Unpacking determinants and consequences of food insecurity for insulin resistance among people living with HIV: Conceptual framework and protocol for the NOURISH-OK study

Marianna S Wetherill, Casey Bakhsh, Lacey Caywood, Mary B Williams, Micah L Hartwell, Denna L Wheeler, Randolph D Hubach, T Kent Teague, Gerwald Köhler, James R Hebert, Sheri D Weiser, Marianna S Wetherill, Casey Bakhsh, Lacey Caywood, Mary B Williams, Micah L Hartwell, Denna L Wheeler, Randolph D Hubach, T Kent Teague, Gerwald Köhler, James R Hebert, Sheri D Weiser

Abstract

Background: Over the past four decades, advances in HIV treatment have contributed to a longer life expectancy for people living with HIV (PLWH). With these gains, the prevention and management of chronic co-morbidities, such as diabetes, are now central medical care goals for this population. In the United States, food insecurity disproportionately impacts PLWH and may play a role in the development of insulin resistance through direct and indirect pathways. The Nutrition to Optimize, Understand, and Restore Insulin Sensitivity in HIV for Oklahoma (NOURISH-OK) will use a novel, multi-level, integrated framework to explore how food insecurity contributes to insulin resistance among PLWH. Specifically, it will explore how food insecurity may operate as an intermediary risk factor for insulin resistance, including potential linkages between upstream determinants of health and downstream consequences of poor diet, other behavioral risk factors, and chronic inflammation.

Methods/design: This paper summarizes the protocol for the first aim of the NOURISH-OK study, which involves purposeful cross-sectional sampling of PLWH (n=500) across four levels of food insecurity to test our conceptual framework. Developed in collaboration with community stakeholders, this initial phase involves the collection of anthropometrics, fasting blood samples, non-blood biomarkers, 24-hour food recall to estimate the Dietary Inflammatory Index (DII®) score, and survey data. A 1-month, prospective observational sub-study (total n=100; n=25 for each food security group) involves weekly 24-hour food recalls and stool samples to identify temporal associations between food insecurity, diet, and gut microbiome composition. Using structural equation modeling, we will explore how upstream risk factors, including early life events, current discrimination, and community food access, may influence food insecurity and its potential downstream impacts, including diet, other lifestyle risk behaviors, and chronic inflammation, with insulin resistance as the ultimate outcome variable. Findings from these analyses of observational data will inform the subsequent study aims, which involve qualitative exploration of significant pathways, followed by development and testing of a low-DII® food as medicine intervention to reverse insulin resistance among PLWH (ClinicalTrials.gov Identifier: NCT05208671).

Discussion: The NOURISH-OK study will address important research gaps to inform the development of food as medicine interventions to support healthy aging for PLWH.

Keywords: HIV- human immunodeficiency virus; community-based participatory research (CBPR); food access; food insecurity; inflammation; insulin resistance; microbiome; structural equation modeling.

Conflict of interest statement

Conflict of interest Unrelated to the present work, MSW previously served as an advisor for Food and Society at the Aspen Institute’s Food is Medicine Initiative and is a consultant for the Sunflower Foundation’s Food is Medicine Initiative. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Conceptual framework for food insecurity and insulin resistance. Created with BioRender.com.
FIGURE 2
FIGURE 2
Study design overview for NOURISH-OK Aim 1, which includes a cross-sectional main study and prospective, observational sub-study.
FIGURE 3
FIGURE 3
Structural equation model of food insecurity and insulin resistance. Additional co-variate demographics not depicted. Created with draw.io.

References

    1. Hernandez-Romieu AC, Garg S, Rosenberg ES, Thompson-Paul AM, Skarbinski J. Is diabetes prevalence higher among HIV-infected individuals compared with the general population? evidence from MMP and NHANES 2009–2010. BMJ Open Diabetes Res. Care (2017) 5(1):e000304–e000304. doi: 10.1136/bmjdrc-2016-000304
    1. Arafath S, Campbell T, Yusuff J, Sharma R. Prevalence of and risk factors for prediabetes in patients infected with HIV. Diabetes Spectr. (2018) 31(2):139. doi: 10.2337/ds17-0009
    1. Price JC, Seaberg EC, Latanich R, Budoff MJ, Kingsley LA, Palella FJ Jr, et al. Risk factors for fatty liver in the multicenter AIDS cohort study. Am. J. Gastroenterol (2014) 109(5):695–704. doi: 10.1038/ajg.2014.32
    1. Khuder SS, Chen S, Letendre S, Marcotte T, Grant I, Franklin D, et al. Impaired insulin sensitivity is associated with worsening cognition in HIV-infected patients. Neurology (2019) 92 (12): e 1344 – 53. doi: 10.1212/WNL.0000000000007125
    1. Pedro MN, Rocha GZ, Guadagnini D, Santos A, Magro DO, Assalin HB, et al. Insulin resistance in HIV-patients: Causes and consequences. Front. Endocrinol. (Lausanne) (2018) 9:514. doi: 10.3389/fendo.2018.00514
    1. The National Institute of Diabetes and Digestive and Kidney Diseases Health Information Center. Insulin resistance & prediabetes (2018). Available at: .
    1. Hulgan T Factors associated with insulin resistance in adults with HIV receiving contemporary antiretroviral therapy: a brief update. Curr. HIV/AIDS Rep (2018) 15(3):223–32. doi: 10.1007/s11904-018-0399-7
    1. Hessol NA, Ameli N, Cohen MH, Urwin S, Weber KM, Tien PC. The association between diet and physical activity on insulin resistance in the women’s interagency HIV study. journal of acquired immune deficiency syndromes. (2013) 62(1):74–80. doi: 10.1097/QAI.0b013e318275d6a4
    1. Weiser SD, Young SL, Cohen CR, Kushel MB, Tsai AC, Tien PC, et al. Conceptual framework for understanding the bidirectional links between food insecurity and HIV/AIDS. Am. J. Clin. Nutr (2011) 94(6):1729S–39S. doi: 10.3945/ajcn.111.012070
    1. Bergmans RS, Palta M, Robert SA, Berger LM, Ehrenthal DB, Malecki KM. Associations between food security status and dietary inflammatory potential within lower-income adults from the united states national health and nutrition examination survey, cycles 2007 to 2014. J. Acad. Nutr. Diet (2018) 118(6):994–1005. doi: 10.1016/j.jand.2017.12.003
    1. Ding M, Keiley MK, Garza KB, Duffy PA, Zizza CA. Food insecurity is associated with poor sleep outcomes among US adults. J. Nutr (2015) 145(3):615–21. doi: 10.3945/jn.114.199919
    1. To QG, Frongillo EA, Gallegos D, Moore JB. Household food insecurity is associated with less physical activity among children and adults in the U.S. population. J. Nutr (2014) 144(11):1797–802. doi: 10.3945/jn.114.198184
    1. Muhammad JN, Fernandez JR, Clay OJ, Saag MS, Overton ET, Willig AL. Associations of food insecurity and psychosocial measures with diet quality in adults aging with HIV. AIDS Care (2019) 31(5):554–62. doi: 10.1080/09540121.2018.1554239
    1. Attia S, Feenstra M, Swain N, Cuesta M, Bandsma RHJ. Starved guts: Morphologic and functional intestinal changes in malnutrition. J. Pediatr. Gastroenterol. Nutr (2017) 65(5):491–5. doi: 10.1097/MPG.0000000000001629
    1. Ecklu-Mensah G, Gilbert J, Devkota S. Dietary selection pressures and their impact on the gut microbiome. Cell. Mol. Gastroenterol. Hepatol (2022) 13(1):7–18. doi: 10.1016/j.jcmgh.2021.07.009
    1. Seligman HK, Schillinger D. Hunger and socioeconomic disparities in chronic disease. New Engl. J. Med (2010) 363(1):6–9. doi: 10.1056/NEJMp1000072
    1. Sirotin N, Hoover DR, Shi Q, Anastos K, Weiser SD. Food insecurity with hunger is associated with obesity among HIV-infected and at risk women in Bronx, NY. PloS One [Electronic Resource] (2014) 9(8):e105957. doi: 10.1371/journal.pone.0105957
    1. Kim-Mozeleski JE, Tsoh JY, Ramirez-Forcier J, Andrews B, Weiser SD, Carrico AW. Smoking predicts food insecurity severity among persons living with HIV. AIDS Behav. (2018) 22(9):2861–7. doi: 10.1007/s10461-018-2069-6
    1. El Zein A, Shelnutt KP, Colby S, Vilaro MJ, Zhou W, Greene G, et al. Prevalence and correlates of food insecurity among U.S. college students: a multi-institutional study. BMC Public Health (2019) 19(1):660. doi: 10.1186/s12889-019-6943-6
    1. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The adverse childhood experiences (ACE) study. Am. J. Prev. Med (1998) 14(4):245–58. doi: 10.1016/S0749-3797(98)00017-8
    1. Chilton M, Knowles M, Bloom SL. The intergenerational circumstances of household food insecurity and adversity. J. Hunger Environ. Nutr (2017) 12 (2):269–97. doi: 10.1080/19320248.2016.1146195
    1. Chilton M, Knowles M, Rabinowich J, Arnold KT. The relationship between childhood adversity and food insecurity: ‘It’s like a bird nesting in your head’. Public Health Nutr. (2015) 18(14):2643–53. doi: 10.1017/S1368980014003036
    1. Campbell JA, Walker RJ, Egede LE. Associations between adverse childhood experiences, high-risk behaviors, and morbidity in adulthood. Am. J. Prev. Med (2016) 50(3):344–52. doi: 10.1016/j.amepre.2015.07.022
    1. Berens AE, Jensen SKG, Nelson CA. Biological embedding of childhood adversity: from physiological mechanisms to clinical implications. BMC Med. (2017) 15(1):135. doi: 10.1186/s12916-017-0895-4
    1. Chen M, Lacey RE. Adverse childhood experiences and adult inflammation: Findings from the 1958 British birth cohort. Brain Behav. Immun (2018) 69:582–90. doi: 10.1016/j.bbi.2018.02.007
    1. Baumeister D, Akhtar R, Ciufolini S, Pariante CM, Mondelli V. Childhood trauma and adulthood inflammation: a meta-analysis of peripheral c-reactive protein, interleukin-6 and tumour necrosis factor-alpha. Mol. Psychiatry (2016) 21(5):642–9. doi: 10.1038/mp.2015.67
    1. Campbell JA, Mosley-Johnson E, Garacci E, Walker RJ, Egede LE. The co-occurrence of diabetes and adverse childhood experiences and its impact on mortality in US adults. J. Affect. Disord (2019) 249:20–5. doi: 10.1016/j.amepre.2015.07.022
    1. Huang H, Yan P, Shan Z, Chen S, Li M, Luo C, et al. Adverse childhood experiences and risk of type 2 diabetes: A systematic review and meta-analysis. Metabolism (2015) 64(11):1408–18. doi: 10.1016/j.metabol.2015.08.019
    1. Gowda C, Hadley C, Aiello AE. The association between food insecurity and inflammation in the US adult population. Am. J. Public Health (2012) 102(8):1579–86. doi: 10.2105/AJPH.2011.300551
    1. Nkinda L, Patel K, Njuguna B, Ngangali JP, Memiah P, Bwire GM, et al. C - reactive protein and interleukin - 6 levels among human immunodeficiency virus-infected patients with dysglycemia in Tanzania. BMC Endocrine Disord. (2019) 19 (1):77–7. doi: 10.1186/s12902-019-0407-y
    1. Galescu O, Bhangoo A, Ten S. Insulin resistance, lipodystrophy and cardiometabolic syndrome in HIV/AIDS. Rev. Endocr. Metab. Disord (2013) 14 (2):133–40. doi: 10.1007/s11154-013-9247-7
    1. Brown TT, Tassiopoulos K, Bosch RJ, Shikuma C, McComsey GA. Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care (2010) 33(10):2244–9. doi: 10.2337/dc10-0633
    1. Zicari S, Sessa L, Cotugno N, Ruggiero A, Morrocchi E, Concato C, et al. Immune activation, inflammation, and non-AIDS Co-morbidities in HIV-infected patients under long-term ART. Viruses (2019) 11(3):200. doi: 10.3390/v11030200
    1. Festa A, D’Agostino R Jr., Williams K, Karter AJ, Mayer-Davis EJ, Tracy RP, et al. The relation of body fat mass and distribution to markers of chronic inflammation. Int. J. Obes. Relat. Metab. Disord (2001) 25(10):1407–15. doi: 10.1038/sj.ijo.0801792
    1. Pérez-Baos S, Prieto-Potin I, Román-Blas JA, Sánchez-Pernaute O, Largo R, Herrero-Beaumont G. Mediators and patterns of muscle loss in chronic systemic inflammation. Front. Physiol (2018) 9:409–9. doi: 10.3389/fphys.2018.00409
    1. Lake JE. The fat of the matter: Obesity and visceral adiposity in treated HIV infection. Curr. HIV/AIDS Rep (2017) 14(6):211–9. doi: 10.1007/s11904-017-0368-6
    1. Grunfeld C, Rimland D, Gibert CL, Powderly WG, Sidney S, Shlipak MG, et al. Association of upper trunk and visceral adipose tissue volume with insulin resistance in control and HIV-infected subjects in the FRAM study. J. Acquir Immune Defic Syndromes (1999) (2007) 46(3):283–90. doi: 10.1097/QAI.0b013e31814b94e2
    1. Hadigan C, Meigs JB, Corcoran C, Rietschel P, Piecuch S, Basgoz N, et al. Metabolic abnormalities and cardiovascular disease risk factors in adults with human immunodeficiency virus infection and lipodystrophy. Clin. Infect. Dis (2001) 32(1):130–9. doi: 10.1086/317541
    1. Apovian CM, Bigornia S, Mott M, Meyers MR, Ulloor J, Gagua M, et al. Adipose macrophage infiltration is associated with insulin resistance and vascular endothelial dysfunction in obese subjects. Arterioscler. Thromb. Vasc. Biol (2008) 28(9):1654–9. doi: 10.1161/ATVBAHA.108.170316
    1. Wada NI, Jacobson LP, Margolick JB, Breen EC, Macatangay B, Penugonda S, et al. The effect of HAART-induced HIV suppression on circulating markers of inflammation and immune activation. AIDS (London England) (2015) 29(4):463–71. doi: 10.1097/QAD.0000000000000545
    1. Leddy AM, Roque A, Sheira LA, Frongillo EA, Landay AL, Adedimeji AA, et al. Food insecurity is associated with inflammation among women living with HIV. J. Infect. Dis (2019) 219(3):429–36. doi: 10.1093/infdis/jiy511
    1. Canfora EE, Meex RCR, Venema K, Blaak EE. Gut microbial metabolites in obesity, NAFLD and T2DM. Nat. Rev. Endocrinol (2019) 15(5):261–73. doi: 10.1038/s41574-019-0156-z
    1. Zheng D, Liwinski T, Elinav E. Interaction between microbiota and immunity in health and disease. Cell Res. (2020) 30(6):492–506. doi: 10.1038/s41422-020-0332-7
    1. Li SX, Armstrong A, Neff CP, Shaffer M, Lozupone CA, Palmer BE. Complexities of gut microbiome dysbiosis in the context of HIV infection and antiretroviral therapy. Clin. Pharmacol. Ther (2016) 99(6):600–11. doi: 10.1002/cpt.363
    1. Dillon SM, Frank DN, Wilson CC. The gut microbiome and HIV-1 pathogenesis: a two-way street. AIDS (London England) (2016) 30(18):2737–51. doi: 10.1097/QAD.0000000000001289
    1. Gelpi M, Vestad B, Hansen SH, Holm K, Drivsholm N, Goetz A, et al. Impact of HIV-related gut microbiota alterations on metabolic comorbidities. Clin. Infect. Dis (2020) 71(8):e359–e67. doi: 10.1093/cid/ciz1235
    1. Benjamin-Neelon S, Differding M, Mueller N. Infants from food insecure households have altered gut microbiota (OR01-03-19). Curr. Develop Nutr (2019) 3(Supplement_1). doi: 10.1093/cdn/nzz040.OR01-03-19
    1. Guenter P, Muurahainen N, Simons G, Kosok A, Cohan GR, Rudenstein R, et al. Relationships among nutritional status, disease progression, and survival in HIV infection. J. Acquir. Immune Defic. Syndr. (1988) (1993) 6 (10):1130–8.
    1. Health Resources and Services Administration (HRSA). Ryan White HIV/AIDS program. In: RSR client characteristics in Oklahoma in 2018. HIV/AIDS Bureau. (2019). Available at: . Accessed January 14, 2022.
    1. Health Resources and Services Administration (HRSA). Ryan White HIV/AIDS program services: Eligible individuals & allowable uses of funds, in policy clarification notice (PCN) #16–02. (2018).
    1. Hebert JR, Shivappa N, Wirth MD, Hussey JR, Hurley TG. Perspective: The dietary inflammatory index (DII)-lessons learned, improvements made, and future directions. Adv. Nutr (2019) 10(2):185–95. doi: 10.1093/advances/nmy071
    1. Shivappa N, Bonaccio M, Hebert JR, Di Castelnuovo A, Costanzo S, Ruggiero E, et al. Association of proinflammatory diet with low-grade inflammation: results from the moli-sani study. Nutrition (2018) 54:182–8. doi: 10.1016/j.nut.2018.04.004
    1. Phillips CM, Shivappa N, Hebert JR, Perry IJ. Dietary inflammatory index and biomarkers of lipoprotein metabolism, inflammation and glucose homeostasis in adults. Nutrients (2018) 10(8):1033. doi: 10.3390/nu10081033
    1. Mtintsilana A, Micklesfield LK, Chorell E, Olsson T, Shivappa N, Hebert JR, et al. Adiposity mediates the association between the dietary inflammatory index and markers of type 2 diabetes risk in middle-aged black south African women. Nutrients (2019) 11(6):1246. doi: 10.3390/nu11061246
    1. Moslehi N, Ehsani B, Mirmiran P, Shivappa N, Tohidi M, Hebert JR, et al. Inflammatory properties of diet and glucose-insulin homeostasis in a cohort of Iranian adults. Nutrients (2016) 8(11):735. doi: 10.3390/nu8110735
    1. Data Resource Center for Child and Adolescent Health. America’s health rankings analysis of U.S. HHS, HRSA, maternal and child health bureau (MCHB), child and adolescent health measurement initiative (CAHMI), national survey of children’s health indicator data set (2020). Available at: .
    1. LaVarnway J, Craven E. An overview of food deserts in Oklahoma: June 2017. (2017). Regional Food Bank of Oklahoma, Oklahoma City, OK
    1. Coleman-Jensen A, Rabbitt MP, Gregory CA, Singh A. Household food security in the united states in 2018. Washington, DC: United States Department of Agriculture; (2019).
    1. United Health Foundation. America’s health rankings annual report 2018.
    1. Health Resources and Services Administration (HRSA). Ending the HIV epidemic: a plan for America. (2019). Available at:
    1. Mercer SL GL, Cargo M, Potter MA, Daniel M, Olds S, Reed-Gross E. Appendix c: Reliability-tested guidelines for assessing participatory research projects. In: Community-based participatory research for health: From process to outcomes. San Francisco: Jossey-Bass; (2008). p. 407–18.
    1. Wetherill M, John R, Wheeler D. Estimating food insecurity and its association with HIV-related health outcomes using self-administered surveys: A community-based research approach. London: SAGE Publications, Ltd; (2017). Available from: .
    1. USDA Economic Research Service. U.S. adult food security survey module: Three-stage design, with screeners. U.S: Department of Agriculture; (2012).
    1. Evans L, Lawler K, Sass S. Gathering sexual orientation data on statewise behavioral risk factor surveillance surveys: a call to action for states. Cahill S, editor. Boston, MA: The Fenway Institute; (2014).
    1. Bauer GR, Braimoh J, Scheim AI, Dharma C. Transgender-inclusive measures of sex/gender for population surveys: Mixed-methods evaluation and recommendations. PloS One (2017) 12(5):e0178043–e0178043. doi: 10.1371/journal.pone.0178043
    1. Centers for Disease Control and Prevention. 2017 behavioral risk factor surveillance system questionnaire (2016). Available at: .
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med (2001) 16(9):606–13. doi: 10.1046/j.1525-1497.2001.016009606.x
    1. Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med (2006) 166(10):1092–7. doi: 10.1001/archinte.166.10.1092
    1. Wu AW, Revicki DA, Jacobson D, Malitz FE. Evidence for reliability, validity and usefulness of the medical outcomes study HIV health survey (MOS-HIV). Qual Life Res. (1997) 6(6):481–93. doi: 10.1023/A:1018451930750
    1. Ware JE Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). i. conceptual framework and item selection. Med. Care (1992) 30(6):473–83.
    1. Feldman BJ, Fredericksen RJ, Crane PK, Safren SA, Mugavero MJ, Willig JH, et al. Evaluation of the single-item self-rating adherence scale for use in routine clinical care of people living with HIV. AIDS Behav. (2013) 17(1):307–18. doi: 10.1007/s10461-012-0326-7
    1. Bethell C, Jones J, Gombojav N, Linkenbach J, Sege R. Positive childhood experiences and adult mental and relational health in a statewide sample: Associations across adverse childhood experiences levels. JAMA Pediatr. (2019) 173(11):e193007–e193007. doi: 10.1001/jamapediatrics.2019.3007
    1. Nightingale VR, Sher TG, Mattson M, Thilges S, Hansen NB. The effects of traumatic stressors and HIV-related trauma symptoms on health and health related quality of life. AIDS Behav. (2011) 15(8):1870–8. doi: 10.1007/s10461-011-9980-4
    1. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc. Sci. Med (2005) 61(7):1576–96. doi: 10.1016/j.socscimed.2005.03.006
    1. Green SH, Glanz K. Development of the perceived nutrition environment measures survey. Am. J. Prev. Med (2015) 49(1):50–61. doi: 10.1016/j.amepre.2015.02.004
    1. USDA. Food access research atlas. Available at: .
    1. University of Wisconsin School of Medicine Public Health. 2015 area deprivation index v2.0 (2020). Available at: .
    1. Waldorf BS. A continuous multi-dimensional measure of rurality: Moving beyond threshold measures. In: 2006 annual meeting. Long Beach, CA: American Agricultural Economics Association; (2006).
    1. Waldorf BS. What is rural and what is urban in Indiana? West Lafayette, IN: Purdue Center for Regional Development; (2007).
    1. Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR. The development of scales to measure social support for diet and exercise behaviors. Prev. Med (1987) 16(6):825–36. doi: 10.1016/0091-7435(87)90022-3
    1. Ball K, Crawford D. An investigation of psychological, social and environmental correlates of obesity and weight gain in young women. Int. J. Obes (2006) 30(8):1240–9. doi: 10.1038/sj.ijo.0803267
    1. Lavelle F, McGowan L, Hollywood L, Surgenor D, McCloat A, Mooney E, et al. The development and validation of measures to assess cooking skills and food skills. Int. J. Behav. Nutr. Phys. Act (2017) 14(1):118. doi: 10.1186/s12966-017-0575-y
    1. Jahns L, Johnson LK, Mayne ST, Cartmel B, Picklo MJ Sr., Ermakov IV, et al. Skin and plasma carotenoid response to a provided intervention diet high in vegetables and fruit: uptake and depletion kinetics. Am. J. Clin. Nutr (2014) 100 (3):930–7. doi: 10.3945/ajcn.114.086900
    1. Nes BM, Janszky I, Vatten LJ, Nilsen TIL, Aspenes St Wisløff U. Estimating V˙O2peak from a nonexercise prediction model: The HUNT study, Norway. Med. Sci. Sports Exercise (2011) 43(11):2024–30. doi: 10.1249/MSS.0b013e31821d3f6f
    1. Prince SA, LeBlanc AG, Colley RC, Saunders TJ. Measurement of sedentary behaviour in population health surveys: a review and recommendations. PeerJ (2017) 5:e4130–0. doi: 10.7717/peerj.4130
    1. Nes BM, Vatten LJ, Nauman J, Janszky I, Wisløff U. A simple nonexercise model of cardiorespiratory fitness predicts long-term mortality. Med. Sci. Sports Exercise (2014) 46(6):1159–65. doi: 10.1249/MSS.0000000000000219
    1. Centers for Disease Control and Prevention. 2020 behavioral risk factor surveillance system questionnaire (2021). Available at: .
    1. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The fagerström test for nicotine dependence: a revision of the fagerström tolerance questionnaire. Br. J. Addict (1991) 86(9):1119–27. doi: 10.1111/j.1360-0443.1991.tb01879.x
    1. Centers for Disease Control and Prevention. Biomonitoring summary: Cotinine (2017). Available at: .
    1. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KAfor the Ambulatory Care Quality Improvement P. The audit alcohol consumption questions (AUDIT-c): An effective brief screening test for problem drinking. Arch. Internal Med (1998) 158(16):1789–95. doi: 10.1001/archinte.158.16.1789
    1. National Cancer Institute. Automated self-administered 24-hour (ASA24®) dietary assessment tool. In: Division of cancer control and population sciences (2017). Available at: .
    1. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J. Health Soc. Behav (1983) 24(4):385–96. doi: 10.2307/2136404
    1. John-Henderson NA, Ginty AT. Historical trauma and social support as predictors of psychological stress responses in American Indian adults during the COVID-19 pandemic. J. Psychos Res (2020) 139:110263. doi: 10.1016/j.jpsychores.2020.110263
    1. MedlinePlus. Calculating body frame size (2020). Available at: .
    1. White JV, Guenter P, Jensen G, Malone A, Schofield M. Consensus statement: Academy of nutrition and dietetics and American society for parenteral and enteral nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN J. Parenter Enteral Nutr (2012) 36(3):275–83. doi: 10.1177/0148607112440285
    1. Centers for Disease Control and Prevention. National health and nutrition examination survey (NHANES): Anthropometry procedures manual. Atlanta, GA: CDC; (2020). Availbale at:
    1. Azuma K, Heilbronn LK, Albu JB, Smith SR, Ravussin E, Kelley DE, et al. Adipose tissue distribution in relation to insulin resistance in type 2 diabetes mellitus. Am. J. Physiol. Endocrinol. Metab (2007) 293(1):E435–42. doi: 10.1152/ajpendo.00394.2006
    1. Rao SS, Disraeli P, McGregor T. Impaired glucose tolerance and impaired fasting glucose. Am. Fam Phys (2004) 69(8):1961–8.
    1. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J. Clin. Endocrinol. Metab (2000) 85(7):2402–10. doi: 10.1210/jcem.85.7.6661
    1. FAO. Declaration of the world food summit on food security. Rome: Food and Agriculture Organization of the United Nations; (2009).
    1. Tarasuk V, McIntyre L, Li J. Low-income women’s dietary intakes are sensitive to the depletion of household resources in one month. J. Nutr (2007) 137 (8):1980–7. doi: 10.1093/jn/137.8.1980
    1. Nyambose J, Koski KG, Tucker KL. High intra/interindividual variance ratios for energy and nutrient intakes of pregnant women in rural Malawi show that many days are required to estimate usual intake. J. Nutr (2002) 132(6):1313–8. doi: 10.1093/jn/132.6.1313
    1. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq illumina sequencing platform. Appl. Environ. Microbiol (2013) 79(17):5112–20. doi: 10.1128/AEM.01043-13
    1. Slade GD, Spencer AJ. Development and evaluation of the oral health impact profile. Community Dent. Health (1994) 11(1):3–11.
    1. Gassman-Pines A, Schenck-Fontaine A. Daily food insufficiency and worry among economically disadvantaged families with young children. J. Marriage Family (2019) 81(5):1269–84. doi: 10.1111/jomf.12593
    1. Lewis SJ, Heaton KW. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol (1997) 32(9):920–4. doi: 10.3109/00365529709011203
    1. Muniyappa R, Madan R, Varghese RT. Assessing insulin sensitivity and resistance in humans. In: Feingold KR, et al., editors. Endotext. South Dartmouth (MA: , Inc; (2000).
    1. Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, et al. Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J. Clin. Endocrinol. Metab (2001) 86(11):5457–64. doi: 10.1210/jcem.86.11.7880
    1. Chen H, Sullivan G, Quon MJ. Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Diabetes (2005) 54(7):1914–25. doi: 10.2337/diabetes.54.7.1914
    1. Katsuki A, Sumida Y, Gabazza EC, Murashima S, Urakawa H, Morioka K, et al. QUICKI is useful for following improvements in insulin sensitivity after therapy in patients with type 2 diabetes mellitus. J. Clin. Endocrinol. Metab (2002) 87(6):2906–8. doi: 10.1210/jcem.87.6.8592
    1. Berkowitz SA, Gao X, Tucker KL. Food-insecure dietary patterns are associated with poor longitudinal glycemic control in diabetes: results from the Boston Puerto Rican health study. Diabetes Care (2014) 37(9):2587–92. doi: 10.2337/dc14-0753
    1. Hendricks KM, Willis K, Houser R, Jones CY. Obesity in HIV-infection: dietary correlates. J. Am. Coll. Nutr (2006) 25(4):321–31. doi: 10.1080/07315724.2006.10719542
    1. Makki K, Deehan EC, Walter J, Backhed F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe (2018) 23(6):705–15. doi: 10.1016/j.chom.2018.05.012
    1. Willett WC. Correction for the effects of measurement error. In: Nutritional epidemiology. New York: Oxford University Press; (2013).
    1. Fowke JH, Hebert JR, Fahey JW. Urinary excretion of dithiocarbamates and self-reported cruciferous vegetable intake: application of the ‘method of triads’ to a food-specific biomarker. Public Health Nutr. (2002) 5(6):791–9. doi: 10.1079/PHN2002345
    1. Van Dam RM, Hunter D. Biochemical indicators of dietary intake. In: Willett WC, editor. Nutritional epidemiology. New York: Oxford University Press; (2013).
    1. Beaton GH, Milner J, Corey P, McGuire V, Cousins M, Stewart E, et al. Sources of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am. J. Clin. Nutr (1979) 32(12):2546–59. doi: 10.1093/ajcn/32.12.2546
    1. Kline RB. Principles and practice of structural equation modeling. Little TD, editor. New York: Guilford Press; (2005).
    1. Glass TA, McAtee MJ. Behavioral science at the crossroads in public health: extending horizons, envisioning the future. Soc. Sci. Med (2006) 62 (7):1650–71. doi: 10.1016/j.socscimed.2005.08.044
    1. Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educ. psychol. Measure (2013) 76(6):913–34. doi: 10.1177/0013164413495237
    1. Hartwell ML, Khojasteh J, Wetherill MS, Croff JM, Wheeler D. Using structural equation modeling to examine the influence of social, behavioral, and nutritional variables on health outcomes based on NHANES data: Addressing complex design, nonnormally distributed variables, and missing information. Curr. Develop Nutr (2019) 3(5). doi: 10.1093/cdn/nzz010
    1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol (2019) 37(8):852–7. doi: 10.1038/s41587-019-0209-9
    1. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. (2011) 12(6): R60–0. doi: 10.1186/gb-2011-12-6-r60
    1. Freeland-Graves J, Babaei M, Sachdev P. Food insecurity and periodontal disease in low-income women (P04-043-19). Curr. Develop Nutr (2019) 3 (Supplement_1). doi: 10.1093/cdn/nzz051.P04-043-19
    1. Santin GC, Martins CC, Pordeus IA, Ferreira FM. Food insecurity and oral health: A systematic review. Pesquisa Bras. em Odontopediatria e Clin Integrada (2014) 144):335–246. doi: 10.4034/PBOCI.2014.144.08
    1. Mataftsi M, Skoura L, Sakellari D. HIV Infection and periodontal diseases: an overview of the post-HAART era. Oral. Dis (2011) 17(1):13–25. doi: 10.1111/j.1601-0825.2010.01727.x
    1. Valentine J, Saladyanant T, Ramsey K, Blake J, Morelli T, Southerland J, et al. Impact of periodontal intervention on local inflammation, periodontitis, and HIV outcomes. Oral. Dis (2016) 22 Suppl 1:87–97. doi: 10.1111/odi.12419
    1. Gorvitovskaia A, Holmes SP, Huse SM. Interpreting prevotella and bacteroides as biomarkers of diet and lifestyle. Microbiome (2016) 4(1):15. doi: 10.1186/s40168-016-0160-7
    1. Most MM. Estimated phytochemical content of the dietary approaches to stop hypertension (DASH) diet is higher than in the control study diet. J. Am. Diet Assoc (2004) 104(11):1725–7. doi: 10.1016/j.jada.2004.08.001
    1. Jahns L, Whigham L, Johnson L, Mayne S, Cartmel B, Ermakov I, et al. Dermal carotenoids as measured by resonance raman spectroscopy as a biomarker of response to a Fruit/Vegetable intervention study. FASEB J. (2012) 26 (1_supplement):131.3–3. doi: 10.1096/fasebj.26.1_supplement.131.3
    1. Robillard AG, Julious CH, Smallwood SW, Douglas M, Gaddist BW, Singleton T. Structural inequities, HIV community-based organizations, and the end of the HIV epidemic. Am. J. Public Health (2022) 112(3):417–25. doi: 10.2105/AJPH.2021.306688

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