Citations

Abstract Background The Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only. Results We identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects. Conclusions Across heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations.

ABSTRACT Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine‐learning models to real‐world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine‐learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best‐performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non‐zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1‐month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1‐month window were statistically significantly associated with clinical worsening events. Our study used machine‐learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.

ABSTRACT In normal health, the liver and lungs enjoy a close anatomic, physiologic, and functional relationship. In the context of pulmonary vascular disease, however, there is accumulating evidence that the interplay between the gut microbiome, hepatic system, and pulmonary vasculature (so‐called “gut‐liver‐lung” axis) plays an important role in driving disease pathogenesis and determining clinical outcomes. Despite recognizing the importance of the gut‐liver‐lung axis in pulmonary vascular disease however, little is known about the clinical characteristics, circulating factors, and physiologic pathways that mediate this important axis of communication. In this clinical and translationally focused review, we provide an overview of liver‐lung communication in normal physiology, and contrast this with gut‐liver‐lung derangements in pulmonary arterial hypertension, portopulmonary hypertension, and hepatopulmonary syndrome. We conclude with identifying key gaps in knowledge that will need to be addressed in order to manipulate the gut‐liver‐lung axis to prevent worsening pulmonary vascular disease, develop novel therapeutics, and improve patient outcomes.

Objective Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f‐FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f‐FTLD. We used a Bayesian linear mixed‐effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia. Methods Participants included 234 asymptomatic or prodromal carriers of C9orf72 , GRN , or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1‐weighted scans. The BLME models established individual voxel‐wise gray matter trajectories using the first 2 scans. Person‐specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time‐varying Cox proportional hazard models covarying for age. Receiver‐operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months. Results The BLME cluster volume predicted conversion to dementia in f‐FTLD mutation carriers overall and separately in C9orf72 , GRN , and MAPT , with comparable hazard ratios observed for atrophy W‐maps and regional volumes. Within a 24‐month timeframe, BLME cluster volume discriminated dementia converters from non‐converters with larger areas under the curve (AUCs) than other approaches. Interpretation Bayesian‐modeled individualized atrophy scores predict dementia progression among asymptomatic f‐FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 20269999:n/a–n/a

Abstract Background Previous studies demonstrate that certain asymptomatic MAPT variant carriers (Asymp‐ MAPT ) exhibit altered baseline gray matter volume (GMV) and functional connectivity (FC) profiles. It remains unclear whether these imaging measures may herald risk for earlier conversion to the symptomatic phase. To address this, we stratified Asymp‐ MAPT based on baseline GMV and longitudinal FC changes to assess whether any Asymp‐ MAPT subgroups showed signs of longitudinal neurodegeneration or cognitive decline. Method We studied 25 Asymp‐ MAPT recruited from the UCSF Memory and Aging Center and ALLFTD Consortia. Using a median split, we divided Asymp‐ MAPT into groups with smaller (LowGMV; n = 12) and larger baseline GMV (HighGMV; n = 13) within regions atrophied in symptomatic MAPT carriers ( n = 20). Next, we stratified Asymp‐ MAPT based on longitudinal FC changes using k‐means clustering on rates of change in FC for four intrinsic connectivity networks associated with common MAPT ‐related syndromes: salience (SN), corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), and default mode (DMN) networks. We compared Asymp‐ MAPT subgroups from each stratification on baseline and longitudinal changes in demographics, plasma neurofilament light (NfL) concentration, GMV within 246 Brainnetome regions, and neuropsychological test scores. Result Stratification of Asymp‐ MAPT based on baseline GMV showed that LowGMV had higher education levels compared to HighGMV, with no other demographic differences. Though LowGMV and HighGMV did not differ in baseline NfL, LowGMV showed greater longitudinal NfL increases. These groups did not differ in baseline or longitudinal GMV changes. LowGMV had lower baseline attention and working memory scores, and greater longitudinal decline in set‐shifting and visuospatial performance. Stratification based on FC changes yielded one group (DecFC; n = 17) exhibiting longitudinal decreases in the SN, PSP, and DMN and another (IncFC; n = 8) showing increases in each network. DecFC and IncFC did not differ in demographics, baseline or longitudinal NfL, or baseline GMV. Longitudinally, DecFC showed greater decline in posterior superior temporal and inferior parietal cortices. DecFC showed lower baseline attention and semantic fluency scores, and greater longitudinal decline in set‐shifting and visuospatial memory performance. Conclusion Lower baseline GMV within regions atrophied during the symptomatic phase and longitudinal FC declines within MAPT ‐relevant networks may signal earlier symptomatic conversion in Asymp‐ MAPT .

Abstract Background A hexanucleotide repeat expansion in C9orf72 is the leading genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis. There remains a critical need for biomarkers that track disease progression across clinical stages. Cross‐sectionally, intrinsic connectivity networks (ICNs) derived from task‐free fMRI (tf‐fMRI) detects abnormalities in C9orf72 expansion carriers ( C9orf72+ ), even when brain structural differences are subtle (Lee et al. , 2017). Few studies have examined longitudinal connectivity changes in C9orf72+ , however. Methods We analyzed longitudinal structural and tf‐fMRI data and plasma neurofilament light chain (NfL) concentrations of C9orf72+ recruited from UCSF and the ALLFTD Consortia. Mean time from first to last scan was 2.4±1.3 years. Participants included 36 asymptomatic (aSx‐ C9 ), 17 prodromal (prodromal‐ C9 ), 29 symptomatic (Sx‐ C9 ) carriers, and 107 healthy controls (HC). For structural MRI analyses, smoothed gray matter maps were parcellated into 246 regions‐of‐interest based on the Brainnetome atlas. We conducted voxelwise seed‐based tf‐fMRI analyses to examine ICNs showing alterations C9orf72+ based on previous literature: salience network (SN), sensorimotor network (SMN), default mode network (DMN), and a medial pulvinar thalamus network (pulvinar). General linear models and linear mixed models (LMM) compared C9orf72+ groups to HC in baseline and longitudinal gray matter and functional connectivity, as well as examined associations between longitudinal functional connectivity changes and baseline NfL concentrations. Results Despite a lack of longitudinal gray matter decline, aSx‐ and Sx‐ C9orf72+ showed functional connectivity changes. Compared to HC, aSx‐ C9 had baseline hypoconnectivity in the SN, SMN, and pulvinar network. Longitudinally, this group showed declines in the SN and pulvinar network, alongside DMN increases. Prodromal‐ C9 exhibited baseline SMN hypoconnectivity and hyperconnectivity in the DMN and pulvinar network, but no significant longitudinal changes. Sx‐ C9 showed baseline hypoconnectivity in SN, SMN, and DMN, with additional regions of DMN hyperconnectivity, while longitudinally, SN and SMN connectivity increased. In aSx‐ C9 , higher baseline NfL concentrations correlated with regions of SMN decreases over time and both increases and decreases in the pulvinar network. In prodromal‐ C9 and Sx‐ C9 combined, higher NfL was associated with DMN increases longitudinally. Conclusions Though gray matter declines were undetectable over a span of several years, functional connectivity networks changed dynamically in C9orf72+ .

Abstract Background TMEM106B encodes a lysosomal protein and is a genetic susceptibility factor for frontotemporal lobar degeneration (FTLD). Aptamer‐based proteomics quantifies TMEM106B protein levels in CSF. The associations of CSF TMEM106B with measures of disease severity have not been previously evaluated, and the value of CSF TMEM106B as a clinical biomarker is unknown. Method CSF TMEM106B and NfL protein levels were quantified with aptamer‐based proteomics in participants from two independent cohorts: sporadic FTLD with neuropathology‐confirmed FTLD‐tau and FTLD‐TDP, clinically diagnosed PSP‐Richardson Syndrome, and controls (UCSF MAC, n = 95; mean age = 69 ± 6 yrs; 48% female) and familial FTLD comprised of presymptomatic and symptomatic FTLD mutation carriers with family non‐carrier controls (ALLFTD, n = 179; mean age = 49 ± 13 yrs; 53% female). The association between biomarkers and diverse measures of disease severity, including the CDR®+NACC‐FTLD and CDR®+NACC‐FTLD sum of boxes, were determined with linear regressions or mixed linear models. Associations with brain volumes were determined within the framework of voxel‐based morphometry. Result In both cohorts, at baseline, CSF TMEM106B did not differ by sex, age, FTLD phenotype, or CSF NfL. CSF TMEM106B was lower in cases with worse disease severity (Figure 1) and in participants with the protective TMEM106B rs1990622 G/G genotype, regardless of pathological diagnosis or FTLD‐causing mutation. Low CSF TMEM106B was associated with more severe longitudinal worsening in FTLD disease severity (Figure 2), basic and instrumental function, global cognition, executive function, and depression scores. These associations were weaker than those of NfL. Low CSF TMEM106B was associated with low brain volumes in frontal, parietal, and temporal regions (FWE‐corrected p < 0.05) (Figure 3A). CSF NfL negatively correlated (FWE‐corrected p < 0.05) with brain volumes in frontotemporal regions (Figure 3B). Conclusion CSF TMEM106B concentrations are influenced by the TMEM106B rs1990622 genotype, and correlate with disease severity and brain volume in both genetic and sporadic FTLD. Lower levels of CSF TMEM106B correlate with more severe disease and brain atrophy. Disease severity is more strongly associated with CSF NfL, while brain volumes are more strongly associated with CSF TMEM106B. CSF TMEM106B may have value as a biomarker of neurodegeneration in FTLD.