The prevalence of hypertension in multiple sclerosis based on 37 million electronic health records from the United States.

BACKGROUND AND PURPOSE: Hypertension (HTN) is a common comorbidity in multiple sclerosis (MS), and it significantly contributes to adverse outcomes. Unfortunately, the distribution of HTN in persons with MS has not been well characterized, and prior estimates have primarily relied on modest sample sizes. The objective of this study was to robustly describe the distribution of HTN in the MS population in comparison to the non-MS population with considerations for age, sex, and race. To date, this is the largest investigation of its kind. METHODS: We conducted a cross-sectional study of 37 million unique electronic health records available in the IBM Explorys Enterprise Performance Management: Explore database (Explorys) spanning the United States. This resource has previously been validated for use in MS. We evaluated the prevalence of HTN in MS (N = 122 660) and non-MS (N = 37 075 350) cohorts, stratifying by age, sex, and race. RESULTS: The prevalence of HTN was significantly greater among those with MS than among those without MS across age, sex, and race subpopulations, even after adjusting for age and sex. HTN was 25% more common in MS. In both MS and non-MS cohorts, the prevalence of HTN progressively increased with age and was higher in Black Americans and in males. DISCUSSION: This study demonstrated that HTN is significantly more common in the MS population compared to the non-MS population, irrespective of sex and race. Because HTN is the leading global risk factor for disability and death, these results emphasize the need for aggressive screening for, and management of, HTN in the MS population.

Metabolome-based signature of disease pathology in MS.

BACKGROUND: Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis. METHODS: To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites. RESULTS: 12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites. CONCLUSION: Our analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment.

Annotation of functional variation within non-MHC MS susceptibility loci through bioinformatics analysis.

There is a strong and complex genetic component to multiple sclerosis (MS). In addition to variation in the major histocompatibility complex (MHC) region on chromosome 6p21.3, 110 non-MHC susceptibility variants have been identified in Northern Europeans, thus far. The majority of the MS-associated genes are immune related; however, similar to most other complex genetic diseases, the causal variants and biological processes underlying pathogenesis remain largely unknown. We created a comprehensive catalog of putative functional variants that reside within linkage disequilibrium regions of the MS-associated genic variants to guide future studies. Bioinformatics analyses were also conducted using publicly available resources to identify plausible pathological processes relevant to MS and functional hypotheses for established MS-associated variants.

Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis.

Investigating genetic interactions (epistasis) has proven difficult despite the recent advances of both laboratory methods and statistical developments. With no 'best' statistical approach available, combining several analytical methods may be optimal for detecting epistatic interactions. Using a multi-stage analysis that incorporated supervised machine learning and methods of association testing, we investigated epistatic interactions with a well-established genetic factor (PTPN22 1858T) in a complex autoimmune disease (rheumatoid arthritis (RA)). Our analysis consisted of four principal stages: Stage I (data reduction)-identifying candidate chromosomal regions in 292 affected sibling pairs, by predicting PTPN22 concordance using multipoint identity-by-descent probabilities and a supervised machine learning algorithm (Random Forests); Stage II (extension analysis)-testing detailed genetic data within candidate chromosomal regions for epistasis with PTPN22 1858T in 677 cases and 750 controls using logistic regression; Stage III (replication analysis)-confirmation of epistatic interactions in 947 cases and 1756 controls; Stage IV (combined analysis)-a pooled analysis including all 1624 RA cases and 2506 control subjects for final estimates of effect size. A total of seven replicating epistatic interactions were identified. SNP variants within CDH13, MYO3A, CEP72 and near WFDC1 showed significant evidence for interaction with PTPN22, affecting susceptibility to RA.