Real-world work productivity is impaired in people with metabolic dysfunction-associated steatotic liver disease in the USA

\ua9 2025Background & Aims: The impact of metabolic dysfunction-associated steatohepatitis (MASH) on patient-reported outcomes is poorly understood. We assessed work productivity burden in a real-world population with suspected/confirmed MASH, comparing work productivity and symptoms across subgroups (risk status, age, BMI, and comorbidities) in the USA using secondary data from Adelphi Real World MASH Disease Specific Programmes™, cross-sectional surveys of physicians and consulting patients in 2019 and 2022. Methods: Physicians (hepatologists, gastroenterologists, and endocrinologists) reported sociodemographic data and signs/symptoms for eight or fewer consecutive participants with MASH. Participants voluntarily completing questionnaires that assessed work productivity, health status, and quality of life/symptoms were categorized with low-, indeterminate-, or high-risk MASH using physician-stated fibrosis stage and derived risk categories. Principal components factor analysis identified factors from MASH signs and symptoms. Elastic net regression determined features associated with work productivity impairment. Results: In total, 87 physicians and 429 individuals with MASH provided data. The impact of MASH on activities was greater in high-versus low-risk MASH (activity impairment score: high risk, 30.8%; low risk, 17%; p <0.001 across all risk categories). Overall work impairment scores were 18.8% in low-risk and 19.9% in high-risk MASH. Of the 14 physician-reported signs/symptoms, 11 were significantly associated with physician-stated fibrosis stage and/or derived risk category. Two clusters of signs and symptoms (‘memory loss/swelling legs/abdomen’ and ‘fatigue/sleep disturbance/insomnia/general weakness’) and female sex were the strongest work impact predictors. Conclusions: Activity impairment was greater in participants with high-risk MASH, whereas overall work impairment was comparable, almost 20%, in low- and high-risk MASH. Non-specific symptoms of fatigue, sleep disturbance, and general weakness associated with MASH and other conditions predicted work impact in low-risk MASH. Thus, early detection and management of MASH could ameliorate work impairment. Impact and implications: Even in its early stages, when otherwise asymptomatic, metabolic dysfunction-associated steatohepatitis (MASH) may impact the ability to work. Using the Work Productivity and Activity Impairment: Specific Health Problem (WPAI:SHP) questionnaire, we identified activity and overall work impairment in people with high- and low-risk MASH. We found a link between difficulty working and other signs, such as tiredness, sleep disturbance, and general weakness, in low-risk MASH that are not specific to MASH but can be associated with liver health. These general, non-specific signs could represent possible early warning signs preceding reduced productivity in patients with MASH

An Iterative Jackknife Approach for Assessing Reliability and Power of fMRI Group Analyses

For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed

The impact of maternal separation on adult mouse behaviour and on the total neuron number in the mouse hippocampus

The maternal separation paradigm has been applied to C57BL/6J mice as an animal developmental model for understanding structural deficits leading to abnormal behaviour. A maternal separation (MS) model was used on postnatal day (PND) 9, where the pups were removed from their mother for 24 h (MS24). When the pups were 10 weeks old, the level of anxiety and fear was measured with two behavioural tests; an open field test and an elevated plus maze test. The Barnes platform maze was used to test spatial learning, and memory by using acquisition trials followed by reverse trial sessions. The MS24 mice spent more time in the open arms of the elevated plus maze compared to controls, but no other treatment differences were found in the emotional behavioural tests. However, in the reverse trial for the Barnes maze test there was a significant difference in the frequency of visits to the old goal, the number of errors made by the MS24 mice compared to controls and in total distance moved. The mice were subsequently sacrificed and the total number of neurons estimated in the hippocampus using the optical fractionator. We found a significant loss of neurons in the dentate gyrus in MS mice compared to controls. Apparently a single maternal separation can impact the number of neurons in mouse hippocampus either by a decrease of neurogenesis or as an increase in neuron apoptosis. This study is the first to assess the result of maternal separation combining behaviour and stereology

Identification and Validation of Novel Cerebrospinal Fluid Biomarkers for Staging Early Alzheimer's Disease

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the 'preclinical' stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85-0.94 95% confidence interval [CI]) and 0.88 (0.81-0.94 CI), respectively.Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions

Dissociating memory accessibility and precision in forgetting

Forgetting involves the loss of information over time; however, we know little about what form this information loss takes. Do memories become less precise over time, or do they instead become less accessible? Here we assessed memory for word–location associations across four days, testing whether forgetting involves losses in precision versus accessibility and whether such losses are modulated by learning a generalizable pattern. We show that forgetting involves losses in memory accessibility with no changes in memory precision. When participants learned a set of related word–location associations that conformed to a general pattern, we saw a strong trade-off; accessibility was enhanced, whereas precision was reduced. However, this trade-off did not appear to be modulated by time or confer a long-term increase in the total amount of information maintained in memory. Our results place theoretical constraints on how models of forgetting and generalization account for time-dependent memory processes. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 4 June 2019. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4368464.v1

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making