Effectiveness of an Advance Care Planning Intervention in Adults Receiving Dialysis and Their Families: A Cluster Randomized Clinical Trial.

Importance: Evidence of effectiveness of advance care planning (ACP) strategies for patients receiving dialysis and their families is needed. Objectives: To test the effectiveness of an ACP intervention to prepare patients and their surrogates for end-of-life (EOL) decision-making and to improve surrogate bereavement outcomes. Design, Setting, and Participants: This cluster randomized clinical trial, An Effectiveness-Implementation Trial of SPIRIT (Sharing Patients' Illness Representations to Increase Trust) in ESRD, was conducted from December 2017 to March 2023 and included 42 dialysis clinics in 5 US states (Georgia, New Mexico, North Carolina, Pennsylvania, and Virginia) randomized to provide intervention or usual care. Recruitment was from February 15, 2018, to January 31, 2022, and patient-surrogate dyads were followed up for 21 months (until January 17, 2023) or until patient death. Intervention: Each clinic selected 1 or 2 health care workers (eg, nurse practitioner, registered nurse, or social worker) to conduct 45- to 60-minute ACP discussions with dyads in the clinic or remotely. After March 13, 2020 (commencement of the COVID-19 emergency declaration), all discussions were conducted remotely. An ACP summary was placed in patients' medical records. Main Outcomes and Measures: The primary, 2-week preparedness outcomes were dyad congruence on EOL goals of care, patient decisional conflict, surrogate decision-making confidence, and a composite of dyad congruence and surrogate decision-making confidence. Secondary bereavement outcomes were anxiety, depression, and posttraumatic distress 3 months after patient death. To adjust for COVID-19 pandemic effects on bereavement outcomes, a variable to indicate the timing of baseline and 3-month assessment relative to the COVID-19 emergency declaration was created. Results: Of the 426 dyads enrolled, 231 were in the intervention clinics, and 195 were in the control clinics. Among all dyads, the mean (SD) patient age was 61.9 (12.7) years, and the mean (SD) surrogate age was 53.7 (15.4) years. At 2 weeks, after adjusting for baseline values, dyad congruence (odds ratio [OR], 1.61; 95% CI, 1.12-2.31; P = .001), decisional conflict scores (ß, -0.10; 95% CI, -0.13 to -0.07; P < .001), and the composite (OR, 1.57; 95% CI, 1.06-2.34; P = .03) were higher in the intervention group than in the control group. Surrogate decision-making confidence was similar between groups (ß, 0.06; 95% CI, -0.01 to 0.13; P = .12). Among 77 bereaved surrogates, after adjusting for baseline values and assessment timing, intervention group anxiety was lower than control group anxiety (ß, -1.55; 95% CI, -3.08 to -0.01; P = .05); however, depression (ß, -0.18; 95% CI, -2.09 to 1.73; P = .84) and posttraumatic distress (ß, -0.96; 95% CI, -7.39 to 5.46; P = .75) were similar. Conclusions and Relevance: In this randomized clinical trial, the ACP intervention implemented by health care workers at dialysis centers improved preparation for EOL decision-making but showed mixed effectiveness on bereavement outcomes. The ACP intervention implemented in dialysis centers may be an effective strategy to the dyad preparation for end-of-life care as opposed to the current focus on advance directives. Trial Registration: ClinicalTrials.gov Identifier: NCT03138564.

Assessing intra- and inter-method agreement of functional data.

Modern medical devices are increasingly producing complex data that could offer deeper insights into physiological mechanisms of underlying diseases. One type of complex data that arises frequently in medical imaging studies is functional data, whose sampling unit is a smooth continuous function. In this work, with the goal of establishing the scientific validity of experiments involving modern medical imaging devices, we focus on the problem of evaluating reliability and reproducibility of multiple functional data that are measured on the same subjects by different methods (i.e. different technologies or raters). Specifically, we develop a series of intraclass correlation coefficient and concordance correlation coefficient indices that can assess intra-method, inter-method, and total (intra + inter) agreement based on multivariate multilevel functional data consisting of replicated functional data measurements produced by each of the different methods. For efficient estimation, the proposed indices are expressed using variance components of a multivariate multilevel functional mixed effect model, which can be smoothly estimated by functional principal component analysis. Extensive simulation studies are performed to assess the finite-sample properties of the estimators. The proposed method is applied to evaluate the reliability and reproducibility of renogram curves produced by a high-tech radionuclide image scan used to non-invasively detect kidney obstruction.

An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction.

Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction.

Computer assisted interpretation of Tc-99m mercaptoacetyltriglycine diuretic scintigraphy enhances resident performance.

OBJECTIVE: iRENEX is a software module that incorporates scintigraphic and clinical data to interpret 99m Tc- mercaptoacetyltriglycine (MAG3) diuretic studies and provide reasons for their conclusions. Our objectives were to compare iRENEX interpretations with those of expert physicians, use iRENEX to evaluate resident performance and determine if iRENEX could improve the diagnostic accuracy of experienced residents. METHODS: Baseline and furosemide 99m Tc-MAG3 acquisitions of 50 patients with suspected obstruction (mean age ± SD, 58.7 ±â€…15.8 years, 60% female) were randomly selected from an archived database and independently interpreted by iRENEX, three expert readers and four nuclear medicine residents with one full year of residency. All raters had access to scintigraphic data and a text file containing clinical information and scored each kidney on a scale from +1.0 to -1.0. Scores ≥0.20 represented obstruction with higher scores indicating greater confidence. Scores +0.19 to -0.19 were indeterminate; scores ≤-0.20 indicated no obstruction. Several months later, residents reinterpreted the studies with access to iRENEX. Receiver operating characteristic (ROC) analysis and concordance correlation coefficient (CCC) quantified agreement. RESULTS: The CCC among experts was higher than that among residents, 0.84, versus 0.39, respectively, P  < 0.001. When residents reinterpreted the studies with iRENEX, their CCC improved from 0.39 to 0.73, P  < 0.001. ROC analysis showed significant improvement in the ability of residents to distinguish between obstructed and non-obstructed kidneys using iRENEX ( P  = 0.036). CONCLUSION: iRENEX interpretations were comparable to those of experts. iRENEX reduced interobserver variability among experienced residents and led to better agreement between resident and expert interpretations.

Racial Differences in Fatal Out-of-Hospital Coronary Heart Disease and the Role of Income in the Atherosclerosis Risk in Communities Cohort Study (1987 to 2017).

Black patients have higher incident fatal coronary heart disease (CHD) rates than do their White counterparts. Racial differences in out-of-hospital fatal CHD could explain the excess risk in fatal CHD among Black patients. We examined racial disparities in in- and out-of-hospital fatal CHD among participants with no history of CHD, and whether socioeconomic status might play a role in this association. We used data from the ARIC (Atherosclerosis Risk in Communities) study, including 4,095 Black and 10,884 White participants, followed between 1987 and 1989 until 2017. Race was self-reported. We examined racial differences in in- and out-of-hospital fatal CHD with hierarchical proportional hazard models. We then examined the role of income in these associations, using Cox marginal structural models for a mediation analysis. The incidence of out-of-hospital and in-hospital fatal CHD was 1.3 and 2.2 in Black participants, and 1.0 and 1.1 in White participants, respectively, per 1,000 person-years. The gender- and age-adjusted hazard ratios comparing out-of-hospital and in-hospital incident fatal CHD in Black with that in White participants were 1.65 (1.32 to 2.07) and 2.37 (1.96 to 2.86), respectively. The income-controlled direct effects of race in Black versus White participants decreased to 1.33 (1.01 to 1.74) for fatal out-of-hospital and to 2.03 (1.61 to 2.55) for fatal in-hospital CHD in Cox marginal structural models. In conclusion, higher rates of fatal in-hospital CHD in Black participants than in their White counterparts likely drive the overall racial differences in fatal CHD. Income largely explained racial differences in both fatal out-of-hospital CHD and fatal in-hospital CHD.

Tensor response quantile regression with neuroimaging data.

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.

Diagnostic evaluation of pharmacokinetic features of functional markers.

The dynamicity of functional (curve) markers from modern clinical studies offers deeper insights into complex disease physiology. A frequent clinical practice is to examine various 'pharmacokinetic features' of functional markers (definite integral, maximum value, time to maximum, etc.) that reflect important physiological underpinnings. For instance, the current diagnostic procedure for kidney obstruction is to examine several pharmacokinetic features of renogram curves characterizing renal function. Motivated by such clinical practices, we develop a statistical framework for evaluating diagnostic accuracy of pharmacokinetic features using area under the receiver operating characteristic curve (AUC). The major challenge is that functional markers are observed at discrete time points with measurement error. To address this challenge, we develop a two-stage non-parametric AUC estimator based on summary functionals providing unified representation of various pharmacokinetic features and study its asymptotic properties. We also propose a sensible adaptation of a semiparametric regression model that can describe heterogeneity of AUC across different subpopulations, while appropriately handling discreteness and noise in observed functional markers. Here, a novel data-driven approach that balances between bias and efficiency of the regression coefficient estimates is introduced. Finally, the framework is applied to rigorously evaluate pharmacokinetic features of renogram curves potentially useful for detecting kidney obstruction.

Racial Disparities in Hospitalization Among Patients Who Receive a Diagnosis of Acute Coronary Syndrome in the Emergency Department.

Background Timely hospitalization of patients who are diagnosed with an acute coronary syndrome (ACS) at the emergency department (ED) is a crucial step to lower the risk of ACS mortality. We examined whether there are racial and ethnic differences in the risk of being discharged home among patients who received a diagnostic code of ACS at the ED and whether having health insurance plays a role. Methods and Results We examined 51 022 910 discharge records of ED visits in Florida, New York, and Utah in the years 2008, 2011, 2014, and 2016/2017 using state-specific data from the Healthcare Cost and Utilization Project. We identified ED admissions for acute myocardial infarction or unstable angina using the International Classification of Diseases, Ninth Revision (ICD-9)/International Statistical Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes. We used generalized estimating equation models to compare the risk of being discharged home across racial and ethnic groups. We used Poisson marginal structural models to estimate the mediating role of health insurance status. The proportion discharged home with a diagnostic code of ACS was 12% among Black patients, 6% among White patients, 9% among Hispanic patients, and 9% among Asian/Pacific Islander patients. The incidence risk ratio for being discharged home was 1.26 (95% CI, 1.18-1.34) in Black patients, 1.23 (95% CI, 1.15-1.32) in Hispanic patients, and 1.11 (95% CI, 0.93-1.31) in Asian/Pacific Islander patients compared with White patients. Race and ethnicity were marginally associated with discharge home via pathways not mediated by health insurance. Conclusions Racial and ethnic disparities exist in the hospitalization of patients who received a diagnostic code of ACS in the ED. Possible causes need to be investigated.

Differences in incident and recurrent myocardial infarction among White and Black individuals aged 35 to 84: Findings from the ARIC community surveillance study.

BACKGROUND: No previous study has examined racial differences in recurrent acute myocardial infarction (AMI) in a community population. We aimed to examine racial differences in recurrent AMI risk, along with first AMI risk in a community population. METHODS: The community surveillance of the Atherosclerosis Risk in Communities Study (2005-2014) included 470,000 people 35 to 84 years old in 4 U.S. communities. Hospitalizations for recurrent and first AMI were identified from ICD-9-CM discharge codes. Poisson regression models were used to compare recurrent and first AMI risk ratios between Black and White residents. RESULTS: Recurrent and first AMI risk per 1,000 persons were 8.8 (95% CI, 8.3-9.2) and 20.7 (95% CI, 20.0-21.4) in Black men, 6.8 (95% CI, 6.5-7.0) and 14.1 (95% CI, 13.8-14.5) in White men, 5.3 (95% CI, 5.0-5.7) and 16.2 (95% CI, 15.6-16.8) in Black women, and 3.1 (95% CI, 3.0-3.3) and 8.8 (95% CI, 8.6-9.0) in White women, respectively. The age-adjusted risk ratios (RR) of recurrent AMI were higher in Black men vs White men (RR, 1.58 95% CI, 1.30-1.92) and Black women vs White women (RR, 2.09 95% CI, 1.64-2.66). The corresponding RRs were slightly lower for first AMI: Black men vs White men, RR, 1.49 (95% CI, 1.30-1.71) and Black women vs White women, RR, 1.65 (95% CI, 1.42-1.92) CONCLUSIONS: Large disparities exist by race for recurrent AMI risk in the community. The magnitude of disparities is stronger for recurrent events than for first events, and particularly among women.

A Bayesian multiple imputation approach to bivariate functional data with missing components.

Existing missing data methods for functional data mainly focus on reconstructing missing measurements along a single function-a univariate functional data setting. Motivated by a renal study, we focus on a bivariate functional data setting, where each sampling unit is a collection of two distinct component functions, one of which may be missing. Specifically, we propose a Bayesian multiple imputation approach based on a bivariate functional latent factor model that exploits the joint changing patterns of the component functions to allow accurate and stable imputation of one component given the other. We further extend the framework to address multilevel bivariate functional data with missing components by modeling and exploiting inter-component and intra-subject correlations. We develop a Gibbs sampling algorithm that simultaneously generates multiple imputations of missing component functions and posterior samples of model parameters. For multilevel bivariate functional data, a partially collapsed Gibbs sampler is implemented to improve computational efficiency. Our simulation study demonstrates that our methods outperform other competing methods for imputing missing components of bivariate functional data under various designs and missingness rates. The motivating renal study aims to investigate the distribution and pharmacokinetic properties of baseline and post-furosemide renogram curves that provide further insights into the underlying mechanism of renal obstruction, with post-furosemide renogram curves missing for some subjects. We apply the proposed methods to impute missing post-furosemide renogram curves and obtain more refined insights.

Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

Nonparametric estimation of broad sense agreement between ordinal and censored continuous outcomes.

The concept of broad sense agreement (BSA) has recently been proposed for studying the relationship between a continuous measurement and an ordinal measurement. They developed a nonparametric procedure for estimating the BSA index, which is only applicable to completely observed data. In this work, we consider the problem of evaluating BSA index when the continuous measurement is subject to censoring. We propose a nonparametric estimation method built upon a derivation of a new functional representation of the BSA index, which allows for accommodating censoring by plugging in the nonparametric survival function estimators. We establish the consistency and asymptotic normality for the proposed BSA estimator. We also investigate an alternative approach based on the strategy of multiple imputation, which is shown to have better empirical performance with small sample sizes than the plug-in method. Extensive simulation studies are conducted to evaluate our proposals. We illustrate our methods via an application to a Surgical Intensive Care Unit study.

A New Functional Representation of Broad Sense Agreement.

We derive a new functional representation of Broad Sense Agreement (BSA) index that evaluates the agreement/alignment between a continuous measurement and an ordinary measurement. Using this result, we develop an alternative BSA estimator, which can offer significant numerical advantages.

A Bayesian Latent Class Model to Predict Kidney Obstruction in the Absence of Gold Standard.

Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection of kidney obstruction. Seeking to develop a computer-aided diagnostic (CAD) tool that can assist practicing radiologists to reduce errors in the interpretation of kidney obstruction, a recent study collected data from diuresis renography, interpretations on the renography data from highly experienced nuclear medicine experts as well as clinical data. To achieve the objective, we develop a statistical model that can be used as a CAD tool for assisting radiologists in kidney interpretation. We use a Bayesian latent class modeling approach for predicting kidney obstruction through the integrative analysis of time-series renogram data, expert ratings, and clinical variables. A nonparametric Bayesian latent factor regression approach is adopted for modeling renogram curves in which the coefficients of the basis functions are parameterized via the factor loadings dependent on the latent disease status and the extended latent factors that can also adjust for clinical variables. A hierarchical probit model is used for expert ratings, allowing for training with rating data from multiple experts while predicting with at most one expert, which makes the proposed model operable in practice. An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. We demonstrate the superiority of the proposed method over several existing methods through extensive simulations. Analysis of the renal study also lends support to the usefulness of our model as a CAD tool to assist less experienced radiologists in the field.

A Smooth Nonparametric Approach to Determining Cut-Points of A Continuous Scale.

The problem of determining cut-points of a continuous scale according to an establish categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, or facilitating interpretations. A general analytic framework was recently proposed for assessing optimal cut-points defined based on some pre-specified criteria. However, the implementation of the existing nonparametric estimators under this framework and the associated inferences can be computationally intensive when more than a few cut-points need to be determined. To address this important issue, a smoothing-based modification of the current method is proposed and is found to substantially improve the computational speed as well as the asymptotic convergence rate. Moreover, a plug-in type variance estimation procedure is developed to further facilitate the computation. Extensive simulation studies confirm the theoretical results and demonstrate the computational benefits of the proposed method. The practical utility of the new approach is illustrated by an application to a mental health study.

A multidimensional analysis of the longitudinal effects of roux en y gastric bypass on fatigue: An association with visceral obesity.

BACKGROUND AND OBJECTIVES: Severe obesity is associated with fatigue, however, the effects of weight loss after bariatric surgery on particular dimensions of fatigue are unknown. In a secondary analysis of a prospective cohort study of women undergoing roux-en-y gastric bypass (RYGB) we explored relationships among multiple dimensions of fatigue and improving adiposity, insulin resistance and inflammation. METHODS: Before, and 1 and 6 months after RYBG, dimensions of fatigue were assessed using the validated, self-report, Multidimensional Fatigue Inventory. Total, abdominal visceral (VAT) and subcutaneous (SAT) adiposity, insulin sensitivity (Si and HOMA) and plasma concentrations of leptin, C-reactive protein (CRP) and interleukin-6 (Il-6) were measured using air displacement plethysmography, computed tomography, glucose tolerance testing and enzyme-linked immunoassay. Associations were assessed using Spearman correlations and linear regression. RESULTS: At baseline, the majority of our female participants (N = 19, body mass index, 46.5 kg/m2, age 37.2 years) were experiencing elevated levels of fatigue. By 6 months, dimensions of physical (-43%), reduced activity (-43%), reduced motivation (-38%), general (-31%; all p < .005), and mental (-18%, p < .05) fatigue improved, concomitant with decreases in markers of adiposity, inflammation and insulin resistance. The decrease in VAT was associated with improvement in mental fatigue (beta, 0.447 ±â€¯0.203, p = .045), independent of other indices of adiposity, IL-6 concentrations, or Si. CONCLUSIONS: In the 6 months after RYGB, fatigue improved, especially physical fatigue. Decreases in mental fatigue were strongly associated with decreases in visceral adiposity. Nevertheless, the biologic mechanisms underlying changes in these specific fatigue dimensions remain undetermined.

Polybrominated Biphenyl Exposure and Menstrual Cycle Function.

BACKGROUND: Brominated flame retardants, including polybrominated biphenyls (PBB), are persistent compounds reported to affect sex hormones in animals; less is known about potential effects in humans. An industrial accident in 1973-1974 exposed Michigan residents to PBB through contaminated food. We examined whether this exposure to PBB had long-term effects on menstrual cycle function. METHODS: In 2004-2006, we recruited reproductive-aged women in the Michigan PBB Registry who were not pregnant, lactating, or taking hormonal medications. Participants kept daily diaries and provided daily urine samples for up to 6 months. We assayed the urine samples for estrone 3-glucuronide (E13G), pregnanediol 3-glucuronide (Pd3G), and follicle stimulating hormone (FSH). We fit linear mixed models among women aged 35-42 years to describe the relation between serum PBB levels and log-transformed, creatinine-adjusted daily endocrine levels among women who were premenarchal during the exposure incident in 1973-1974 (n = 70). RESULTS: We observed that high (>3.0 parts per billion [ppb]) and medium (>1.0-3.0 ppb) PBB exposure were associated with lower E13G levels across the menstrual cycle and lower FSH levels during the follicular phase, compared with low PBB exposure (≤1.0 ppb). High PBB exposure was also associated with lower Pd3G levels across the cycle compared with low PBB exposure, whereas Pd3G levels were similar in women with medium and low PBB exposure. CONCLUSION: Our results are consistent with a hypothesized effect of exposure to an exogenous estrogen agonist but the modest sample size of the study requires cautious interpretation.

Assessing alignment between functional markers and ordinal outcomes based on broad sense agreement.

Functional markers and their quantitative features (eg, maximum value, time to maximum, area under the curve [AUC], etc) are increasingly being used in clinical studies to diagnose diseases. It is thus of interest to assess the diagnostic utility of functional markers by assessing alignment between their quantitative features and an ordinal gold standard test that reflects the severity of disease. The concept of broad sense agreement (BSA) has recently been introduced for studying the relationship between continuous and ordinal measurements, and provides a promising tool to address such a question. Our strategy is to adopt a general class of summary functionals (SFs), each of which flexibly captures a different quantitative feature of a functional marker, and study its alignment according to an ordinal outcome via BSA. We further illustrate the proposed framework using three special classes of SFs (AUC-type, magnitude-specific, and time-specific) that are widely used in clinical settings. The proposed BSA estimator is proven to be consistent and asymptotically normal given a consistent estimator for the SF. We further provide an inferential framework for comparing a pair of candidate SFs in terms of their importance on the ordinal outcome. Our simulation results demonstrate satisfactory finite-sample performance of the proposed framework. We demonstrate the application of our methods using a renal study.

The effects of roux en y gastric bypass surgery on neurobehavioral symptom domains associated with severe obesity.

BACKGROUND: Neurobehavioral symptoms and cognitive dysfunction related to mood disorders are present in individuals with severe obesity. We sought to determine acute improvements in these symptoms and relationships with adiposity, inflammation, and insulin sensitivity after roux-en-y gastric bypass (RYGB) surgery. METHODS: The self-report Zung Depression Rating (ZDRS) and Neurotoxicity Rating (NRS) scales were administered before, and at 6-months after RYGB surgery in severely obese women (body mass index > 35 kg/m2; N = 19). Symptom domains corresponding to depressed mood/suicide ideation, anxiety, cognitive, somatic, and neurovegetative symptoms were assessed. Biologic measures were of adiposity [leptin, abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue], inflammation [IL-6, C-reactive protein (CRP)], and insulin sensitivity (Si). Spearman correlations and linear regression (adjusted for biologic measures) assessed relationships between changes in biologic measures and changes in neurobehavioral domains. RESULTS: By 6-months after RYGB, VAT, SAT, Si, CRP, and IL-6 had improved (p < .05). Anxiety, somatic, and neurovegetative symptoms domains improved (p < .05), but depressed mood/suicidal ideation and cognitive domains did not. Reductions in VAT were associated with decreases in neurovegetative symptoms (beta = 295 ±â€¯85, p < .01). We also found significant positive longitudinal associations between IL-6 concentrations and minor changes in cognitive symptoms. CONCLUSION: Anxiety, somatic and neurovegetative symptoms, improved within 6 months after RYGB, but depressed mood/suicidal ideation and cognitive symptoms did not improve. Associations between visceral adiposity, IL-6 concentrations and neurovegetative and cognitive symptoms support links between obesity, inflammation and distinct neurobehavioral symptoms.