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Threat vigilance and socioeconomic disparities in metabolic health

Published online by Cambridge University Press:  22 November 2017

Camelia E. Hostinar ,
Kharah M. Ross ,
Meanne Chan ,
Edith Chen  and
Gregory E. Miller
Camelia E. Hostinar*
Affiliation:
University of California–Davis
Kharah M. Ross
Affiliation:
University of California–Los Angeles
Meanne Chan
Affiliation:
University of Toronto
Edith Chen
Affiliation:
Northwestern University
Gregory E. Miller
Affiliation:
Northwestern University
*
Address correspondence and reprint requests to: Camelia E. Hostinar, Psychology Department, University of California–Davis, 202 Cousteau Place, Davis, CA 95618; E-mail: [email protected].
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Abstract

A quarter of the global population meets diagnostic criteria for metabolic syndrome (MetS). MetS prevalence stratifies by socioeconomic status (SES), such that low SES is associated with higher MetS risk starting in childhood. Despite this trend, some low-SES children maintain good metabolic health across the life span, but the factors responsible for their resilience are not well understood. This study examined the role of threat vigilance as either a moderator or a mediator of the effects of low early life SES on adult metabolic risk. Three hundred twenty-five Canadians aged 15–55 participated (M = 36.4 years, SD = 10.7; 55.4% female). We coded parental occupational status between the ages of 0 and 5 to index early life SES. We used the International Diabetes Federation case definition for MetS based on waist circumference, blood pressure, triglyceride levels, HDL cholesterol, and glycosylated hemoglobin measures. Threat vigilance was assessed using the Weapons Identification Procedure, a visual discrimination paradigm that captures implicit perceptions of threat. Analyses supported the moderator hypothesis: low early life SES was associated with MetS diagnosis exclusively among those with high levels of threat vigilance. This suggests that low early life SES environments that heighten vigilance to threat might be particularly detrimental for metabolic health. Conversely, low threat vigilance may buffer against the metabolic risks associated with socioeconomic disadvantage.

Type
Special Issue Articles
Information
Development and Psychopathology , Volume 29 , Special Issue 5: Biological and Behavioral Effects of Early Adversity on Multiple Levels of Development , December 2017 , pp. 1721 - 1733
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

We thank the participants for their contribution to this project. This research was supported by NIH Grants R01 HD058502 and F32 HD078048.

References

Abraham, N. G., Brunner, E. J., & Eriksson, J. W. (2007). Metabolic syndrome: Psychosocial, neuroendocrine, and classical risk factors in type 2 diabetes. Annals of the New York Academy of Sciences, 275, 256–275. doi:10.1196/annals.1391.015 Google Scholar
Ainsworth, M. D. S., Bell, S. M., & Stayton, D. J. (1974). Infant–mother attachment and social development: Socialisation as a product of reciprocal responsiveness to signals. In Richards, M. P. M. (Ed.), The integration of a child into a social world (pp. 99–137). New York: Cambridge University Press.Google Scholar
Alberti, K. G. M. M., Zimmet, P., & Shaw, J. (2006). Metabolic syndrome—A new world-wide definition. A consensus statement from the International Diabetes Federation. Diabetic Medicine, 23, 469–480. doi:10.1111/j.1464-5491.2006.01858.x CrossRefGoogle ScholarPubMed
Aldhafeeri, F., Mackenzie, I., Kay, T., Alghamdi, J., & Sluming, V. (2014). Regional brain responses to pleasant and unpleasant IAPS pictures: Different networks. Neuroscience Letters, 512, 94–98. doi:10.1016/j.neulet.2012.01.064 Google Scholar
Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25, 464–469. doi:10.1111/j.1467-842X.2001.tb00294.x Google Scholar
Brito, N. H., & Noble, K. G. (2014). Socioeconomic status and structural brain development. Frontiers in Neuroscience, 8, 1–12. doi:10.3389/fnins.2014.00276 CrossRefGoogle ScholarPubMed
Brody, G. H., Lei, M., Chen, E., & Miller, G. E. (2014). Neighborhood poverty and allostatic load in African American youth. Pediatrics, 134, e1362–e1368. doi:10.1542/peds.2014-1395 Google Scholar
Brunner, E. J., Hemingway, H., Walker, B. R., Page, M., Clarke, P., Juneja, M., … Marmot, M. G. (2002). Adrenocortical, autonomic, and inflammatory causes of the metabolic syndrome. Circulation, 106, 2659–2665. doi:10.1161/01.CIR.0000038364.26310.BD Google Scholar
Brunner, E. J., Marmot, M., Nanchahal, K., Shipley, M., Stansfeld, S., Juneja, M., & Alberti, K. (1997). Social inequality in coronary risk: Central obesity and the metabolic syndrome. Evidence from the Whitehall II study. Diabetologia, 40, 1341–1349. doi:10.1007/s001250050830 Google Scholar
Carlson, E. A., Sroufe, L. A., & Egeland, B. (2004). The construction of experience: A longitudinal study of representation and behavior. Child Development, 75, 66–83. doi:10.1111/j.1467-8624.2004.00654.x CrossRefGoogle ScholarPubMed
Chan, M., Chen, E., Hibbert, A. S., Wong, J. H. K., & Miller, G. E. (2011). Implicit measures of early-life family conditions: Relationships to psychosocial characteristics and cardiovascular disease risk in adulthood. Health Psychology, 30, 570–578. doi:10.1037/a0024210 CrossRefGoogle ScholarPubMed
Chen, E., Langer, D. A., Raphaelson, Y. E., & Matthews, K. A. (2004). Socioeconomic status and health in adolescents: The role of stress interpretations. Child Development, 75, 1039–1052. doi:10.1111/j.1467-8624.2004.00724.x Google Scholar
Chen, E., & Matthews, K. A. (2001). Cognitive appraisal biases: An approach to understanding the relation between socioeconomic status and cardiovascular reactivity in children. Annals of Behavioral Medicine, 23, 101–111. doi:10.1207/S15324796ABM2302_4 Google Scholar
Chen, E., & Matthews, K. A. (2003). Development of the cognitive appraisal and understanding of social events (CAUSE) videos. Health Psychology, 22, 106–110. doi:10.1037/0278-6133.22.1.106 Google Scholar
Chen, E., & Miller, G. E. (2013). Socioeconomic status and health: Mediating and moderating factors. Annual Review of Clinical Psychology, 9, 723–749. doi:10.1146/annurev-clinpsy-050212-185634 CrossRefGoogle ScholarPubMed
Chen, E., Miller, G. E., Kobor, M. S., & Cole, S. W. (2011). Maternal warmth buffers the effects of low early-life socioeconomic status on pro-inflammatory signaling in adulthood. Molecular Psychiatry, 16, 729–737. doi:10.1038/mp.2010.53 Google Scholar
Chichlowska, K. L., Rose, K. M., Diez-Roux, A. V., Golden, S. H., McNeill, A. M., & Heiss, G. (2009). Life course socioeconomic conditions and metabolic syndrome in adults: The Atherosclerosis Risk in Communities (ARIC) Study. Annals of Epidemiology, 19, 875–883. doi:10.1016/j.annepidem.2009.07.094 Google Scholar
Choi, B., Lee, D., Chun, E., & Lee, J. (2014). The relationship between metabolic syndrome and childhood maternal education level, job status. Findings from the Korean National Health and Nutrition Examination, 2007–2009. Korean Journal of Family Medicine, 35, 207–215. doi:10.4082/kjfm.2014.35.4.207 Google Scholar
Cicchetti, D., & Blender, J. A. (2006). A multiple-levels-of-analysis perspective on resilience: Implications for the developing brain, neural plasticity, and preventive interventions. Annals of the New York Academy of Sciences, 1094, 248–258. doi:10.1196/annals.1376.029 Google Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600. doi:10.1017/S0954579400007318 Google Scholar
Cicchetti, D., & Valentino, K. (2007). Toward the application of a multiple-levels-of-analysis perspective to research in development and psychopathology. In Masten, A. S. (Ed.), Multilevel dynamics in developmental psychopathology. Pathways to the future: The Minnesota symposia on child psychology (pp. 243–284). Mahwah, NJ: Erlbaum. doi:10.4324/9780203936429 Google Scholar
Cohen, S., Tyrrell, D., Russell, M., Jarvis, M., & Smith, A. (1993). Smoking, alcohol consumption, and susceptibility to the common cold. American Journal of Public Health, 83, 1277–83.Google Scholar
Cornier, M.-A., Dabelea, D., Hernandez, T. L., Lindstrom, R. C., Steig, A. J., Stob, N. R., … Eckel, R. H. (2008). The metabolic syndrome. Endocrine Reviews, 29, 777–822. doi:10.1210/er.2008-0024 Google Scholar
Dallman, M. F. (2010). Stress-induced obesity and the emotional nervous system. Trends in Endocrinology and Metabolism, 21, 159–165. doi:10.1016/j.tem.2009.10.004 CrossRefGoogle ScholarPubMed
Davis, C., Usher, N., Dearing, E., Barkai, A., Crowell-Doom, C., Neupert, S., … Crowell, J. (2014). Attachment and the metabolic syndrome in midlife: The role of interview-based discourse patterns. Psychosomatic Medicine, 76, 611–621. doi:10.1097/PSY.0000000000000107 Google Scholar
Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic conduct problems in adolescence. Psychological Bulletin, 39, 349–371. doi:10.1037/0012-1649.39.2.349 Google Scholar
Doom, J. R., Gunnar, M. R., & Clark, C. J. (2016). Maternal relationship during adolescence predicts cardiovascular disease risk in adulthood. Health Psychology, 35, 376–386. doi.10.1037/hea0000285 Google Scholar
Emerging Risk Factors Collaboration. (2011). Diabetes mellitus, fasting glucose, and risk of cause-specific death. New England Journal of Medicine, 364, 829–841. doi:10.1056/NEJMoa1008862 Google Scholar
Evans, G. W., & English, K. (2002). The environment of poverty: Multiple stressor exposure, psychophysiological stress, and socioemotional adjustment. Child Development, 73, 1238–1248. doi:10.1111/1467-8624.00469 Google Scholar
Evans, G. W., & Kim, P. (2010). Multiple risk exposure as a potential explanatory mechanism for the socioeconomic status-health gradient. Annals of the New York Academy of Sciences, 1186, 174–189. doi:10.1111/j.1749-6632.2009.05336.x Google Scholar
Evans, G. W., Kim, P., Ting, A. H., Tesher, H. B., & Shannis, D. (2007). Cumulative risk, maternal responsiveness, and allostatic load among young adolescents. Developmental Psychology, 43, 341–351. doi:10.1037/0012-1649.43.2.341 Google Scholar
Faienza, M. F., Wang, D. Q. H., Fruhbeck, G., Garruti, G., & Portincasa, P. (2016). The dangerous link between childhood and adulthood predictors of obesity and metabolic syndrome. Internal and Emergency Medicine, 11, 175–182. doi:10.1007/s11739-015-1382-6 Google Scholar
Fisher, P. A., Beauchamp, K. G., Roos, L. E., Noll, L. K., Flannery, J., & Delker, B. C. (2016). The neurobiology of intervention and prevention in early adversity. Annual Review of Clinical Psychology, 12, 331–357. doi:10.1146/annurev-clinpsy-032814-112855 Google Scholar
Gami, A. S., Witt, B. J., Howard, D. E., Erwin, P. J., Gami, L. A., Somers, V. K., & Montori, V. M. (2007). Metabolic syndrome and risk of incident cardiovascular events and death. Journal of the American College of Cardiology, 49, 403–414. doi:10.1016/j.jacc.2006.09.032 Google Scholar
Gannar, F., De León, A. C., Díaz, B. B., Del, M., Rodríguez, C., Rodríguez, I. M., … Attia, N. (2015). Social class and metabolic syndrome in populations from Tunisia and Spain. Diabetology and Metabolic Syndrome, 7, 1–7. doi:10.1186/s13098-015-0084-6 CrossRefGoogle ScholarPubMed
Gianaros, P. J., Horenstein, J. A., Hariri, A. R., Sheu, L. K., Manuck, S. B., Matthews, K. A., & Cohen, S. (2008). Potential neural embedding of parental social standing. Social Cognitive and Affective Neuroscience, 3, 91–96. doi:10.1093/scan/nsn003 Google Scholar
Gianaros, P. J., & Manuck, S. B. (2010). Neurobiological pathways linking socioeconomic position and health. Psychosomatic Medicine, 72, 450–461. doi:10.1097/PSY.0b013e3181e1a23c Google Scholar
Grinshteyn, E., & Hemenway, D. (2016). Violent death rates: The US compared with other high-income OECD countries, 2010. American Journal of Medicine, 129, 266–273. doi:10.1016/j.amjmed.2015.10.025 Google Scholar
Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, Thrombosis, and Vascular Biology, 28, 629–636. doi:10.1161/ATVBAHA.107.151092 CrossRefGoogle ScholarPubMed
Grundy, S. M. (2016). Metabolic syndrome update. Trends in Cardiovascular Medicine, 26, 364–373. doi:10.1016/j.tcm.2015.10.004 Google Scholar
Gump, B. B., Matthews, K. A., & Räikkönen, K. (1999). Modeling relationships among socioeconomic status, hostility, cardiovascular reactivity, and left ventricular mass in African American and White children. Health Psychology, 18, 140–150. doi:10.1037/0278-6133.18.2.140 Google Scholar
Gunnar, M. R., & Quevedo, K. (2007). The neurobiology of stress and development. Annual Review of Psychology, 58, 145–173. doi:10.1146/annurev.psych.58.110405.085605 Google Scholar
Gustafsson, P. E., & Hammarström, A. (2012). Socioeconomic disadvantage in adolescent women and metabolic syndrome in mid-adulthood: An examination of pathways of embodiment in the Northern Swedish Cohort. Social Science and Medicine, 74, 1630–1638. doi:10.1016/j.socscimed.2012.01.044 Google Scholar
Gustafsson, P. E., Persson, M., & Om, A. H. (2011). Life course origins of the metabolic syndrome in middle-aged women and men: The role of socioeconomic status and metabolic risk factors in adolescence and early adulthood. Annals of Epidemiology, 21, 103–110. doi:10.1016/j.annepidem.2010.08.012 Google Scholar
Hackman, D. A., & Farah, M. J. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13, 65–73. doi:10.1016/j.tics.2008.11.003 CrossRefGoogle ScholarPubMed
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11, 651–659. doi:10.1038/nrn2897 CrossRefGoogle ScholarPubMed
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.Google Scholar
Hossain, P., Kawar, B., & El Nahas, M. (2007). Obesity and diabetes in the developing world— A growing challenge. New England Journal of Medicine, 356, 213–215. doi:10.1056/NEJMp068177 Google Scholar
Hostinar, C. E., Ross, K. M., Chen, E., & Miller, G. E. (2017). Early-life and adult socioeconomic disadvantage as related to metabolic syndrome. Psychosomatic Medicine. Advance online publiction. doi:10.1097/psy.0000000000000455 Google Scholar
Hostinar, C. E., Sullivan, R. M., & Gunnar, M. R. (2014). Psychobiological mechanisms underlying the social buffering of the hypothalamic-pituitary-adrenocortical axis: A review of animal models and human studies across development. Psychological Bulletin, 140, 256–282. doi:10.1037/a0032671 CrossRefGoogle Scholar
Hotamisligil, G. S. (2006). Inflammation and metabolic disorders. Nature, 444, 860–867. doi:10.1038/nature05485 Google Scholar
Jackson, R. W., Treiber, F. A., Turner, J. R., Davis, H., & Strong, W. B. (1999). Effects of race, sex, and socioeconomic status upon cardiovascular stress responsivity and recovery in youth. International Journal of Psychophysiology, 31, 111–119. doi:10.1016/S0167-8760(98)00044-0 Google Scholar
Javanbakht, A., King, A. P., Evans, G. W., Swain, J. E., Angstadt, M., Phan, K. L., & Liberzon, I. (2015). Childhood poverty predicts adult amygdala and frontal activity and connectivity in response to emotional faces. Frontiers in Behavioral Neuroscience, 9, 1–8. doi:10.3389/fnbeh.2015.00154 CrossRefGoogle ScholarPubMed
Johnson, P., & Neyman, J. (1936). Tests of certain linear hypotheses and their applications to some educational problems. Statistical Research Memoirs, 1, 57–93.Google Scholar
Kim, P., Evans, G. W., Angstadt, M., Ho, S. S., Sripada, C. S., & Swain, J. E. (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences, 110, 18442–18447. doi:10.1073/pnas.1308240110 Google Scholar
Kyrou, I., & Tsigos, C. (2009). Stress hormones: Physiological stress and regulation of metabolism. Current Opinion in Pharmacology, 9, 787–793. doi:10.1016/j.coph.2009.08.007 Google Scholar
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual (Technical Report A-8). Gainesville, FL: University of Florida.Google Scholar
Langenberg, C., Kuh, D., Wadsworth, M. E. J., Brunner, E., & Hardy, R. (2006). Social circumstances and education: Life course origins of social inequalities in metabolic risk in a prospective national birth cohort. American Journal of Public Health, 96, 2216–2221. doi:10.2105/AJPH.2004.049429 Google Scholar
Lawlor, D. A., Ebrahim, S., & Smith, G. D. (2002). Socioeconomic position in childhood and adulthood and insulin resistance: Cross sectional survey using data from British women's heart and health study. British Medical Journal, 325, 1–5.Google Scholar
Lehman, B., Taylor, S., Kiefe, C., & Seeman, T. (2005). Relation of childhood socioeconomic status and family environment to adult metabolic functioning in the CARDIA study. Psychosomatic Medicine, 67, 846–854. doi:10.1097/01.psy.0000188443.48405.eb Google Scholar
Lucove, J. C., Kaufman, J. S., & James, S. A. (2007). Association between adult and childhood socioeconomic status and prevalence of the metabolic syndrome in African Americans: The Pitt County Study. American Journal of Public Health, 97, 234–236. doi:10.2105/AJPH.2006.087429 Google Scholar
Lupien, S. J., King, S., Meaney, M. J., & McEwen, B. S. (2000). Child's stress hormone levels correlate with mother's socioeconomic status and depressive state. Biological Psychiatry, 48, 976–980. doi:10.1016/S0006-3223(00)00965-3 Google Scholar
McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences, 1186, 190–222. doi:10.1111/j.1749-6632.2009.05331.x CrossRefGoogle ScholarPubMed
Meaney, M. J. (2010). Epigenetics and the biological definition of Gene × Environment interactions. Child Development, 81, 41–79. doi:10.1111/j.1467-8624.2009.01381.x Google Scholar
Meaney, M. J., & Szyf, M. (2005). Environmental programming of stress responses through DNA methylation: Life at the interface between a dynamic environment and a fixed genome. Dialogues in Clinical Neuroscience, 7, 103–123.Google Scholar
Miller, G. E., Chen, E., & Parker, K. J. (2011). Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms. Psychological Bulletin, 137, 959–997. doi:10.1037/a0024768 Google Scholar
Miller, G. E., Cohen, S., Rabin, B. S., Skoner, D. P., & Doyle, W. J. (1999). Personality and tonic cardiovascular, neuroendocrine, and immune parameters. Brain, Behavior, and Immunity, 123, 109–123.Google Scholar
Miller, G. E., Lachman, M. E., Chen, E., Gruenewald, T. L., Karlamangla, A. S., & Seeman, T. E. (2011). Pathways to resilience: Maternal nurturance as a buffer against the effects of childhood poverty on metabolic syndrome at midlife. Psychological Science, 22, 1591–1599. doi:10.1177/0956797611419170 Google Scholar
Muscatell, K. A., Morelli, S. A., Falk, E. B., Way, B. M., Pfeifer, J. H., Galinsky, A. D., … Eisenberger, N. I. (2012). Social status modulates neural activity in the mentalizing network. NeuroImage, 60, 1771–1777. doi:10.1016/j.neuroimage.2012.01.080 CrossRefGoogle ScholarPubMed
Non, A. L., Rewak, M., Kawachi, I., Gilman, S. E., Loucks, E. B., Appleton, A. A., … Kubzansky, L. D. (2014). Childhood social disadvantage, cardiometabolic risk, and chronic disease in adulthood. American Journal of Epidemiology, 180, 263–271. doi:10.1093/aje/kwu127 Google Scholar
Nusslock, R., & Miller, G. E. (2016). Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis. Biological Psychiatry, 80, 23–32. doi:10.1016/j.biopsych.2015.05.017 CrossRefGoogle ScholarPubMed
Ong, K. L., Tso, A. W., Lam, K. S., Cherny, S. S., Sham, P. C., & Cheung, B. M. (2010). Using glycosylated hemoglobin to define the metabolic syndrome in United States adults. Diabetes Care, 33, 1856–1858. doi:10.2337/dc10-0190 Google Scholar
Paffenbarger, R. S., Blair, S. N., Lee, I.-M., & Hyde, R. T. (1993). Measurement of physical activity to assess health effects in free-living populations. Medicine and Science in Sports and Exercise, 25, 60–70.Google Scholar
Park, M. J., Yun, K. E., Lee, G. U., Cho, H. J., & Park, H. S. (2007). A cross-sectional study of socioeconomic status and the Metabolic Syndrome in Korean adults. Annals of Epidemiology, 17, 320–326. doi:10.1016/j.annepidem.2006.10.007 Google Scholar
Park, Y.-W., Zhu, S., Palaniappan, L., Heshka, S., Carnethon, M. R., & Heymsfield, S. B. (2003). The metabolic syndrome. Archives of Internal Medicine, 163, 1988–1994. doi:10.1001/archinte.163.4.427 Google Scholar
Parker, L., Lamont, D. W., Unwin, N., Pearce, M. S., Bennett, S. M. A., Dickinson, H. O., … Craft, A. W. (2003). A lifecourse study of risk for hyperinsulinaemia, dyslipidaemia and obesity (the central metabolic syndrome) at age 49–51 years. Diabetic Medicine, 20, 406–415. doi:10.1046/j.1464-5491.2003.00949.x CrossRefGoogle ScholarPubMed
Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. doi:10.1037/0022-3514.81.2.181 Google Scholar
Payne, B. K. (2006). Weapon bias: Split-second decisions and unintended stereotyping. Current Directions in Psychological Science, 15, 287–291. doi:10.1111/j.1467-8721.2006.00454.x Google Scholar
Pervanidou, P., & Chrousos, G. P. (2012). Metabolic consequences of stress during childhood and adolescence. Metabolism, 61, 611–619. doi:10.1016/j.metabol.2011.10.005 Google Scholar
Pollak, S. D. (2008). Mechanisms linking early experience and the emergence of emotions illustrations from the study of maltreated children. Current Directions in Psychological Science, 17, 370–375. doi:10.1111/j.1467-8721.2008.00608.x Google Scholar
Saland, J. M. (2007). Update on the metabolic syndrome in children. Current Opinion in Pediatrics, 19, 183–191. doi:10.1097/MOP.0b013e3280208519 Google Scholar
Schooling, C. M., Jiang, C. Q., Lam, T. H., Zhang, W. S., Cheng, K. K., & Leung, G. M. (2008). Life-course origins of social inequalities in metabolic risk in the population of a developing country. American Journal of Epidemiology, 16, 419–428. doi:10.1093/aje/kwm329 Google Scholar
Silventoinen, K., Pankow, J., Jousilahti, P., Hu, G., & Tuomilehto, J. (2005). Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women. International Journal of Epidemiology, 34, 327–334. doi:10.1093/ije/dyi007 Google Scholar
Sroufe, L. A., Egeland, B., Carlson, E. A., & Collins, W. A. (2005). The development of the person: The Minnesota study of risk and adaptation from birth to adulthood. New York: Guilford Press.Google Scholar
Tamayo, T., Christian, H., & Rathmann, W. (2010). Impact of early psychosocial factors (childhood socioeconomic factors and adversities) on future risk of type 2 diabetes, metabolic disturbances and obesity: A systematic review. BMC Public Health, 10, 525. doi:10.1186/1471-2458-10-525 Google Scholar
Taylor, S. E., Lerner, J. S., Sage, R. M., Lehman, B. J., & Seeman, T. E. (2004). Early environment, emotions, responses to stress, and health. Journal of Personality, 72, 1365–1394. doi:10.1111/j.1467-6494.2004.00300.x Google Scholar
Treiber, F., Harshfield, G., Davis, H., Kapuku, G., & Moore, D. (1990). Stress responsivity and body fatness: Links between socioeconomic status and cardiovascular risk factors in youth. Annals of the New York Academy of Sciences, 6295, 435–438. doi:10.1111/j.1749-6632.1999.tb08163.x Google Scholar
Wiley, J. F., Gruenewald, T. L., Karlamangla, A. S., & Seeman, T. E. (2016). Modeling multisystem physiological dysregulation. Psychosomatic Medicine, 78, 1–12. doi:10.1097/PSY.0000000000000288 Google Scholar