EDITORIAL COMMENT
Allostatic Load and the Assessment of Cumulative Biological Risk in Biobehavioral Medicine: Challenges and Opportunities Abstract: Allostatic load provides a useful framework for conceptualizing the multisystem physiological impact of sustained stress and its effects on health and well-being. Research across two decades shows that allostatic load indices predict health outcomes including all-cause mortality and vary with stress and related psychosocial constructs. The study by Slopen and colleagues in this issue provides an example both of the utility of the allostatic load framework and of limitations in related literature, such as inconsistencies in conceptualization and measurement across studies, and the frequent application of cross-sectional designs. The current article describes these limitations and provides suggestions for further research to enhance the value and utility of the allostatic load framework in biobehavioral medicine research. Key words: allostatic load.
Allostatic load provides an integrative framework for understanding the physiological processes through which chronic stress and other sustained psychosocial factors affect health and well-being (1). This model was introduced by McEwen and Stellar (2) to describe the biologic toll exacted by prolonged activation of primary markers in the autonomic nervous system and hypothalamic-pituitary-adrenocortical system, as an organism attempts to maintain ‘‘allostasis’’ (i.e., physiological homeostasis) in the face of environmental, psychological, and behavioral challenges. The cumulative stress responses can have damaging effects on multiple downstream secondary physiological functions, thereby increasing morbidity and mortality risks, conceptualized as tertiary outcomes in the allostatic load model (1). The model recognizes that there is wide variation in physiological and health consequences of chronic stress as a function of interacting genetic, environmental, and individual influences (3,4). In contrast to the common practice of examining risk factors within a single physiological system, the allostatic load framework provides an integrative approach that may better characterize the cumulative impact of dynamic and nonlinear influences across major biological regulatory systems. Several recent literature reviews summarizing nearly two decades of research have concluded that allostatic load predicts health outcomes including cardiovascular disease, functional decline, frailty, and all-cause mortality (5Y7). The model has also proven useful in elucidating the physiological consequences of psychosocial and socioeconomic antecedents of stress and their implications for health disparities (5,6,8,9). Although early allostatic load studies were conducted in a single cohort with limited sociodemographic variability (10,11), subsequent research has examined diverse populations and varied social constructs (e.g., socioeconomic status and immigration) (5,6). This work has strengthened the evidence for the allostatic load framework and its utility in understanding health and social correlates therein (5,6).
DOI: 10.1097/PSY.0000000000000095 478
In the current issue, Slopen and colleagues (12) report associations between childhood adversity and allostatic loadVhere, termed ‘‘cumulative biological risk’’Vin 550 participants from the Chicago Community Adult Health Study. They found that participants who reported experiencing greater adversity in childhood had increased dysregulation across physiological systems, but only if they also resided as adults in neighborhoods characterized by low affluence (operationalized using census data). The authors concluded that the resources inherent to an affluent environment could buffer the harmful physiological consequences of early life adversity. Through this application of the allostatic load framework, the study provides a unique contribution toward understanding the life course impact of early stress exposure on a range of deleterious physiological outcomes, as moderated by neighborhood context. The study also highlights several limitations of the extant allostatic load literature that deserve further consideration. In particular, the research provides an example of unsettled questions regarding the optimal representation of allostatic load (5,6). Allostatic load is typically operationalized as a composite of biological markers representing multiple systems, especially the neuroendocrine, cardiovascular, metabolic, and immune systems. Allostatic load composite scores often combine primary mediators of the stress response (e.g., stress hormones and proinflammatory cytokines) and secondary outcomes of cardiovascular, metabolic, and immune dysregulation (e.g., blood pressure, waist circumference, and glycosylated hemoglobin), measured at a single point in time. However, the research base is notable for the substantial variability in the specific indicators chosen, the number of indicators used both across and within physiological systems, whether continuous or categorical scores are combined, how categorical thresholds are derived, whether or not the biomarkers are adjusted for use of medications, and how indicators are combined into composite scores (e.g., whether or not a weighted approach is used; for a detailed description of approaches used to operationalize allostatic load, see Ref. (5)). A common method is to form a simple composite by counting the number of physiological parameters at relatively ‘‘high’’ or ‘‘low’’ levels based on the distribution of scores in the study Psychosomatic Medicine 76:478Y480 (2014)
0033-3174/14/7607Y0478 Copyright * 2014 by the American Psychosomatic Society
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
EDITORIAL COMMENT sample (e.g., extreme quartile approach), either overall or specific to sex groups. However, because the distributions depend on the population, this method compromises uniformity across studies. Moreover, sample-based thresholds may be useful for research purposes, but their psychosocial, medical, and public health applications are less clear, and the statistical underpinnings of these composite scores generally are not well documented. To bypass this concern, some studies have instead applied a priori clinically defined thresholds to categorize participants as high or low risk. However, not all physiological components selected to define allostatic load have known clinical thresholds, including the primary neuroendocrine and inflammatory biomarkers. Other investigators have tested complex scoring methods to create allostatic load indices, for example, relying on recursive partitioning (13), canonical correlation (14), and ‘‘grade of membership’’ (15,16) multivariate models. The limited comparative research suggests that the choice of indicators and the approach used to combine them have only a modest bearing on predictive utility (4,13,14), although maintaining the continuum of scores and/or incorporating both high and low extremes may enhance the ability to detect associations of allostatic load with a range of health outcomes (5,15,17,18). The study by Slopen and colleagues applied a composite of eight cardiovascular, metabolic, and immune system indicators with known clinically relevant thresholds. Notably, none of the primary biomarkers were included in the composite, and this may be a reason that the authors applied the term ‘‘cumulative biological risk,’’ instead of allostatic load. This practice is relatively common, even though the primary mediators are considered a fundamental element in the allostatic load framework. The approach to conceptualizing allostatic load in large epidemiological studies is often determined pragmatically according to the available information, and most of these trials (e.g., the National Health and Nutrition Surveys) do not include neuroendrocrine assessments. Moreover, the primary biomarkers are difficult to measure consistently, as they may be collected from varied biological samples (e.g., blood, saliva, and urine) and are prone to circadian fluctuations as well as rapid responses from external stimuli (8,19). Thus, a single point-in-time assessment may not adequately capture true dysregulation. Also notable is the fact that several of the indicators used in the study by Slopen et al. overlap with those used to diagnose the metabolic syndrome (i.e., waist circumference, systolic and diastolic blood pressure, high-density lipoprotein cholesterol, and glycosylated hemoglobin, which captures similar information to fasting glucose) (20). The similarity between allostatic load and the metabolic syndrome, or other composite systems such as the Framingham Risk Score, has been noted previously (11). There is some evidence that metabolic syndrome and allostatic load are conceptually distinct (21) and that the variance in health predicted by allostatic load is greater than that predicted by the metabolic syndrome (14,22). Nonetheless, additional research to better understand the relative utility of these and related composite frameworks is indicated. In addition, it remains important for investigators to describe findings for individual components of allostatic
load, as Slopen and colleagues have done, to enhance comparability across studies and identify the specific components of allostatic load that contribute to results based on composite scores. Like much prior research, the study by Slopen et al. also applied a cross-sectional design, creating unanswered questions about the directionality and temporal progression of observed associations. The allostatic load model fundamentally proposes that the physiological burden of stress is cumulative over timeVa premise that cannot explicitly be examined within a cross-sectional design. Even longitudinal studies may inadequately address this central tenet if the follow-up period is limited, given that the duration of exposure necessary for primary mediators to induce secondary and tertiary outcomes is unknown. Similarly, a cross-sectional design prevents determination of whether or not the secondary physiological dysregulation is in fact subsequent to primary mediators and to the stress exposure. Reverse-causation or loop mechanisms may also be relevant because individuals with greater physiological dysregulation could experience, and thus report, higher or added stressors at the point of mutual assessment (22). These complex associations cannot directly be explored through a cross-sectional study with a single assessment occasion. The current study by Slopen et al. circumvents this concern to some extent by (retrospectively) assessing previous childhood experience as exposure. Nonetheless, only a few prospective studies to date support the applicability of the allostatic load framework (23,24) and the need for additional longitudinal research has been repeatedly emphasized (5,25,26). Although research suggests that social and health correlates are generally consistent across different operationalizations of allostatic load, definitional variability may impede the adoption and application of the allostatic load framework in research and practice. The absence of a ‘‘gold standard’’ representation also limits the comparisons that can be made across applications. Such variability is common in the early stages of biomarker panel development (19), but to further advance the field, agreement is needed regarding the biological indicators and systems that should optimally be used to operationalize allostatic load (5,19). Moreover, additional observational studies in humans and targeted experimental investigations are warranted to evaluate the health implications of allostatic load, especially in longterm prospective cohorts with data on interacting factors (genetic influences, health behaviors). Such studies will improve understanding of the life course dynamics of allostatic load-related processes in health and disease (25Y27). Further research is also needed in diverse samples to investigate the consistency of observed findings and the utility of specific allostatic load operationalizations, across different populations. In sum, the allostatic load framework and its potential for advancing biobehavioral medicine research and practice will be enhanced by efforts to standardize operationalization; to disentangle the temporal sequence among hypothesized antecedents, mediators, and physiological consequences; and to examine the generalizability of these and other advances in allostatic load across diverse groups.
Psychosomatic Medicine 76:478Y480 (2014)
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
479
EDITORIAL COMMENT LINDA C. GALLO, PHD Department of Psychology San Diego State University, San Diego, California ADDIE L. FORTMANN, PHD Scripps Whittier Diabetes Institute The Scripps Research Institute, San Diego, California JOSIEMER MATTEI, PHD, MPH Department of Nutrition Harvard School of Public Health Boston, Massachusetts REFERENCES 1. McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med 1998;338:171Y9. 2. McEwen BS, Stellar E. Stress and the individual. Mechanisms leading to disease. Arch Intern Med 1993;153:2093Y101. 3. McEwen BS, Gianaros PJ. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 2010;1186:190Y222. 4. Seeman T, Epel E, Gruenewald T, Karlamangla A, McEwen BS. Socioeconomic differentials in peripheral biology: cumulative allostatic load. Ann N Y Acad Sci 2010;1186:223Y39. 5. Beckie TM. A systematic review of allostatic load, health, and health disparities. Biol Res Nurs 2012;14:311Y46. 6. Juster RP, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 2010; 35:2Y16. 7. Leahy R, Crews DE. Physiological dysregulation and somatic decline among elders: modeling, applying and re-interpreting allostatic load. Coll Antropol 2012;36:11Y22. 8. Dowd JB, Simanek AM, Aiello AE. Socio-economic status, cortisol and allostatic load: a review of the literature. Int J Epidemiol 2009;38: 1297Y309. 9. Szanton SL, Gill JM, Allen JK. Allostatic load: a mechanism of socioeconomic health disparities? Biol Res Nurs 2005;7:7Y15. 10. Seeman TE, Singer BH, Rowe JW, Horwitz RI, McEwen BS. Price of adaptationVallostatic load and its health consequences. MacArthur studies of successful aging. Arch Intern Med 1997;157:2259Y68. 11. Seeman TE, McEwen BS, Rowe JW, Singer BH. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc Natl Acad Sci U S A 2001;98:4770Y5. 12. Slopen N, Non A, Williams DR, Roberts AL, Albert MA. Childhood adversity, adult neighborhood context, and cumulative biological risk for chronic diseases in adulthood. Psychosom Med 2014;76:XXYXX.
480
13. Gruenewald TL, Seeman TE, Ryff CD, Karlamangla AS, Singer BH. Combinations of biomarkers predictive of later life mortality. Proc Natl Acad Sci U S A 2006;103:14158Y63. 14. Karlamangla AS, Singer BH, McEwen BS, Rowe JW, Seeman TE. Allostatic load as a predictor of functional decline: MacArthur studies of successful aging. J Clin Epidemiol 2002;55:696Y710. 15. Seplaki CL, Goldman N, Glei D, Weinstein M. A comparative analysis of measurement approaches for physiological dysregulation in an older population. Exp Gerontol 2005;40:438Y49. 16. Seplaki CL, Goldman N, Weinstein M, Lin YH. Measurement of cumulative physiological dysregulation in an older population. Demography 2006;43:165Y83. 17. Hampson SE, Goldberg LR, Vogt TM, Hillier TA, Dubanoski JP. Using physiological dysregulation to assess global health status: associations with self-rated health and health behaviors. J Health Psychol 2009;14:232Y41. 18. Seplaki CL, Goldman N, Weinstein M, Lin YH. How are biomarkers related to physical and mental well-being? J Gerontol A Biol Sci Med Sci 2004;59:B201Y17. 19. Loucks EB, Juster RP, Pruessner JC. Neuroendocrine biomarkers, allostatic load, and the challenge of measurement: a commentary on Gersten. Soc Sci Med 2008;66:525Y30. 20. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640Y5. 21. McCaffery JM, Marsland AL, Strohacker K, Muldoon MF, Manuck SB. Factor structure underlying components of allostatic load. PloS One 2012; 7:e47246. 22. Mattei J, Demissie S, Falcon LM, Ordovas JM, Tucker K. Allostatic load is associated with chronic conditions in the Boston Puerto Rican Health Study. Soc Sci Med 2010;70:1988Y96. 23. Hwang AC, Peng LN, Wen YW, Tsai YW, Chang LC, Chiou ST, Chen LK. Predicting all-cause and cause-specific mortality by static and dynamic measurements of allostatic load: a 10-year population-based cohort study in Taiwan. J Am Med Dir Assoc 2014;15:490Y6. 24. Karlamangla AS, Singer BH, Seeman TE. Reduction in allostatic load in older adults is associated with lower all-cause mortality risk: MacArthur studies of successful aging. Psychosom Med 2006;68:500Y7. 25. Juster RP, Marin MF, Sindi S, Nair NP, Ng YK, Pruessner JC, Lupien SJ. Allostatic load associations to acute, 3-year and 6-year prospective depressive symptoms in healthy older adults. Physiol Behav 2011;104:360Y4. 26. Piazza JR, Almeida DM, Dmitrieva NO, Klein LC. Frontiers in the use of biomarkers of health in research on stress and aging. J Gerontol B Psychol Sci Soc Sci 2010;65:513Y25. 27. Goldman N, Turra CM, Glei DA, Lin YH, Weinstein M. Physiological dysregulation and changes in health in an older population. Exp Gerontol 2006;41:862Y70.
Psychosomatic Medicine 76:478Y480 (2014)
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
Allostatic Load and the Assessment of Cumulative Biological Risk in Biobehavioral Medicine: Challenges and Opportunities Abstract: Allostatic load provides a useful framework for conceptualizing the multisystem physiological impact of sustained stress and its effects on health and well-being. Research across two decades shows that allostatic load indices predict health outcomes including all-cause mortality and vary with stress and related psychosocial constructs. The study by Slopen and colleagues in this issue provides an example both of the utility of the allostatic load framework and of limitations in related literature, such as inconsistencies in conceptualization and measurement across studies, and the frequent application of cross-sectional designs. The current article describes these limitations and provides suggestions for further research to enhance the value and utility of the allostatic load framework in biobehavioral medicine research. Key words: allostatic load.
Allostatic load provides an integrative framework for understanding the physiological processes through which chronic stress and other sustained psychosocial factors affect health and well-being (1). This model was introduced by McEwen and Stellar (2) to describe the biologic toll exacted by prolonged activation of primary markers in the autonomic nervous system and hypothalamic-pituitary-adrenocortical system, as an organism attempts to maintain ‘‘allostasis’’ (i.e., physiological homeostasis) in the face of environmental, psychological, and behavioral challenges. The cumulative stress responses can have damaging effects on multiple downstream secondary physiological functions, thereby increasing morbidity and mortality risks, conceptualized as tertiary outcomes in the allostatic load model (1). The model recognizes that there is wide variation in physiological and health consequences of chronic stress as a function of interacting genetic, environmental, and individual influences (3,4). In contrast to the common practice of examining risk factors within a single physiological system, the allostatic load framework provides an integrative approach that may better characterize the cumulative impact of dynamic and nonlinear influences across major biological regulatory systems. Several recent literature reviews summarizing nearly two decades of research have concluded that allostatic load predicts health outcomes including cardiovascular disease, functional decline, frailty, and all-cause mortality (5Y7). The model has also proven useful in elucidating the physiological consequences of psychosocial and socioeconomic antecedents of stress and their implications for health disparities (5,6,8,9). Although early allostatic load studies were conducted in a single cohort with limited sociodemographic variability (10,11), subsequent research has examined diverse populations and varied social constructs (e.g., socioeconomic status and immigration) (5,6). This work has strengthened the evidence for the allostatic load framework and its utility in understanding health and social correlates therein (5,6).
DOI: 10.1097/PSY.0000000000000095 478
In the current issue, Slopen and colleagues (12) report associations between childhood adversity and allostatic loadVhere, termed ‘‘cumulative biological risk’’Vin 550 participants from the Chicago Community Adult Health Study. They found that participants who reported experiencing greater adversity in childhood had increased dysregulation across physiological systems, but only if they also resided as adults in neighborhoods characterized by low affluence (operationalized using census data). The authors concluded that the resources inherent to an affluent environment could buffer the harmful physiological consequences of early life adversity. Through this application of the allostatic load framework, the study provides a unique contribution toward understanding the life course impact of early stress exposure on a range of deleterious physiological outcomes, as moderated by neighborhood context. The study also highlights several limitations of the extant allostatic load literature that deserve further consideration. In particular, the research provides an example of unsettled questions regarding the optimal representation of allostatic load (5,6). Allostatic load is typically operationalized as a composite of biological markers representing multiple systems, especially the neuroendocrine, cardiovascular, metabolic, and immune systems. Allostatic load composite scores often combine primary mediators of the stress response (e.g., stress hormones and proinflammatory cytokines) and secondary outcomes of cardiovascular, metabolic, and immune dysregulation (e.g., blood pressure, waist circumference, and glycosylated hemoglobin), measured at a single point in time. However, the research base is notable for the substantial variability in the specific indicators chosen, the number of indicators used both across and within physiological systems, whether continuous or categorical scores are combined, how categorical thresholds are derived, whether or not the biomarkers are adjusted for use of medications, and how indicators are combined into composite scores (e.g., whether or not a weighted approach is used; for a detailed description of approaches used to operationalize allostatic load, see Ref. (5)). A common method is to form a simple composite by counting the number of physiological parameters at relatively ‘‘high’’ or ‘‘low’’ levels based on the distribution of scores in the study Psychosomatic Medicine 76:478Y480 (2014)
0033-3174/14/7607Y0478 Copyright * 2014 by the American Psychosomatic Society
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
EDITORIAL COMMENT sample (e.g., extreme quartile approach), either overall or specific to sex groups. However, because the distributions depend on the population, this method compromises uniformity across studies. Moreover, sample-based thresholds may be useful for research purposes, but their psychosocial, medical, and public health applications are less clear, and the statistical underpinnings of these composite scores generally are not well documented. To bypass this concern, some studies have instead applied a priori clinically defined thresholds to categorize participants as high or low risk. However, not all physiological components selected to define allostatic load have known clinical thresholds, including the primary neuroendocrine and inflammatory biomarkers. Other investigators have tested complex scoring methods to create allostatic load indices, for example, relying on recursive partitioning (13), canonical correlation (14), and ‘‘grade of membership’’ (15,16) multivariate models. The limited comparative research suggests that the choice of indicators and the approach used to combine them have only a modest bearing on predictive utility (4,13,14), although maintaining the continuum of scores and/or incorporating both high and low extremes may enhance the ability to detect associations of allostatic load with a range of health outcomes (5,15,17,18). The study by Slopen and colleagues applied a composite of eight cardiovascular, metabolic, and immune system indicators with known clinically relevant thresholds. Notably, none of the primary biomarkers were included in the composite, and this may be a reason that the authors applied the term ‘‘cumulative biological risk,’’ instead of allostatic load. This practice is relatively common, even though the primary mediators are considered a fundamental element in the allostatic load framework. The approach to conceptualizing allostatic load in large epidemiological studies is often determined pragmatically according to the available information, and most of these trials (e.g., the National Health and Nutrition Surveys) do not include neuroendrocrine assessments. Moreover, the primary biomarkers are difficult to measure consistently, as they may be collected from varied biological samples (e.g., blood, saliva, and urine) and are prone to circadian fluctuations as well as rapid responses from external stimuli (8,19). Thus, a single point-in-time assessment may not adequately capture true dysregulation. Also notable is the fact that several of the indicators used in the study by Slopen et al. overlap with those used to diagnose the metabolic syndrome (i.e., waist circumference, systolic and diastolic blood pressure, high-density lipoprotein cholesterol, and glycosylated hemoglobin, which captures similar information to fasting glucose) (20). The similarity between allostatic load and the metabolic syndrome, or other composite systems such as the Framingham Risk Score, has been noted previously (11). There is some evidence that metabolic syndrome and allostatic load are conceptually distinct (21) and that the variance in health predicted by allostatic load is greater than that predicted by the metabolic syndrome (14,22). Nonetheless, additional research to better understand the relative utility of these and related composite frameworks is indicated. In addition, it remains important for investigators to describe findings for individual components of allostatic
load, as Slopen and colleagues have done, to enhance comparability across studies and identify the specific components of allostatic load that contribute to results based on composite scores. Like much prior research, the study by Slopen et al. also applied a cross-sectional design, creating unanswered questions about the directionality and temporal progression of observed associations. The allostatic load model fundamentally proposes that the physiological burden of stress is cumulative over timeVa premise that cannot explicitly be examined within a cross-sectional design. Even longitudinal studies may inadequately address this central tenet if the follow-up period is limited, given that the duration of exposure necessary for primary mediators to induce secondary and tertiary outcomes is unknown. Similarly, a cross-sectional design prevents determination of whether or not the secondary physiological dysregulation is in fact subsequent to primary mediators and to the stress exposure. Reverse-causation or loop mechanisms may also be relevant because individuals with greater physiological dysregulation could experience, and thus report, higher or added stressors at the point of mutual assessment (22). These complex associations cannot directly be explored through a cross-sectional study with a single assessment occasion. The current study by Slopen et al. circumvents this concern to some extent by (retrospectively) assessing previous childhood experience as exposure. Nonetheless, only a few prospective studies to date support the applicability of the allostatic load framework (23,24) and the need for additional longitudinal research has been repeatedly emphasized (5,25,26). Although research suggests that social and health correlates are generally consistent across different operationalizations of allostatic load, definitional variability may impede the adoption and application of the allostatic load framework in research and practice. The absence of a ‘‘gold standard’’ representation also limits the comparisons that can be made across applications. Such variability is common in the early stages of biomarker panel development (19), but to further advance the field, agreement is needed regarding the biological indicators and systems that should optimally be used to operationalize allostatic load (5,19). Moreover, additional observational studies in humans and targeted experimental investigations are warranted to evaluate the health implications of allostatic load, especially in longterm prospective cohorts with data on interacting factors (genetic influences, health behaviors). Such studies will improve understanding of the life course dynamics of allostatic load-related processes in health and disease (25Y27). Further research is also needed in diverse samples to investigate the consistency of observed findings and the utility of specific allostatic load operationalizations, across different populations. In sum, the allostatic load framework and its potential for advancing biobehavioral medicine research and practice will be enhanced by efforts to standardize operationalization; to disentangle the temporal sequence among hypothesized antecedents, mediators, and physiological consequences; and to examine the generalizability of these and other advances in allostatic load across diverse groups.
Psychosomatic Medicine 76:478Y480 (2014)
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
479
EDITORIAL COMMENT LINDA C. GALLO, PHD Department of Psychology San Diego State University, San Diego, California ADDIE L. FORTMANN, PHD Scripps Whittier Diabetes Institute The Scripps Research Institute, San Diego, California JOSIEMER MATTEI, PHD, MPH Department of Nutrition Harvard School of Public Health Boston, Massachusetts REFERENCES 1. McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med 1998;338:171Y9. 2. McEwen BS, Stellar E. Stress and the individual. Mechanisms leading to disease. Arch Intern Med 1993;153:2093Y101. 3. McEwen BS, Gianaros PJ. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 2010;1186:190Y222. 4. Seeman T, Epel E, Gruenewald T, Karlamangla A, McEwen BS. Socioeconomic differentials in peripheral biology: cumulative allostatic load. Ann N Y Acad Sci 2010;1186:223Y39. 5. Beckie TM. A systematic review of allostatic load, health, and health disparities. Biol Res Nurs 2012;14:311Y46. 6. Juster RP, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 2010; 35:2Y16. 7. Leahy R, Crews DE. Physiological dysregulation and somatic decline among elders: modeling, applying and re-interpreting allostatic load. Coll Antropol 2012;36:11Y22. 8. Dowd JB, Simanek AM, Aiello AE. Socio-economic status, cortisol and allostatic load: a review of the literature. Int J Epidemiol 2009;38: 1297Y309. 9. Szanton SL, Gill JM, Allen JK. Allostatic load: a mechanism of socioeconomic health disparities? Biol Res Nurs 2005;7:7Y15. 10. Seeman TE, Singer BH, Rowe JW, Horwitz RI, McEwen BS. Price of adaptationVallostatic load and its health consequences. MacArthur studies of successful aging. Arch Intern Med 1997;157:2259Y68. 11. Seeman TE, McEwen BS, Rowe JW, Singer BH. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc Natl Acad Sci U S A 2001;98:4770Y5. 12. Slopen N, Non A, Williams DR, Roberts AL, Albert MA. Childhood adversity, adult neighborhood context, and cumulative biological risk for chronic diseases in adulthood. Psychosom Med 2014;76:XXYXX.
480
13. Gruenewald TL, Seeman TE, Ryff CD, Karlamangla AS, Singer BH. Combinations of biomarkers predictive of later life mortality. Proc Natl Acad Sci U S A 2006;103:14158Y63. 14. Karlamangla AS, Singer BH, McEwen BS, Rowe JW, Seeman TE. Allostatic load as a predictor of functional decline: MacArthur studies of successful aging. J Clin Epidemiol 2002;55:696Y710. 15. Seplaki CL, Goldman N, Glei D, Weinstein M. A comparative analysis of measurement approaches for physiological dysregulation in an older population. Exp Gerontol 2005;40:438Y49. 16. Seplaki CL, Goldman N, Weinstein M, Lin YH. Measurement of cumulative physiological dysregulation in an older population. Demography 2006;43:165Y83. 17. Hampson SE, Goldberg LR, Vogt TM, Hillier TA, Dubanoski JP. Using physiological dysregulation to assess global health status: associations with self-rated health and health behaviors. J Health Psychol 2009;14:232Y41. 18. Seplaki CL, Goldman N, Weinstein M, Lin YH. How are biomarkers related to physical and mental well-being? J Gerontol A Biol Sci Med Sci 2004;59:B201Y17. 19. Loucks EB, Juster RP, Pruessner JC. Neuroendocrine biomarkers, allostatic load, and the challenge of measurement: a commentary on Gersten. Soc Sci Med 2008;66:525Y30. 20. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640Y5. 21. McCaffery JM, Marsland AL, Strohacker K, Muldoon MF, Manuck SB. Factor structure underlying components of allostatic load. PloS One 2012; 7:e47246. 22. Mattei J, Demissie S, Falcon LM, Ordovas JM, Tucker K. Allostatic load is associated with chronic conditions in the Boston Puerto Rican Health Study. Soc Sci Med 2010;70:1988Y96. 23. Hwang AC, Peng LN, Wen YW, Tsai YW, Chang LC, Chiou ST, Chen LK. Predicting all-cause and cause-specific mortality by static and dynamic measurements of allostatic load: a 10-year population-based cohort study in Taiwan. J Am Med Dir Assoc 2014;15:490Y6. 24. Karlamangla AS, Singer BH, Seeman TE. Reduction in allostatic load in older adults is associated with lower all-cause mortality risk: MacArthur studies of successful aging. Psychosom Med 2006;68:500Y7. 25. Juster RP, Marin MF, Sindi S, Nair NP, Ng YK, Pruessner JC, Lupien SJ. Allostatic load associations to acute, 3-year and 6-year prospective depressive symptoms in healthy older adults. Physiol Behav 2011;104:360Y4. 26. Piazza JR, Almeida DM, Dmitrieva NO, Klein LC. Frontiers in the use of biomarkers of health in research on stress and aging. J Gerontol B Psychol Sci Soc Sci 2010;65:513Y25. 27. Goldman N, Turra CM, Glei DA, Lin YH, Weinstein M. Physiological dysregulation and changes in health in an older population. Exp Gerontol 2006;41:862Y70.
Psychosomatic Medicine 76:478Y480 (2014)
Copyright © 2014 by the American Psychosomatic Society. Unauthorized reproduction of this article is prohibited.
