Experimental Neurology

BackgroundLocomotor adaptation is essential for adjusting walking patterns to complex environments. This study investigated locomotor adaptation deficits in people with Parkinsons disease (PD) and examined oscillatory activity in the globus pallidus internus (GPi) during walking adaptation. We hypothesized that elevated beta-band activity in the GPi is associated with reduced locomotor adaptability in PD. MethodsTwelve PD patients with GPi deep brain stimulation (DBS) (eleven bilateral and one unilateral) were included. Local field potentials (LFPs) were recorded from DBS electrodes during split-belt treadmill walking. Patients were tested in the medication-off, DBS-off state. Locomotor adaptation was measured as the change in step length asymmetry during split-belt walking, with smaller changes indicating greater adaptation deficits. ResultsWe found that GPi high beta (20-30 Hz) and low gamma (30-60 Hz) oscillations were modulated during split-belt walking. Compared to adapters, non-adapters showed decreased movement-related beta suppression during walking. Across participants, beta activity in the GPi contralateral to the fast leg was negatively associated with adaptation magnitude (Spearmans {rho} = -0.65 to -0.75). ConclusionsGPi oscillations are dynamically modulated during locomotor adaptation in PD. Increased beta activity may underlie impaired sensorimotor adaptation during walking. These findings provide novel insight into basal ganglia mechanisms of gait adaptation in PD and suggest that elevated GPi beta activity may serve as a marker of locomotor adaptation deficits.

Background: Statins have gained increasing interest for their potential therapeutic effect in Parkinson's disease (PD). Beyond their cholesterol-lowering effect, statins decrease synthesis of isoprenoids, which is believed to account for their pleiotropic effects. Isoprenylation is important for proper membrane localization and function of the Rho GTPases, including RhoA. RhoA signalling has emerged as a possible underlying signalling pathway involved in the pathogenesis of PD and other neurodegenerative diseases. Methods: In the present study, we investigated the effects of simvastatin on neurodegeneration-associated phenotypes using human induced pluripotent stem cell-derived dopaminergic (DA) neurons from both PD patients and isogenic PARK2-/- cell lines. The dependence on RhoA was confirmed using direct RhoA inhibition using rhosin. Assessed phenotypes included structural integrity, mitochondrial and lysosomal characteristics, cytokine secretion, and cell viability. To understand the relevance of RhoA in PD, RhoA activity was measured in 32 PD patient iPSC-derived lines with different familial PD-related mutations and in healthy controls. Results: Simvastatin rescued multiple PD-associated phenotypes, including impaired DA neurite outgrowth, mitochondrial and lysosomal alterations, cytokine release, and cell death. RhoA inhibition was associated with changes in mitophagy- and autophagy-related markers, suggesting improved autophagic and mitophagic turnover. Furthermore, we performed the first systematic screen of RhoA activity across 32 iPSC-derived DA neuron lines representing multiple genetic forms of PD (PINK1 loss of function, parkin loss of function, LRRK2 (G2019S), LRRK2 (R1441C), GBA (L44P), GBA (N370S), A53T, and SNCA triplication) and healthy controls. RhoA activity was perturbated across several genetic forms of PD subtypes and was significantly increased in many, although not all, patient lines compared with healthy controls, highlighting disease heterogeneity and supporting RhoA dysregulation as a shared pathogenic mechanism in a subset of PD. Conclusions: Our findings identify aberrant RhoA signalling as a convergent pathogenic mechanism across multiple forms of genetic PD and demonstrate that simvastatin ameliorates PD-associated phenotypes through RhoA inhibition. These results support RhoA as a promising therapeutic target while emphasizing the importance of patient stratification based on RhoA activity.

Introduction: Parkinson's disease (PD) and aging both disrupt hypothalamic-pituitary-adrenal (HPA) axis function and peripheral immune homeostasis. Whether aging or -synuclein (-syn) pathology alters glucocorticoid (GC) sensitivity of peripheral immune cells has not been investigated. Methods: Using an ex vivo GC sensitivity assay, we assessed the responsiveness of isolated and lipopolysaccharide (LPS)-stimulated splenocytes to the anti-inflammatory effects of increasing doses of corticosterone (CORT) in a wild-type (WT) aging cohort and in a PD -syn transgenic mouse model and respective age-matched controls. Results: Compared with splenocytes from 6-month-old WT mice, splenocytes from 20-month-old WT mice were less sensitive to 0.1 and 0.5 M CORT. Isolated splenocytes from PD vs. control mice were less sensitive to 0.05, 0.1, and 0.5 M CORT specifically at 16 months of age, but not at 6 or 20 months of age. As peripheral immune phenotyping revealed neither differences in HPA axis-related parameters nor in splenic GC receptor expression between PD and age-matched control mice at 6, 16, and 20 months, splenic GC resistance in PD mice at 16 months of age seems to be mediated by downstream GR signaling dysfunction. Conclusion: Together, our results support the hypothesis that -syn pathology accelerates an aging-associated decline in the peripheral sensitivity to anti-inflammatory GCs and may thereby sustain systemic and neuroinflammatory processes in PD.

Background. GBA2-associated hereditary spastic paraplegia (SPG46) is a rare autosomal recessive neurodegenerative disorder caused by loss-of-function mutations in GBA2, encoding the non-lysosomal glucocerebrosidase 2. GBA2 deficiency leads to glucosylceramide (GlcCer) accumulation and glucosylated cholesterol (GlcChol) depletion, causing cytoskeletal defects in immature neurons. However, the mechanisms linking lipid dysregulation to neuronal dysfunction remain poorly understood. Methods. We modelled GBA2 loss of function by chronic pharmacological inhibition in mouse cerebellar granule neurons (CGNs) and assessed neuronal morphology, synaptic organization, Ca2+ dynamics, mitochondrial function and actin cytoskeleton during maturation. Proteomic profiling was performed in GBA2-inhibited and GlcChol-supplemented neurons. Findings were validated in a zebrafish gba2 crispant model by evaluating motor behavior, cerebellar development, neuronal organization and mitochondrial function, and in patient-derived fibroblasts carrying a homozygous pathogenic GBA2 variant (NM_020944). The role of RAC1 was studied in both neurons and patients' cultured skin fibroblasts, and upon rac1 pharmacological inhibition in zebrafish crispants. Results. Chronic GBA2 inhibition impaired axonal outgrowth in immature CGNs but not neurite complexity in mature neurons, suggesting morphological compensation. Nevertheless, mature neurons displayed enlarged presynaptic terminals, impaired synaptic vesicle clustering and altered Ca2+ responses to potassium and glutamate, the latter associated with NMDA receptor redistribution without changes in total receptor levels. Mitochondrial alterations were observed in CGNs, patient fibroblasts and zebrafish, consistent with defective architecture of the mitochondrial network. Proteomics revealed convergent alterations in actin cytoskeleton, synaptic pathways and cellular metabolism following both GBA2 inhibition and GlcChol supplementation. GlcChol bidirectionally regulated RAC1 function, likely altering its spatial distribution rather than its global activation. Confocal imaging confirmed abnormal RAC1 and F-actin localization in patient fibroblasts. Zebrafish gba2 crispants recapitulated motor deficits, Purkinje cell loss, motor neuron disorganization and mitochondrial abnormalities. Pharmacological Rac1 inhibition rescued motor behavior and neuronal organization, linking cytoskeletal disorganization to the observed phenotype in the zebrafish model. Conclusions. Our findings identify a pathogenic GlcChol-RAC1-actin signalling axis linking lipid imbalance to synaptic disorganization, NMDA receptor redistribution and mitochondrial dysfunction in SPG46. The selective vulnerability of corticospinal neurons, cerebellar granule neurons and Purkinje cells may reflect their dependence on this pathway. Rac1 inhibition rescues disease phenotypes in vivo, highlighting this pathway as a promising therapeutic target.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive dysfunction and loss of upper and lower motor neurons. Although motor neuron degeneration ultimately drives paralysis, neuronal dysfunction may precede cell death by a prolonged interval, suggesting that vulnerable neurons engage stress-adaptive programs that permit survival despite impaired function. Cellular senescence represents one such persistent stress response and has increasingly been implicated in neurodegenerative disease, including disorders associated with TDP-43 pathology. Here, we investigated whether senescence-associated molecular states are present in vulnerable motor neurons in ALS and whether they differ according to anatomical region and phosphorylated TDP-43 (pTDP-43) pathology. Postmortem primary motor cortex, cervical spinal cord, and lumbar spinal cord were obtained from the Department of Veterans Affairs Biorepository Brain Bank from individuals with ALS classified as pTDP-43-positive or pTDP-43-negative, together with non-ALS controls. Targeted bulk transcriptomic profiling was combined with GeoMx Digital Spatial Profiling of individual motor neurons to characterize disease-, region-, and pathology-associated molecular phenotypes while preserving anatomical context. Across ALS cases, we identified alterations in pathways related to cell-cycle regulation, RNA processing, mitochondrial function, proteostasis, inflammation, and synaptic signaling. These signatures varied by anatomical region and pTDP-43 status, indicating substantial heterogeneity in the molecular response to ALS pathology. Despite these differences, both ALS groups exhibited convergent proteomic and transcriptomic features associated with cellular senescence. These findings identify senescence-associated molecular states within vulnerable neuronal populations in ALS and support a model in which persistent stress adaptation may permit neuronal survival while contributing to progressive cellular dysfunction. This spatially resolved analysis links neuronal phenotype to anatomical and pathological context and supports further evaluation of senescence-associated pathways as therapeutic vulnerabilities in ALS.

Hirschsprung disease (HSCR) is a severe birth defect where ganglia of the enteric nervous system (ENS) are missing from distal bowel. The aganglionic segment is also characterized by increased epithelial permeability and pro-inflammatory immune activation. These problems may sequentially lead to translocation of gut microbes into the colon wall and systemic circulation, resulting in enterocolitis and sepsis. Current HSCR treatment via surgical resection of the aganglionic segment is lifesaving but not curative, often leaving patients with persistent gastrointestinal complications including recurrent risk of enterocolitis. As alternative, we are developing a regenerative medicine strategy based on in situ stimulation of tissue-resident ENS progenitors via rectal administration of the neurotrophic factor GDNF. Here, we report that GDNF-based therapy has pleiotropic gastrointestinal effects in a mouse model of short-segment HSCR, beyond its role in ENS regeneration. Interestingly, we found that these protective effects are not restricted to the aganglionic distal colon, also positively impacting the ENS-containing proximal colon. GDNF treatment reduces bacterial translocation both locally and in peripheral organs, and this is associated with recovery of the key epithelial junction proteins CLDN3, ZO1 and DSG2. Furthermore, multiparameter flow cytometry-based analysis of 55 lymphoid and 17 myeloid cell subtypes revealed that GDNF treatment has global anti-inflammatory effects, preferentially affecting innate over adaptive immunity. Overall, these findings highlight a critical role for GDNF treatment in reestablishing proper epithelial and immune cell homeostasis, offering promising therapeutic avenues not only for HSCR but also potentially for other intestinal disorders with overlapping pathophysiology.

Background: Serial glial fibrillary acidic protein (GFAP) trajectories have become an important framework for contextualizing evolving secondary-injury pathophysiology after moderate-to-severe traumatic brain injury (msTBI). However, total GFAP pools release and clearance signals that may be less useful for longitudinal bedside decisions than a proteoform-resolved assay. We compared total GFAP with neoGFAP, defined here as calpain-generated GFAP proteoforms intended to index active astroglial proteolysis during the subacute phase. Methods: We analyzed 651 serial serum samples from 95 msTBI patients from a previously described single-site cohort. Total GFAP and neoGFAP were measured on the same MSD platform from 6 to 240 hours after injury. Early (6 to 72 h) and late (96 to 240 h) windows, data-derived tertiles, and serial trajectory summaries were calculated directly from serial samples. Models were benchmarked against age plus admission post-resuscitation Glasgow Coma Scale (GCS) and the admission IMPACT extended risk score using five-fold stratified cross-validation. Outcomes were unfavorable outcome (GOSE 1 to 4), less-than-good recovery (GOSE 1 to 6), Disability Rating Scale (DRS) [≥]15, mortality, and neuroimaging worsening at 6 months. Results: The cohort contributed 95 serial biomarker profiles, with 90 participants evaluable for 6-month GOSE and 89 for DRS. Unfavorable outcome occurred in 57/90 (63.3%), and less-than-good recovery in 79/90 (87.8%). For unfavorable outcome, IMPACT plus early neoGFAP reached AUROC 0.85 versus 0.84 for IMPACT plus early total GFAP and 0.81 for IMPACT alone. For less-than-good recovery, IMPACT plus late neoGFAP achieved AUROC 0.90 versus 0.84 for late total GFAP and 0.82 for IMPACT alone. Secondary analyses for DRS, mortality, and neuroimaging worsening showed smaller differences. Conclusions: In this retrospective analysis, neoGFAP provided clearer incremental value than total GFAP for recovery-oriented monitoring, especially when late-window reassessment of patients who remained at risk for less-than-good recovery was required. Results support prospective testing of neoGFAP as a pathophysiology-informed adjunct to serial bedside decision making, repeat-assessment thresholds, and recovery stratification.

Background: Unruptured intracranial aneurysms (UIAs) are increasingly identified incidentally, and management balances rupture risk against treatment risk. UIA diagnosis has been linked to psychological distress, but psychotropic medication initiation after UIA discovery has not been compared across the full UIA management spectrum. Methods: We conducted a retrospective cohort study using IBM MarketScan claims (CCAE, MDCD, and MDCR; 2009-2023) among adults with a UIA diagnosis, continuous enrollment for 365 days before and after the index date, and no SAH/rupture on or before the index date. We compared the prevalence of 6 mental-health diagnoses before versus after UIA discovery and used adjusted logistic regression to examine psychotropic medication initiation within 365 days by management strategy (untreated observation as the reference). Results: Among 54,945 patients (untreated, 78.5%; endovascular, 11.3%; clipping, 3.0%; other/uncertain, 7.2%), prevalence of every mental-health diagnosis was higher after UIA discovery, most for depression (+4.6 percentage points) and anxiety (+4.5 points). Medication initiation was most common for benzodiazepines (8.7%). Endovascular treatment was associated with higher adjusted odds of benzodiazepine (aOR, 1.21), SSRI (aOR, 1.20), and sedative-hypnotic (aOR, 1.25) initiation.Surgical clipping demonstrated the broadest association, with higher odds across 5 of 6 classes, including benzodiazepines (aOR, 1.71) and sedative-hypnotics (aOR, 1.86). Benzodiazepines had the lowest 1-year persistence (10.5%) despite being the most commonly initiated class. Findings were consistent across sensitivity analyses, with the exception of the increase in panic disorder, which was no longer observed after applying a 30-day post-index lag. Conclusions: Mental-health diagnoses and psychotropic medication initiation increased after UIA discovery, and medication initiation was most pronounced among patients treated with surgical clipping. These findings support psychological assessment as part of aneurysm management regardless of strategy.

Artificial intelligence applied to routine electrocardiograms (ECGs) has largely focused on detecting existing disease or predicting individual cardiovascular outcomes. Whether ECGs can support prediction of multiple future diseases across organ systems remains unclear. We developed ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients. Discrimination was highest for heart, brain, kidney and lung endpoints, with organ-level C-indices ranging from 0.796 to 0.825, whereas liver and pancreatic endpoints showed lower discrimination. The ECG-only model achieved strong discrimination across most endpoints, whereas the incremental improvement gained by incorporating ECG and laboratory inputs beyond demographic information varied substantially across endpoints. Across the nine exploratory aggregated outcomes, Kaplan Meier curves showed clear separation among model-score tertiles. Discrimination was highest for dementia (C-index, 0.891) and heart failure (C-index, 0.857). These findings support the feasibility of ECG-based longitudinal risk prediction across multiple diseases. External validation and competing-risk analyses are required to assess generalisability and clinical utility.

Dopamine replacement therapy (DRT) alleviates motor symptoms in Parkinson's disease (PD) but can trigger hallucinations in a subset of patients, yet the neural basis of this selective vulnerability is unknown. Hallucinations are among the most disabling non-motor symptoms of PD, linked to social isolation, dementia and institutionalization. Using a validated robotic paradigm to induce and quantify hallucinations in real-time, combined with resting-state fMRI in a crossover On/Off DRT design, we studied patients with PD with (PD-H) and without (PD-nH) hallucinations. DRT selectively amplified sensitivity to robot-induced hallucinations in patients with pre-existing hallucinatory phenotype (PD-H, but not PD-nH) and was accompanied by cortico-striatal and large-scale network hyperconnectivity. Rather than supporting a uniform hallucinogenic effect of dopamine in PD, these findings indicate that DRT interacts with an intrinsic neural vulnerability that varies in patients. Prospective studies will establish whether this pharmacological-behavioural signature identifies patients at risk before clinical hallucinations emerge.

We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient's DE. The first method uses a non linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra rare, patient' specific trials. They can inform methodological design for future ARCA precision therapies.

Objective: Whether a scheduled antiseizure medication (ASM) continues on schedule across the ICU-to-floor transfer has not been characterized. We quantified ASM administration-gap frequency across this transfer and compared it with gap frequency during matched non-transfer intervals in the same patient and drug. Methods: In this retrospective MIMIC-IV (version 3.1) cohort study, we identified epilepsy and status-epilepticus admissions with an ICU stay followed by floor transfer and a scheduled ASM order active at ICU departure. A gap was defined as an interval exceeding 1.5 times the expected dosing interval between the last ICU dose and first floor dose, or no further dose before discharge, and compared with a matched non-transfer control interval in the same patient and drug (paired McNemar test). A multivariable model evaluated six prespecified clinical predictors; sociodemographic variables were summarized descriptively. Results: Among 2,469 ASM transition-by-drug observations (1,583 admissions, 1,335 patients), an administration gap occurred in 251 (10.2%; 95% CI, 8.7%-11.7%). Gap frequency across the transfer exceeded frequency during matched non-transfer control intervals in the same patient and drug: a paired rate difference of 5.8 percentage points (95% CI, 4.4-7.1; 7.5% vs 1.7%; P = 7.3 x 10^-22) before the transfer and 6.4 percentage points (95% CI, 4.9-7.9; 8.9% vs 2.5%; P = 1.9 x 10^-23) after. Gap rates were similar for intravenous-available (9.9%) and oral-only (11.4%) drugs (rate difference, 1.5 percentage points; 95% CI, -1.6 to 4.5; P = .34). None of six prespecified predictors reached significance after correction. Significance: An antiseizure medication administration gap occurred in approximately 1 of every 10 drug-transition observations at the ICU-to-floor transfer, exceeding matched non-transfer gap rates by 5.8 to 6.4 percentage points. This transfer-associated excess, rather than any single medication or patient characteristic, supports a structured medication-continuity check.

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease, leading to progressive muscle weakness and motor impairment. Growing evidence indicates that cerebellar Purkinje cells, which play a central role in motor coordination, are also affected in ALS. However, it is unclear whether the molecular events that initiate neurodegeneration in these ALS-relevant motor-controlling neurons are shared or distinct. Here, we used a MATR3 S85C knock-in (KI) mouse model of early-stage ALS with stage-specific motor phenotypes and selective vulnerability of motor neurons and Purkinje cells to decipher the molecular events underlying neurodegeneration in these two neuronal populations. We found that a profound reduction in detectable MATR3 S85C immunoreactivity (hereafter referred to as MATR3 loss) in both motor neurons and Purkinje cells precedes the onset of motor dysfunction and neuropathology, implicating MATR3 loss as the earliest detectable molecular event. Our bulk cerebellar RNA profiling and motor neuron-specific RNA profiling data at the onset of MATR3 loss revealed distinct molecular signatures. In the cerebellum, Ngfr expression emerged in Purkinje cells before the onset of neuronal loss and remained elevated throughout the disease course. This increase was accompanied by activation of the JNK-mediated cell death pathway. In the motor neurons, elevated Fgf21 and integrated stress response (ISR) gene expression were the first to be observed and persisted throughout disease progression, consistent with previous findings in SOD1 mouse models. Our findings provide mechanistic insights into the initiation of neurodegeneration in ALS-relevant motor-controlling neurons and implicate potential neuron type-specific targets for future therapeutics.

PACS1 syndrome is a rare neurodevelopmental disorder caused by a recurrent de novo missense variant (p.R203W) in the PACS1 protein. However, it remains unclear whether the p.R203W variant acts through a loss-of-function or alternative mechanism. Here, we used isogenic iPSC-derived neurons (iNs) to directly compare the effects of PACS1 p.R203W to complete loss of PACS1 function. Using a combination of proteomic, biochemical and electrophysiological approaches, we identified molecular and functional phenotypes associated with each genotype. While PACS1(+/R203W) and PACS1(-/-) iNs shared phenotypic abnormalities, the overall molecular and functional consequences of the p.R203W variant were distinct from those caused by PACS1 deficiency. Notably, PACS1(+/R203W) presented with unique proteomic and kinase signaling signatures and a shift in stimulus dependent excitability. These findings demonstrate that PACS1 syndrome is not caused by a simple loss of function and instead support a non-loss-of-function mechanism. Lastly, our interactome analysis suggests that the p.R203W variant retains aspects of canonical PACS1 function while acquiring novel molecular interactions that could contribute to PACS1 syndrome pathogenesis. Altogether, these findings provide a framework for future mechanistic studies and therapeutic development in PACS1 syndrome. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/747101v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d1522corg.highwire.dtl.DTLVardef@69e4dforg.highwire.dtl.DTLVardef@30eebcorg.highwire.dtl.DTLVardef@899b9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Kinaesthetic motor imagery (kMI) is widely used in sport to enhance motor performance by engaging cortical sensorimotor networks. Neurofeedback may further support kMI, but the optimal neural target to reinforce remains unclear. Maximal sensorimotor event-related desynchronisation (SMR-ERD) represents a relevant target as it may index sensorimotor cortex engagement, yet sport expertise has been associated with reduced SMR-ERD, potentially reflecting neural efficiency. The optimal neurofeedback target may therefore depend on sport expertise, movement expertise, and individual kMI ability. This study examined how these factors influence sensorimotor activity during kMI. We compared 17 basketball players (Experts) and 16 individuals without formal basketball training (Novices). kMI ability and frequency of use were assessed using questionnaires, while SMR-ERD was quantified using electroencephalography (EEG) during kMI. Participants imagined either a basketball-specific movement (Free throw), for which only Experts had extensive experience, or a generic movement (Box lifting), familiar to both groups. Experts reported greater kMI ability and more frequent kMI use than Novices. Only Experts exhibited significant and sustained SMR-ERD during kMI. Moreover, SMR-ERD was stronger in Experts than Novices specifically during Free throw kMI, corresponding to their movement of expertise. Nonetheless, within the Expert group, higher kMI ability was associated with reduced SMR-ERD. These findings suggest that sport expertise initially enhances voluntary recruitment of sensorimotor networks during kMI, whereas greater kMI ability may subsequently promote neural efficiency, resulting in reduced overall sensorimotor cortical activation. These results highlight the need to tailor kMI-based neurofeedback training to users' sport expertise and kMI ability levels.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

Background: Traditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-dimensional data loss. We used causal machine learning (Causal ML) to unmask heterogeneous treatment effects in diabetic kidney disease (DKD). Methods: Using data from 4,588 patients within the Japanese J-CKD-DB-Ex registry, we implemented a doubly robust (DR) learning framework (Linear DR-learner with XGBoost) to compare SGLT2 and DPP4 inhibitors. Outcomes included the chronic eGFR slope and a composite renal endpoint ([≥] 50% eGFR decline or end-stage kidney disease). Heterogeneity was explored via causal SHAP and decision trees. Results: At the population level, SGLT2 inhibitors modestly slowed chronic eGFR decline (average treatment effect [ATE] = 0.14 [95% CI: -0.86, 1.15] mL/min/1.73m^2/year) and reduced composite endpoint risk by 9% (ATE: -0.09 [-0.11, -0.08]) versus DPP4 inhibitors. However, individual-level counterfactual analysis suggested that for the chronic eGFR slope, non-glinide users with stable pre-treatment trajectories who were also taking ACE inhibitors had a greater benefit from SGLT2 inhibitors (ATE: 2.95 [-0.68, 6.58]). Conversely, glinide users with steep pre-treatment decline had a greater benefit from DPP4 inhibitors (ATE: -8.98 [-16.11, -1.85]). For composite renal events, SGLT2 inhibitors had a 28% absolute risk reduction within the algorithmically identified high-risk subgroup (eGFR [≤] 28.1 mL/min/1.73 m^2 and positive proteinuria; ATE: -0.28 [-0.33, -0.23]). Even non-proteinuric decliners demonstrated a 8% risk reduction with SGLT2 inhibitors (ATE: -0.08 [-0.10, -0.06]). Conclusion: Causal ML advances precision medicine in DKD, shifting from uniform prescribing to individualized, data-driven therapy targeting distinct intrarenal pathways.

Background: Higher Body Mass Index (BMI) is an established risk factor of sleep disturbance. It is not known if the effect is homogeneous across the lifecourse or if there is a particular time point in life that might be best to target. Methods: Two-sample Mendelian randomization (MR) was used to investigated the effect of childhood adiposity (adjusting on adulthood adiposity and obstructive sleep apnea (OSA)) on insomnia, morning chronotype, sleep duration, daytime sleepiness and daytime napping. Similarly, total, and direct effect of adulthood adiposity on these outcomes was explored. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank for childhood and adulthood adiposity (n=453,169) and large-scale consortia of OSA (Million Veteran Program) (n=410,268), insomnia, and chronotype (23andMe) (n=1,978,022 and n=248,1000, respectively). Results: Two-sample univariable MR analysis provided no evidence of an effect of genetically predicted childhood adiposity on later life insomnia (Odds ratio (OR)= 0.94, 95% Confidence interval (CI)= 0.87, 1.03). Whereas, multivariable MR (adjusted for adulthood adiposity) analysis provide strong evidence of direct protective effect of genetically predicted childhood adiposity on later life insomnia (OR= 0.70, CI= 0.64, 0.77). Further, both in univariable and multivariable MR, a strong positive effect of increased childhood body size on morning chronotype was observed (OR= 1.16, CI= 1.01, 1.33 and OR= 1.36, CI= 1.15, 1.62, respectively) after accounting for adulthood body size. In both analysis the estimate did not change considerably after aditionally adjusting for OSA. However, childhood and adulthood adiposity found to be associated with OSA and OSA with insomnia. In both univariable and multivariable analysis, increased body size in adulthood increased the risk of having insomnia and a morning chronotype. Conclusions: The findings suggest that higher body size in childhood is not a risk factor for later life insomnia, whereas higher body size in adulthood was. Further, if healthy body size is maintained in adulthood, high childhood adiposity may decrease the risk of insomnia and increase the risk of being a morning person in later life. Keywords: childhood, adulthood, obesity, insomnia, morning chronotype, medelian randomization

Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.

Polycystic Ovary Syndrome is a common endocrine disorder characterized by ovulatory dysfunction, hyperandrogenism, and/or polycystic ovarian morphology, with significant reproductive and metabolic consequences. Due to heterogeneous symptom profiles, Polycystic Ovary Syndrome is frequently underdiagnosed or diagnosed late. In this study, we develop machine learning models for early Polycystic Ovary Syndrome prediction using a structured clinical dataset with 42 features and 542 patient records. After data cleaning and normalization, correlation-based feature selection was applied to retain the most predictive variables. Multiple models were trained and evaluated, including Logistic Regression, Decision Tree, KNN, and Random Forest. Results demonstrate that Random Forest achieves the best overall performance (approximately 88% accuracy), suggesting that ensemble models can effectively capture non-linear feature interactions in clinical data. We also contextualize findings with international clinical guidance and recent work on explainable and clinically applicable Polycystic Ovary Syndrome prediction systems.