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Dyslipidemia refers to abnormalities in the composition, concentration, or size of lipids and lipoproteins in the circulation, and it is a key modifiable risk factor for cardiovascular (CV) disease. With respect to lipid-modifying therapies, the greatest commercial success has come via agents reducing low-density lipoprotein (LDL)-cholesterol, but research and development focusing on triglycerides, high-density lipoprotein (HDL)-cholesterol, and other novel biomarkers is ongoing, with the aim of ultimately providing CV benefits. During the 2017-2027 forecast period, the dyslipidemia market is expected to have two distinct phases: 1) early contraction owing largely to the genericization of the dominant statin class, and 2) marked growth due to the emergence of novel therapies, including Amgen’s Repatha, Sanofi/Regeneron Pharmaceuticals’ Praluent, Amarin’s Vascepa, Kowa Pharmaceuticals’ Parmodia (pemafibrate), The Medicines Company/Alnylam Pharmaceuticals’ inclisiran, Esperion Therapeutics’ bempedoic acid, and AstraZeneca’s Epanova.
Disease Landscape & Forecast: Comprehensive market intelligence providing world-class epidemiology, keen insight into current treatment paradigms, in-depth pipeline assessments, and drug forecasts supported by detailed primary and secondary research.
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Surbhi Gupta is an Insights analyst in the Cardiovascular, Metabolic and Renal disorders team at Decision Resources Group and has worked on contents on pulmonary hypertension and obesity. She completed her Masters with specialization in Pharmacology, and a Bachelor's degree in Pharmacy from the University Of Delhi. Before joining DRG, she has worked as a Senior Business Analyst at the research firm Evalueserve, where she was involved in competitive intelligence projects across multiple indications (such as atrial fibrillation, ACS, VTE, renal anemia, and several oncology indications). She also worked as a Medical writer at the clinical research organization, Apcer Pharma.
Ema is an epidemiologist with expertise in forecasting incident and prevalent populations within oncology, as well as some cardiovascular indications such as venous thromboembolism. She has significant experience with statistical methods such as multivariate linear regression, conditional logistic regression, principal components analysis, mixed models, hierarchical modeling, and path analysis to account for the complex relationships among various predictors of health outcomes, particularly correlated variables. She completed her master’s and doctoral degree (MPH, ) in Environmental Health at Boston University School of Public Health, where she worked on projects investigating significant predictors of various health outcomes including central nervous system cancer, cognitive function, and birth outcomes.