Human restricted CHRFAM7A gene increases brain efficiency.

Introduction: CHRFAM7A, a uniquely human fusion gene, has been associated with neuropsychiatric disorders including Alzheimer's disease, schizophrenia, anxiety, and attention deficit disorder. Understanding the physiological function of CHRFAM7A in the human brain is the first step to uncovering its role in disease. CHRFAM7A was identified as a potent modulator of intracellular calcium and an upstream regulator of Rac1 leading to actin cytoskeleton reorganization and a switch from filopodia to lamellipodia implicating a more efficient neuronal structure. We performed a neurocognitive-MRI correlation exploratory study on 46 normal human subjects to explore the effect of CHRFAM7A on human brain. Methods: Dual locus specific genotyping of CHRFAM7A was performed on genomic DNA to determine copy number (TaqMan assay) and orientation (capillary sequencing) of the CHRFAM7A alleles. As only the direct allele is expressed at the protein level and affects α7 nAChR function, direct allele carriers and non-carriers are compared for neuropsychological and MRI measures. Subjects underwent neuropsychological testing to measure motor (Timed 25-foot walk test, 9-hole peg test), cognitive processing speed (Symbol Digit Modalities Test), Learning and memory (California Verbal Learning Test immediate and delayed recall, Brief Visuospatial Memory Test-Revised immediate and delayed recall) and Beck Depression Inventory-Fast Screen, Fatigue Severity Scale. All subjects underwent MRI scanning on the same 3 T GE scanner using the same protocol. Global and tissue-specific volumes were determined using validated cross-sectional algorithms including FSL's Structural Image Evaluation, using Normalization, of Atrophy (SIENAX) and FSL's Integrated Registration and Segmentation Tool (FIRST) on lesion-inpainted images. The cognitive tests were age and years of education-adjusted using analysis of covariance (ANCOVA). Age-adjusted analysis of covariance (ANCOVA) was performed on the MRI data. Results: CHRFAM7A direct allele carrier and non-carrier groups included 33 and 13 individuals, respectively. Demographic variables (age and years of education) were comparable. CHRFAM7A direct allele carriers demonstrated an upward shift in cognitive performance including cognitive processing speed, learning and memory, reaching statistical significance in visual immediate recall (FDR corrected p = 0.018). The shift in cognitive performance was associated with smaller whole brain volume (uncorrected p = 0.046) and lower connectivity by resting state functional MRI in the visual network (FDR corrected p = 0.027) accentuating the cognitive findings. Conclusion: These data suggest that direct allele carriers harbor a more efficient brain consistent with the cellular biology of actin cytoskeleton and synaptic gain of function. Further larger human studies of cognitive measures correlated with MRI and functional imaging are needed to decipher the impact of CHRFAM7A on brain function.

Lower arterial cerebral blood flow is associated with worse neuroinflammation and immunomodulation composite proteomic scores.

BACKGROUND: Brain hypoperfusion is linked with worse physical, cognitive and MRI outcomes in multiple sclerosis (MS). Understanding the proteomic signatures related to hypoperfusion could provide insights into the pathophysiological mechanism. METHODS: 140 people with MS (pwMS; 86 clinically isolated syndrome (CIS)/relapsing-remitting (RRMS) and 54 progressive (PMS)) were included. Cerebral arterial blood flow (CABF) was determined using ultrasound Doppler measurement as the sum of blood flow in the bilateral common carotid arteries and vertebral arteries. Proteomic analysis was performed using the Multiple Sclerosis Disease Activity (MSDA) test assay panel performed on the Olink™ platform. The MSDA test measures the concentrations of 18 proteins that are age and sex-adjusted. It utilizes a stacked classifier logistic regression model to determine 4 disease pathway scores (immunomodulation, neuroinflammation, myelin biology, and neuroaxonal integrity) as well as an overall disease activity score (1 to 10). MRI measures of T2 lesion volume (LV) and whole brain volume (WBV) were derived. RESULTS: The pwMS were on average 54 years old and had an average CABF of 951 mL/min. There were no differences in CABF between CIS/RRMS vs. PMS groups. Lower CABF levels were correlated with the overall disease activity score (r = -0.26, p = 0.003) and with the neuroinflammation (r = -0.29, p = 0.001), immunomodulation (r = -0.26, p = 0.003) and neuroaxonal integrity (r = -0.23, p = 0.007) pathway scores. After age and body mass index (BMI)-adjustment, lower CABF remained associated with the neuroinflammatory (r = -0.23, p = 0.011) and immunomodulation (r = -0.20, p = 0.024) pathway scores. The relationship between CABF and the neuroinflammation pathway score remained significant after adjusting for T2-LV and WBV (p = 0.038). Individual analyses identified neurofilament light chain, CCL-20 and TNFSF13B as contributors. When compared to the highest quartile (>1133.5 mL/min), the pwMS in the lowest CABF quartile (<764 mL/min) had greater overall disease activity score (p = 0.003), neuroinflammation (p = 0.001), immunomodulation (p = 0.004) and neuroaxonal integrity pathway scores (p = 0.007). CONCLUSION: Lower cerebral arterial perfusion in MS is associated with changes in neuroinflammatory/immunomodulation pathways and their respective proteomic biomarkers. These findings may suggest a relationship between the hypoperfusion and pro-inflammatory MS changes rather than being merely an epiphenomenon subsequent to lower energy demands.

Neuroimaging assessment of facility-bound severely-affected MS reveals the critical role of cortical gray matter pathology: results from the CASA-MS case-controlled study.

BACKGROUND: A subgroup of people with multiple sclerosis (pwMS) will develop severe disability. The pathophysiology underlying severe MS is unknown. The comprehensive assessment of severely affected MS (CASA-MS) was a case-controlled study that compared severely disabled in skilled nursing (SD/SN) (EDSS ≥ 7.0) to less-disabled (EDSS 3.0-6.5) community dwelling (CD) progressive pwMS, matched on age-, sex- and disease-duration (DDM). OBJECTIVES: To identify neuroimaging and molecular biomarker characteristics that distinguish SD/SN from DDM-CD progressive pwMS. METHODS: This study was carried at SN facility and at a tertiary MS center. The study collected clinical, molecular (serum neurofilament light chain, sNfL and glial acidic fibrillary protein, sGFAP) and MRI quantitative lesion-, brain volume-, and tissue integrity-derived measures. Statistical analyses were controlled for multiple comparisons. RESULTS: 42 SD/SN and 42 DDM-CD were enrolled. SD/SN pwMS showed significantly lower cortical volume (CV) (p < 0.001, d = 1.375) and thalamic volume (p < 0.001, d = 0.972) compared to DDM-CD pwMS. In a logistic stepwise regression model, the SD/SN pwMS were best differentiated from the DDM-CD pwMS by lower CV (p < 0.001) as the only significant predictor, with the accuracy of 82.3%. No significant differences between the two groups were observed for medulla oblongata volume, a proxy for spinal cord atrophy and white matter lesion burden, while there was a statistical trend for numerically higher sGFAP in SD/SN pwMS. CONCLUSIONS: The CASA-MS study showed significantly more gray matter atrophy in severe compared to less-severe progressive MS.

Authors' response to letter to editor.

Authors' Response to Letter to Editor from Hinpetch Daungsupawong and Viroj Wiwanitkit.

Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation.

BACKGROUND: Atrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology. OBJECTIVES: To determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS). METHODS: Proteomic and MRI data for 202 pwMS (148 clinically isolated syndrome /relapsing-remitting MS and 54 progressive MS (PMS)) were acquired at baseline and at 5.4-year follow-up. The concentrations of 21 proteins related to multiple MS pathophysiology pathways were derived using a custom-developed Proximity Extension Assay on the Olink™ platform. The accrual of aLV was determined as the volume of baseline T2-weighted lesions that were replaced by cerebrospinal fluid over the follow-up. Regression models and age-adjusted analysis of covariance (ANCOVA) were used. RESULTS: Older age (standardized beta = 0.176, p = 0.022), higher glial fibrillary acidic protein (standardized beta = 0.312, p = 0.001), and lower myelin oligodendrocyte glycoprotein levels (standardized beta = -0.271, p = 0.002) were associated with accrual of aLV over follow-up. This relationship was driven by the pwPMS population. The presence of LMCE at the follow-up visit was not predicted by any baseline proteomic biomarker nor cross-sectionally associated with any protein concentration. CONCLUSION: Proteomic markers of glial activation are associated with chronic lesional WM pathology (measured as aLV) and may be specific to the progressive MS phenotype. LMCE presence in MS does not appear to relate to proteomic measures.

Dynamic disability measures decrease the clinico-radiological gap in people with severely affected multiple sclerosis.

BACKGROUND: Expanded Disability Status Scale (EDSS) is limited when utilized in highly disabled people with multiple sclerosis (pwMS). OBJETIVE: To explore the relationship between disability measures and MRI outcomes in severely-affected pwMS. METHODS: PwMS recruited from The Boston Home (TBH), a specialized residential facility for severly-affected pwMS and University at Buffalo (UB) MS Center were assessed using EDSS, MS Severity Scale, age-related MSS, Scripps Neurological Rating Scale (SNRS) and Combinatorial Weight-Adjusted Disability Score (CombiWISE). In all scores except SNRS, higher score indicates greater disability. MRI measures of T1, T2-lesion volume (LV), whole brain, gray matter, medulla oblongata and thalamic volumes (WBV, GMV, MOV, TV) and thalamic dysconnectivity were obtained. RESULTS: Greatest disability differences between the TBH and UB pwMS were in SNRS (24.4 vs 71.9, p < 0.001, Cohen's d = 4.05) and CombiWISE (82.3 vs. 38.9, p < 0.001, Cohen's d = 4.02). In combined analysis of all pwMS, worse SNRS scores were correlated with worse MRI pathology in 8 out of 9 outcomes. EDSS only with 3 measures (GMV, MOV and TV). In severely-affected pwMS, SNRS was associated with T1-LV, T2-LV and WBV (not surviving false discovery rate (FDR) correction for multiple comparisons) whereas EDSS did not. CONCLUSION: Granular and dynamic disability measures may bridge the clinico-radiologcal gap present in severely affected pwMS.

Non-neurological factors associated with serum neurofilament levels in the United States population.

OBJECTIVE: To model interdependencies of serum neurofilament light chain (sNfL), a clinically useful biomarker of axonal injury in neurological diseases, with demographic, anthropometric, physiological, and disease biomarkers in the United States population. METHODS: sNfL and 80 biomarkers were obtained from the National Health and Nutrition Examination Survey (n = 2071, age: 20-75 years). Body habitus and composition, electrolytes, blood cell, metabolic, liver, and kidney function biomarkers, and common diseases were assessed with weighted regression adjusted for age, sex, and race/ethnicity. Salient biomarkers were modeled with ensemble learning; a Bayesian network structure was obtained for interdependencies. RESULTS: Age was strongly associated with sNfL. sNfL levels were 13% higher in men versus women. Mexican Americans had 18.5% lower sNfL versus Non-Hispanic Whites. sNfL was similar in pregnant versus nonpregnant women. Lymphocyte, and neutrophil numbers, and phosphorus, and chloride levels were associated with sNfL. Multiple liver function (e.g., albumin and gamma-glutamyltransferase), renal function (e.g., creatinine and urea), and carbohydrate/lipid metabolism markers (e.g., glucose and triglycerides) were associated with sNfL. A 50% greater creatinine was associated with 26.8% greater sNfL. Diabetes, kidney disease, congestive heart failure, and stroke were associated with sNfL. The ensemble learning algorithm predicted high sNfL outliers with 5.06%-9.16% test error. Bayesian network modeling indicated sNfL had neighbor dependencies with age, creatinine, albumin, and chloride. INTERPRETATION: sNfL is associated with age, kidney and liver function, diabetes, blood cell subsets, and electrolytes. sNfL may be a useful biomarker for biological age of the whole body and major organ systems including the brain.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

An ultra-sensitive and high-throughput trapping-micro-LC-MS method for quantification of circulating vitamin D metabolites and application in multiple sclerosis patients.

Quantitative analysis of the biologically-active metabolites of vitamin D (VitD), which are crucial in regulating various physiological and pathological processes, is important for clinical investigations. Liquid chromatography-tandem mass spectrometry (LC-MS) has been widely used for this purpose but existing LC-MS methods face challenges in achieving highly sensitive and accurate quantification of low-abundance VitD metabolites while maintaining high throughput and robustness. Here we developed a novel pipeline that combines a trapping-micro-LC-(T-µLC) with narrow-window-isolation selected-reaction monitoring MS(NWI-SRM) for ultra-sensitive, robust and high-throughput quantification of VitD metabolites in serum samples after derivatization. The selective-trapping and delivery approach efficiently removes matrix components, enabling high-capacity sample loading and enhancing sensitivity, throughput, and robustness. The NWI-SRM further improves the sensitivity by providing high selectivity. The lower limits of quantification (LOQs) achieved were markedly lower than any existing LC-MS methods: 1.0 pg/mL for 1,25(OH)2D3, 5.0 pg/mL for 24,25(OH)2D3, 30 pg/mL for both 25(OH)D2 and 25(OH)D3, all within a 9-min cycle. The method is applied to quantify VitD metabolites from 218 patients with multiple sclerosis. This study revealed negative correlations(r=- 0.44 to - 0.51) between the levels of 25(OH)D2 and all the three D3 metabolites in multiple sclerosis patients.

Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis.

BACKGROUND: A quantitative measurement of serum proteome biomarkers that would associate with disease progression endpoints can provide risk stratification for persons with multiple sclerosis (PwMS) and supplement the clinical decision-making process. MATERIALS AND METHODS: In total, 202 PwMS were enrolled in a longitudinal study with measurements at two time points with an average follow-up time of 5.4 years. Clinical measures included the Expanded Disability Status Scale, Timed 25-foot Walk, 9-Hole Peg, and Symbol Digit Modalities Tests. Subjects underwent magnetic resonance imaging to determine the volumetric measures of the whole brain, gray matter, deep gray matter, and lateral ventricles. Serum samples were analyzed using a custom immunoassay panel on the Olink™ platform, and concentrations of 18 protein biomarkers were measured. Linear mixed-effects models and adjustment for multiple comparisons were performed. RESULTS: Subjects had a significant 55.6% increase in chemokine ligand 20 (9.7 pg/mL vs. 15.1 pg/mL, p < 0.001) and neurofilament light polypeptide (10.5 pg/mL vs. 11.5 pg/mL, p = 0.003) at the follow-up time point. Additional changes in CUB domain-containing protein 1, Contactin 2, Glial fibrillary acidic protein, Myelin oligodendrocyte glycoprotein, and Osteopontin were noted but did not survive multiple comparison correction. Worse clinical performance in the 9-HPT was associated with neurofilament light polypeptide (p = 0.001). Increases in several biomarker candidates were correlated with greater neurodegenerative changes as measured by different brain volumes. CONCLUSION: Multiple proteins, selected from a disease activity test that represent diverse biological pathways, are associated with physical, cognitive, and radiographic outcomes. Future studies should determine the utility of multiple protein assays in routine clinical care.

Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model.

To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.

Exploratory 5-year follow-up study of retinol, tocopherols, and carotenoids in multiple sclerosis.

BACKGROUND: Retinol, tocopherols, and carotenoids (RTC) have physiological roles as vitamins, pro-vitamins, and antioxidants, and provide biomarkers of dietary vegetable and fruit intake. The goal was to investigate RTC in multiple sclerosis (MS). METHODS: This exploratory study included 106 people with MS (71 relapsing-remitting MS or RR-MS; and 35 progressive MS or PMS) and 31 healthy controls (HC) at baseline and 5-year follow-up (5YFU). Serum retinol, α-carotene, ß-carotene, α-tocopherol, δ-tocopherol, γ-tocopherol, ß-cryptoxanthin, lutein/zeaxanthin, and lycopene were measured using high performance liquid chromatography. Serum neurofilament light chain (sNfL) levels were measured using the single molecule array method. Expanded Disability Status Scale (EDSS) and low contrast letter acuity (LCLA) were used as disability measures. RESULTS: Retinol in MS was positively correlated with α-carotene, ß-carotene, ß-cryptoxanthin, lutein/zeaxanthin, and α-tocopherol but negatively correlated with δ-tocopherol. EDSS was associated with α-tocopherol, δ-tocopherol, and lycopene. Greater retinol levels were associated with greater LCLA in RR-MS and PMS; high contrast visual acuity was not associated. Greater γ-tocopherol levels were associated with lower LCLA and high contrast visual acuity in PMS. CONCLUSIONS: RTC exhibit distinctive associations with LCLA and EDSS in MS.

Modelling the progression of illicit substance use patterns from real-world evidence.

AIMS: To investigate an innovative pharmacometrics approach that addresses the challenges of using real-world evidence to model the progression of illicit substance use. METHODS: The modelling strategy analysed real-world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug-naïve, active users and nonusers. The transitions between categories were modelled using interval-censored parametric survival analysis. The resulting hazard rate functions were used as time-dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. RESULTS: AddHealth enrolled 6504 American teenagers (median age 16 years, range 11-21 years); this cohort was followed with five interviews over a 22-year period; the median age at the last interview was 38 years (range 34-45 years). The percentages of illicit drug users at Interviews 1-5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age-dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug-naïve-active user transition rates but not the active user-nonuser and nonuser-active user transition rates. The evidence did not support an interaction between sex and depression. CONCLUSIONS: The model provided a satisfactory approximation for the age-dependent progression of illicit substance use from preadolescence to early middle age.

Repurposing Artificial Intelligence Tools for Disease Modeling: Case Study of Face Recognition Deficits in Neurodegenerative Diseases.

Face recognition deficits occur in diseases such as prosopagnosia, autism, Alzheimer's disease, and dementias. The objective of this study was to evaluate whether degrading the architecture of artificial intelligence (AI) face recognition algorithms can model deficits in diseases. Two established face recognition models, convolutional-classification neural network (C-CNN) and Siamese network (SN), were trained on the FEI faces data set (~ 14 images/person for 200 persons). The trained networks were perturbed by reducing weights (weakening) and node count (lesioning) to emulate brain tissue dysfunction and lesions, respectively. Accuracy assessments were used as surrogates for face recognition deficits. The findings were compared with clinical outcomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Face recognition accuracy decreased gradually for weakening factors less than 0.55 for C-CNN, and 0.85 for SN. Rapid accuracy loss occurred at higher values. C-CNN accuracy was similarly affected by weakening any convolutional layer whereas SN accuracy was more sensitive to weakening of the first convolutional layer. SN accuracy declined gradually with a rapid drop when nearly all nodes were lesioned. C-CNN accuracy declined rapidly when as few as 10% of nodes were lesioned. CNN and SN were more sensitive to lesioning of the first convolutional layer. Overall, SN was more robust than C-CNN, and the findings from SN experiments were concordant with ADNI results. As predicted from modeling, brain network failure quotient was related to key clinical outcome measures for cognition and functioning. Perturbation of AI networks is a promising method for modeling disease progression effects on complex cognitive outcomes.

Proteomics and relationship with axonal pathology in multiple sclerosis: 5-year diffusion tensor imaging study.

Blood-based biomarkers can be economic and easily accessible tools for monitoring and predicting disease activity in multiple sclerosis. The objective of this study was to determine the predictive value of a multivariate proteomic assay for concurrent and future microstructural/axonal brain pathology in a longitudinal study of a heterogeneous group of people with multiple sclerosis. A proteomic analysis was obtained on serum samples from 202 people with multiple sclerosis (148 relapsing-remitting and 54 progressive) at baseline and 5-year follow-up. The concentration of 21 proteins related to multiple pathways of multiple sclerosis pathophysiology was derived using Proximity Extension Assay on the Olink platform. Patients were imaged on the same 3T MRI scanner at both timepoints. Тhe rate of whole brain, white matter and grey matter atrophy over the 5-year follow-up was determined using the multi-timepoint Structural Image Evaluation, using Normalisation, of Atrophy algorithms. Lesion burden measures were also assessed. The severity of microstructural axonal brain pathology was quantified using diffusion tensor imaging. Fractional anisotropy and mean diffusivity of normal-appearing brain tissue, normal-appearing white matter, grey matter, T2 and T1 lesions were calculated. Age, sex and body mass index-adjusted step-wise regression models were used. Glial fibrillary acidic protein was the most common and highest-ranked proteomic biomarker associated with greater concurrent microstructural central nervous system alterations (P < 0.001). The rate of whole brain atrophy was associated with baseline levels of glial fibrillary acidic protein, protogenin precursor, neurofilament light chain and myelin oligodendrocyte (P < 0.009), whereas grey matter atrophy was associated with higher baseline neurofilament light chain, higher osteopontin and lower protogenin precursor levels (P < 0.016). Higher baseline glial fibrillary acidic protein level was a significant predictor of future severity of the microstructural CNS alterations as measured by normal-appearing brain tissue fractional anisotropy and mean diffusivity (standardized ß = -0.397/0.327, P < 0.001), normal-appearing white matter fractional anisotropy (standardized ß = -0.466, P < 0.0012), grey matter mean diffusivity (standardized ß = 0.346, P < 0.011) and T2 lesion mean diffusivity (standardized ß = 0.416, P < 0.001) at the 5-year follow-up. Serum levels of myelin-oligodendrocyte glycoprotein, neurofilament light chain, contactin-2 and osteopontin proteins were additionally and independently associated with worse concomitant and future axonal pathology. Higher glial fibrillary acidic protein levels were associated with future disability progression (Exp(B) = 8.65, P = 0.004). Multiple proteomic biomarkers are independently associated with greater severity of axonal brain pathology as measured by diffusion tensor imaging in multiple sclerosis. Baseline serum glial fibrillary acidic protein levels can predict future disability progression.

Antioxidant defense enzymes in multiple sclerosis: A 5-year follow-up study.

BACKGROUND AND PURPOSE: Oxidative stress biomarkers are increased in multiple sclerosis (MS) lesions. Antioxidant defense enzymes regulate reactive oxygen species that can cause tissue injury in MS. METHODS: The study of 91 subjects included 64 relapsing-remitting MS (RR-MS; 72% female, baseline age ± SD = 44.6 ± 11 years, disease duration = 13.3 ± 8.8 years, median Expanded Disability Status Scale [EDSS] = 2.0, interquartile range = 1.8) and 27 healthy controls (HC) at baseline and 5-year follow-up (5YFU). Serum glutathione peroxidase (GPX), glutathione-S-transferase (GST), glutathione reductase (GSHR), superoxide dismutase, and paraoxonase-1 (PON1) arylesterase and paraoxonase activities were measured using kinetic enzyme assays. Total cholesterol (TC), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), and an apolipoprotein (Apo) panel with ApoA-I, ApoA-II, ApoB, ApoC-II, and ApoE were obtained. Serum neurofilament (sNfL) was used to assess axonal injury. Disability was measured on the EDSS. RESULTS: GSHR activity was lower in HC compared to RR-MS at baseline and 5YFU. GPX (p = 0.008) and PON1 arylesterase and paraoxonase activities (both p = 0.05) increased between baseline and 5YFU in HC but did not increase in RR-MS. At baseline and 5YFU, GPX and GST were associated with TC, LDL-C, and ApoA-II; GSHR was associated with ApoA-II and ApoC-II. Antioxidant enzymes were not associated with sNfL or EDSS in RR-MS. CONCLUSIONS: RR-MS patients did not exhibit the changes in antioxidant enzyme activities over 5YFU found in HC; however, the differences were modest. Antioxidant enzyme activities are not associated with disability.

Lithium's effects on therapeutic targets and MRI biomarkers in Parkinson's disease: A pilot clinical trial.

Background: Lithium has a wide range of neuroprotective actions, has been effective in Parkinson's disease (PD) animal models and may account for the decreased risk of PD in smokers. Methods: This open-label pilot clinical trial randomized 16 PD patients to "high-dose" (n = 5, lithium carbonate titrated to achieve serum level of 0.4-0.5 mmol/L), "medium-dose" (n = 6, 45 mg/day lithium aspartate) or "low-dose" (n = 5, 15 mg/day lithium aspartate) lithium therapy for 24-weeks. Peripheral blood mononuclear cell (PBMC) mRNA expression of nuclear receptor-related-1 (Nurr1) and superoxide dismutase-1 (SOD1) were assessed by qPCR in addition to other PD therapeutic targets. Two patients from each group received multi-shell diffusion MRI scans to assess for free water (FW) changes in the dorsomedial nucleus of the thalamus and nucleus basalis of Meynert, which reflect cognitive decline in PD, and the posterior substantia nigra, which reflects motor decline in PD. Results: Two of the six patients receiving medium-dose lithium therapy withdrew due to side effects. Medium-dose lithium therapy was associated with the greatest numerical increases in PBMC Nurr1 and SOD1 expression (679% and 127%, respectively). Also, medium-dose lithium therapy was the only dosage associated with mean numerical decreases in brain FW in all three regions of interest, which is the opposite of the known longitudinal FW changes in PD. Conclusion: Medium-dose lithium aspartate therapy was associated with engagement of blood-based therapeutic targets and improvements in MRI disease-progression biomarkers but was poorly tolerated in 33% of patients. Further PD clinical research is merited examining lithium's tolerability, effects on biomarkers and potential disease-modifying effects.

Metabolomic profiles in relapsing-remitting and progressive multiple sclerosis compared to healthy controls: a five-year follow-up study.

INTRODUCTION AND OBJECTIVES: Multiple sclerosis (MS) is a disease of the central nervous system associated with immune dysfunction, demyelination, and neurodegeneration. The disease has heterogeneous clinical phenotypes such as relapsing-remitting MS (RRMS) and progressive multiple sclerosis (PMS), each with unique pathogenesis. Metabolomics research has shown promise in understanding the etiologies of MS disease. However, there is a paucity of clinical studies with follow-up metabolomics analyses. This 5-year follow-up (5YFU) cohort study aimed to investigate the metabolomics alterations over time between different courses of MS patients and healthy controls and provide insights into metabolic and physiological mechanisms of MS disease progression. METHODS: A cohort containing 108 MS patients (37 PMS and 71 RRMS) and 42 controls were followed up for a median of 5 years. Liquid chromatography-mass spectrometry (LC-MS) was applied for untargeted metabolomics profiling of serum samples of the cohort at both baseline and 5YFU. Univariate analyses with mixed-effect ANCOVA models, clustering, and pathway enrichment analyses were performed to identify patterns of metabolites and pathway changes across the time effects and patient groups. RESULTS AND CONCLUSIONS: Out of 592 identified metabolites, the PMS group exhibited the most changes, with 219 (37%) metabolites changed over time and 132 (22%) changed within the RRMS group (Bonferroni adjusted P < 0.05). Compared to the baseline, there were more significant metabolite differences detected between PMS and RRMS classes at 5YFU. Pathway enrichment analysis detected seven pathways perturbed significantly during 5YFU in MS groups compared to controls. PMS showed more pathway changes compared to the RRMS group.

Cholesterol pathway biomarkers are associated with neuropsychological measures in multiple sclerosis.

BACKGROUND: Cognitive impairment (CI) is frequent in persons with multiple sclerosis (PwMS) and is linked to neurodegeneration. Cholesterol pathway biomarkers (CPB) are associated with blood-brain barrier breakdown, lesions, and neurodegeneration in multiple sclerosis (MS). CPB could influence CI. METHODS: This cross-sectional study (n = 163) included 74 relapsing-remitting MS (RR-MS), 48 progressive MS (P-MS) and 41 healthy control (HC) subjects. The assessed physical disability and cognitive measures were: Nine-hole Peg Test (NHPT), Timed 25-Foot Walk, Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test-3, and Beck Depression Inventory-Fast Screen. CPB panel included plasma total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and the apolipoproteins (Apo), ApoA-I, ApoA-II, ApoB, ApoC-II and ApoE. Disability and cognitive measures were assessed as dependent variables in regression analyzes with age, sex, body mass index, years of education, HC vs. RR-MS vs. P-MS status, CPB, and a HC vs. RR-MS vs. P-MS status × CPB interaction term as predictors. RESULTS: SDMT was associated with the interaction terms for HDL-C (p = 0.045), ApoA-I (p = 0.032), ApoB (p = 0.032), TC/HDL-C (p = 0.013), and ApoB/ApoA-I (p = 0.008) ratios. CPB associations of SDMT were not abrogated upon adjusting for brain parenchymal volume. NHPT performance was associated with the interaction terms for TC (p = 0.047), LDL-C (p = 0.017), ApoB (p = 0.001), HDL-C (p = 0.035), ApoA-I (p = 0.032), ApoC-II (p = 0.049) and ApoE (p = 0.037), TC/HDL-C (p < 0.001), and ApoB/ApoA-I ratios (p < 0.001). CONCLUSIONS: The LDL to HDL proportion is associated with SDMT and NHPT in MS. The findings are consistent with a potential role for CPB in CI.

Generative models for age, race/ethnicity, and disease state dependence of physiological determinants of drug dosing.

Dosing requires consideration of diverse patient-specific factors affecting drug pharmacokinetics and pharmacodynamics. The available pharmacometric methods have limited capacity for modeling the inter-relationships and patterns of variability among physiological determinants of drug dosing (PDODD). To investigate whether generative adversarial networks (GANs) can learn a generative model from real-world data that recapitulates PDODD distributions. A GAN architecture was developed for modeling a PDODD panel comprised of: age, sex, race/ethnicity, body weight, body surface area, total body fat, lean body weight, albumin concentration, glomerular filtration rate (EGFR), urine flow rate, urinary albumin-to-creatinine ratio, alanine aminotransferase to alkaline phosphatase R-value, total bilirubin, active hepatitis B infection status, active hepatitis C infection status, red blood cell, white blood cell, and platelet counts. The panel variables were derived from National Health and Nutrition Examination Survey (NHANES) data sets. The dependence of GAN-generated PDODD on age, race, and active hepatitis infections was assessed. The continuous PDODD biomarkers had diverse non-normal univariate distributions and bivariate trend patterns. The univariate distributions of PDODD biomarkers from GAN simulations satisfactorily approximated those in test data. The joint distribution of the continuous variables was visualized using three 2-dimensional projection methods; for all three methods, the points from the GAN simulation random variate vectors were well dispersed amongst the test data. The age dependence trend patterns in GAN data were similar to those in test data. The histograms for R-values and EGFR from GAN simulations overlapped extensively with test data histograms for the Hispanic, White, African American, and Other race/ethnicity groups. The GAN-simulated data also mirrored the R-values and EGFR changes in active hepatitis C and hepatitis B infection. GANs are a promising approach for simulating the age, race/ethnicity and disease state dependencies of PDODD.