Background Although nontremor and tremor Part 3 Movement Disorder Society–Unified Parkinson's Disease Rating Scale items measure different impairment domains, their distinct progression and drug responsivity remain unstudied longitudinally. The total score may obscure important time‐based and treatment‐based changes occurring in the individual domains. Objective Using the unique advantages of item response theory (IRT), we developed novel longitudinal unidimensional and multidimensional models to investigate nontremor and tremor changes occurring in an interventional Parkinson's disease (PD) study. Method With unidimensional longitudinal IRT, we assessed the 33 Part 3 item data (22 nontremor and 10 tremor items) of 336 patients with early PD from the STEADY‐PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine for PD, placebo vs. isradipine) study. With multidimensional longitudinal IRT, we assessed the progression rates over time and treatment (in overall motor severity, nontremor, and tremor domains) using Markov Chain Monte Carlo implemented in Stan. Results Regardless of treatment, patients showed significant but different time‐based deterioration rates for total motor, nontremor, and tremor scores. Isradipine was associated with additional significant deterioration over placebo in total score and nontremor scores, but not in tremor score. Further highlighting the 2 separate latent domains, nontremor and tremor severity changes were positively but weakly correlated (correlation coefficient, 0.108). Conclusions Longitudinal IRT analysis is a novel statistical method highly applicable to PD clinical trials. It addresses limitations of traditional linear regression approaches and previous IRT investigations that either applied cross‐sectional IRT models to longitudinal data or failed to estimate all parameters simultaneously. It is particularly useful because it can separate nontremor and tremor changes both over time and in response to treatment interventions.

Background Longitudinal item response theory (IRT) models previously suggested that the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) motor examination has two salient domains, tremor and nontremor, that progress in time and in response to treatment differently. Objective Apply longitudinal IRT modeling, separating tremor and nontremor domains, to reanalyze outcomes in the previously published clinical trial (Study of Urate Elevation in Parkinson's Disease, Phase 3) that showed no overall treatment effects. Methods We applied unidimensional and multidimensional longitudinal IRT models to MDS‐UPDRS motor examination items in 298 participants with Parkinson's disease from the Study of Urate Elevation in Parkinson's Disease, Phase 3 (placebo vs. inosine) study. We separated 10 tremor items from 23 nontremor items and used Bayesian inference to estimate progression rates and sensitivity to treatment in overall motor severity and tremor and nontremor domains. Results The progression rate was faster in the tremor domain than the nontremor domain before levodopa treatment. Inosine treatment had no effect on either domain relative to placebo. Levodopa treatment was associated with greater slowing of progression in the tremor domain than the nontremor domain regardless of inosine exposure. Linear patterns of progression were observed. Despite different domain‐specific progression patterns, tremor and nontremor severities at baseline and over time were significantly correlated. Conclusions Longitudinal IRT analysis is a novel statistical method addressing limitations of traditional linear regression approaches. It is particularly useful because it can simultaneously monitor changes in different, but related, domains over time and in response to treatment interventions. We suggest that in neurological diseases with distinct impairment domains, clinical or anatomical, this application may identify patterns of change unappreciated by standard statistical methods. © 2022 International Parkinson and Movement Disorder Society.

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Mulberry (Morus alba L.) leaf, a “source of both medicine and food”, contains antioxidant ingredients such as flavonoids, alkaloids and polyphenols. The effects of 6-benzylaminopurine (6-BA) treatment on plant growth and flavonoid contents in mulberry leaves were investigated in this study. The expression of rutin (Rut), chlorogenic acid (ChA), isoquercitrin (IQ) and astragaloside IV (Ast) related genes in the flavonoid synthesis pathways was investigated in mulberry leaves. The results showed that 6-BA treatment significantly promoted mulberry differentiation and growth as well as, increased the numbers of new shoots and buds compared to the control. In addition, 30 mg/L 6-BA significantly increased the contents of Rut, IQ and Ast, and it strongly induced the expression of flavonoid biosynthesis-related genes, including flavonoid 3-O-glucosyltransferase (F3GT), 4-xoumarate-CoA ligase (4CL), phenylalanine (PAL) and chalcone synthase (CHS). The dietary risk assessment of mulberry leaves was based on hormone residues 5 days after treatment with 30 mg /L 6-BA, and the results showed that the dietary exposure risk of 6-BA was extremely low without causing any health concern. Thus, treatment with 30 mg/L 6-BA is a new method to improve the medicinal quality and development of high-value mulberry leaf foods without any potential risk.