Highly Efficient Carbon Dioxide Electroreduction via DNA-Directed Catalyst Immobilization.

Electrochemical reduction of carbon dioxide (CO2) is a promising route to up-convert this industrial byproduct. However, to perform this reaction with a small-molecule catalyst, the catalyst must be proximal to an electrode surface. Efforts to immobilize molecular catalysts on electrodes have been stymied by the need to optimize the immobilization chemistries on a case-by-case basis. Taking inspiration from nature, we applied DNA as a molecular-scale "Velcro" to investigate the tethering of three porphyrin-based catalysts to electrodes. This tethering strategy improved both the stability of the catalysts and their Faradaic efficiencies (FEs). DNA-catalyst conjugates were immobilized on screen-printed carbon and carbon paper electrodes via DNA hybridization with nearly 100% efficiency. Following immobilization, a higher catalyst stability at relevant potentials is observed. Additionally, lower overpotentials are required for the generation of carbon monoxide (CO). Finally, high FE for CO generation was observed with the DNA-immobilized catalysts as compared to the unmodified small-molecule systems, as high as 79.1% FE for CO at -0.95 V vs SHE using a DNA-tethered catalyst. This work demonstrates the potential of DNA "Velcro" as a powerful strategy for catalyst immobilization. Here, we demonstrated improved catalytic characteristics of molecular catalysts for CO2 valorization, but this strategy is anticipated to be generalizable to any reaction that proceeds in aqueous solutions.

2D Time-Domain Spectroscopy for Determination of Energy and Momentum Relaxation Rates of Hydrogen-Like Donor States in Germanium.

We present measurements of the coherence times of excited states of hydrogen-like arsenic impurities in germanium (Ge:As) using a table-top two-dimensional time-domain spectroscopy (2D-TDS) system. We show that this laboratory system is capable of resolving the coherence lifetimes of atomic-like excited levels of impurity centers in semiconductors, such as those used in solid-state quantum information technologies, on a subpicosecond time scale. By fitting the coherent nonlinear response of the system with the known intracenter transition frequencies, we are able to monitor coherent population transfer and decay of the transitions from the 2p0 and 2p± states for different low excitation pulse fields. Furthermore, by examining the off-diagonal resonances in the 2D frequency-domain map, we are able to identify coherences between excited electronic states that are not visible via conventional single-frequency pump-probe or Hahn-echo measurements.

Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data.

BACKGROUND: Diagnosis-code-based algorithms to identify fall injuries in Medicare data are useful for ascertaining outcomes in interventional and observational studies. However, these algorithms have not been validated against a fully external reference standard, in ICD-10-CM, or in Medicare Advantage (MA) data. METHODS: We linked self-reported fall injuries leading to medical attention (FIMA) from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial (reference standard) to Medicare fee-for-service (FFS) and MA data from 2015-2019. We measured the area under the receiver operating characteristic curve (AUC) based on sensitivity and specificity of a diagnosis-code-based algorithm against the reference standard for presence or absence of ≥1 FIMA within a specified window of dates, varying the window size to obtain points on the curve. We stratified results by source (FFS versus MA), trial arm (intervention versus control), and STRIDE's ten participating healthcare systems. RESULTS: Both reference standard data and Medicare data were available for 4941 (of 5451) participants. The reference standard and algorithm identified 2054 and 2067 FIMA, respectively. The algorithm had 45% sensitivity (95% confidence interval [CI], 43%-47%) and 99% specificity (95% CI, 99%-99%) to identify reference standard FIMA within the same calendar month. The AUC was 0.79 (95% CI, 0.78-0.81) and was similar by FFS or MA data source or trial arm, but showed variation among STRIDE healthcare systems (AUC range by healthcare system, 0.71 to 0.84). CONCLUSIONS: An ICD-10-CM algorithm to identify fall injuries demonstrated acceptable performance against an external reference standard, in both MA and FFS data.

Associations of Social Support with Physical and Mental Health Symptom Burden After COVID-19 Hospitalization Among Older Adults.

BACKGROUND: Despite significant support system disruptions during the COVID-19 pandemic, little is known about the relationship between social support and symptom burden among older adults following COVID-19 hospitalization. METHODS: From a prospective cohort of 341 community-living persons aged ≥60 years hospitalized with COVID-19 between June 2020-June 2021 who underwent follow-up at 1, 3, and 6 months after discharge, we identified 311 participants with ≥1 follow-up assessment. Social support pre-hospitalization was ascertained using a 5-item version of the Medical Outcomes Study Social Support Survey (range, 5-25), with low social support defined as a score ≤15. At hospitalization and each follow-up assessment, 14 physical symptoms were assessed using a modified Edmonton Symptom Assessment System inclusive of COVID-19-relevant symptoms. Mental health symptoms were assessed using Patient Health Questionnaire-4. Longitudinal associations between social support and physical and mental health symptoms, respectively, were evaluated through multivariable regression. RESULTS: Participants' mean age was 71.3 years (standard deviation, 8.5), 52.4% were female, and 34.2% were of Black race or Hispanic ethnicity. 11.8% reported low social support. Over the 6-month follow-up period, low social support was independently associated with higher burden of physical symptoms (adjusted rate ratio [aRR], 1.26; 95% confidence interval [CI], 1.05-1.52), but not mental health symptoms (aRR, 1.14; 95% CI, 0.85-1.53). CONCLUSIONS: Low social support is associated with greater physical, but not mental health, symptom burden among older survivors of COVID-19 hospitalization. Our findings suggest a potential need for social support screening and interventions to improve post-COVID-19 symptom management in this vulnerable group.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Applying Machine Learning Approach to Explore Childhood Circumstances and Self-Rated Health in Old Age - China and the US, 2020-2021.

Introduction: Childhood circumstances impact senior health, prompting the introduction of machine learning methods to assess their individual and collective contributions to senior health. Methods: Using health and retirement study (HRS) and China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,434 American and 5,612 Chinese participants aged 60 and above. Conditional inference trees and forests were employed to estimate the influence of childhood circumstances on self-rated health (SRH). Results: The conventional method estimated higher inequality of opportunity (IOP) values in both China (0.039, accounting for 22.67% of the total Gini coefficient 0.172) and the US (0.067, accounting for 35.08% of the total Gini coefficient 0.191). In contrast, the conditional inference tree yielded lower estimates (China: 0.022, accounting for 12.79% of 0.172; US: 0.044, accounting for 23.04% of 0.191), as did the forest (China: 0.035, accounting for 20.35% of 0.172; US: 0.054, accounting for 28.27% of 0.191). Childhood health, financial status, and regional differences were key determinants of senior health. The conditional inference forest consistently outperformed others in predictive accuracy, as demonstrated by lower out-of-sample mean squared error (MSE). Discussion: The findings emphasize the need for early-life interventions to promote health equity in aging populations. Machine learning showcases the potential in identifying contributing factors.

National Estimates of Short- and Longer-Term Hospital Readmissions After Major Surgery Among Community-Living Older Adults.

Importance: Nationally representative estimates of hospital readmissions within 30 and 180 days after major surgery, including both fee-for-service and Medicare Advantage beneficiaries, are lacking. Objectives: To provide population-based estimates of hospital readmission within 30 and 180 days after major surgery in community-living older US residents and examine whether these estimates differ according to key demographic, surgical, and geriatric characteristics. Design, Setting, and Participants: A prospective longitudinal cohort study of National Health and Aging Trends Study data (calendar years 2011-2018), linked to records from the Centers for Medicare & Medicaid Services (CMS). Data analysis was conducted from April to August 2023. Participants included community-living US residents of the contiguous US aged 65 years or older who had at least 1 major surgery from 2011 to 2018. Data analysis was conducted from April 10 to August 28, 2023. Main Outcomes and Measures: Major operations and hospital readmissions within 30 and 180 days were identified through data linkages with CMS files that included both fee-for-service and Medicare Advantage beneficiaries. Data on frailty and dementia were obtained from the annual National Health and Aging Trends Study assessments. Results: A total of 1780 major operations (representing 9 556 171 survey-weighted operations nationally) were identified from 1477 community-living participants; mean (SD) age was 79.5 (7.0) years, with 56% being female. The weighted rates of hospital readmission were 11.6% (95% CI, 9.8%-13.6%) for 30 days and 27.6% (95% CI, 24.7%-30.7%) for 180 days. The highest readmission rates within 180 days were observed among participants aged 90 years or older (36.8%; 95% CI, 28.3%-46.3%), those undergoing vascular surgery (45.8%; 95% CI, 37.7%-54.1%), and persons with frailty (36.9%; 95% CI, 30.8%-43.5%) or probable dementia (39.0%; 95% CI, 30.7%-48.1%). In age- and sex-adjusted models with death as a competing risk, the hazard ratios for hospital readmission within 180 days were 2.29 (95% CI, 1.70-3.09) for frailty and 1.58 (95% CI, 1.15-2.18) for probable dementia. Conclusions and Relevance: In this nationally representative cohort study of community-living older US residents, the likelihood of hospital readmissions within 180 days after major surgery was increased among older persons who were frail or had probable dementia, highlighting the potential value of these geriatric conditions in identifying those at increased risk.

The dementia care study (D-CARE): Recruitment strategies and demographic characteristics of participants in a pragmatic randomized trial of dementia care.

INTRODUCTION: Pragmatic research studies that include diverse dyads of persons living with dementia (PLWD) and their family caregivers are rare. METHODS: Community-dwelling dyads were recruited for a pragmatic clinical trial evaluating three approaches to dementia care. Four clinical trial sites used shared and site-specific recruitment strategies to enroll health system patients. RESULTS: Electronic health record (EHR) queries of patients with a diagnosis of dementia and engagement of their clinicians were the main recruitment strategies. A total of 2176 dyads were enrolled, with 80% recruited after the onset of the pandemic. PLWD had a mean age of 80.6 years (SD 8.5), 58.4% were women, and 8.8% were Hispanic/Latino, and 11.9% were Black/African American. Caregivers were mostly children of the PLWD (46.5%) or spouses/partners (45.2%), 75.8% were women, 9.4% were Hispanic/Latino, and 11.6% were Black/African American. DISCUSSION: Health systems can successfully enroll diverse dyads in a pragmatic clinical trial.

Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank.

BACKGROUND AND OBJECTIVES: Poor oral health is a modifiable risk factor that is associated with clinically observed cardiovascular disease. However, the relationship between oral and brain health is not well understood. We tested the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in middle-aged persons without stroke or dementia. METHODS: We performed a 2-stage cross-sectional neuroimaging study using UK Biobank data. First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian randomization (MR) analyses to test for association between genetically determined poor oral health and the same neuroimaging markers. Poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants linked to increased composite risk of dentures or teeth that are decayed, missing, or filled. Neuroimaging markers of brain health included white matter hyperintensity (WMH) volume and aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), 2 metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging across 48 brain regions. RESULTS: We included 40,175 persons (mean age 55 years, female sex 53%) enrolled from 2006 to 2010, who underwent a dedicated research brain MRI between 2014 and 2016. Among participants, 5,470 (14%) had poor oral health. Poor oral health was associated with a 9% increase in WMH volume (ß = 0.09, SD = 0.014, p < 0.001), 10% change in aggregate FA score (ß = 0.10, SD = 0.013, p < 0.001), and 5% change in aggregate MD score (ß = 0.05, SD = 0.013, p < 0.001). Genetically determined poor oral health was associated with a 30% increase in WMH volume (ß = 0.30, SD = 0.06, p < 0.001), 43% change in aggregate FA score (ß = 0.43, SD = 0.06, p < 0.001), and 10% change in aggregate MD score (ß = 0.10, SD = 0.03, p < 0.01). DISCUSSION: Among middle age Britons without stroke or dementia, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potentially causal association. Because the neuroimaging markers evaluated in this study precede and are established risk factors of stroke and dementia, our results suggest that oral health, an easily modifiable process, may be a promising target for very early interventions focused on improving brain health.

Terminal Decline in Physical Function in Older Adults.

BACKGROUND: It is currently unclear whether (and when) physical function exhibits a terminal decline phase, that is, a substantial acceleration of decline in the very last years before death. METHODS: 702 deceased adults aged 70 years and older from the Yale PEP Study provided 4 133 measurements of physical function (Short Physical Performance Battery, SPPB) up to 20 years before death. In addition, continuous gait and chair rise subtest scores (in seconds) were assessed. Generalized mixed regression models with random change points were used to estimate the onset and the steepness of terminal decline in physical function. RESULTS: Decline accelerated in the last years of life in all 3 measures of physical function. The onset of terminal decline occurred 1 year before death for the SPPB, and at 2.5 and 2.6 years before death for chair rise and gait speed test scores, respectively. Terminal declines in physical function were 6-8 times steeper than pre-terminal declines. Relative to those whose condition leading to death was frailty, participants who died from dementia and cancer had an up to 6 months earlier and 3 months later onset of terminal decline in SPPB, respectively. CONCLUSIONS: Terminal decline in physical function among older adults is comparable to the more established terminal decline phenomenon in cognition. Our results provide additional evidence of late-life rapid decline in physical function due to impending death.

Relationship Between Distressing Symptoms and Changes in Disability After Major Surgery Among Community-living Older Persons.

OBJECTIVES: To evaluate the relationship between distressing symptoms and changes in disability after major surgery and to determine whether this relationship differs according to the timing of surgery (nonelective vs elective), sex, multimorbidity, and socioeconomic disadvantage. BACKGROUND: Major surgery is a common and serious health event that has pronounced deleterious effects on both distressing symptoms and functional outcomes in older persons. METHODS: From a cohort of 754 community-living persons, aged 70 or older, 392 admissions for major surgery were identified from 283 participants who were discharged from the hospital. The occurrence of 15 distressing symptoms and disability in 13 activities were assessed monthly for up to 6 months after major surgery. RESULTS: Over the 6-month follow-up period, each unit increase in the number of distressing symptoms was associated with a 6.4% increase in the number of disabilities [adjusted rate ratio (RR): 1.064; 95% CI: 1.053, 1.074]. The corresponding increases were 4.0% (adjusted RR: 1.040; 95% CI: 1.030, 1.050) and 8.3% (adjusted RR: 1.083; 95% CI: 1.066, 1.101) for nonelective and elective surgeries. Based on exposure to multiple (ie, 2 or more) distressing symptoms, the adjusted RRs (95% CI) were 1.43 (1.35, 1.50), 1.24 (1.17, 1.31), and 1.61 (1.48, 1.75) for all, nonelective, and elective surgeries. Statistically significant associations were observed for each of the other subgroups with the exception of individual-level socioeconomic disadvantage for the number of distressing symptoms. CONCLUSIONS: Distressing symptoms are independently associated with worsening disability, providing a potential target for improving functional outcomes after major surgery.

A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. Objective: A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. Design Setting and Participants: We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. Exposure: DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-V max ) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12,599 participants were included in the echocardiographic study (YNHHS: n =8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n =3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-V max (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-V max . This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Is residential exposure to oil refineries a novel contextual risk factor for coronary heart disease?

Despite a multi-decade decrease in cardiovascular disease, geographic disparities have widened, with excess mortality concentrated within the United States (U.S.) South. Petroleum production and refining, a major contributor to climate change, is concentrated within the U.S. South and emits multiple classes of atherogenic pollutants. We investigated whether residential exposure to oil refineries could explain variation in self-reported coronary heart disease (CHD) prevalence among adults in southern states for the year 2018, where the majority of oil refinery activity occurs (Alabama, Mississippi, Louisiana, Arkansas, Texas, New Mexico, and Oklahoma). We examined census tract-level association between oil refineries and CHD prevalence. We used a double matching method to adjust for measured and unmeasured spatial confounders: one-to-n distance matching and one-to-one generalized propensity score matching. Exposure metrics were constructed based on proximity to refineries, activities of refineries, and wind speed/direction. For all census tracts within 10 km of refineries, self-reported CHD prevalence ranged from 1.2% to 17.6%. Compared to census tracts located at ≥5 km and <10 km, one standard deviation increase in the exposure within 5 km of refineries was associated with a 0.33 (95% confidence interval: 0.04, 0.63) percentage point increase in the prevalence. A total of 1119.0 (123.5, 2114.2) prevalent cases or 1.6% (0.2, 3.1) of CHD prevalence in areas within 5 km from refineries were potentially explained by exposure to oil refineries. At the census tract-level, the prevalence of CHD explained by exposure to oil refineries ranged from 0.02% (0.00, 0.05) to 47.4% (5.2, 89.5). Thus, although we cannot rule out potential confounding by other personal risk factors, CHD prevalence was found to be higher in populations living nearer to oil refineries, which may suggest that exposure to oil refineries can increase CHD risk, warranting further investigation.

Suboptimal Sleep Duration Is Associated With Poorer Neuroimaging Brain Health Profiles in Middle-Aged Individuals Without Stroke or Dementia.

BACKGROUND: The American Heart Association's Life's Simple 7, a public health construct capturing key determinants of cardiovascular health, became the Life's Essential 8 after the addition of sleep duration. The authors tested the hypothesis that suboptimal sleep duration is associated with poorer neuroimaging brain health profiles in asymptomatic middle-aged adults. METHODS AND RESULTS: The authors conducted a prospective magnetic resonance neuroimaging study in middle-aged individuals without stroke or dementia enrolled in the UK Biobank. Self-reported sleep duration was categorized as short (<7 hours), optimal (7-<9 hours), or long (≥9 hours). Evaluated neuroimaging markers included the presence of white matter hyperintensities (WMHs), volume of WMH, and fractional anisotropy, with the latter evaluated as the average of 48 white matter tracts. Multivariable logistic and linear regression models were used to test for an association between sleep duration and these neuroimaging markers. The authors evaluated 39 771 middle-aged individuals. Of these, 28 912 (72.7%) had optimal, 8468 (21.3%) had short, and 2391 (6%) had long sleep duration. Compared with optimal sleep, short sleep was associated with higher risk of WMH presence (odds ratio, 1.11 [95% CI, 1.05-1.18]; P<0.001), larger WMH volume (beta=0.06 [95% CI, 0.04-0.08]; P<0.001), and worse fractional anisotropy profiles (beta=-0.04 [95% CI, -0.06 to -0.02]; P=0.001). Compared with optimal sleep, long sleep duration was associated with larger WMH volume (beta=0.04 [95% CI, 0.01-0.08]; P=0.02) and worse fractional anisotropy profiles (beta=-0.06 [95% CI, -0.1 to -0.02]; P=0.002), but not with WMH presence (P=0.6). CONCLUSIONS: Among middle-aged adults without stroke or dementia, suboptimal sleep duration is associated with poorer neuroimaging brain health profiles. Because these neuroimaging markers precede stroke and dementia by several years, these findings are consistent with other findings evaluating early interventions to improve this modifiable risk factor.

Association of Childhood Adversity With Frailty and the Mediating Role of Unhealthy Lifestyle: A Lifespan Analysis.

OBJECTIVES: Childhood adversity and lifestyle have been associated with frailty in later life, but not much is known about factors that may explain these associations. Therefore, this study aims to investigate the association of childhood adversity with frailty, and the mediating role of unhealthy lifestyle in the association. METHODS: This lifespan analysis included 152,914 adults aged 40-69 years old from the UK Biobank. We measured childhood adversity with five items: physical neglect, emotional neglect, sexual abuse, physical abuse, and emotional abuse through online mental health survey. Frailty was measured by the frailty index; an unhealthy lifestyle score (range: 0-5) was calculated based on unhealthy body mass index, smoking, alcohol consumption, physical inactivity, and unhealthy diet at the baseline survey. Multiple logistic regression and mediation analysis were performed. RESULTS: A total of 10,078 participants (6.6%) were defined as having frailty. Participants with any childhood adversity had higher odds of frailty. For example, in the fully adjusted model, with a one-point increase in cumulative score of childhood adversity, the odds of frailty increased by 38% (odds ratio: 1.38; 95% Confidence Interval: 1.36, 1.40). Unhealthy lifestyle partially mediated the associations of childhood adversity with frailty (mediation proportion: 4.4%-7.0%). The mediation proportions were largest for physical (8.2%) and sexual (8.1%) abuse. CONCLUSIONS: Childhood adversity was positively associated with frailty, and unhealthy lifestyle partially mediated the association. This newly identified pathway highlights the potential of lifestyle intervention strategies among those who experienced childhood adversity (in particular, physical, and sexual abuse) to promote healthy aging.

Understanding multimorbidity requires sign-disease networks and higher-order interactions, a perspective.

Background: Count scores, disease clustering, and pairwise associations between diseases remain ubiquitous in multimorbidity research despite two major shortcomings: they yield no insight into plausible mechanisms underlying multimorbidity, and they ignore higher-order interactions such as effect modification. Objectives: We argue that two components are currently missing but vital to develop novel multimorbidity metrics. Firstly, networks should be constructed which consists simultaneously of signs, symptoms, and diseases, since only then could they yield insight into plausible shared biological mechanisms underlying diseases.Secondly, learning pairwise associations is insufficient to fully characterize the correlations in a system. That is, synergistic (e.g., cooperative or antagonistic) effects are widespread in complex systems, where two or more elements combined give a larger or smaller effect than the sum of their individual effects. It can even occur that pairs of symptoms have no pairwise associations whatsoever, but in combination have a significant association. Therefore, higher-order interactions should be included in networks used to study multimorbidity, resulting in so-called hypergraphs. Methods: We illustrate our argument using a synthetic Bayesian Network model of symptoms, signs and diseases, composed of pairwise and higher-order interactions. We simulate network interventions on both individual and population levels and compare the ground-truth outcomes with the predictions from pairwise associations. Conclusion: We find that, when judged purely from the pairwise associations, interventions can have unexpected 'side-effects' or the most opportune intervention could be missed. The hypergraph uncovers links missed in pairwise networks, giving a more complete overview of sign and disease associations.