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Cerebral cortex thinning and cerebral blood flow (CBF) reduction are typically observed during normal healthy aging. However, imaging-based age prediction models have primarily used morphological features of the brain. Complementary physiological CBF information might result in an improvement in age estimation. In this study, T1-weighted structural magnetic resonance imaging and arterial spin labeling CBF images were acquired in 146 healthy participants across the adult life span. Sixty-eight cerebral cortex regions were segmented, and the cortical thickness and mean CBF were computed for each region. Linear regression with age was computed for each region and data type, and laterality and correlation matrices were computed. Sixteen predictive models were trained with the cortical thickness and CBF data alone as well as a combination of both data types. The age explained more variance in the cortical thickness data (average R2 of 0.21) than in the CBF data (average R2 of 0.09). All 16 models performed significantly better when combining both measurement types and using feature selection, and thus, we conclude that the inclusion of CBF data marginally improves age estimation.

10-Year Update on ClinicalTrials.gov A decade after ClinicalTrials.gov launched a database for reporting results of clinical trials, the database includes results of approximately 36,000 trials. Th...

Importance Extenuating circumstances can trigger unplanned changes to randomized trials and introduce methodological, ethical, feasibility, and analytical challenges that can potentially compromise the validity of findings. Numerous randomized trials have required changes in response to the COVID-19 pandemic, but guidance for reporting such modifications is incomplete. Objective As a joint extension for the CONSORT and SPIRIT reporting guidelines, CONSERVE (CONSORT and SPIRIT Extension for RCTs Revised in Extenuating Circumstances) aims to improve reporting of trial protocols and completed trials that undergo important modifications in response to extenuating circumstances. Evidence A panel of 37 international trial investigators, patient representatives, methodologists and statisticians, ethicists, funders, regulators, and journal editors convened to develop the guideline. The panel developed CONSERVE following an accelerated, iterative process between June 2020 and February 2021 involving (1) a rapid literature review of multiple databases (OVID Medline, OVID EMBASE, and EBSCO CINAHL) and gray literature sources from 2003 to March 2021; (2) consensus-based panelist meetings using a modified Delphi process and surveys; and (3) a global survey of trial stakeholders. Findings The rapid review yielded 41 673 citations, of which 38 titles were relevant, including emerging guidance from regulatory and funding agencies for managing the effects of the COVID-19 pandemic on trials. However, no generalizable guidance for all circumstances in which trials and trial protocols might face unanticipated modifications were identified. The CONSERVE panel used these findings to develop a consensus reporting guidelines following 4 rounds of meetings and surveys. Responses were received from 198 professionals from 34 countries, of whom 90% (n = 178) indicated that they understood the concept definitions and 85.4% (n = 169) indicated that they understood and could use the implementation tool. Feedback from survey respondents was used to finalize the guideline and confirm that the guideline’s core concepts were applicable and had utility for the trial community. CONSERVE incorporates an implementation tool and checklists tailored to trial reports and trial protocols for which extenuating circumstances have resulted in important modifications to the intended study procedures. The checklists include 4 sections capturing extenuating circumstances, important modifications, responsible parties, and interim data analyses. Conclusions and Relevance CONSERVE offers an extension to CONSORT and SPIRIT that could improve the transparency, quality, and completeness of reporting important modifications to trials in extenuating circumstances such as COVID-19.

Elucidating the brain regions and networks associated with cognitive processes has been the mainstay of task-based fMRI, under the assumption that BOLD signals are uncompromised by vascular function. This is despite the plethora of research highlighting BOLD modulations due to vascular changes induced by disease, drugs, and aging. On the other hand, BOLD fMRI-based assessment of cerebrovascular reactivity (CVR) is often used as an indicator of the brain's vascular health and has been shown to be strongly associated with cognitive function. This review paper considers the relationship between BOLD-based assessments of CVR, cognition and task-based fMRI. How the BOLD response reflects both CVR and neural activity, and how findings of altered CVR in disease and in normal physiology are associated with cognition and BOLD signal changes are discussed. These are pertinent considerations for fMRI applications aiming to understand the biological basis of cognition. Therefore, a discussion of how the acquisition of BOLD-based CVR can enhance our ability to map human brain function, with limitations and potential future directions, is presented.

Inclusion and subsequent reporting of minority participants in clinical trials are critical for ensuring external validity and detecting differences among subgroups, however reports suggest that ongoing gaps persist. ClinicalTrials.gov began requiring the reporting of race/ethnicity information (if collected) during results submission for trials in April 2017. For this study, we downloaded and compared trial race/ethnicity information from ClinicalTrials.gov submitted before (N = 3540) and after (N = 3542) the requirement date. We found that 42.0% of pre-requirement trials compared to 91.4% of post-requirement trials reported race/ethnicity information in ClinicalTrials.gov ; 8.6% of post-requirement trials indicated race/ethnicity information was not collected. Use of NIH/U.S. Office of Management and Budget (OMB) classification categories was slightly higher in the post-requirement (77.1%) compared to pre-requirement (72.8%) samples. Additionally, we examined two 10% random samples of post-requirement trials – one with customized race/ethnicity reporting in ClinicalTrials.gov and the other with corresponding results publications available in PubMed. In the first random sample, 95.9% of customized categories included race information and 52.7% included ethnicity information. In the other random sample, 33.1% had a corresponding results publication, of which 62.4% reported race/ethnicity information in the publication. Among trials without published race/ethnicity information, 90.0% reported race/ethnicity information on ClinicalTrials.gov . This analysis demonstrates that the requirement has advanced public availability of information on the inclusion of minorities in research, but that further work remains to systematically ensure collection and complete reporting of race/ethnicity information.

IntroductionUsing the tenets underlying social identity theory and the theory of planned behavior, the current study compared the perceptions, attitudes, and behaviors of e-cigarette users that reported vaping as self-defining ("vapers") compared to users that denied vaping was central to their identity ("non-vapers").MethodSecondary analyses of data from the 2017-2018 California Student Tobacco Survey were utilized. A weighted, multivariable regression model (N = 82,217) compared the demographic characteristics, beliefs, and behaviors of vapers and non-vapers. A path analytic model examined whether norms and attitudes mediated the relationship between vaper identity and use behavior.ResultsSelf-identified sexual and/or gender minority youth were more likely to identify as vapers compared to heterosexual and cisgender respondents. Youth that identified as vapers viewed e-cigarette use as more normative, held more favorable attitudes (i.e., lower harm beliefs), used e-cigarettes more frequently and in greater quantities, were more likely to use fruit and mint flavored e-cigarettes, and were more likely to acquire e-cigarettes from commercial sellers (i.e., vape/tobacco shops; all p < 0.05). Additionally, descriptive norms and attitudes mediated the relationship between vaper identity and use frequency/quantity.ConclusionsDifferences in e-cigarette beliefs and behaviors were found for youth e-cigarette users that perceived vaping as self-defining versus those that did not view vaping as part of their self-concept. Future studies are needed to examine causal directionality between identity, norms, attitudes, and behavior. Tobacco control efforts might use these findings to further denormalize vaping using evidence-based media campaigns and policy implementation.

Purpose To examine the chemical composition of JUUL pods collected from a convenience sample of 16 high schools in California to identify possible consumer modification or counterfeit use. Methods Using Gas Chromatography–Mass Spectrometry, we quantitatively analyzed the nicotine, propylene glycol (PG), and vegetable glycerin (VG) in JUUL pods (n = 26) collected from California high schools and compared results to commercial 3% (n = 15) and 5% (n = 24) JUUL pods purchased online. Results Most of the collected JUUL pods (24/26 pods) had a nicotine concentration (43.3 mg/ml, 95% PI: 21.5–65.1) outside the prediction intervals (PI) of the 3% (33.5 mg/ml, 95% PI: 31.8–35.2) and 5% (55.0 mg/ml, 95% PI: 51.5–58.3) commercial JUUL pods. Most (73%) collected JUUL pods had VG concentrations (583.5 mg/ml, PI: 428.9–738.1) lower than the 3% (722.2 mg/ml, PI: 643.0–801.4) and 5% (710.5 mg/ml, PI: 653.1–767.8) commercial JUUL pods. Conclusions Used JUUL products collected from high school students or found on school grounds were not chemically consistent with the manufacturer’s stated formulations.

The phenomenon of cortical thinning with age has been well established; however, the measured rate of change varies between studies. The source of this variation could be image acquisition techniques including hardware and vendor specific differences. Databases are often consolidated to increase the number of subjects but underlying differences between these datasets could have undesired effects. We explore differences in cerebral cortex thinning between 4 databases, totaling 1382 subjects. We investigate several aspects of these databases, including: 1) differences between databases of cortical thinning rates versus age, 2) correlation of cortical thinning rates between regions for each database, and 3) regression bootstrapping to determine the effect of the number of subjects included. We also examined the effect of different databases on age prediction modeling. Cortical thinning rates were significantly different between databases in all 68 parcellated regions (ANCOVA, P < 0.001). Subtle differences were observed in correlation matrices and bootstrapping convergence. Age prediction modeling using a leave-one-out cross-validation approach showed varying prediction performance (0.64 < R2 < 0.82) between databases. When a database was used to calibrate the model and then applied to another database, prediction performance consistently decreased. We conclude that there are indeed differences in the measured cortical thinning rates between these large-scale databases.

In the decade following the journal editors’ trial registration policy, a global trial reporting system (TRS) has arisen to supplement journal publication by increasing the transparency and accountability of the clinical research enterprise (CRE), which ultimately advances evidence-based medicine. Trial registration a foundation component of the TRS. In this article, we assess impact of the trial registration on the CRE with respect to two key goals: (1) establishing a publicly accessible and structured public record of all trials and (2) ensuring access to date-stamped protocol details that change during a study. After characterizing international trial registry landscape, we summarize the published evidence of the impact of the registration laws and policies on the CRE to date. We present three analyses using ClinicalTrials.gov registration data to illustrate approaches for assessing and monitoring the TRS: (1) timing of registration (i.e., prior to trial initiation [prospective] or after trial initiation [retrospective or “late”]; (2) degree of specificity and consistency of registered primary outcome measures compared to descriptions in study protocols and published articles; and (3) a survey of the published literature to characterize how ClinicalTrials.gov data has been used in research on the CRE. These findings suggest that, while the TRS is largely moving towards goals, key stakeholders need to do more in the next decade.

The final rule for reporting clinical trial results has now been issued by the Department of Health and Human Services. It aims to increase accountability in the clinical research enterprise, making key information available to researchers, funders, and the public.

OBJECTIVES: Little evidence is available on the association of e-cigarettes with health indices. We investigated the association of e-cigarette use with diagnosed respiratory disorder among adults with data from the Behavioral Risk Factor Surveillance Survey (BRFSS). METHODS: The 2016 Hawaii BRFSS, a cross-sectional random-dial telephone survey, had 8,087 participants; mean age was 55 years. Items asked about e-cigarette use, cigarette smoking, and being diagnosed by a health professional with (a) asthma or (b) chronic obstructive pulmonary disease. Multivariable analyses tested associations of e-cigarette use with the respiratory variables controlling for smoking and demographic, physical, and psychosocial variables. RESULTS: Controlling for the covariates and smoking there was a significant association of e-cigarette use with chronic pulmonary disorder in the total sample (AOR = 2.58, CI 1.36 – 4.89, p < .01) and a significant association with asthma among nonsmokers (AOR = 1.33, CI 1.00 – 1.77, p < .05). The associations were stronger among nonsmokers than among smokers. Results were similar for analyses based on relative risk and absolute risk. There was also a greater likelihood of respiratory disorder for smokers, females, and persons with overweight, financial stress, and secondhand smoke exposure. CONCLUSIONS: This is the first study to show a significant independent association of e-cigarette use with chronic respiratory disorder. Several aspects of the data are inconsistent with the possibility that e-cigarettes were being used for smoking cessation by persons with existing respiratory disorder. Theoretical mechanisms that might link e-cigarettes use and respiratory symptoms are discussed.

This chapter describes the evolving role of clinical trial registries and results databases. The first part describes the important public health policy goals of registration and results reporting, its history, and key policies in the United States and abroad. The second part focuses on practical issues, policies, and procedures for submitting data to and using data from ClinicalTrials.gov , the largest publicly available clinical trials database in the world. The future role of systematic sharing of individual participant data also is discussed.

A new method is proposed for obtaining cerebral perfusion measurements whereby blood oxygen level dependent (BOLD) MRI is used to dynamically monitor hyperoxia-induced changes in the concentration of deoxygenated hemoglobin in the cerebral vasculature. The data is processed using kinetic modeling to yield perfusion metrics, namely: cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Ten healthy human subjects were continuously imaged with BOLD sequence while a hyperoxic (70% O2) state was interspersed with baseline periods of normoxia. The BOLD time courses were fit with exponential uptake and decay curves and a biophysical model of the BOLD signal was used to estimate oxygen concentration functions. The arterial input function was derived from end-tidal oxygen measurements, and a deconvolution operation between the tissue and arterial concentration functions was used to yield CBF. The venous component of the CBV was calculated from the ratio of the integrals of the estimated tissue and arterial concentration functions. Mean gray and white matter measurements were found to be: 61.6 ± 13.7 and 24.9 ± 4.0 ml 100 g−1 min−1 for CBF; 1.83 ± 0.32 and 1.10 ± 0.19 ml 100 g−1 for venous CBV; and 2.94 ± 0.52 and 3.73 ± 0.60 s for MTT, respectively. We conclude that it is possible to derive CBF, CBV and MTT metrics within expected physiological ranges via analysis of dynamic BOLD fMRI acquired during a period of hyperoxia.

Cerebral amyloid angiopathy (CAA) is a small-vessel disease preferentially affecting posterior brain regions. Recent evidence has demonstrated the efficacy of functional MRI in detecting CAA-related neurovascular injury, however, it is unknown whether such perturbations are associated with changes in the hemodynamic response function (HRF). Here we estimated HRFs from two different brain regions from block design activation data, in light of recent findings demonstrating how block designs can accurately reflect HRF parameter estimates while maximizing signal detection. Patients with a diagnosis of probable CAA and healthy controls performed motor and visual stimulation tasks. Time-to-peak (TTP), full-width at half-maximum (FWHM), and area under the curve (AUC) of the estimated HRFs were compared between groups and to MRI features associated with CAA including cerebral microbleed (CMB) count. Motor HRFs in CAA patients showed significantly wider FWHM ( P = 0.006) and delayed TTP ( P = 0.03) compared to controls. In the patient group, visual HRF FWHM was positively associated with CMB count ( P = 0.03). These findings indicate that hemodynamic abnormalities in patients with CAA may be reflected in HRFs estimated from block designs across different brain regions. Moreover, visual FWHM may be linked to structural MR indications associated with CAA.

Reliance on the hemodynamic response as a surrogate marker of neural activity imposes an intrinsic limit on the spatial specificity of functional MRI. An alternative approach based on diffusion-weighted functional MRI (DfMRI) has been reported as a contrast less reliant on hemodynamic effects, however current evidence suggests that both hemodynamic and unique neural sources contribute to the diffusion signal. Here we compare activation patterns obtained with the standard blood oxygenation level-dependent (BOLD) contrast to DfMRI in order to gain a deeper understanding of how the BOLD proportion contributes to the observable diffusion signal. Both individual and group-level activation patterns obtained with DfMRI and BOLD to a visual field stimulation paradigm were analyzed. At the individual level, the DfMRI contrast showed a strong, positive relationship between the volumes of cortex activated in response to quadrant- and hemi-field visual stimulation. This was not observed in the corresponding BOLD experiment. Overall, the DfMRI response indicated less between-subject variability, with random effects analyses demonstrating higher statistical values at the peak voxel for DfMRI. Furthermore, the spatial extent of the activation was more restricted to the primary visual region for DfMRI than BOLD. However, the diffusion signal was sensitive to the hemodynamic response in a manner dependent on experimental manipulation. It was also limited by its low signal-to-noise ratio (SNR), demonstrating lower sensitivity than BOLD. Together these findings both support DfMRI as a contrast that bears a closer spatial relationship to the underlying neural activity than BOLD, and raise important caveats regarding its utilization. Models explaining the DfMRI signal change need to consider the dynamic vascular contributions that may vary with neural activity.

Use of electronic cigarettes (e-cigarettes) is prevalent among adolescents, but there is little knowledge about the consequences of their use. We examined, longitudinally, how e-cigarette use among adolescents is related to subsequent smoking behaviour.Longitudinal school-based survey with a baseline sample of 2338 students (9th and 10th graders, mean age 14.7 years) in Hawaii surveyed in 2013 (time 1, T1) and followed up 1 year later (time 2, T2). We assessed e-cigarette use, tobacco cigarette use, and psychosocial covariates (demographics, parental support and monitoring, and sensation seeking and rebelliousness). Regression analyses including the covariates tested whether e-cigarette use was related to the onset of smoking among youth who had never smoked cigarettes, and to change in smoking frequency among youth who had previously smoked cigarettes.Among T1 never-smokers, those who had used e-cigarettes at T1 were more likely to have smoked cigarettes at T2; for a complete-case analysis, adjusted OR=2.87, 95% CI 2.03 to 4.05, p0.0001. Among ever-smokers at T1, using e-cigarettes was not related to significant change in their frequency of smoking at T2. Uptake of e-cigarette use among T1 never-users of either product was predicted by age, Caucasian or Native Hawaiian (vs Asian-American) ethnicity, lower parental education and parental support, higher rebelliousness, and perception of e-cigarettes as healthier.Adolescents who use e-cigarettes are more likely to start smoking cigarettes. This result together with other findings suggests that policies restricting adolescents' access to e-cigarettes may have a rationale from a public health standpoint.

This study examines the extent to which trials registered in ClinicalTrials.gov by individual sponsors for the same drug and condition were available publicly in ClinicalTrials.gov and/or PubMed.

INTRODUCTION This study examined change in tobacco use over 4 years among the general population of patients in six diverse health care organizations using electronic medical record data. METHODS The study cohort (N = 34 393) included all patients age 18 years or older who were identified as smokers in 2007, and who then had at least one primary care visit in each of the following 4 years. RESULTS In the 4 years following 2007, this patient cohort had a median of 13 primary care visits, and 38.6% of the patients quit smoking at least once. At the end of the fourth follow-up year, 15.4% had stopped smoking for 1 year or more. Smokers were more likely to become long-term quitters if they were 65 or older (OR = 1.32, 95% CI = [1.16, 1.49]), or had a diagnoses of cancer (1.26 [1.12, 1.41]), cardiovascular disease (1.22 [1.09, 1.37]), asthma (1.15 [1.06, 1.25]), or diabetes (1.17 [1.09, 1.27]). Characteristics associated with lower likelihood of becoming a long-term quitter were female gender (0.90 [0.84, 0.95]), black race (0.84 [0.75, 0.94]) and those identified as non-Hispanic (0.50 [0.43, 0.59]). CONCLUSIONS Among smokers who regularly used these care systems, one in seven had achieved long-term cessation after 4 years. This study shows the practicality of using electronic medical records for monitoring patient smoking status over time. Similar methods could be used to assess tobacco use in any health care organization to evaluate the impact of environmental and organizational programs.

Introduction Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research. Methods We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment. Results The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases. Conclusion The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.

Background Clinical trials that end prematurely (or “terminate”) raise financial, ethical, and scientific concerns. The extent to which the results of such trials are disseminated and the reasons for termination have not been well characterized. Methods and Findings A cross-sectional, descriptive study of terminated clinical trials posted on the ClinicalTrials.gov results database as of February 2013 was conducted. The main outcomes were to characterize the availability of primary outcome data on ClinicalTrials.gov and in the published literature and to identify the reasons for trial termination. Approximately 12% of trials with results posted on the ClinicalTrials.gov results database (905/7,646) were terminated. Most trials were terminated for reasons other than accumulated data from the trial (68%; 619/905), with an insufficient rate of accrual being the lead reason for termination among these trials (57%; 350/619). Of the remaining trials, 21% (193/905) were terminated based on data from the trial (findings of efficacy or toxicity) and 10% (93/905) did not specify a reason. Overall, data for a primary outcome measure were available on ClinicalTrials.gov and in the published literature for 72% (648/905) and 22% (198/905) of trials, respectively. Primary outcome data were reported on the ClinicalTrials.gov results database and in the published literature more frequently (91% and 46%, respectively) when the decision to terminate was based on data from the trial. Conclusions Trials terminate for a variety of reasons, not all of which reflect failures in the process or an inability to achieve the intended goals. Primary outcome data were reported most often when termination was based on data from the trial. Further research is needed to identify best practices for disseminating the experience and data resulting from terminated trials in order to help ensure maximal societal benefit from the investments of trial participants and others involved with the study.