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.

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.

Authors' response to letter to editor.

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

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.

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.

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.

Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity.

AIMS: Modelling biomarker profiles for under-represented race/ethnicity groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. The aim was to investigate generative adversarial networks (GANs), an artificial intelligence technology that enables realistic simulations of complex patterns, for modelling clinical biomarker profiles of under-represented groups. METHODS: GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modelling univariate and joint distributions of a panel of 16 diabetes-relevant biomarkers from the National Health and Nutrition Examination Survey, which contains laboratory and clinical biomarker data from a population-based sample of individuals of all ages, racial groups and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data. RESULTS: The biomarkers exhibited non-normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modelled with generator and discriminator neural networks consisting of 2 dense layers with rectified linear unit-activation. The distributions of GAN-generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN-generated data were dispersed and overlapped with the joint distribution of the test data as assessed by 3 multidimensional projection methods. Conditional GANs satisfactorily modelled the joint distribution of the biomarker panel in the Black, Hispanic, White and Other race/ethnicity categories. CONCLUSION: GAN is a promising artificial intelligence approach for generating virtual patient data with realistic biomarker distributions for under-represented race/ethnicity groups.

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.

Lower cerebral arterial blood flow is associated with greater serum neurofilament light chain levels in multiple sclerosis patients.

BACKGROUND AND PURPOSE: Hypoperfusion, vascular pathology, and cardiovascular risk factors are associated with disease severity in multiple sclerosis (MS). We aimed to assess relationships between cerebral arterial blood flow (CABF) and serum neurofilament light chain (sNfL) as neuronal damage biomarkers. METHODS AND MATERIALS: Total CABF was measured in 137 patients (86 with clinically isolated syndrome/relapsing-remitting (RR) MS and 51 with progressive MS [PMS]) and 48 healthy controls using Doppler ultrasonography. sNfL was quantitated using a single-molecule assay (Simoa). Examination using 3.0-T magnetic resonance imaging (MRI) allowed quantification of T2 lesions and whole-brain volume (WBV). Multiple linear regression models determined the sNfL association with CABF after correction for demographic and MRI-derived variables. RESULTS: After adjustment for age, sex and body mass index (BMI), total CABF remained statistically significant and model comparisons showed that CABF explained an additional 2.6% of the sNfL variance (ß = -0.167, p = 0.044). CABF also remained significant in a stepwise regression model (ß = 0.18, p = 0.034) upon the inclusion of T2 lesion burden and WBV effects. Patients in the lowest CABF quartile (CABF ≤ 761 ml/min) had significantly higher sNfL levels (34.6 vs. 23.9 pg/ml, age and BMI-adjusted-p = 0.042) when compared to the highest quartile (CABF ≥ 1130 ml/min). CONCLUSION: Lower CABF is associated with increased sNfL in MS patients, highlighting the relationship between cerebral hypoperfusion and axonal pathology.

Machine learning in pharmacometrics: Opportunities and challenges.

The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression.

The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.

Hypertension and hypertension severity in Hispanics/Latinx with MS.

BACKGROUND: Vascular comorbidities (VCs) including hypertension (HTN) are associated with worse multiple sclerosis (MS) outcomes. HTN is common in Latinx, but the prevalence and relationship with disability are unknown in Latinx with MS. METHODS: Latinx (n = 451) from the Alliance for Research in Hispanic MS (ARHMS) seen between 2007 and 2019 were included. HTN, diabetes (DM), hyperlipidemia (HLD), ischemic events, and smoking were considered VC. Blood pressures (BPs) were classified using the American Heart Association (AHA) criteria. Logistic regression determined associations between VC and ambulatory disability accounting for age, sex, and disease duration. RESULTS: Medical comorbidities were found in 41.9% and VC in 24.2%. Smoking (13.6%) and HTN (7.3%) were the most common. HTN was the most common over the age of 40 (12.6%). The odds of having severe disability were three times higher for those with HTN (odds ratio [OR], 3.12; 95% confidence interval (CI), 1.37-7.12). Stage II HTN according to AHA also tripled the odds (OR, 2.89; 95%CI, 1.11-7.55). AHA BP confirmed HTN in 27.5% (compared to 7.3% with established diagnosis). CONCLUSION: HTN diagnosis and stage II HTN defined by AHA were independently associated with severe ambulatory disability in Latinx with MS. HTN was underdiagnosed. Future studies should assess whether HTN treatment control would prevent disability in MS.

Neurofilament levels are associated with blood-brain barrier integrity, lymphocyte extravasation, and risk factors following the first demyelinating event in multiple sclerosis.

BACKGROUND: Increased blood brain barrier (BBB) permeability, CNS inflammation and neuroaxonal damage are pathological hallmarks in early multiple sclerosis (MS). OBJECTIVE: To investigate the associations of neurofilament light chain (NfL) levels with measures of BBB integrity and central nervous system (CNS) inflammation in MS during the first demyelinating event. METHODS: Blood and cerebrospinal fluid (CSF) were obtained from 142 MS (McDonald 2017) treatment-naive patients from the SET study (63% female; age: 29.7 ± 7.9 years) following the disease onset. NfL, albumin, immunoglobulin G (IgG), and immunoglobulin M (IgM) levels were measured in CSF and blood samples. Albumin quotient was computed as a marker of BBB integrity. Immune cell subset counts in CSF were measured using flow cytometry. MS risk factors, such as Human leukocyte antigen DRB1 locus gene (HLA DRB1)*1501, anti-Epstein-Barr virus (EBV) antibodies, and 25-hydroxy vitamin D3, were also measured. RESULTS: Higher serum NfL (sNfL) levels were associated with higher albumin quotient (p < 0.001), CSF CD80+ (p = 0.012), and CD80+ CD19+ (p = 0.015) cell frequency. sNfL levels were also associated with contrast-enhancing and T2 lesions on brain magnetic resonance imaging (MRI; all p ⩽ 0.001). Albumin quotient was not associated with any of the MS risk factors assessed. sNfL levels were associated with anti-EBV viral capsid antigen (VCA) IgG levels (p = 0.0026). CONCLUSION: sNfL levels during the first demyelinating event of MS are associated with greater impairment of BBB integrity, immune cell extravasation, and brain lesion activity on MRI.

Lipoprotein(a) Levels Are Associated with the Size of Extracranial Arteries in Multiple Sclerosis.

BACKGROUND: Hyperlipidemia is associated with worse clinical and radiological outcomes in persons with multiple sclerosis (PwMS) and studies show greater MS extracranial arterial vessel pathology. OBJECTIVE: The aim of this study was to determine the effect of lipid profile measures on extracranial arterial vessels in PwMS and healthy controls (HCs). METHODS: Non-contrast magnetic resonance angiography was conducted on 104 PwMS and 41 HCs. The cross-sectional area (CSA) of the common carotid artery (CCA) and vertebral artery (VA) was measured using a semi-automated edge-detection/contouring method at cervical levels C4-C7. The lipid profile was obtained at the time of the scan. Repeated measures analyses adjusted for age, gender, and body mass index were used. RESULTS: In PwMS, age was associated with CCA CSA (F = 7.65, η2 = 0.083, p = 0.007) and lipoprotein(a) [Lp(a)] with VA CSA (F = 13.4, η2 = 0.13, p < 0.001). These associations were not present in HCs. PwMS with Lp(a) ≥30 and ≥50 mg/dL, and within the highest Lp(a) quartile had significantly larger CCA and VA when compared to those with lower Lp(a) threshold values. Total cholesterol and low- and high-density lipoprotein cholesterol were not associated with CCA or VA CSA. CONCLUSIONS: Lp(a) levels are associated with CSA of major extracranial arterial vessels in PwMS but not in HCs. The clinical and pathological significance of these associations, if any, remains unknown.

Higher EBV response is associated with more severe gray matter and lesion pathology in relapsing multiple sclerosis patients: A case-controlled magnetization transfer ratio study.

BACKGROUND: Epstein-Barr virus (EBV) infection has been associated with higher clinical activity and risk of multiple sclerosis (MS). OBJECTIVE: To evaluate associations between EBV-specific humoral response and magnetization transfer ratio (MTR)-derived measure in MS patients and healthy controls (HCs). METHODS: The study included 101 MS patients (69 relapsing-remitting multiple sclerosis (RRMS) and 32 secondary-progressive multiple sclerosis (SPMS)) and 41 HCs who underwent clinical, serological, and magnetic resonance imaging (MRI) investigations. MTR values of T1 or T2 lesion volume (LV), normal-appearing (NA) brain tissue (NABT), gray matter (NAGM), and white matter (NAWM) were obtained. Enzyme-linked immunosorbent assay was used to quantify EBV antibody levels. Partial correlations corrected for MRI strength were used, and Benjamini-Hochberg-adjusted p-values < 0.05 were considered significant. RESULTS: MS patients had significantly higher anti-EBV nuclear antigen-1 (EBNA-1) titer when compared to HCs (107.9 U/mL vs 27.8 U/mL, p < 0.001). Within the MS group, higher serum anti-EBNA-1 titer was significantly correlated with lower T1-LV MTR (r = -0.287, p = 0.035). Within the RRMS group, higher serum anti-EBNA-1 titer was associated with T1-LV MTR (r = -0.524, p = 0.001) and NAGM MTR (r = -0.308, p = 0.043). These associations were not present in HCs or SPMS patients. CONCLUSION: Greater EBV humoral response is associated with lower GM MTR changes and focal destructive lesion pathology in RRMS patients.

Fatigue at enrollment predicts EDSS worsening in the New York State Multiple Sclerosis Consortium.

BACKGROUND: Fatigue is one of the most common and distressing symptoms among persons with multiple sclerosis (pwMS). OBJECTIVE: The aim of this study is to evaluate fatigue as a predictor for disease worsening among pwMS. METHODS: In this retrospective cohort study of New York State MS Consortium (NYSMSC) registry, MS patients reporting moderate-to-severe fatigue at study enrollment (n = 2714) were frequency matched to less-fatigued subjects (n = 2714) on age, baseline Kurtzke Expanded Disability Status Scale (EDSS), disease duration, and MS phenotype. Change from baseline patient-reported outcomes (PROs), as measured by LIFEware™, categorized participants into two groups: those with stable/improved outcomes and those who worsened. In a subgroup of patients with longitudinal data (n = 1951), sustained EDSS worsening was analyzed using Cox proportional hazards modeling to explore the effect of fatigue. RESULTS: The median survival time from study enrollment to sustained EDSS worsening was 8.7 years (CI: 7.2-10.1). Participants who reported fatigue at baseline were more likely to experience sustained EDSS worsening during follow-up (HR: 1.4, 95% CI: 1.2-1.7). Patients who were fatigued at baseline were also more likely to report worsening psychosocial limitations (all ps ⩽ 0.01). CONCLUSION: In addition to being a common symptom of MS, severe fatigue was a significant predictor for EDSS worsening in the NYSMSC.

Application of Brunauer-Emmett-Teller (BET) theory and the Guggenheim-Anderson-de Boer (GAB) equation for concentration-dependent, non-saturable cell-cell interaction dose-responses.

To systematically assess the characteristics and potential utility of the Guggenheim-Anderson-de Boer (GAB) formulation of the Brunauer-Emmett-Teller (BET) equation from physical chemistry for modeling dose-responses in pharmaceutical applications. The GAB-BET equation was derived using pharmacodynamic first principles to underscore the assumptions involved and the functional characteristics of the equation were investigated. The properties of the GAB-BET equation were compared to the familiar Michaelis-Menten and Hill equations and its utility for pharmacokinetic-pharmacodynamic modeling was assessed by fitting the model equations to four diverse data sets from the literature. The results enabled the salient characteristics of the unconstrained GAB-BET equation and the corresponding GAB-BET equation with finite layers for modeling pharmacodynamic effects to be critically assessed. The GAB-BET approach allows for the accumulation of heterogeneous stacks containing multiple cells or molecules at the target site. The unconstrained GAB-BET equation is capable of describing concentration-dependent dose-response curves that do not exhibit saturation. The GAB-BET equation for finite layers exhibits saturation but increases more slowly than the comparable Michaelis-Menten and Hill equations. The fitting results of the model equations to literature data sets provided support for key aspects of the GAB-BET model. The GAB-BET equation may be a useful method for mechanistic modeling of diverse immune processes and drugs that recruit immune cell activity at the site of action.

Oxysterols and apolipoproteins in multiple sclerosis: a 5 year follow-up study.

The purpose of this work was to investigate whether changes in oxysterol and apolipoprotein levels over 5 years are associated with disease course and disability progression in multiple sclerosis (MS). This study included 139 subjects [39 healthy controls (HCs), 61 relapsing-remitting MS (RR-MS) patients, and 39 progressive MS (P-MS) patients]. Oxysterols [24-hydroxycholesterol (24HC), 25-hydroxycholesterol (25HC), 27-hydroxycholesterol (27HC), 7α-hydroxycholesterol (7αHC), and 7-ketocholesterol (7KC)] were measured at baseline and 5 years using a novel mass spectrometric method, and apolipoproteins were measured using immunoturbidometric diagnostic kits. Levels of 24HC (P = 0.004), 25HC (P = 0.029), and 27HC (P = 0.026) increased in P-MS patients. 7KC (P = 0.047) and 7αHC (P = 0.001) levels decreased in RR-MS patients, and there were no changes in any oxysterols in HCs. In MS patients, ApoC-II (all P ≤ 0.01) and ApoE (all P ≤ 0.01) changes were positively associated with all oxysterol levels. Increases in 24HC (P = 0.038) and ApoB (P = 0.038) and decreases in 7KC (P = 0.020) were observed in RR-MS patients who converted to secondary P-MS (SP-MS) at follow-up and in SP-MS patients compared with RR-MS patients. Oxysterols and their associations with apolipoproteins differed between MS patients and HCs over 5 years. Oxysterol and apolipoprotein changes were associated with conversion to SP-MS.

Serum lipid profile changes predict neurodegeneration in interferon-ß1a-treated multiple sclerosis patients.

The purpose of this work was to determine whether changes in cholesterol profiles after interferon-ß (IFN-ß)1a treatment initiation following the first demyelinating event suggestive of multiple sclerosis are associated with clinical and MRI outcomes over 4 years. A group of 131 patients (age: 27.9 ± 7.8 years, 63% female) with serial 3-monthly clinical and 12-monthly MRI follow-ups over 4 years were investigated. Serum cholesterol profiles, including total cholesterol (TC), HDL cholesterol (HDL-C), and LDL cholesterol (LDL-C) were obtained at baseline, 1 month, 3 months, and every 6 months thereafter. IFN-ß1a initiation caused rapid decreases in serum HDL-C, LDL-C, and TC within 1 month of IFN-ß1a initiation (all P < 0.001) that returned slowly toward baseline. In predictive mixed model analyses, greater percent decreases in HDL-C after 3 months of IFN-ß1a treatment initiation were associated with less brain atrophy over the 4 year time course, as assessed by percent brain volume change (P < 0.001), percent gray matter volume change (P < 0.001), and percent lateral ventricle volume change (P = 0.005). Decreases in cholesterol biomarkers following IFN-ß1a treatment are associated with brain atrophy outcomes over 4 years. Pharmacological interventions targeting lipid homeostasis may be clinically beneficial for disrupting neurodegenerative processes.