Citations

359881 Background: Sunitinib is approved for advanced gastrointestinal stromal tumor (GIST) after imatinib failure. Ripretinib, a broad-spectrum KIT and PDGFRA switch-control tyrosine kinase inhibitor (TKI), is indicated for the treatment of adult patients (pts) with GIST who received prior treatment with 3 or more TKIs, including imatinib. We compared the efficacy and safety of ripretinib vs sunitinib in pts with advanced GIST who progressed on or were intolerant to imatinib. Methods: This multicenter, global, randomized, open-label phase 3 study (NCT03673501) enrolled adult pts with GIST who progressed on or had intolerance to imatinib. Pts were randomized 1:1 to ripretinib 150 mg once daily (QD) or sunitinib 50 mg QD (4 wks on/2 wks off). Randomization was stratified by KIT mutational status and imatinib intolerance. The primary endpoint was progression-free survival (PFS) by independent radiologic review (IRR) using modified RECIST version 1.1. Key secondary endpoints were objective response rate (ORR) by IRR and overall survival (OS). Hierarchical testing was performed for primary and key secondary endpoints in a prespecified sequence; testing pts with a KIT exon 11 primary mutation (Ex11 intention-to-treat [ITT] population) preceded the all-patient (AP) ITT population. Data cutoff was 1 Sep 2021; final analyses of PFS and ORR and the first interim analysis of OS were conducted. Results: A total of 453 pts were randomized to ripretinib (n = 226; Ex11 ITT, n = 163) or sunitinib (n = 227; Ex11 ITT, n = 164). Median age was 60 yrs (range 18–88) and most pts were white (66.2%) males (62.0%). PFS was not statistically different between ripretinib and sunitinib in the Ex11 ITT (hazard ratio [HR] 0.88, 95% CI 0.66, 1.16; P = 0.36; median 8.3 vs 7.0 mos) or in the AP populations (HR 1.05, 95% CI 0.82, 1.33; P = 0.72; median 8.0 vs 8.3 mos). ORR was numerically higher for ripretinib vs sunitinib in the Ex11 ITT (23.9% vs 14.6%; difference 9.3%, 95% CI 0.7, 17.8; nominal P = 0.03) and AP ITT populations (21.7% vs 17.6%; difference 4.2%, 95% CI −3.2, 11.5; nominal P = 0.27). OS data was highly immature; median OS was not reached in either arm. Fewer pts in the ripretinib arm experienced Grade 3-4 (G3-4) treatment-emergent adverse events (TEAEs) vs sunitinib (41.3% vs 65.6%). Among G3-4 TEAEs with a difference ≥5% between arms, ripretinib had fewer events vs sunitinib (hypertension [8.5% vs 26.7%], palmar-plantar erythrodysesthesia [1.3% vs 10.0%], neutropenia [0% vs 6.3%], and neutrophil count decreased [0% vs 7.2%]). Conclusions: The PFS in both arms was longer than PFS achieved by sunitinib in its pivotal phase 3 trial. While the PFS for ripretinib did not meet the primary endpoint of superiority vs sunitinib, meaningful clinical activity and fewer G3-4 TEAEs were observed in pts with advanced GIST treated with ripretinib after imatinib failure. Clinical trial information: NCT03673501.

ABSTRACT Background Critically ill patients with severe acute kidney injury (AKI) requiring kidney replacement therapy (KRT) have a grim prognosis. Recently, multiple studies focused on the impact of KRT initiation time [i.e., accelerated versus watchful waiting KRT initiation (WWS-KRT)] on patient outcomes. We aim to review the results of all related clinical trials. Methods In this systematic review, we searched all relevant randomized clinical trials from January 2000 to April 2021. We assessed the impacts of accelerated versus WWS-KRT on KRT dependence, KRT-free days, mortality and adverse events, including hypotension, infection, arrhythmia and bleeding. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. Results A total of 4932 critically ill patients with AKI from 10 randomized clinical trials were included in this analysis. The overall 28-day mortality rate was 38.5%. The 28-day KRT-dependence rate was 13.0%. The overall incident of KRT in the accelerated group was 97.4% and 62.8% in the WWS-KRT group. KRT in the accelerated group started 36.7 h earlier than the WWS-KRT group. The two groups had similar risks of 28-day [pooled log odds ratio (OR) 1.001, P = 0.982] and 90-day (OR 0.999, P = 0.991) mortality rates. The accelerated group had a significantly higher risk of 90-day KRT dependence (OR 1.589, P = 0.007), hypotension (OR 1.687, P < 0.001) and infection (OR 1.38, P = 0.04) compared with the WWS-KRT group. Conclusions This meta-analysis revealed that accelerated KRT leads to a higher probability of 90-day KRT dependence and dialysis-related complications without any impact on mortality rate when compared with WWS-KRT. Therefore, we suggest the WWS-KRT strategy for critically ill patients.

Abstract Study Objectives Post-traumatic stress disorder (PTSD) and rapid eye movement (REM) sleep behavior disorder (RBD) share some common features including prominent nightmares and sleep disturbances. We aimed to comparatively analyze REM sleep without atonia (RSWA) between patients with chronic PTSD with and without dream enactment behavior (DEB), isolated RBD (iRBD), and controls. Methods In this retrospective study, we comparatively analyzed 18 PTSD with DEB (PTSD+DEB), 18 PTSD without DEB, 15 iRBD, and 51 controls matched for age and sex. We reviewed medical records to determine PTSD clinical features and quantitatively analyzed RSWA. We used nonparametric analyses to compare clinical and polysomnographic features. Results PTSD patients, both with and without DEB, had significantly higher RSWA than controls (all p < .025, excepting submentalis phasic duration in PTSD+DEB). Most RSWA measures were also higher in PTSD+DEB than in PTSD without DEB patients (all p < .025). Conclusions PTSD patients have higher RSWA than controls, whether DEB is present or not, indicating that REM sleep atonia control is abnormal in chronic PTSD. Further prospective studies are needed to determine whether neurodegenerative risk and disease markers similar to RBD might occur in PTSD patients.

Background: Sequencing Mendelian arrhythmia genes in individuals without an indication for arrhythmia genetic testing can identify carriers of pathogenic or likely pathogenic (P/LP) variants. However, the extent to which these variants are associated with clinically meaningful phenotypes before or after return of variant results is unclear. In addition, the majority of discovered variants are currently classified as variants of uncertain significance, limiting clinical actionability. Methods: The eMERGE-III study (Electronic Medical Records and Genomics Phase III) is a multicenter prospective cohort that included 21 846 participants without previous indication for cardiac genetic testing. Participants were sequenced for 109 Mendelian disease genes, including 10 linked to arrhythmia syndromes. Variant carriers were assessed with electronic health record–derived phenotypes and follow-up clinical examination. Selected variants of uncertain significance (n=50) were characterized in vitro with automated electrophysiology experiments in HEK293 cells. Results: As previously reported, 3.0% of participants had P/LP variants in the 109 genes. Herein, we report 120 participants (0.6%) with P/LP arrhythmia variants. Compared with noncarriers, arrhythmia P/LP carriers had a significantly higher burden of arrhythmia phenotypes in their electronic health records. Fifty-four participants had variant results returned. Nineteen of these 54 participants had inherited arrhythmia syndrome diagnoses (primarily long-QT syndrome), and 12 of these 19 diagnoses were made only after variant results were returned (0.05%). After in vitro functional evaluation of 50 variants of uncertain significance, we reclassified 11 variants: 3 to likely benign and 8 to P/LP. Conclusions: Genome sequencing in a large population without indication for arrhythmia genetic testing identified phenotype-positive carriers of variants in congenital arrhythmia syndrome disease genes. As the genomes of large numbers of people are sequenced, the disease risk from rare variants in arrhythmia genes can be assessed by integrating genomic screening, electronic health record phenotypes, and in vitro functional studies. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier; NCT03394859.

Background: A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. Objective: We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). Methods: Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. Results: The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. Conclusion: The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Background Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. Methods To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. Results The mean acute effect across all studies was −0.21 ml/min per 1.73 m 2 (95% confidence interval, −0.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, −2.50 to +2.08 ml/min per 1.73 m 2 ). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. Conclusion The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD.

Abstract Background: Real world data (RWD) is one of the key components to our understanding of the treatment trajectory of patients with multiple myeloma (MM) in clinical practice. However, treatment responses recorded in patients' medical records are not often standardized. We have developed an algorithm to derive real-world response (dR) for MM patients based on the International Myeloma Working Group (IMWG) criteria to overcome issues with missing values (Xu et al. Pharmacoepidemiol Drug Saf 2021). In this study, we evaluate its association with overall survival (OS) in a RWD cohort. Methods: This study included patients diagnosed with MM between Jan 1, 2011 and Jan 31, 2021 from the US-based Flatiron Health electronic health record (EHR)-derived de-identified database. We derived overall response (partial response [PR] or better, e.g. > 50% reduction of serum M-protein from baseline) to 1L treatment in newly diagnosed MM patients based on the IMWG response criteria, taking into account missing data. Associations between dR and OS were evaluated at individual- and treatment-level. At individual-level, associations were assessed using landmark analyses at approximating 3/4/5 cycles of treatment and at 6 months. Patients who died before the landmark were excluded from the analysis. At treatment-level, we stratified the cohort by year of treatment initiation (every two years from 2011-2021). Within each stratum, treatment groups were compared to estimate the hazard ratios (HRs) of OS and odds ratios (ORs) of dR, both adjusted for potential confounding factors (age, ECOG status, cytogenetic risk groups [high vs standard], time between diagnosis and first-line [1L] start date). The association between HRs and ORs was assessed by coefficient of determination (R 2) from a stratum-size weighted linear regression model, where values close to 1 imply a strong correlation and 0 indicates no association. Only significant HRs and ORs (P < 0.05) in the multivariable analysis were used in the analysis. Results: Of 6806 patients in the Flatiron Health MM database, 70% (n=4830) had serum myeloma (M) protein measured by protein electrophoresis within 30 days before the start of 1L treatment, 27% (n=1838) had urine M protein and 57.8% (n=3935) had serum free light chains (FLCs). Patients with at least one serum M protein or FLC measurement were eligible for study entry (n=5609). During 1L treatment, 4727 patients (46% female, mean ± standard deviation age 67.8 ± 10.4 years) had valid laboratory test results for dR assessment (for each patient we required a baseline measurement plus at least one additional measurement after treatment start of the same lab type), of which 71.7% (n=3387) had PR or better. The majority of the patients (n=2188, 46.3%) received a proteasome inhibitor (PI)+steroid+immunomodulatory drug (IMiD) treatment regimen, followed by PI+chemo+steroid (n=639, 13.5%), PI+steroid (n=621, 13.1%), and steroid+IMiD (n=607, 12.8%). Other treatment groups had fewer than 100 patients and were not considered in the treatment-level analysis (Table 1). At the individual-level analysis, dR was significantly associated with OS at all landmarks (HRs 0.80 to 0.81, P < 0.001, Table 2). At treatment-level, the association between dR and OS was R 2=0.67 (P < 0.001, Figure 1). Subgroup analyses were conducted for each pair of treatment groups; however, only PI+IMiD+steroid vs PI+steroid had sufficient sample sizes, which showed R 2 of 0.82 (P = 0.02). Conclusions: In the absence of recorded response data in a patient's EHR, laboratory assessments can successfully be applied using our algorithm to determine, based on part of the IMWG response criteria, the response status for patients with MM. In the RWD cohort, patients who had PR or better to 1L treatment demonstrated longer survival than non-responders. A moderate association was observed at the treatment-level between dR at 1L and OS. However, these associations might be specific to certain treatment groups and the underlying mode of action of the drugs. Further analysis will be needed to validate these results. Figure 1 Figure 1. Disclosures Xu: F. Hoffmann-La Roche AG: Current Employment. Tyanova: F. Hoffmann-La Roche: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; AbbVie: Other: Received travel sponsorship ; F. Hoffmann-La Roche Ltd.: Current Employment; NHS: Ended employment in the past 24 months; Sanofi: Consultancy; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Roche: Current equity holder in publicly-traded company; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Celgene: Honoraria, Other: Received travel sponsorship ; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag Ltd: Honoraria, Speakers Bureau. Yousefi: Roche: Current Employment, Current equity holder in publicly-traded company. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Real-world data (RWD) derived from electronic health records are becoming increasingly important for deriving insights from clinical practice to complement findings from clinical trials. Response to treatment in multiple myeloma (MM) is assessed using the International Myeloma Working Group (IMWG) response criteria based on MM-specific laboratory measures (i.e. monoclonal [M] protein in serum and urine, free light chain [FLC] levels), as well as radiological images and bone marrow (BM) investigations when appropriate. As healthcare providers do not routinely collect all the information required by the IMWG response criteria, RWD data are often incomplete. Here, we present a derived real-world response (dR) algorithm based on IMWG criteria that accounts for lab measures routinely collected in the clinical care of MM patients and evaluate its ability to accurately assess response to treatment using clinical trial patient-level data. Methods: Treatment response is assessed using a rule-based algorithm integrating longitudinal laboratory measures routinely captured in RWD (Xu et al. Pharmacoepidemiol Drug Saf 2021) as e.g. by the Flatiron MM database. The rules are based on 'relaxed' IMWG criteria, which entail exclusion of BM biopsies data and of imaging results, and reduction in either serum or urine M protein levels (rather than both) to assign partial response. This algorithm was applied to patient-level clinical trial data from the Bellini trial (Kumar et al. Lancet Oncol 2020) with response assignment made by an independent review committee (IRC) in the trial used as the 'ground truth'. Agreement between the IRC's and algorithm's assignments of response was estimated using Cohen's Kappa statistic. Differences in overall response rate (ORR) between treatment and placebo arms were calculated using stratified Cochran-Mantel-Haenszel tests based on strata at randomization with number of prior lines of therapy and previous proteasome inhibitor treatment status variables used for stratification. Results: The Bellini trial is a Phase 3 clinical trial with 2:1 design enrolling 194 and 97 patients in its treatment and placebo arms, respectively (291 patients in total). Regular assessments of M protein and FLC levels were performed (median 13 or 14 measurements), providing detailed trajectory of patient response (Table 1). Comparison between the IRC's and algorithm's assignments of responders classified as Partial Response or better (PR+) resulted in almost perfect agreement with Cohen's Kappa 0.82 (274/291 assignments in agreement). It is worth noting that the Cohen's Kappa between the IRC's and Investigator's assessments is 0.85, indicating that the algorithm's error is within some expected uncertainty. Due to the exclusion of BM information, agreement decreased to 0.56 when depths of response were considered separately as opposed to grouping PR+ patients together with most cases being overestimated as complete response (CR) or stringent CR. In assessing treatment effect, differences in ORR between the intervention and placebo arms in the trial based on IRC's assessment could be accurately recapitulated (68% [66/97] vs 71% [69/97] responders in the placebo arm and 82% [159/194] vs 87% [169/194] in the intervention arm for IRC's and algorithm's assessment, respectively). Based on these results, algorithm response assignment led to consistent conclusions about treatment efficacy in the Bellini trial. Implementation of criteria to characterize very good partial response or better (VGPR+) also led to a conclusion consistent with the Bellini trial IRC's assessment, while implementation of criteria of CR+ resulted in estimates of treatment efficacy that were higher, but directionally consonant (Table 2). Conclusions: We present a fully automated rule-based algorithm for response assessment in MM relying on longitudinal lab measurements during treatment. The algorithm uses 'relaxed' IMWG criteria to account for routine clinical practice settings and demonstrates very high agreement with the assessment of trained clinicians, while reliably reproducing the efficacy analysis in the Bellini trial when ORR and VGPR+ are considered. We envision that, if the concordance observed here is confirmed in other independent cohorts, this algorithm can be used in the assessment of response in RWD and can facilitate response assessment when suboptimal amounts of data are available. Figure 1 Figure 1. Disclosures Tyanova: F. Hoffmann-La Roche: Current Employment. Xu: F. Hoffmann-La Roche AG: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; Janssen-Cilag Ltd: Honoraria, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Other: Received travel sponsorship ; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Roche: Current equity holder in publicly-traded company; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Sanofi: Consultancy; NHS: Ended employment in the past 24 months; F. Hoffmann-La Roche Ltd.: Current Employment; AbbVie: Other: Received travel sponsorship . Hong: Imago BioSciences: Current Employment; Genentech, Inc.: Ended employment in the past 24 months; Stock options in Imago BioSciences: Current equity holder in publicly-traded company. Jin: Genentech Inc: Current Employment; Roche: Current equity holder in publicly-traded company. Karve: AbbVie: Current Employment, Current equity holder in publicly-traded company. Roose: Flatiron Health, Inc.: Current Employment; Roche: Current equity holder in publicly-traded company. Sawas: Seattle Genetics: Honoraria; Affimed: Research Funding; Roche: Current equity holder in publicly-traded company; Flat Iron Health: Current Employment; Acrotech: Honoraria; Daiichi-Sankyo: Speakers Bureau; Seattle Genetics: Speakers Bureau; Gilead: Speakers Bureau. Kumar: Antengene: Consultancy, Honoraria; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Introduction Ductal carcinoma in situ (DCIS) of the breast is a non-obligate precursor of invasive breast cancer (IBC). Many DCIS patients are either undertreated or overtreated. The overarching goal of the study described here is to facilitate detection of patients with DCIS at risk of IBC development. Here, we propose to use risk factor data and formalin-fixed paraffin-embedded (FFPE) DCIS tissue from a large, ethnically diverse, population-based cohort of 8175 women with a first diagnosis of DCIS and followed for subsequent IBC to: identify/validate miRNA expression changes in DCIS tissue associated with risk of subsequent IBC; evaluate ipsilateral IBC risk in association with two previously identified marker sets (triple immunopositivity for p16, COX-2, Ki67; Oncotype DX Breast DCIS score); examine the association of risk factor data with IBC risk. Methods and analysis We are conducting a series of case–control studies nested within the cohort. Cases are women with DCIS who developed subsequent IBC; controls (2/case) are matched to cases on calendar year of and age at DCIS diagnosis. We project 485 cases/970 controls in the aim focused on risk factors. We estimate obtaining FFPE tissue for 320 cases/640 controls for the aim focused on miRNAs; of these, 173 cases/346 controls will be included in the aim focused on p16, COX-2 and Ki67 immunopositivity, and of the latter, 156 case–control pairs will be included in the aim focused on the Oncotype DX Breast DCIS score®. Multivariate conditional logistic regression will be used for statistical analyses. Ethics and dissemination Ethics approval was obtained from the Institutional Review Boards of Albert Einstein College of Medicine (IRB 2014-3611), Kaiser Permanente Colorado, Kaiser Permanente Hawaii, Henry Ford Health System, Mayo Clinic, Marshfield Clinic Research Institute and Hackensack Meridian Health, and from Lifespan Research Protection Office. The study results will be presented at meetings and published in peer-reviewed journals.