Phenome-wide examination of comorbidity burden and multiple sclerosis disease severity

Zhang, Tingting; Goodman, Matthew; Zhu, Feng; Healy, Brian C.; Carruthers, Robert; Chitnis, Tanuja; Weiner, Howard L.; Cai, Tianxi; Jager, Philip De; Tremlett, Helen; Xia, Zongqi

doi:10.1212/nxi.0000000000000864

Cited by 20 publications

(15 citation statements)

References 33 publications

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“…We obtained the EHR data for eligible participants from the Mass General Brigham (MGB; formerly the Partners) HealthCare system during the same period. 25 , 26 Mass General Brigham began recording electronic prescriptions in 2005. We compared dimethyl fumarate vs fingolimod for the standard-efficacy DMT comparison and natalizumab vs rituximab for the higher-efficacy DMT comparison.…”

Section: Methodsmentioning

confidence: 99%

“… 21 , 22 With advances in analytical capability, 23 , 24 electronic health record (EHR) data provide unique features to complement registries. Our group previously integrated registry data from a well-characterized, longitudinal clinic-based cohort with EHR data for developing models to classify MS diagnosis and severity, 25 assessing comorbidity burden, 26 examining long-term disease activity trends, 27 and predicting future relapse. 28 Here, we compared 2 DMT pairs using registry-annotated MS relapse as the outcome and high-dimensional EHR features and doubly robust (DR) estimation strategies to extensively correct for confounding biases.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

et al. 2021

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Key Points Question In the management of multiple sclerosis, is there a difference in relapse outcomes associated with commonly prescribed, standard-efficacy medications as well as with common higher-efficacy medications? Findings This comparative effectiveness study integrated electronic health records with research registry data and found no significant differences in relapse outcomes between dimethyl fumarate and fingolimod after correcting for confounding biases. Rituximab was associated with a lower relapse rate when compared with natalizumab after bias correction. Meaning The study illustrates the value of incorporating electronic health record data as high-dimensional covariates in real-world comparative effectiveness analysis of multiple sclerosis medications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

et al. 2021

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“…We previously integrated research registry data from a well-characterized, longterm, clinic-based cohort 19,20 with EHR data for developing EHR-based models of MS classification and neurological disability. 15,21 Here, we leveraged clinical and associated EHR data to develop and test a clinically deployable model for predicting 1-year relapse risk in MS patients.…”

Section: Introductionmentioning

confidence: 99%

“…Increasing analytical capability 17,18 has enabled the use of electronic health records (EHR) data to facilitate clinical discovery by providing complementary features otherwise unavailable from traditional research registries. We previously integrated research registry data from a well‐characterized, long‐term, clinic‐based cohort 19,20 with EHR data for developing EHR‐based models of MS classification and neurological disability 15,21 . Here, we leveraged clinical and associated EHR data to develop and test a clinically deployable model for predicting 1‐year relapse risk in MS patients.…”

Section: Introductionmentioning

confidence: 99%

Leveraging electronic health records data to predict multiple sclerosis disease activity

Ahuja

Kim

Liang

et al. 2021

Ann Clin Transl Neurol

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Objective No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk. Methods Using data from a clinic‐based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1‐year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor‐intensive chart review is impractical. We pursued two‐stage algorithm development: (1) L1‐regularized logistic regression (LASSO) to phenotype past 1‐year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1‐year relapse risk using imputed prior 1‐year relapse status and other algorithm‐selected features. Results The final model, comprising age, disease duration, and imputed prior 1‐year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1‐year relapse history. The predicted risk probability declined with disease duration and age. Conclusion Our novel machine‐learning algorithm predicts 1‐year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR‐based two‐stage approach of outcome prediction may have application to neurological disease beyond MS.

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“…The full picture, however, is more complex. Several human phenome-wide association studies identified causal genetic loci that are shared between metabolic and neurological phenotypes [2][3][4][5][6][7][8], suggesting some degree of pleiotropy-a phenomenon whereby one gene variant affects multiple traits.…”

Section: Introductionmentioning

confidence: 99%

System genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

Šilhavý

et al. 2021

Preprint

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Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetic methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular disease and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival. We subsequently fine-mapped a key phenotype to a locus that includes the telo2-interacting protein 2 gene (Tti2)—a chaperone that modulates the activity and stability of PIKK kinases. To validate variants in or near Tti2 as a cause for differences in neurogenesis and glucose levels, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic and molecular link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

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Phenome-wide examination of comorbidity burden and multiple sclerosis disease severity

Cited by 20 publications

References 33 publications

Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

Leveraging electronic health records data to predict multiple sclerosis disease activity

System genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

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