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Midlife in the United States (MIDUS 2): Biomarker Project, 2004-2009

Version
v5
Resource Type
Dataset : clinical data, experimental data, survey data
Creator
  • Ryff, Carol D.
  • Seeman, Teresa
  • Weinstein, Maxine
Other Title
  • Version 5 (Subtitle)
Collective Title
  • Midlife in the United States (MIDUS) Series
Publication Date
2010-09-24
Publication Place
Ann Arbor, Michigan
Publisher
  • Inter-University Consortium for Political and Social Research
Funding Reference
  • United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging
Language
English
Free Keywords
Schema: ICPSR
adults; biomarkers; disease prevention; health; health behavior; human behavior; medical evaluation; medications; physical condition; psychological evaluation; psychological wellbeing; psychosocial assessment
Description
  • Abstract

    These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.The Biomarker study is Project 4 of the MIDUS longitudinal study, a national survey of more than 7,000 Americans (aged 25 to 74) begun in 1994. The purpose of the larger study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. With support from the National Institute on Aging, a longitudinal follow-up of the original MIDUS samples [core sample (N = 3,487), metropolitan over-samples (N = 757), twins (N = 957 pairs), and siblings (N = 950)] was conducted in 2004-2006. Guiding hypotheses, at the most general level, were that behavioral and psychosocial factors are consequential for health (physical and mental). A description of the study and findings from it are available on the MIDUS Web site. The Biomarker Project (Project 4) of MIDUS II contains data from 1,255 respondents. These respondents include two distinct subsamples, all of whom completed the Project 1 Survey: (1) longitudinal survey sample (n = 1,054) and (2) Milwaukee sample (n = 201). The Milwaukee group contained individuals who participated in the baseline MIDUS Milwaukee study, initiated in 2005. The purpose of the Biomarker Project (Project 4) was to add comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biology. The broad aim is to identify biopsychosocial pathways that contribute to diverse health outcomes. A further theme is to investigate protective roles that behavioral and psychosocial factors have in delaying morbidity and mortality, or in fostering resilience and recovery from health challenges once they occur. The research was not disease-specific, given that psychosocial factors have relevance across multiple health endpoints. Biomarker data collection was carried out at three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allow for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities, bone densitometry, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Project 1, participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor. The MIDUS and MIDJA Biomarker Clinic Visits include collection of comprehensive information about medications of all types, as well as basic information about allergic reactions to any type of medication. Respondents were instructed to bring all their medications, or information about their medications, to the clinic visit to ensure the information about those medications was recorded accurately. Information regarding Prescription Medications (FDA approved medications prescribed by someone authorized/licensed under the Western medical tradition, or medications prescribed by individuals authorized under Japanese law to prescribe Western and/or Eastern/Chinese traditional medicine), Quasi Medications (including Over the Counter Medications i.e. vitamins, minerals, non-prescription pain relief, antacids, etc. that can be purchased without a prescription) and Alternative Medications (i.e. herbs, herbal blends (excluding herbal teas), homeopathic remedies, and other alternative remedies that may be purchased over the counter or "prescribed" by a health care practitioner trained in a non-western tradition)was collected at this time.The following information was collected for each medication type Medication name, dosage, and route of administration; How often the medication is taken(frequency); How long the participant has been taking a given medication; Why they think they are taking the medication; After basic cleaning protocols were completed, standardized protocols were applied to both MIDUS and MIDJA medication data to link medications first to Generic Names and associated DrugIDs and then to therapeutic and pharmacologic class information from the Lexicomp Lexi-Data database, and also to code text data describing why participants think they are taking a given medication. The scope of this collected medication data lends itself to within person analysis of medication use, thus the medication data are also released in a standalone stacked format. The stacked file only contains data about medications used where each case represents an individual medication, thus it does not include any data about medication allergies.
  • Methods

    ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes..
  • Methods

    Presence of Common Scales: Data users interested in the scales used for this study should refer to the scaling documentation provided on both the ICPSR and NACDA Web site.
  • Methods

    Response Rates: The response rate was 39.3 percent for each of the 2 samples (longitudinal survey sample, and Milwaukee).
  • Abstract

    Datasets:

    • DS0: Study-Level Files
    • DS1: Aggregated Data
    • DS2: Stacked Medication Data
Temporal Coverage
  • Time period: 2004-07-30--2009-05-31
  • 2004-07-30 / 2009-05-31
  • Collection date: 2004-07-30--2009-05-31
  • 2004-07-30 / 2009-05-31
Geographic Coverage
  • United States
Sampled Universe
Adult non-institutionalized population of the United States. Smallest Geographic Unit: No geographic information is included other than for the Milwaukee cases.
Sampling
All respondents participating in MIDUS II (ICPSR 4652) or the Milwaukee study (ICPSR 22840) who completed Project 1 were eligible to participate in the Biomarker assessments.
Collection Mode
  • face-to-face interview
  • mixed mode
  • on-site questionnaire
Note
2019-03-27 This collection was updated to include MIDUS-MIDJA Medication Data Documentation.2018-10-31 This collection is being updated to include 700+ new variables in the Aggregated Data file. An additional dataset now accompanies this release. Please note this is a stacked file. That is, there is one row per medication reported, thus the 'N' indicates the total number of medications, not the number of cases. Supporting documentation is included and has also been updated.2017-11-21 This collection is being updated, per request from the PI, to reflect a title change; the corresponding downloadable files are only being updated to reflect the title change, where applicable.2013-12-20 Variable formats were edited per P.I. request; the data files have been updated.2013-05-02 The Acknowledgement for MIDUS II Biomarker Project (P4) Publications and the M2_P4 Biomarker IRB Approval and Certificate of Confidentiality were added to the collection2013-04-23 Technical corrections were made to data formats.2013-01-11 The study documentation has been updated, in particular, details about new variables have been added to: (1) the Blood, Urine, and Saliva documentation; (2) the Documentation of Scales and Constructed Variables; (3) the Psychophysiology Protocol Documentation; and (4) the DataFile Notes. New variables have been added to the data file including: (1) new biomarkers Insulin, Glucose, and Insulin-like Growth Factor 1 (IGF-1) along with an indicator of insulin resistance (HOMAIR); (2) a set of filter variables for the Psychophysiology data; (3) a new administrative Bone Scan variable; and (4) newly coded additional prescription, over-the-counter, and alternative medication data. Further details about the new variables and changes to documentation can be found in the README document available for download from the ICPSR and NACDA Web sites. The data and documentation files that have been uploaded are intended to replace the existing versions at ICPSR. Extant files that are not being replaced should remain available.2011-10-25 The document titled DDI codebook has been renamed Codebook. Question text has been incorporated with this study and is included in the updated SDA for this study. Lastly, a number of undocumented missing values codes have been updated and included within this data release. Funding institution(s): United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging (P01-AG020166).
Availability
Download
This version of the study is no longer available on the web. If you need to acquire this version of the data, you have to contact ICPSR User Support (ICPSR-help@umich.edu).
Alternative Identifiers
  • 29282 (Type: ICPSR Study Number)
Relations
  • Is previous version of
    DOI: 10.3886/ICPSR29282.v6
  • Is new version of
    DOI: 10.3886/ICPSR29282.v4
Publications
  • De Geus, Eco J.C., Gianaros, Peter J., Brindle, Ryan C., Jennings, J.R, Berntson, Gary G.. Should heart rate variability be 'corrected' for heart rate? Biological, quantitative, and interpretive considerations. Psychophysiology.56, (2), e132872019.
    • ID: 10.1111/psyp.13287 (DOI)
  • Ellis, Erin M., Prather, Aric A., Grenen, Emily G., Ferrer, Rebecca A.. Direct and indirect associations of cognitive reappraisal and suppression with disease biomarkers. Psychology and Health.1-19.2019.
    • ID: 10.1080/08870446.2018.1529313 (DOI)
  • Hisler, Garrett C., Brenner, Rachel E.. Does sleep partially mediate the effect of everyday discrimination on future mental and physical health?. Social Science and Medicine.221, 115-123.2019.
    • ID: 10.1016/j.socscimed.2018.12.002 (DOI)
  • Schwartz, Joseph A., Wright, Emily M., Valgardson, Bradon A.. Adverse childhood experiences and deleterious outcomes in adulthood: A consideration of the simultaneous role of genetic and environmental influences in two independent samples from the United States. Child Abuse and Neglect.88, 420-431.2019.
    • ID: 10.1016/j.chiabu.2018.12.022 (DOI)
  • Deighton, Stephanie, Neville, Alexandra, Pusch, Dennis, Dobson, Keith. Biomarkers of adverse childhood experiences: A scoping review. Psychiatry Research.1-39.2018.
    • ID: 10.1016/j.psychres.2018.08.097 (DOI)
  • French, Kimberly, Allen, Tammy, Henderson, Tyler. Challenge and hindrance stressors and metabolic risk factors. Journal of Occupational Health Psychology.2018.
    • ID: 10.1037/ocp0000138 (DOI)
  • Gough, Margaret, Godde, Kanya. A multifaceted analysis of social stressors and chronic inflammation. SSM - Population Health.6, 136-140.2018.
    • ID: 10.1016/j.ssmph.2018.09.005 (DOI)
  • Gough, Margaret, Godde, Kanya. Accelerated aging: The role of socioeconomic, social, demographic, and biological factors on bone mineral density. Research on Aging.2018.
    • ID: 10.1177/0164027518816516 (DOI)
  • Graham, E.K., Bastarache, E.D., Milad, E., Turiano, N. A., Cotter, K.A., Mroczek, D.K.. Physical activity mediates the association between personality and biomarkers of inflammation. SAGE Open Medicine.6, 1-10.2018.
    • ID: 10.1177/2050312118774990 (DOI)
  • Kitayama, S., Park, J., Miyamoto, Y., Date, H., Boylan, J.M., Markus, H.R., Karasawa, M., Kawakami, N., Coe, C.L., Love, G.D., Ryff, C.D.. Behavioral adjustment moderates the link between neuroticism and biological health risk: A U.S.-Japan comparison study. Personality and Social Psychology Bulletin.44, (6), 809-822.2018.
    • ID: 10.1177/0146167217748603 (DOI)
  • Lee, C., Tsenkova, V.K., Boylan, J.M., Ryff, C.D.. Gender differences in the pathways from childhood disadvantage to metabolic syndrome in adulthood: An examination of health lifestyles. SSM - Population Health.4, 216-224.2018.
    • ID: 10.1016/j.ssmph.2018.01.003 (DOI)
  • Ozga, Jenny E., Felicione, Nicholas J., Blank, Melissa D., Turiano, Nicholas A.. Cigarette smoking duration mediates the association between future thinking and norepinephrine level. Addictive Behaviors.87, 33-38.2018.
    • ID: https://doi.org/10.1016/j.addbeh.2018.06.004 (DOI)
  • Patel, P.C., Wolfe, M.T., Williams, T.A.. Self-employment and allostatic load. Journal of Business Venturing.2018.
    • ID: 10.1016/j.jbusvent.2018.05.004 (DOI)
  • Phillips, Amanda S., Guarnaccia, Charles A.. Responses to the perceived stress scale are not associated with cortisol levels or insulin resistance in adults with type 2 diabetes. Journal of Articles in Support of the Null Hypothesis.15, (1), 91-105.2018.
    • ID: http://www.jasnh.com/pdf/Vol14-No2-article2.pdf (URL)
  • Piazza, Jennifer R., Stawski, Robert S., Sheffler, Julia L.. Age, daily stress processes, and allostatic load: A longitudinal study. Journal of Aging and Health.1-21.2018.
    • ID: 10.1177/0898264318788493 (DOI)
  • Priest, J.B.. Examining differentiation of self as a mediator in the biobehavioral family model. Journal of Marital and Family Therapy.2018.
    • ID: 10.1111/jmft.12301 (DOI)
  • Radler, B.T., Rigotti, A., Ryff, C.D.. Persistently high psychological well-being predicts better HDL cholesterol and triglyceride levels: Findings from the Midlife in the U.S. (MIDUS) longitudinal study. Lipids in Health and Disease.17, (1), 1-9.2018.
    • ID: 10.1186/s12944-017-0646-8 (DOI)
  • Ransome, Y., Slopen, N., Karlsson, O., Williams, D.R.. Elevated inflammation in association with alcohol abuse among blacks but not whites: Results from the MIDUS biomarker study. Journal of Behavioral Medicine.41, (3), 374-384.2018.
    • ID: 10.1007/s10865-017-9905-4 (DOI)
  • Roberson, P.N.E., Shorter, R.L., Woods, S., Priest, J.. How health behaviors link romantic relationship dysfunction and physical health across 20 years for middle-aged and older adults. Social Science and Medicine.201, 18-26.2018.
    • ID: 10.1016/j.socscimed.2018.01.037 (DOI)
  • Roberson, Patricia, Fincham, Frank. Is relationship quality linked to diabetes risk and management?: It depends on what you look at. Families, Systems, and Health.36, (3), 2018.
    • ID: 10.1037/fsh0000336 (DOI)
  • Robinette, Jennifer W., Beam, Christopher R.. A genetically-informed study of neighborhoods and health: Results from the MIDUS twin sample. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences.2018.
    • ID: 10.1093/geronb/gby157 (DOI)
  • Stephan, Y., Sutin, A.R., Bovier-Lapierre, G., Terracciano, A.. Personality and walking speed across adulthood: Prospective evidence from five samples. Social Psychological and Personality Science.9, (7), 773-780.2018.
    • ID: 10.1177/1948550617725152 (DOI)
  • Bei, B., Seeman, T. E.p, Carroll, J. E., Wiley, J. F.. Sleep and physiological dysregulation: A closer look at sleep intraindividual variability. Sleep.2017.
    • ID: 10.1093/sleep/zsx109 (DOI)
  • Boylan, Jennifer Morozink, Robert, Stephanie A.. Neighborhood SES is particularly important to the cardiovascular health of low SES individuals. Social Science and Medicine.188, 60-68.2017.
    • ID: 10.1016/j.socscimed.2017.07.005 (DOI)
  • Chopik, William J., Newton, Nicky J., Ryan, Lindsay H., Kashdan, Todd B., Jarden, Aaron J.. Gratitude across the life span: Age differences and links to subjective well-being. Journal of Positive Psychology.1-11.2017.
    • ID: 10.1080/17439760.2017.1414296 (DOI)
  • Chung, Joon. Social support, social strain, sleep quality, and actigraphic sleep characteristics: Evidence from a national survey of US adults. Sleep Health.3, (1), 22-27.2017.
    • ID: 10.1016/j.sleh.2016.10.003 (DOI)
  • Cosco, T.D., Prina, M., Stubbs, B., Wu, Y.T.. Reliability and validity of the center for epidemiologic studies depression scale in a population-based cohort of middle-aged U.S. Adults. Journal of Nursing Measurement.25, (3), 476-485.2017.
    • ID: 10.1891/1061-3749.25.3.476 (DOI)
  • Curtis, David S., Fuller-Rowell, Thomas E., El-Sheikh, Mona, Carnethon, Mercedes R., Ryff, Carol D.. Habitual sleep as a contributor to racial differences in cardiometabolic risk. Proceedings of the National Academy of Sciences of the United States of America.14, (33), 8889-8894.2017.
    • ID: 10.1073/pnas.1618167114 (DOI)
  • Elliot, Ari J., Heffner, Kathi L., Mooney, Christopher J., Moynihan, Jan A., Chapman, Benjamin P.. Social relationships and inflammatory markers in the MIDUS cohort. Journal of Aging and Health.2017.
    • ID: 10.1177/0898264317698551 (DOI)
  • Elliot, Ari J., Turiano, Nicholas A., Chapman, Benjamin P.. Socioeconomic status interacts with conscientiousness and neuroticism to predict circulating concentrations of inflammatory markers. Annals of Behavioral Medicine.51, (2), 240-250.2017.
    • ID: 10.1007/s12160-016-9847-z (DOI)
  • Hisler, G., Krizan, Z.. Anger tendencies and sleep: Poor anger control is associated with objectively measured sleep disruption. Journal of Research in Personality.71, 17-26.2017.
    • ID: 10.1016/j.jrp.2017.08.009 (DOI)
  • Hostinar, Camelia E., Davidson, Richard J., Graham, Eileen K., Mroczek, Daniel K., Lachman, Margie E., Seeman, Teresa E., van Reekum, Carien M., Miller, Gregory E.. Frontal brain asymmetry, childhood maltreatment, and low-grade inflammation at midlife. Psychoneuroendocrinology.75, 152-163.2017.
    • ID: 10.1016/j.psyneuen.2016.10.026 (DOI)
  • Kitayama, Shinobu, Park, Jiyoung. Emotion and biological health: The socio-cultural moderation. Current Opinion in Psychology.17, 99-105.2017.
    • ID: 10.1016/j.copsyc.2017.06.016 (DOI)
  • Liebzeit, Daniel, Phelan, Cynthia, Moon, Chooza, Brown, Roger, Bratzke, Lisa. Rest-activity patterns in older adults with heart failure and healthy older adults. Journal of Aging and Physical Activity.25, (1), 116-122.2017.
    • ID: 10.1123/japa.2016-0058 (DOI)
  • Magidson, Jessica F., Robustelli, Briana L., Seitz-Brown, C.J., Whisman, Mark A.. Activity enjoyment, not frequency, is associated with alcohol-related problems and heavy episodic drinking. Psychology of Addictive Behaviors.31, (1), 73-78.2017.
    • ID: 10.1037/adb0000220 (DOI)
  • Nielsen, Lisbeth. Measuring the subjective well-being of diverse populations in the U.S. Applied Research in Quality of Life.12, (2), 237-240.2017.
    • ID: 10.1007/s11482-017-9518-7 (DOI)
  • Ong, A.D., Williams, D.R., Nwizu, U., Gruenewald, T.L.. Everyday unfair treatment and multisystem biological dysregulation in African American adults. Cultural Diversity and Ethnic Minority Psychology.23, (1), 27-35.2017.
    • ID: 10.1037/cdp0000087 (DOI)
  • Owens, Sherry L., Hunte, Haslyn E.R., Sterkel, Amanda, Johnson, Dayna A., Johnson-Lawrence, Vicki. Association between discrimination and objective and subjective sleep measures in the Midlife in the United States study adult sample. Psychosomatic Medicine.79, (4), 469-478.2017.
    • ID: 10.1097/PSY.0000000000000428 (DOI)
  • Ransome, Yusuf, Slopen, Natalie, Karlsson, Oskar, Williams, David R.. The association between alcohol abuse and neuroendocrine system dysregulation: Race differences in a national sample. Brain, Behavior, and Immunity.2017.
    • ID: 10.1016/j.bbi.2017.07.154 (DOI)
  • Ryff, Carol D.. Eudaimonic well-being, inequality, and health: Recent findings and future directions. International Review of Economics.64, (2), 159-178.2017.
    • ID: 10.1007/s12232-017-0277-4 (DOI)
  • Schwartz, Joseph A.. Long-term physical health consequences of perceived inequality: Results from a twin comparison design. Social Science and Medicine.187, 184-192.2017.
    • ID: 10.1016/j.socscimed.2017.06.006 (DOI)
  • Schwartz, Joseph A., Portnoy, Jill. Lower catecholamine activity is associated with greater levels of anger in adults. International Journal of Psychophysiology.120, 33-41.2017.
    • ID: 10.1016/j.ijpsycho.2017.07.005 (DOI)
  • Shah, Krupa N., Lin, Feng V., Yu, Fang, McMahon, James M.. Activity engagement and physical function in old age sample. Archives of Gerontology and Geriatrics.69, 55-60.2017.
    • ID: 10.1016/j.archger.2016.11.007 (DOI)
  • Sloan, Richard P., Schwarz, Emilie, McKinley, Paula S., Weinstein, Maxine, Love, Gayle, Ryff, Carol, Mroczek, Daniel, Choo, Tse-Hwei, Lee, Seonjoo, Seeman, Teresa. Vagally-mediated heart rate variability and indices of well-being: Results of a nationally representative study. Health Psychology.36, (1), 73-81.2017.
    • ID: 10.1037/hea0000397 (DOI)
  • Slopen, Natalie, Chen, Ying, Guida, Jennifer L., Albert, Michelle A., Williams, David R.. Positive childhood experiences and ideal cardiovascular health in midlife: Associations and mediators. Preventive Medicine.97, 72-79.2017.
    • ID: 10.1016/j.ypmed.2017.01.002 (DOI)
  • Stepanikova, Irena, Bateman, Lori B., Oates, Gabriela R.. Systemic inflammation in midlife: Race, socioeconomic status, and perceived discrimination. American Journal of Preventive Medicine.52, (1), S63-S76.2017.
    • ID: 10.1016/j.amepre.2016.09.026 (DOI)
  • Tsenkova, Vera K.. Leisure-time, occupational, household physical activity and insulin resistance (HOMAIR) in the Midlife in the United States (MIDUS) national study of adults. Preventive Medicine Reports.5, 224-227.2017.
    • ID: 10.1016/j.pmedr.2016.12.025 (DOI)
  • Tsenkova, Vera K., Lee, Chioun, Boylan, Jennifer Morozink. Childhood socioeconomic disadvantage, occupational, leisure-time, and household physical activity, and diabetes in adulthood. Journal of Physical Activity and Health.2017.
    • ID: 10.1123/jpah.2016-0438 (DOI)
  • Vadiveloo, Maya, Mattei, Josiemer. Perceived weight discrimination and 10-year risk of allostatic load among US adults. Annals of Behavioral Medicine.51, (1), 94-104.2017.
    • ID: 10.1007/s12160-016-9831-7 (DOI)
  • Wang, Diana, Gruenewald, Tara. The psychological costs of social support imbalance: Variation across relationship context and age. Journal of Health Psychology.2017.
    • ID: 10.1177/1359105317692854 (DOI)
  • White, Kaitlin Hanley, Rumble, Meredith E., Benca, Ruth M.. Sex differences in the relationship between depressive symptoms and actigraphic assessments of sleep and rest-activity rhythms in a population-based sample. Psychosomatic Medicine.79, (4), 479-484.2017.
    • ID: 10.1097/psy.0000000000000434 (DOI)
  • Wiley, J.F., Gruenewald, T.L., Karlamangla, A.S., Seeman, T.E.. The authors reply: Pursuing the optimal operationalization of allostatic load. Psychosomatic Medicine.79, (1), 119-121.2017.
    • ID: 10.1097/PSY.0000000000000416 (DOI)
  • Wolfe, Marcus, Patel, Pankaj C.. Two are better than one: Cortisol as a contingency in the association between epinephrine and self-employment. Journal of Business Venturing.8, 78-86.2017.
    • ID: 10.1016/j.jbvi.2017.07.002 (DOI)
  • Yang, Yang Claire, Gerken, Karen, Schorpp, Kristen, Boen, Courtney, Harris, Kathleen Mullan. Early-life socioeconomic status and adult physiological functioning: A life course examination of biosocial mechanisms. Biodemography and Social Biology.63, (2), 87-103.2017.
    • ID: 10.1080/19485565.2017.1279536 (DOI)
  • Yoo, Jiah, Miyamoto, Yuri, Rigotti, Attilio, Ryff, Carol D.. Linking positive affect to blood lipids: A cultural perspective. Psychological Science.2017.
    • ID: 10.1177/0956797617713309 (DOI)
  • Boehm, J.K., Chen, Y., Williams, D.R., Ryff, C.D., Kubzansky, L.D.. Subjective well-being and cardiometabolic health: An 8-11 year study of midlife adults. Journal of Psychosomatic Research.85, 1-8.2016.
    • ID: 10.1016/j.jpsychores.2016.03.018 (DOI)
  • Devine, Jaime K., Wolf, Jutta M.. Integrating nap and night-time sleep into sleep patterns reveals differential links to health-relevant outcomes. Journal of Sleep Research.2016.
    • ID: 10.1111/jsr.12369 (DOI)
  • Elliot, Ari J., Chapman, Benjamin P.. Socioeconomic status, psychological resources, and inflammatory markers: Results from the MIDUS Study. Health Psychology.35, (11), 1205-1213.2016.
    • ID: 10.1037/hea0000392 (DOI)
  • Fuller-Rowell, Thomas E., Curtis, David S., El-Sheikh, Mona, Chae, David H., Boylan, Jennifer M., Ryff, Carol D.. Racial disparities in sleep: The role of neighborhood disadvantage. Sleep Medicine.27-28, (1), 82016.
    • ID: 10.1016/j.sleep.2016.10.008 (DOI)
  • Galen Buckwalter, J., Castellani, Brian, Mcewen, Bruce, Karlamangla, Arun S., Rizzo, Albert A., John, Bruce, O'donnell, Kyle, Seeman, Teresa. Allostatic load as a complex clinical construct: A case-based computational modeling approach. Complexity.2016.
    • ID: 10.1002/cplx.21743 (DOI)
  • Hamdi, N.R., South, S.C., Krueger, R.F.. Does education lower allostatic load? A co-twin control study. Brain, Behavior, and Immunity.2016.
    • ID: 10.1016/j.bbi.2016.01.014 (DOI)
  • Kim, Tae Ho, Carroll, Judith E., An, Suk Kyoon, Seeman, Teresa E., Namkoong, Kee, Lee, Eun. Associations between actigraphy-assessed sleep, inflammatory markers, and insulin resistance in the midlife development in the United States (MIDUS) study. Sleep Medicine.27, 72-79.2016.
    • ID: 10.1016/j.sleep.2016.07.023 (DOI)
  • Krueger, Diane, Siglinsky, Ellen, Buehring, Bjoern, Binkley, Neil. Total body less head measurement is most appropriate for lean mass assessment in adults. Journal of Clinical Densitometry.20, (1), 128-129.2016.
    • ID: 10.1016/j.jocd.2016.08.068 (DOI)
  • Miller, Robert, Stalder, Tobias, Jarczok, Marc, Almeida, David M., Badrick, Ellena, Bartels, Meike, Boomsma, Dorret I., Coe, Christopher L., Dekker, Marieke C.J., Donzella, Bonny, Fischer, Joachim E., Gunnar, Megan R., Kumari, Meena, Lederbogen, Florian, Power, Christine, Ryff, Carol D., Subramanian, S.V., Tiemeier, Henning, Watamura, Sarah E., Kirschbaum, Clemens. The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies. Psychoneuroendocrinology.73, 16-23.2016.
    • ID: 10.1016/j.psyneuen.2016.07.201 (DOI)
  • Murdock, Kyle W., LeRoy, Angie S., Lacourt, Tamara E., Duke, Danny C., Heijnen, Cobi J., Fagundes, Christopher P.. Executive functioning and diabetes: The role of anxious arousal and inflammation. Psychoneuroendocrinology.71, 102-109.2016.
    • ID: 10.1016/j.psyneuen.2016.05.006 (DOI)
  • Parker, Elizabeth Oshrin, Maier, Candice, Wojciak, Armeda. Childhood abuse and family obligation in middle adulthood: Fndings from the MIDUS II National Survey. Journal of Family Therapy.2016.
    • ID: 10.1111/1467-6427.12114 (DOI)
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Update Metadata: 2019-03-27 | Issue Number: 6 | Registration Date: 2015-07-01

Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine (2010): Midlife in the United States (MIDUS 2): Biomarker Project, 2004-2009. Version 5. Midlife in the United States (MIDUS) Series. Version: v5. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR29282.v5