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      A Biobehavioral Framework to Address the Emerging Challenge of Multimorbidity :

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          Abstract

          Multimorbidity, the co-occurrence of multiple physical or psychological illnesses, is prevalent particularly among older adults. The number of Americans with multiple chronic diseases is projected to increase from 57 million in 2000 to 81 million in 2020. However, behavioral medicine and health psychology, while focusing on the co-occurrence of psychological/psychiatric disorders with primary medical morbidities, have historically tended to ignore the co-occurrence of primary medical comorbidities, such as diabetes and cancer, and their biopsychosocial implications. This approach may hinder our ecologically valid understanding of the etiology, prevention, and treatment for individual patients with multimorbidity. In this selective review, we propose a heuristic behavioral framework for the etiology of multimorbidity. More acknowledgment and systematic research on multiple, co-existing disorders in behavioral medicine are consistent with the biopsychosocial model's emphasis on treating the "whole person," which means not considering any single illness, its symptoms, risk factors, or mechanisms, in isolation. As systems analytics, big data, machine learning, and mixed-model trajectory analyses, among others, come online and become more widely available, we may be able to tackle multimorbidity more holistically, efficiently, and satisfactorily.

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          Most cited references37

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          Cumulative illness rating scale.

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            The implications of human metabolic network topology for disease comorbidity.

            Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.
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              Anger, anxiety, and depression as risk factors for cardiovascular disease: the problems and implications of overlapping affective dispositions.

              Several recent reviews have identified 3 affective dispositions--depression, anxiety, and anger-hostility--as putative risk factors for coronary heart disease. There are, however, mixed and negative results. Following a critical summary of epidemiological findings, the present article discusses the construct and measurement overlap among the 3 negative affects. Recognition of the overlap necessitates the development of more complex affect-disease models and has implications for the interpretation of prior studies, statistical analyses, prevention, and intervention in health psychology and behavioral medicine. The overlap among the 3 negative dispositions also leaves open the possibility that a general disposition toward negative affectivity may be more important for disease risk than any specific negative affect.
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                Author and article information

                Journal
                Psychosomatic Medicine
                Psychosomatic Medicine
                Ovid Technologies (Wolters Kluwer Health)
                0033-3174
                2016
                April 2016
                : 78
                : 3
                : 281-289
                Article
                10.1097/PSY.0000000000000294
                4889007
                26867072
                af389986-3784-4e3e-b7dc-f4c1ad958c2c
                © 2016
                History

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