Teprotumumab and Orbital Decompression for the Management of Proptosis in Patients With Thyroid Eye Disease.

PURPOSE: To compare outcomes of patients with thyroid eye disease treated with teprotumumab or orbital decompression, or both in sequence. METHODS: Patients with thyroid eye disease and treated with decompression, teprotumumab, or both were included. Four groups were defined: decompression only, teprotumumab only, teprotumumab first with decompression later, and decompression first with teprotumumab later. The primary outcome was change in exophthalmometry. Secondary outcomes included change in extraocular muscle motility, strabismus, diplopia, and side effects. RESULTS: One hundred and thirty-nine patients were included. The mean duration for early follow-up was 1.2 months for both decompression and teprotumumab groups. The mean late follow-up was 14.4 and 8.2 months for the decompression and teprotumumab groups respectively. Mean change in exophthalmometry was significantly greater for the decompression group (3.5 mm) compared with teprotumumab (2.0 mm) at late follow-up. Improvement in total extraocular muscle restriction was significantly greater in the teprotumumab group (14.7 degrees) than in the decompression group (2.6 degrees). The teprotumumab group had a significantly higher percentage of patients with diplopia score >1 at baseline and late follow-up (p < 0.01) compared with the decompression group. Additional treatment with teprotumumab or decompression when previously treated with the opposite had similar proptosis reduction effect as that therapy alone. CONCLUSIONS: Surgical decompression has a greater proptosis reduction effect than teprotumumab, whereas teprotumumab better improves extraocular muscle motility. The addition of teprotumumab or decompression to a previous course of the opposite adds a similar effect to the supplemental treatment alone.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid.

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid.

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.