. Prev Chronic Dis 2013; 10:E202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 2000; 342:836–43. [DOI] [PubMed] [Google Scholar]
- 16. Weiser SD, Bangsberg DR, Kegeles S, Ragland K, Kushel MB, Frongillo EA. Food insecurity among homeless and marginally housed individuals living with HIV/AIDS in San Francisco. AIDS Behav 2009; 13:841–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Kalichman SC, Hernandez D, Cherry C, Kalichman MO, Washington C, Grebler T. Food insecurity and other poverty indicators among people living with HIV/AIDS: effects on treatment and health outcomes. J Community Health 2014; 39:1133–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Coleman-Jensen A, Rabbitt MP, Gregory CA, et al. . Household food security in the United States in 2015. Washington, DC:US Department of Agriculture, Economic Research Service, 2016. [Google Scholar]
- 19. Moreira FP, Medeiros JR, Lhullier AC, et al. . Cocaine abuse and effects in the serum levels of cytokines IL-6 and IL-10. Drug Alcohol Depend 2016; 158:181–5. [DOI] [PubMed] [Google Scholar]
- 20. Pellowski JA, Huedo-Medina TB, Kalichman SC. Food insecurity, substance use, and sexual transmission risk behavior among people living with HIV: A daily level analysis. Arch Sex Behav 2018; 47:1899–907. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Wang EA, McGinnis KA, Fiellin DA, et al. ; VACS Project Team Food insecurity is associated with poor virologic response among HIV-infected patients receiving antiretroviral medications. J Gen Intern Med 2011; 26:1012–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Vaidya SA, Korner C, Sirignano MN, et al. . Tumor necrosis factor α is associated with viral control and early disease progression in patients with HIV type 1 infection. J Infect Dis 2014; 210:1042–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Lopez-Garcia E, Schulze MB, Fung TT, et al. . Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr 2004; 80:1029–35. [DOI] [PubMed] [Google Scholar]
- 24. Schulze MB, Hoffmann K, Manson JE, et al. . Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr 2005; 82:675–84; quiz 714–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Shivappa N, Tavani A, Hébert JR, Rosato V, La Vecchia C. Dietary inflammatory index and acute myocardial infarction in a large Italian case-control study. Eur J Public Health 2018; 28:161–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Wirth MD, Hébert JR, Shivappa N, et al. . Anti-inflammatory dietary inflammatory index scores are associated with healthier scores on other dietary indices. Nutr Res 2016; 36:214–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. National Cancer Institute. Multifactor screener in the 2000 National Health Interview Survey Cancer Control Supplement: overview https://epi.grants.cancer.gov/nhis/multifactor/. Accessed 3 September 2018.
- 28. Young S, Wheeler AC, McCoy SI, Weiser SD. A review of the role of food insecurity in adherence to care and treatment among adult and pediatric populations living with HIV and AIDS. AIDS Behav 2014; 18 (Suppl 5):S505–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Weiser SD, Frongillo EA, Ragland K, Hogg RS, Riley ED, Bangsberg DR. Food insecurity is associated with incomplete HIV RNA suppression among homeless and marginally housed HIV-infected individuals in San Francisco. J Gen Intern Med 2009; 24:14–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Dinour LM, Bergen D, Yeh MC. The food insecurity-obesity paradox: a review of the literature and the role food stamps may play. J Am Diet Assoc 2007; 107:1952–61. [DOI] [PubMed] [Google Scholar]
- 31. Seligman HK, Bindman AB, Vittinghoff E, Kanaya AM, Kushel MB. Food insecurity is associated with diabetes mellitus: results from the National Health Examination and Nutrition Examination Survey (NHANES) 1999–2002. J Gen Intern Med 2007; 22:1018–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Drewnowski A, Darmon N. The economics of obesity: dietary energy density and energy cost. Am J Clin Nutr 2005; 82(Suppl 1):265S–73S. [DOI] [PubMed] [Google Scholar]
- 33. Wilde PE, Ranney CK. The monthly food stamp cycle: shopping frequency and food intake decisions in an endogenous switching regression framework. Am J Agricult Econ 2000; 82:200–13. [Google Scholar]
- 34. Laraia BA, Siega-Riz AM, Gundersen C, Dole N. Psychosocial factors and socioeconomic indicators are associated with household food insecurity among pregnant women. J Nutr 2006; 136:177–82. [DOI] [PubMed] [Google Scholar]
- 35. Whitaker RC, Phillips SM, Orzol SM. Food insecurity and the risks of depression and anxiety in mothers and behavior problems in their preschool-aged children. Pediatrics 2006; 118:e859–68. [DOI] [PubMed] [Google Scholar]
- 36. Marsland AL, Walsh C, Lockwood K, John-Henderson NA. The effects of acute psychological stress on circulating and stimulated inflammatory markers: A systematic review and meta-analysis. Brain Behav Immun 2017; 64:208–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Mehraj V, Routy JP. Tryptophan catabolism in chronic viral infections: handling uninvited guests. Int J Tryptophan Res 2015; 8:41–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Clemente JC, Manasson J, Scher JU. The role of the gut microbiome in systemic inflammatory disease. BMJ 2018; 360:j5145. [DOI] [PMC free article] [PubMed] [Google Scholar]