Biomarkers of neurodegeneration and neural injury as potential predictors for delirium.

OBJECTIVES: Determine if biomarkers of Alzheimer's disease and neural injury may play a role in the prediction of delirium risk. METHODS: In a cohort of older adults who underwent elective surgery, delirium case-no delirium control pairs (N = 70, or 35 matched pairs) were matched by age, sex and vascular comorbidities. Biomarkers from CSF and plasma samples collected prior to surgery, including amyloid beta (Aß)42 , Aß40 , total (t)-Tau, phosphorylated (p)-Tau181 , neurofilament-light (NfL), and glial fibrillary acid protein (GFAP) were measured in cerebrospinal fluid (CSF) and plasma using sandwich enzyme-linked immunosorbent assays (ELISAs) or ultrasensitive single molecule array (Simoa) immunoassays. RESULTS: Plasma GFAP correlated significantly with CSF GFAP and both plasma and CSF GFAP values were nearly two-fold higher in delirium cases. The median paired difference between delirium case and control without delirium for plasma GFAP was not significant (p = 0.074) but higher levels were associated with a greater risk for delirium (odds ratio 1.52, 95% confidence interval 0.85, 2.72 per standard deviation increase in plasma GFAP concentration) in this small study. No matched pair differences or associations with delirium were observed for NfL, p-Tau 181, Aß40 and Aß42 . CONCLUSIONS: These preliminary findings suggest that plasma GFAP, a marker of astroglial activation, may be worth further investigation as a predictive risk marker for delirium.

Patterns and Persistence of Perioperative Plasma and Cerebrospinal Fluid Neuroinflammatory Protein Biomarkers After Elective Orthopedic Surgery Using SOMAscan.

BACKGROUND: The neuroinflammatory response to surgery can be characterized by peripheral acute plasma protein changes in blood, but corresponding, persisting alterations in cerebrospinal fluid (CSF) proteins remain mostly unknown. Using the SOMAscan assay, we define acute and longer-term proteome changes associated with surgery in plasma and CSF. We hypothesized that biological pathways identified by these proteins would be in the categories of neuroinflammation and neuronal function and define neuroinflammatory proteome changes associated with surgery in older patients. METHODS: SOMAscan analyzed 1305 proteins in blood plasma (n = 14) and CSF (n = 15) samples from older patients enrolled in the Role of Inflammation after Surgery for Elders (RISE) study undergoing elective hip and knee replacement surgery with spinal anesthesia. Systems biology analysis identified biological pathways enriched among the surgery-associated differentially expressed proteins in plasma and CSF. RESULTS: Comparison of postoperative day 1 (POD1) to preoperative (PREOP) plasma protein levels identified 343 proteins with postsurgical changes ( P < .05; absolute value of the fold change [|FC|] > 1.2). Comparing postoperative 1-month (PO1MO) plasma and CSF with PREOP identified 67 proteins in plasma and 79 proteins in CSF with altered levels ( P < .05; |FC| > 1.2). In plasma, 21 proteins, primarily linked to immune response and inflammation, were similarly changed at POD1 and PO1MO. Comparison of plasma to CSF at PO1MO identified 8 shared proteins. Comparison of plasma at POD1 to CSF at PO1MO identified a larger number, 15 proteins in common, most of which are regulated by interleukin-6 (IL-6) or transforming growth factor beta-1 (TGFB1) and linked to the inflammatory response. Of the 79 CSF PO1MO-specific proteins, many are involved in neuronal function and neuroinflammation. CONCLUSIONS: SOMAscan can characterize both short- and long-term surgery-induced protein alterations in plasma and CSF. Acute plasma protein changes at POD1 parallel changes in PO1MO CSF and suggest 15 potential biomarkers for longer-term neuroinflammation that warrant further investigation.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans.

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.

Preclinical and translational models for delirium: Recommendations for future research from the NIDUS delirium network.

Delirium is a common, morbid, and costly syndrome that is closely linked to Alzheimer's disease (AD) and AD-related dementias (ADRD) as a risk factor and outcome. Human studies of delirium have advanced our knowledge of delirium incidence and prevalence, risk factors, biomarkers, outcomes, prevention, and management. However, understanding of delirium neurobiology remains limited. Preclinical and translational models for delirium, while challenging to develop, could advance our knowledge of delirium neurobiology and inform the development of new prevention and treatment approaches. We discuss the use of preclinical and translational animal models in delirium, focusing on (1) a review of current animal models, (2) challenges and strategies for replicating elements of human delirium in animals, and (3) the utility of biofluid, neurophysiology, and neuroimaging translational markers in animals. We conclude with recommendations for the development and validation of preclinical and translational models for delirium, with the goal of advancing awareness in this important field.

Identification of Plasma Proteome Signatures Associated With Surgery Using SOMAscan.

OBJECTIVES: To characterize the proteomic signature of surgery in older adults and association with postoperative outcomes. SUMMARY OF BACKGROUND DATA: Circulating plasma proteins can reflect the physiological response to and clinical outcomes after surgery. METHODS: Blood plasma from older adults undergoing elective surgery was analyzed for 1305 proteins using SOMAscan. Surgery-associated proteins underwent Ingenuity Pathways Analysis. Selected surgery-associated proteins were independently validated using Luminex or enzyme-linked immunosorbent assay methods. Generalized linear models estimated correlations with postoperative outcomes. RESULTS: Plasma from a subcohort (n = 36) of the Successful Aging after Elective Surgery (SAGES) study was used for SOMAscan. Systems biology analysis of 110 proteins with Benjamini-Hochberg (BH) corrected P value ≤0.01 and an absolute foldchange (|FC|) ≥1.5 between postoperative day 2 (POD2) and preoperative (PREOP) identified functional pathways with major effects on pro-inflammatory proteins. Chitinase-3-like protein 1 (CHI3L1), C-reactive protein (CRP), and interleukin-6 (IL-6) were independently validated in separate validation cohorts from SAGES (n = 150 for CRP, IL-6; n = 126 for CHI3L1). Foldchange CHI3L1 and IL-6 were associated with increased postoperative complications [relative risk (RR) 1.50, 95% confidence interval (95% CI) 1.21-1.85 and RR 1.63, 95% CI 1.18-2.26, respectively], length of stay (RR 1.35, 95% CI 0.77-1.92 and RR 0.98, 95% CI 0.52-1.45), and risk of discharge to postacute facility (RR 1.15, 95% CI 1.04-1.26 and RR 1.11, 95% CI 1.04-1.18); POD2 and PREOP CRP difference was associated with discharge to postacute facility (RR 1.14, 95% CI 1.04-1.25). CONCLUSION: SOMAscan can identify novel and clinically relevant surgery-induced protein changes. Ultimately, proteomics may provide insights about pathways by which surgical stress contributes to postoperative outcomes.

Plasma and cerebrospinal fluid inflammation and the blood-brain barrier in older surgical patients: the Role of Inflammation after Surgery for Elders (RISE) study.

BACKGROUND: Our understanding of the relationship between plasma and cerebrospinal fluid (CSF) remains limited, which poses an obstacle to the identification of blood-based markers of neuroinflammatory disorders. To better understand the relationship between peripheral and central nervous system (CNS) markers of inflammation before and after surgery, we aimed to examine whether surgery compromises the blood-brain barrier (BBB), evaluate postoperative changes in inflammatory markers, and assess the correlations between plasma and CSF levels of inflammation. METHODS: We examined the Role of Inflammation after Surgery for Elders (RISE) study of adults aged ≥ 65 who underwent elective hip or knee surgery under spinal anesthesia who had plasma and CSF samples collected at baseline and postoperative 1 month (PO1MO) (n = 29). Plasma and CSF levels of three inflammatory markers previously identified as increasing after surgery were measured using enzyme-linked immunosorbent assay: interleukin-6 (IL-6), C-reactive protein (CRP), and chitinase 3-like protein (also known as YKL-40). The integrity of the BBB was computed as the ratio of CSF/plasma albumin levels (Qalb). Mean Qalb and levels of inflammation were compared between baseline and PO1MO. Spearman correlation coefficients were used to determine the correlation between biofluids. RESULTS: Mean Qalb did not change between baseline and PO1MO. Mean plasma and CSF levels of CRP and plasma levels of YKL-40 and IL-6 were higher on PO1MO relative to baseline, with a disproportionally higher increase in CRP CSF levels relative to plasma levels (CRP tripled in CSF vs. increased 10% in plasma). Significant plasma-CSF correlations for CRP (baseline r = 0.70 and PO1MO r = 0.89, p < .01 for both) and IL-6 (PO1MO r = 0.48, p < .01) were observed, with higher correlations on PO1MO compared with baseline. CONCLUSIONS: In this elective surgical sample of older adults, BBB integrity was similar between baseline and PO1MO, plasma-CSF correlations were observed for CRP and IL-6, plasma levels of all three markers (CRP, IL-6, and YKL-40) increased from PREOP to PO1MO, and CSF levels of only CRP increased between the two time points. Our identification of potential promising plasma markers of inflammation in the CNS may facilitate the early identification of patients at greatest risk for neuroinflammation and its associated adverse cognitive outcomes.

Association of Plasma Neurofilament Light with Postoperative Delirium.

OBJECTIVE: To examine the association of the plasma neuroaxonal injury markers neurofilament light (NfL), total tau, glial fibrillary acid protein, and ubiquitin carboxyl-terminal hydrolase L1 with delirium, delirium severity, and cognitive performance. METHODS: Delirium case-no delirium control (n = 108) pairs were matched by age, sex, surgery type, cognition, and vascular comorbidities. Biomarkers were measured in plasma collected preoperatively (PREOP), and 2 days (POD2) and 30 days postoperatively (PO1MO) using Simoa technology (Quanterix, Lexington, MA). The Confusion Assessment Method (CAM) and CAM-S (Severity) were used to measure delirium and delirium severity, respectively. Cognitive function was measured with General Cognitive Performance (GCP) scores. RESULTS: Delirium cases had higher NfL on POD2 and PO1MO (median matched pair difference = 16.2pg/ml and 13.6pg/ml, respectively; p < 0.05). Patients with PREOP and POD2 NfL in the highest quartile (Q4) had increased risk for incident delirium (adjusted odds ratio [OR] = 3.7 [95% confidence interval (CI) = 1.1-12.6] and 4.6 [95% CI = 1.2-18.2], respectively) and experienced more severe delirium, with sum CAM-S scores 7.8 points (95% CI = 1.6-14.0) and 9.3 points higher (95% CI = 3.2-15.5). At PO1MO, delirium cases had continued high NfL (adjusted OR = 9.7, 95% CI = 2.3-41.4), and those with Q4 NfL values showed a -2.3 point decline in GCP score (-2.3 points, 95% CI = -4.7 to -0.9). INTERPRETATION: Patients with the highest PREOP or POD2 NfL levels were more likely to develop delirium. Elevated NfL at PO1MO was associated with delirium and greater cognitive decline. These findings suggest NfL may be useful as a predictive biomarker for delirium risk and long-term cognitive decline, and once confirmed would provide pathophysiological evidence for neuroaxonal injury following delirium. ANN NEUROL 2020;88:984-994.

Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.

BACKGROUND: Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort. METHODS: We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical settings, including interview and medical record data. A large feature set included 71 potential predictors. A smaller set of 18 features was selected by an expert panel using a consensus process, and this smaller feature set was considered with and without a measure of pre-operative mental status. RESULTS: The area under the receiver operating characteristic curve (AUC) was higher in the large feature set conditions (range of AUC, 0.62-0.71 across algorithms) versus the selected feature set conditions (AUC range, 0.53-0.57). The restricted feature set with mental status had intermediate AUC values (range, 0.53-0.68). In the full feature set condition, algorithms such as gradient boosting, cross-validated logistic regression, and neural network (AUC = 0.71, 95% CI 0.58-0.83) were comparable with a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor. CONCLUSIONS: We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

The association of urine osmolality with decreased kidney function and/or albuminuria in the United States.

BACKGROUND: Decreased kidney function is commonly caused by hypovolemia. When hypovolemic, the kidney reabsorbs water resulting in concentrated urine. Osmolality is a measure of urine concentration which is more objective than self-reported fluid intake. It has a positive association with hypovolemia. However, it remains controversial whether osmolality is associated with decreased kidney function and/or albuminuria. METHODS: We conducted a cross-sectional analysis of the 2009-2012 National Health and Nutrition Examination Survey, a standardized survey in the U.S. POPULATION: Participants aged 18-70 years old with random urine osmolality were included. Osmolality was categorized as quartiles. Decreased kidney function was defined by estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 and albuminuria was defined by albumin-to-creatinine ratio ≥ 30 mg/gm. We performed multivariable regression via four sequential models. RESULTS: Our study sample included 7,373 participants. The mean age was 42.9 ± 0.4 years. Overall, 51.4% were male and 67.3% were white. The mean osmolality was 603.8 mOsm/kg and 629.1 mOsm/kg in those with and without decreased eGFR and/or albuminuria, respectively. The number of cases was 610 (6.7%). The prevalence from the lowest to highest quartiles of osmolality was 116 (6.2%), 213 (8.6%), 179 (7.5%), and 102 (4.3%), respectively (p-value for trend = 0.02). The relationship between osmolality and eGFR appeared nonlinear. After adjustment for demographic, social, cardiovascular, and dietary risk factors, there was no significant association of osmolality quartiles with decreased eGFR and/or albuminuria (odds ratio [OR] 0.77, 95% confidence interval [CI] 0.56, 1.07). In sensitivity analyses, osmolality ≥ 500 mOsm/kg was associated with lower eGFR (adjusted ß -1.13, 95% CI -1.98, -0.28). In pre-specified subgroup analyses, osmolality had a statistically significant negative correlation with eGFR among individuals with eGFR ≥ 60 mL/min/1.73m2, but a positive correlation among those with eGFR < 60 mL/min/1.73m2 (adjusted ß -0.19, 95% CI -0.36, -0.01 versus adjusted ß 0.50, 95% CI 0.05, 0.96; p-value for interaction = 0.016). CONCLUSIONS: Higher osmolality was significantly associated with lower eGFR among adults with eGFR ≥ 60 mL/min/1.73m2 Future research should examine the relationship between osmolality and change in kidney function over time among adults with normal eGFR.

New Delirium Severity Indicators: Generation and Internal Validation in the Better Assessment of Illness (BASIL) Study.

BACKGROUND: Delirium is a common and preventable geriatric syndrome. Moving beyond the binary classification of delirium present/absent, delirium severity represents a potentially important outcome for evaluating preventive and treatment interventions and tracking the course of patients. Although several delirium severity assessment tools currently exist, most have been developed in the absence of advanced measurement methodology and have not been evaluated with rigorous validation studies. OBJECTIVE: We aimed to report our development of new delirium severity items and the results of item reduction and selection activities guided by psychometric analysis of data derived from a field study. METHODS: Building on our literature review of delirium instruments and expert panel process to identify domains of delirium severity, we adapted items from existing delirium severity instruments and generated new items. We then fielded these items among a sample of 352 older hospitalized patients. RESULTS: We used an expert panel process and psychometric data analysis techniques to narrow a set of 303 potential items to 17 items for use in a new delirium severity instrument. The 17-item set demonstrated good internal validity and favorable psychometric characteristics relative to comparator instruments, including the Confusion Assessment Method - Severity (CAM-S) score, the Delirium Rating Scale Revised 98, and the Memorial Delirium Assessment Scale. CONCLUSION: We more fully conceptualized delirium severity and identified characteristics of an ideal delirium severity instrument. These characteristics include an instrument that is relatively quick to administer, is easy to use by raters with minimal training, and provides a severity rating with good content validity, high internal consistency reliability, and broad domain coverage across delirium symptoms. We anticipate these characteristics to be represented in the subsequent development of our final delirium severity instrument.

Apolipoprotein E genotype and the association between C-reactive protein and postoperative delirium: Importance of gene-protein interactions.

INTRODUCTION: Apolipoprotein E (APOE) status may modify the risk of postoperative delirium conferred by inflammation. METHODS: We tested whether APOE modifies the established association between C-reactive protein (CRP) and delirium incidence, severity, and duration in 553 noncardiac surgical patients aged 70 and older. High postoperative plasma CRP (≥234.12 mg/L) was defined by the highest sample-based quartile. Delirium was determined using the Confusion Assessment Method and chart review, and severity was determined by the Confusion Assessment Method-Severity score. RESULTS: APOE ε4 carrier prevalence was 19%, and postoperative delirium occurred in 24%. The relationship between CRP and delirium incidence, severity, and duration differed by ε4 status. Among ε4 carriers, there was a strong relationship between high CRP (vs. low CRP) and delirium incidence (relative risk [95% confidence interval], 3.0 [1.4-6.7]); however, no significant association was observed among non-ε4 carriers (relative risk [95% CI], 1.2 [0.8-1.7]). DISCUSSION: Our findings raise the possibility that APOE ε4 carrier status may modify the relationship between postoperative day 2 CRP levels and postoperative delirium.

The Association Between C-Reactive Protein and Postoperative Delirium Differs by Catechol-O-Methyltransferase Genotype.

OBJECTIVE: Catechol-O-methyltransferase (COMT), a key enzyme in degrading catecholamines associated with the stress response, may influence susceptibility to delirium. Individuals with the COMT (rs4680) Val/Val genotype (designated "warriors") withstand the onset of neuropsychiatric disorders and cognitive decline, whereas individuals with Met/Met and Val/Met genotypes ("nonwarriors") are more susceptible to these conditions. We evaluated whether COMT genotype modifies the established association between acute phase reactant (stress marker) C-reactive protein (CRP) and postoperative delirium. METHODS: This was a prospective cohort study conducted at two academic medical centers. The study involved 547 patients aged 70 or older undergoing major noncardiac surgery. We collected blood, extracted DNA, and performed COMT genotyping using allele-specific polymerase chain reaction assays, considering warriors versus nonwarriors. High plasma CRP, measured on postoperative day 2 using enzyme-linked immunosorbent assay, was defined by the highest sample-based quartile (≥234.12 mg/L). Delirium was determined using the Confusion Assessment Method, augmented by a validated chart review. We used generalized linear models adjusted for age, sex, surgery type, and race/ethnicity, stratified by COMT genotype, to determine whether the association between CRP and delirium differed by COMT. RESULTS: Prevalence of COMT warriors was 26%, and postoperative delirium occurred in 23%. Among COMT warriors, high CRP was not associated with delirium (relative risk [RR] 1.0, 95% confidence interval [CI] 0.4-2.6). In contrast, among nonwarriors, we found the expected relationship of high CRP and delirium (RR 1.5, 95% CI 1.1-2.2). CONCLUSION: COMT warriors may be protected against the increased risk of delirium associated with high CRP on postoperative day 2. With further confirmation, COMT genotype may help target interventions for delirium prevention in the vulnerable nonwarrior group.

Use of an expert panel to identify domains and indicators of delirium severity.

PURPOSE: Our purpose was to create a content domain framework for delirium severity to inform item development for a new instrument to measure delirium severity. METHODS: We used an established, multi-stage instrument development process during which expert panelists discussed best approaches to measure delirium severity and identified related content domains. We conducted this work as part of the Better ASsessment of ILlness (BASIL) study, a prospective, observational study aimed at developing and testing measures of delirium severity. Our interdisciplinary expert panel consisted of twelve national delirium experts and four expert members of the core research group. Over a one-month period, experts participated in two rounds of review. RESULTS: Experts recommended that the construct of delirium severity should reflect both the phenomena and the impact of delirium to create an accurate, patient-centered instrument useful to interdisciplinary clinicians and family caregivers. Final content domains were Cognitive, Level of consciousness, Inattention, Psychiatric-Behavioral, Emotional dysregulation, Psychomotor features, and Functional. Themes debated by experts included reconciling clinical geriatrics and psychiatric content, mapping symptoms to one specific domain, and accurate capture of unclear clinical presentations. CONCLUSIONS: We believe this work represents the first application of instrument development science to delirium. The identified content domains are inclusive of various, wide-ranging domains of delirium severity and are reflective of a consistent framework that relates delirium severity to potential clinical outcomes. Our content domain framework provides a foundation for development of delirium severity instruments that can help improve care and quality of life for patients with delirium.

Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium.

BACKGROUND: The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. METHODS: Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. RESULTS: We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. CONCLUSIONS: Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

Quantifying the Severity of a Delirium Episode Throughout Hospitalization: the Combined Importance of Intensity and Duration.

BACKGROUND: The ability to determine which episodes of delirium are likely to lead to poor clinical outcomes has remained a major area of challenge. OBJECTIVE: To quantify delirium severity and course over an entire hospitalization using several measures, and to evaluate their predictive validity for 30- and 90-day outcomes post-discharge. DESIGN: Two prospective cohort studies. PARTICIPANTS: Analysis was conducted in two independent cohorts of adult patients aged ≥70. MAIN MEASURES: Nine delirium episode severity measures were examined: (1) measures reflecting delirium intensity (peak Confusion Assessment Method-Severity [CAM-S] and mean CAM-S score), (2) a measure reflecting delirium intensity and duration (sum of all CAM-S scores, sum of all CAM-S scores on delirium days only, peak CAM-S score x days with delirium), (3) measures requiring information on delirium duration and delirium at discharge (total number of delirium days, percentage of delirium days, delirium at discharge), and (4) a measure of cognitive change. Associations of the delirium episode severity measures with 30- and 90-day post-hospital outcomes (death, nursing home placement, and readmission) relevant to delirium were examined. KEY RESULTS: The delirium episode severity measure that required information on both delirium intensity and duration (sum of all CAM-S scores) was the most strongly associated with 30- and 90-day post-hospital outcomes. Using this measure, the relative risk [95 % confidence interval] for death at 30-days increased across levels of sum of all CAM-S scores from 1.0 (referent) to 2.1 [0.8, 5.4] for 'low,' to 2.9 [1.2, 7.1] for 'moderate,' to 6.4 [2.9, 14.0] for 'high' (p for trend <.01). CONCLUSIONS: The delirium episode severity measure that included both intensity and duration had the strongest association with important post-hospital outcomes.

The long-arm of adolescent weight status on later life depressive symptoms.

BACKGROUND: given the increase in worldwide obesity among children and adolescents, the long-term consequences of childhood obesity on the risk of adverse health outcomes in later life has garnered increased attention. Much of the work on earlier life weight status and later life health has focused on cardiovascular-related outcomes in mid- to late-adulthood; however, little is known about the later life mental health consequences of adolescent body weight. METHODS: data came from the Wisconsin Longitudinal Study. We estimated gender-stratified logistic regression models to characterise the relationship between adolescent weight status using standardised relative body mass ascertained from high school photograph portraits in 1957 and depressive symptoms at age 65 using the Center for Epidemiologic Studies Depression Scale measured in 2004. RESULTS: women who were overweight in adolescence were significantly more likely to experience depressive symptoms in later adulthood than their normal weight counterparts (odds ratio [OR] = 1.740) when the full set of controls was included. This relationship was not observed among men. The relationship between women's adolescent weight status and later life depressive symptoms was moderated by childhood socioeconomic status, and adolescent overweight was more predictive of later life depressive symptoms for women who were raised in low- and middle-income families (OR = 2.568 and OR = 2.763) than in high-income families (OR = 1.643). CONCLUSION: these findings provide further evidence for the wide range of long-term consequences of adolescent overweight on later life well-being and are notable for the gender differences in the connection between early life circumstances and later life mental health.

Comparison of the frailty index and frailty phenotype and their associations with postoperative delirium incidence and severity.

BACKGROUND: Recent studies have reported an association between presurgical frailty and postoperative delirium. However, it remains unclear whether the frailty-delirium relationship differs by measurement tool (e.g., frailty index vs. frailty phenotype) and whether frailty is associated with delirium, independent of preoperative cognition. METHODS: We used the successful aging after elective surgery (SAGES) study, a prospective cohort of older adults age ≥70 undergoing major non-cardiac surgery (N = 505). Preoperative measurement of the modified mini-mental (3MS) test, frailty index and frailty phenotype were obtained. The confusion assessment method (CAM), supplemented by chart review, identified postoperative delirium. Delirium feature severity was measured by the sum of CAM-severity (CAM-S) scores. Generalized linear models were used to determine the relative risk of each frailty measure with delirium incidence and severity. Subsequent models adjusted for age, sex, surgery type, Charlson comorbidity index, and 3MS. RESULTS: On average, patients were 76.7 years old (standard deviation 5.22), 58.8% of women. For the frailty index, the incidence of delirium was 14% in robust, 17% in prefrail, and 31% in frail patients (p < 0.001). For the frailty phenotype, delirium incidence was 13% in robust, 21% in prefrail, and 27% in frail patients (p = 0.016). Frailty index, but not phenotype, was independently associated with delirium after adjustment for comorbidities (relative risk [RR] 2.13, 95% confidence interval [CI] 1.23-3.70; RR 1.61, 95% CI 0.77-3.37, respectively). Both frailty measures were associated with delirium feature severity. After adjustment for preoperative cognition, only the frailty index was associated with delirium incidence; neither index nor phenotype was associated with delirium feature severity. CONCLUSION: Both the frailty index and phenotype were associated with the development of postoperative delirium. The index showed stronger associations that remained significant after adjusting for baseline comorbidities and preoperative cognition. Measuring frailty prior to surgery can assist in identifying patients at risk for postoperative delirium.

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

The Association Between Active Transportation and Serum Total 25-Hydroxyvitamin D Levels Among US Childbearing-Aged Women.

BACKGROUND: Serum total 25-hydroxyvitamin D (25[OH]D) and physical activity (PA) both play important roles in maternal-fetal health. However, a high prevalence of vitamin D and PA insufficiency has been observed in women of childbearing age. Active transportation may increase overall PA levels and potentially boost serum 25(OH)D levels. METHODS: Data from the National Health and Nutrition Examination Survey between 2007 and 2014 were used. A total of 5601 women aged 18-49 years were included. Transportation PA (TPA) was quantified as metabolic equivalents of task and serum 25(OH)D levels was measured. Multivariable logistic regression models adjusted for potential confounders were conducted. RESULTS: The corresponding adjusted odds ratios associated with vitamin D insufficiency (<50 nmol/L) were 1.09 (95% confidence interval, 0.87-1.37) for 1 to 499 MET minutes per week of TPA, 0.69 (0.52-0.91) for 500 to 1000 MET minutes per week of TPA, and 0.95 (0.72-1.26) for >1000 MET minutes per week of TPA, respectively, compared with no TPA. Using vitamin D deficiency (<30 nmol/L) as the outcome led to similar results. The association between TPA and serum 25(OH)D levels was more robust in high sedentary time. CONCLUSIONS: A moderate level of TPA is related to lower odds of suboptimal vitamin D status among women of childbearing age.