Multi-omics in stress and health research: study designs that will drive the field forward.

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights: The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Dynamic lipidome alterations associated with human health, disease and ageing.

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling.

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

Adverse childhood experiences, diabetes and associated conditions, preventive care practices and health care access: A population-based study.

Our objective was to examine how Adverse Childhood Experiences (ACEs) are associated with diabetes mellitus, diabetes-related conditions, and preventive care practices. We used data from the Behavioral Risk Factor Surveillance System (BRFSS) 2009-2012, a cross-sectional, population-based survey, to assess ACEs, diabetes, and health care access in 179,375 adults. In those with diabetes (n = 21,007), we assessed the association of ACEs with myocardial infarction, stroke, and five Healthy People 2020 (HP2020) diabetes-related preventive-care objectives (n = 13,152). Healthcare access indicators included lack of a regular health care provider, insurance, and difficulty affording health care. Regression analyses adjusted for age, sex, and race. The adjusted odds ratio (AOR) of diabetes increased in a stepwise fashion by ACE exposure, ranging from 1.2 (95% CI 1.1-1.3) for 1 ACE to 1.7 (95% CI 1.6-1.9) for ≥4 ACEs, versus having no ACEs. In persons with diabetes, those with ≥4 ACEs had an elevated adjusted odds of myocardial infarction (AOR = 1.6, 95% CI 1.2-2.0) and stroke (AOR = 1.8, 95% CI 1.3-2.4), versus having no ACEs. ACEs were also associated with a reduction in the adjusted percent of HP2020 diabetes objectives met: 72.9% (95% CI 71.3-74.5) for those with no ACEs versus only 66.5% (95% CI 63.8-69.3%) for those with ≥4 ACEs (p = 0.0002). Finally, ACEs predicted worse health care access in a stepwise fashion for all indicators. In conclusion, ACEs are associated with greater prevalence of diabetes and associated disease conditions, and with meeting fewer HP2020 prevention goals. Implementing ACE screening and trauma-informed health care practices are thus recommended.

Depression, health comorbidities, cognitive symptoms and their functional impact: Not just a geriatric problem.

OBJECTIVE: To compare the prevalence of cognitive symptoms and their functional impact by age group accounting for depression and number of other health conditions. METHODS: We analyzed data from the 2011 Behavioral Risk Factor Surveillance System, a population-based, cross-sectional telephone survey of US adults. Twenty-one US states asked participants (n = 131, 273) about cognitive symptoms (worsening confusion or memory loss in the past year) and their functional impact (interference with activities and need for assistance). We analyzed the association between age, depression history and cognitive symptoms and their functional impact using logistic regression and adjusted for demographic characteristics and other health condition count. RESULTS: There was a significant interaction between age and depression (p < 0.0001). In adults reporting depression, the adjusted odds of cognitive symptoms in younger age groups (<75 years) were comparable or greater to those in the oldest age group (≥75 years) with a peak in the middle age (45-54 years) group (OR 1.9 (95% Confidence Interval: 1.4-2.5). In adults without depression, adults <75 years had a significantly lower adjusted odds of cognitive symptoms compared to the oldest age group with the exception of the middle-aged group where the difference was not statistically significant. Over half of adults under age 65 with depression reported that cognitive symptoms interfered with life activities compared to 35.7% of adults ≥65 years. CONCLUSIONS: Cognitive symptoms are not universally higher in older adults; middle-aged adults are also particularly vulnerable. Given the adverse functional impact associated with cognitive symptoms in younger adults, clinicians should assess cognitive symptoms and their functional impact in adults of all ages and consider treatments that impact both cognition and functional domains.

Wearable sensors enable personalized predictions of clinical laboratory measurements.

Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.

The 5-HTTLPR long allele predicts two-year longitudinal increases in cortisol and declines in verbal memory in older adults.

OBJECTIVES: The short form or s-allele variant of the serotonin transporter polymorphism (5-HTTLPR), as compared with the long-form or l-allele variant, has been associated with the presence of cognitive dysfunction, and particularly memory impairment in older adults. This body of cross-sectional work has culminated in the hypothesis that presence of the s-allele predicts greater memory decline in older adults. Yet, to date, there are no longitudinal studies that have investigated this issue. METHODS/DESIGN: Here, we examine 109 community-dwelling older adults (mean and SD of age = 70.7 ± 8.7 years) who underwent blood draw for genotyping, cognitive, and psychological testing at baseline, 12-, and 24-monthfollow-ups. RESULTS: Multilevel modeling found that s-allele carriers (ss or ls) performed worse than ll homozygotes at baseline on delayed verbal recall. Yet, s-allele carriers' memory performance was stable over the two-yearfollow-up period, while l-allele homozygotes experienced significant memory decline. l-allele homozygote status was associated with both increased cortisol and decreased memory over time, resulting in attenuated verbal memory performance differences compared to s-allele carriers with age. CONCLUSIONS: Overall, our findings do not support the hypothesis that presence of the 5-HTTLPRs-allele is a marker for memory decline in older adults. J Am Geriatr Soc 68:-, 2020.

Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

Personal aging markers and ageotypes revealed by deep longitudinal profiling.

The molecular changes that occur with aging are not well understood1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.

A longitudinal big data approach for precision health.

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

Subjective but Not Objective Sleep is Associated with Subsyndromal Anxiety and Depression in Community-Dwelling Older Adults.

OBJECTIVE: To examine the relationship between subclinical anxiety and depressive symptoms and objective sleep architecture measures and subjective sleep reports in older adults. METHODS: Community-dwelling older adults (N = 167) self-rated their current severity of anxiety symptoms, depressive symptoms, daytime sleepiness, and global sleep quality. Participants received overnight ambulatory polysomnography to assess sleep architecture. Multivariate linear regression models examined associations between anxiety and depressive symptoms and objective and subjective sleep measures. RESULTS: Significant findings emerged for subjective sleep, with higher depression and anxiety scores associated with worse global sleep quality and greater anxiety scores associated with greater daytime sleepiness. No significant associations were observed between subclinical levels of anxiety or depressive symptoms with sleep architecture. CONCLUSION: Subclinical levels of late-life anxiety and depression have distinct associations with subjective sleep disturbance. Findings implicate subjective measures of sleep quality and daytime sleepiness as stronger trait markers for subthreshold psychiatric symptoms than objective sleep biomarkers.

Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.

A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

Potentially Avoidable Hospitalizations among People at Different Activity of Daily Living Limitation Stages.

OBJECTIVE: To determine whether higher activity of daily living (ADL) limitation stages are associated with increased risk of hospitalization, particularly for ambulatory care sensitive (ACS) conditions. DATA SOURCE: Secondary data analysis, including 8,815 beneficiaries from 2005 to 2006 Medicare Current Beneficiary Survey (MCBS). STUDY DESIGN: ADL limitation stages (0-IV) were determined at the end of 2005. Hospitalization rates were calculated for 2006 and age adjusted using direct standardization. Multivariate negative binomial regression, adjusting for baseline demographic and health characteristics, with the outcome hospitalization count was performed to estimate the adjusted rate ratio of ACS and non-ACS hospitalizations for beneficiaries with ADL stages > 0 compared to beneficiaries without limitations. DATA COLLECTION: Baseline ADL stage and health conditions were assessed using 2005 MCBS data and count of hospitalization determined using 2006 MCBS data. PRINCIPAL FINDINGS: Referenced to stage 0, the adjusted rate ratios (95 percent confidence interval) for stage I to stage IV ranged from 1.9 (1.4-2.5) to 4.1 (2.2-7.8) for ACS hospitalizations compared with from 1.6 (1.3-1.9) to 1.8 (1.4-2.5) for non-ACS hospitalizations. CONCLUSIONS: Hospitalization rates for ACS conditions increased more dramatically with ADL limitation stage than did rates for non-ACS conditions. Adults with ADL limitations appear particularly vulnerable to potentially preventable hospitalizations for conditions typically manageable in ambulatory settings.

Identifying neuropsychiatric disorders in the Medicare Current Beneficiary Survey: the benefits of combining health survey and claims data.

BACKGROUND: To address the impact of using multiple sources of data in the United States Medicare Current Beneficiary Survey (MCBS) compared to using only one source of data to identify those with neuropsychiatric diagnoses. METHODS: Our data source was the 2010 MCBS with associated Medicare claims files (N = 14, 672 beneficiaries). The MCBS uses a stratified multistage probability sample design to select a nationally representative sample of Medicare beneficiaries. We excluded those participants in Medicare Health Maintenance Organizations (n = 3894) and performed a cross-sectional analysis. We classified neuropsychiatric conditions according to four broad categories: intellectual/developmental disorders, neurological conditions affecting the central nervous system (Neuro-CNS), dementia, and psychiatric conditions. To account for different baseline prevalence differences of the categories we calculated the relative increase in prevalence that occurred from adding information from claims in addition to the absolute increase to allow comparison among categories. RESULTS: The estimated proportion of the sample with neuropsychiatric disorders increased to 50.0 (both sources) compared to 38.9 (health survey only) and 33.2 (claims only) with an overlap between sources of only 44.1 %. Augmenting health survey data with claims led to an increase in estimated percentage of intellectual/developmental disorders, psychiatric disorders, Neuro-CNS disorders and dementia of 1.3, 5.9, 11.5 and 3.8 respectively. In the community sample, the largest relative increases were seen for dementia (147.6 %) and Neuro-CNS disorders (87.4 %). With the exception of dementia, larger relative increases were seen in the facility sample with the greatest being for intellectual/developmental disorders (121.5 %) and Neuro-CNS disorders (93.8 %). CONCLUSIONS: The magnitude of potentially underestimated sample proportions using health survey only data varied strikingly according to the category of diagnosis and setting. Augmentation of survey data with claims appears essential particularly when attempting to estimate proportion of the sample affected by conditions that cause cognitive impairment which may affect ability to self-report. Augmenting proxy survey data with claims data also appears to be essential when ascertaining proportion of the facility-dwelling sample affected by neuropsychiatric disorders.

Adverse Childhood Experiences, Support, and the Perception of Ability to Work in Adults with Disability.

OBJECTIVE: To examine the impact of adverse childhood experiences (ACEs) and support on self-reported work inability of adults reporting disability. PARTICIPANTS: Adults (ages 18-64) who participated in the Behavioral Risk Factor Surveillance System in 2009 or 2010 and who reported having a disability (n = 13,009). DESIGN AND MAIN OUTCOME MEASURES: The study used a retrospective cohort design with work inability as the main outcome. ACE categories included abuse (sexual, physical, emotional) and family dysfunction (domestic violence, incarceration, mental illness, substance abuse, divorce). Support included functional (perceived emotional/social support) and structural (living with another adult) support. Logistic regression was used to adjust for potential confounders (age, sex and race) and to evaluate whether there was an independent effect of ACEs on work inability after adding other important predictors (support, education, health) to the model. RESULTS: ACEs were highly prevalent with almost 75% of the sample reporting at least one ACE category and over 25% having a high ACE burden (4 or more categories). ACEs were strongly associated with functional support. Participants experiencing a high ACE burden had a higher adjusted odds ratio (OR) [95% confidence interval] of 1.9 [1.5-2.4] of work inability (reference: zero ACEs). Good functional support (adjusted OR 0.52 [0.42-0.63]) and structural support (adjusted OR 0.48 [0.41-0.56]) were protective against work inability. After adding education and health to the model, ACEs no longer appeared to have an independent effect. Structural support remained highly protective, but functional support only appeared to be protective in those with good physical health. CONCLUSIONS: ACEs are highly prevalent in working-age US adults with a disability, particularly young adults. ACEs are associated with decreased support, lower educational attainment and worse adult health. Health care providers are encouraged to screen for ACEs. Addressing the effects of ACEs on health and support, in addition to education and retraining, may increase ability to work in those with a disability.

Increasing Discussion Rates of Incontinence in Primary Care: A Randomized Controlled Trial.

BACKGROUND: A minority of women with urinary incontinence (UI) and even fewer with fecal incontinence (FI) report having discussed it with a health care provider in the past year. Thus our aim was to evaluate whether the use of an electronic pelvic floor assessment questionnaire (ePAQ-PF) improves communication about incontinence in primary care. METHODS: Women 40 years and older who were scheduled for an annual wellness physical at an internal medicine clinic between August 2007 and August 2008 were randomized to complete the ePAQ-PF prior to (n = 145) or after (n = 139) their visit. Clinicians of women in the intervention group received the ePAQ-PF report prior to the visit. Outcome measures from clinic note abstraction included mention of UI (primary) and FI. Participant-reported outcome measures included discussion of UI and FI and initiator of discussion. RESULTS: Discussions of UI was more common in the intervention group than the control group: (27% vs. 19%; odds ratio [OR], 1.6 95% confidence interval [95%CI] 0.9-2.8, particularly for women over 60 (33% vs. 12%; OR 3.8, 95%CI 1.2-11.8) and for women with UI (42% vs. 25%; OR 2.2, 95%CI 1.1-4.1). The intervention primarily led to an increase in clinician-initiated UI discussions which were more common in the intervention group (18% vs. 4%, OR 4.8, 95%CI 1.9-12.0) Participants in the intervention group more frequently reported discussion of FI (14% vs. 6%; OR 2.5, 95%CI 1.1-6.0) which was clinician initiated in over half the cases (9% vs. 3%; OR 3.5, 95%CI 1.1-11.0). CONCLUSIONS: Use of the ePAQ-PF prior to clinic visits increases discussion of UI and FI, particularly clinician-initiated discussion. These findings suggest that such instruments may increase the detection and treatment of this often "silent" affliction.

Traumatic brain injury in U.S. Veterans with traumatic spinal cord injury.

Patients with both a spinal cord injury (SCI) and traumatic brain injury (TBI) are often very difficult to manage and can strain the resources of clinical units specialized in treating either diagnosis. However, a wide range of estimates exists on the extent of this problem. The aim of this study was to describe the scope of the problem in a well-defined population attending a comprehensive SCI unit. Electronic medical records of all patients with SCI being followed by the SCI unit in a U.S. Veterans' hospital were searched to identify those with concurrent TBI. The data were analyzed for age, sex, cause of injury, level and completeness of SCI, cognitive impairment, relationship with Active Duty military, and date of injury. Of 409 Veterans with a traumatic SCI, 99 (24.2%) were identified as having had a concurrent TBI. The occurrence did not appear to be closely related to military conflict. Reports of TBI were much more common in the last 20 yr than in previous decades. Documentation of TBI in patients with SCI was inconsistent. Improved screening and documentation could identify all patients with this dual diagnosis and facilitate appropriate management.

Activity Limitation Stages empirically derived for Activities of Daily Living (ADL) and Instrumental ADL in the U.S. Adult community-dwelling Medicare population.

BACKGROUND: Stages quantify severity like conventional measures but further specify the activities that people are still able to perform without difficulty. OBJECTIVE: To develop Activity Limitation Stages for defining and monitoring groups of adult community-dwelling Medicare beneficiaries. DESIGN: Cross-sectional. SETTING: Community. PARTICIPANTS: There were 14,670 respondents to the 2006 Medicare Current Beneficiary Survey. METHODS: Stages were empirically derived for the Activities of Daily Living (ADLs) and the Instrumental Activities of Daily Living (IADLs) by profiling the distribution of performance difficulties as reported by beneficiaries or their proxies. Stage prevalence estimates were determined, and associations with demographic and health variables were examined for all community-dwelling Medicare beneficiaries. MAIN OUTCOME MEASUREMENTS: ADL and IADL stage prevalence. RESULTS: Stages (0-IV) define 5 groups across the separate ADL and IADL domains according to hierarchically organized profiles of retained abilities and difficulties. For example, at ADL-I, people are guaranteed to be able to eat, toilet, dress, and bathe/shower without difficulty, whereas they experience limitations getting in and out of bed or chairs and/or difficulties walking. In 2006, an estimated 6.0, 2.9, 2.2, and 0.5 million beneficiaries had mild (ADL-I), moderate (ADL-II), severe (ADL-III), and complete (ADL-IV) difficulties, respectively, with estimates for IADL stages even higher. ADL and IADL stages showed expected associations with age and health-related concepts, supporting construct validity. Stages showed the strongest associations with conditions that impair cognition. CONCLUSIONS: Stages as aggregate measures reveal the ADLs and IADLs that people are still able to do without difficulty, along with those activities in which they report having difficulty, consequently emphasizing how groups of people with difficulties can still participate in their own lives. Over the coming decades, stages applied to populations served by vertically integrated clinical practices could facilitate large-scale planning, with the goal of maximizing personal autonomy among groups of community-dwelling people with disabilities.

Adverse childhood experiences and disability in U.S. adults.

OBJECTIVE: To assess relationships between adverse childhood experiences and self-reported disabilities in adult life. DESIGN: Cross-sectional, random-digit-dialed, state-population-based survey (Behavioral Risk Factor Surveillance System). SETTING: Fourteen states and the District of Columbia. PARTICIPANTS: Noninstitutionalized adults ages ≥18 years surveyed in 2009 and/or in 2010 (n = 81,184). METHODS: The Behavioral Risk Factor Surveillance System Adverse Childhood Experience (ACE) Module asks about abuse (physical, sexual, emotional), family dysfunction (exposures to domestic violence, living with mentally ill, substance abusing, or incarcerated family member(s), and/or parental separation and/or divorce) that occurred before age 18 years. The ACE score sums affirmed ACE categories (range, 0-8). We controlled for demographic characteristics (age, race, education, income, and marital status) and self-reported physical health conditions (stroke, myocardial infarction, diabetes, coronary heart disease, asthma). Five states asked participants about mental health conditions (anxiety, depression). A subset analysis of participants in these states evaluated the effect of adjusting for these conditions. MAIN OUTCOME MEASUREMENTS: The primary outcome was disability (self-reported activity limitation and/or assistive device use). RESULTS: More than half of participants (57%) reported at least 1 adverse childhood experience category, and 23.2% reported disability. The odds ratio (95% confidence interval) of disability increased in a graded fashion from odds ratio 1.3 (95% confidence interval, 1.2-1.4) among those who experienced 1 adverse experience to odds ratio 5.8 (95% confidence interval, 4.6-7.5) among those with 7-8 adverse experiences compared with those with no such experiences when adjusting for demographic factors. The relationship between adverse experiences and disability remained strong after adjusting for physical and mental health conditions. CONCLUSIONS: There is a strong graded relationship between childhood exposure to abuse and household dysfunction and self-reported disability in adulthood, even after adjusting for potentially mediating health conditions. Greater clinician, researcher, and policymaker awareness of the impact of childhood adversity on disability is crucial to help those affected by childhood adversity lead more functional lives.