The Precision of Cardiovascular Disease Prevention Begins With a Zip Code.
Ontology highlight
ABSTRACT:
SUBMITTER: Obuobi S
PROVIDER: S-EPMC8311274 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
The Precision of Cardiovascular Disease Prevention Begins With a Zip Code.
Obuobi Shirlene S Belardo Danielle D Gulati Martha M
JACC. Case reports 20210616 6
Similar Datasets
Project description:BackgroundArea-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown.MethodsThis is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment.ResultsA total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003).ConclusionThe concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.
| S-EPMC10852236 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, and is among the most aggressive and still incurable cancers. Innovative and successful therapeutic strategies are extremely needed. Peptides represent a versatile and promising tool to achieve tumor targeting, thanks to their ability to recognize specific target proteins (over)expressed on the surface of cancer cells. A7R is one such peptide, binding neuropilin-1 (NRP-1) and VEGFR2. Since PDAC expresses these receptors, the aim of this study was to test if A7R-drug conjugates could represent a PDAC-targeting strategy. PAPTP, a promising mitochondria-targeted anticancer compound, was selected as the cargo for this proof-of-concept study. Derivatives were designed as prodrugs, using a bioreversible linker to connect PAPTP to the peptide. Both the retro-inverso (DA7R) and the head-to-tail cyclic (cA7R) protease-resistant analogs of A7R were tested, and a tetraethylene glycol chain was introduced to improve solubility. Uptake of a fluorescent DA7R conjugate, as well as of the PAPTP-DA7R derivative into PDAC cell lines was found to be related to the expression levels of NRP-1 and VEGFR2. Conjugation of DA7R to therapeutically active compounds or nanovehicles might allow PDAC-targeted drug delivery, improving the efficacy of the therapy and reducing off-target effects.
| S-EPMC10221132 | biostudies-literature
Project description:The purpose of this review is to describe novel pharmacologic and nonpharmacologic preventive therapies, as well as new strategies to improve delivery of available therapies. Cardiovascular disease (CVD) is the leading cause of death worldwide, and prevention plays a critical role in curbing the global epidemic. Despite available treatment for tobacco addiction, platelet inhibition, blood pressure, and lipid lowering for reduction of atherosclerotic disease, significant gaps in treatment of total CVD remain. We review a range of new preventive treatment options, including drugs for tobacco cessation, platelet/thrombotic inhibition, lipid- and blood pressure-lowering; nonpharmacologic options such as left atrial appendage closure devices and caloric restriction; and strategies such as fixed-dose combination drugs, laboratory screening for drug tailoring, and community-based prevention programs. CVD preventive research continues to evolve and provide clinicians and patients with novel pharmacologic and nonpharmacologic therapies, including new preventive strategies.
| S-EPMC3911909 | biostudies-other
Project description:BackgroundPolygenic risk score (PRS) quantifies the cumulative effects of common genetic variants across the genome, including both coding and non-coding regions, to predict the risk of developing common diseases. In cardiovascular medicine, PRS enhances risk stratification beyond traditional clinical risk factors, offering a precision medicine approach to coronary artery disease (CAD) prevention. This study evaluates the predictive performance of a multi-ancestry PRS framework for cardiovascular risk assessment using the All of Us (AoU) short-read whole-genome sequencing dataset comprising over 225,000 participants.MethodsWe developed PRSs for lipid traits (LDL-C, HDL-C, triglycerides) and cardiometabolic conditions (type 2 diabetes, hypertension, atrial fibrillation) and constructed two metaPRSs: one integrating lipid and cardiometabolic PRSs (risk factor metaPRS) and another incorporating CAD PRSs in addition to these risk factors (risk factor + CAD metaPRS). Predictive performance was evaluated separately for each trait-specific PRS and for both metaPRSs to assess their effectiveness in CAD risk prediction across diverse ancestries. Model predictive performance, including calibration, was assessed separately for each ancestry group, ensuring that all metrics were ancestry-specific and that PRSs remain generalizable across diverse populations Results: PRSs for lipids and cardiometabolic conditions demonstrated strong predictive performance across ancestries. The risk factors metaPRS predicted CAD risk across multiple ancestries. The addition of a CAD-specific PRS to the risk factors metaPRS improved predictive performance, highlighting a genetic component in CAD etiopathology that is not fully captured by traditional risk factors, whether clinically measured or genetically inferred. Model calibration and validation across ancestries confirmed the broad applicability of PRS-based approaches in multi-ethnic populations.ConclusionPRS-based risk stratification provides a reliable, ancestry-inclusive framework for personalized cardiovascular disease prevention, enabling better targeted interventions such as pharmacological therapy and lifestyle modifications. By incorporating genetic information from both coding and non-coding regions, PRSs refine risk prediction across diverse populations, advancing the integration of genomics into precision medicine for common diseases.
| S-EPMC11901995 | biostudies-literature
Project description:ImportanceThere has been large geographic inequity in vaccination coverage across Chicago, Illinois, with higher vaccination rates in zip codes with residents who predominantly have high incomes and are White.ObjectiveTo determine the association between inequitable zip code-level vaccination coverage and COVID-19 mortality in Chicago.Design, setting, and participantsThis retrospective cohort study used Chicago Department of Public Health vaccination and mortality data and Cook County Medical Examiner mortality data from March 1, 2020, through November 6, 2021, to assess the association of COVID-19 mortality with zip code-level vaccination rates. Data were analyzed from June 1, 2021, to April 13, 2022.ExposuresZip code-level first-dose vaccination rates before the Alpha and Delta waves of COVID-19.Main outcomes and measuresThe primary outcome was deaths from COVID-19 during the Alpha and Delta waves. The association of a marginal increase in zip code-level vaccination rate with weekly mortality rates was estimated with a mixed-effects Poisson regression model, and the total number of preventable deaths in the least vaccinated quartile of zip codes was estimated with a linear difference-in-difference design.ResultsThe study population was 2 686 355 Chicago residents in 52 zip codes (median [IQR] age 34 [32-38] years; 1 378 658 [51%] women; 773 938 Hispanic residents [29%]; 783 916 non-Hispanic Black residents [29%]; 894 555 non-Hispanic White residents [33%]). Among residents in the least vaccinated quartile, 80% were non-Hispanic Black, compared with 8% of residents identifying as non-Hispanic Black in the most vaccinated quartile (P < .001). After controlling for age distribution and recovery from COVID-19, a 10-percentage point increase in zip code-level vaccination 6 weeks before the peak of the Alpha wave was associated with a 39% lower relative risk of death from COVID-19 (incidence rate ratio [IRR], 0.61 [95% CI, 0.52-0.72]). A 10-percentage point increase in zip code vaccination rate 6 weeks before the peak of the Delta wave was associated with a 24% lower relative risk of death (IRR, 0.76 [95% CI, 0.66-0.87]). The difference-in-difference estimate was that 119 Alpha wave deaths (72% [95% CI, 63%-81%]) and 108 Delta wave deaths (75% [95% CI, 66%-84%]) might have been prevented in the least vaccinated quartile of zip codes if it had had the vaccination coverage of the most vaccinated quartile.Conclusions and relevanceThese findings suggest that low zip code-level vaccination rates in Chicago were associated with more deaths during the Alpha and Delta waves of COVID-19 and that inequitable vaccination coverage exacerbated existing racial and ethnic disparities in COVID-19 deaths.
| S-EPMC9142872 | biostudies-literature
Project description:We assessed the added value and limitations of generating directly estimated ZIP Code-level estimates by aggregating 5 years of data from an annual cross-sectional survey, the New York City Community Health Survey (n = 44,886) from 2009 to 2013, that were designed to provide reliable estimates only of larger geographies. Survey weights generated directly-observed ZIP Code (n = 128) level estimates. We assessed the heterogeneity of ZIP Code-level estimates within coarser United Hospital Fund (UHF) neighborhood areas (n = 34) by using the Rao-Scott Chi-Square test and one-way ANOVA. Orthogonal linear contrasts assessed whether there were linear trends at the UHF level from 2009 to 2013. 22 of 37 health indicators were reliable in over 50% of ZIP Codes. 14 of the 22 variables showed heterogeneity in ≥4 UHFs. Variables for drinking, nutrition, and HIV testing showed heterogeneity in the most UHFs (9-24 UHFs). In half of the 32 UHFs, >20% variables had within-UHF heterogeneity. Flu vaccination and sugary beverage consumption showed significant time trends in the largest number of UHFs (12 or more UHFs). Overall, heterogeneity of ZIP Code-level estimates suggests that there is value in aggregating 5 years of data to make direct small area estimates.
| S-EPMC7392987 | biostudies-literature
Project description:Precision medicine is an integrative approach to cardiovascular disease prevention and treatment that considers an individual's genetics, lifestyle, and exposures as determinants of their cardiovascular health and disease phenotypes. This focus overcomes the limitations of reductionism in medicine, which presumes that all patients with the same signs of disease share a common pathophenotype and, therefore, should be treated similarly. Precision medicine incorporates standard clinical and health record data with advanced panomics (ie, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics) for deep phenotyping. These phenotypic data can then be analyzed within the framework of molecular interaction (interactome) networks to uncover previously unrecognized disease phenotypes and relationships between diseases, and to select pharmacotherapeutics or identify potential protein-drug or drug-drug interactions. In this review, we discuss the current spectrum of cardiovascular health and disease, population averages and the response of extreme phenotypes to interventions, and population-based versus high-risk treatment strategies as a pretext to understanding a precision medicine approach to cardiovascular disease prevention and therapeutic interventions. We also consider the search for resilience and Mendelian disease genes and argue against the theory of a single causal gene/gene product as a mediator of the cardiovascular disease phenotype, as well as an Erlichian magic bullet to solve cardiovascular disease. Finally, we detail the importance of deep phenotyping and interactome networks and the use of this information for rational polypharmacy. These topics highlight the urgent need for precise phenotyping to advance precision medicine as a strategy to improve cardiovascular health and prevent disease.
| S-EPMC6021027 | biostudies-literature
Project description:IntroductionWe have demonstrated that transposons derived from ctDNA can be transferred between cancer cells. The present research aimed to investigate the cellular uptake and intracellular trafficking of Multiple Myeloma-zip code (MM-ZC), a cell-specific zip code, in myeloma cell lines. We demonstrated that MM-ZC uptake by myeloma cells was concentration-, time- and cell-type-dependent.MethodsFlow cytometry and confocal microscopy methods were used to identify the level of internalization of the zip codes in MM cells. To screen for the mechanism of internalization, we used multiple inhibitors of endocytosis. These experiments were followed by biotin pulldown and confocal microscopy for validation. Single interference RNA (siRNA) targeting some of the proteins involved in endocytosis was used to validate the role of this pathway in ZC cell internalization.ResultsEndocytosis inhibitors identified that Monensin and Chlorpromazine hydrochloride significantly reduced MM-ZC internalization. These findings suggested that Clathrin-mediated endocytosis and endosomal maturation play a crucial role in the cellular uptake of MM-ZC. Biotin pulldown and confocal microscopic studies revealed the involvement of proteins such as Clathrin, Rab5a, Syntaxin-6, and RCAS1 in facilitating the internalization of MM-ZC. Knockdown of Rab5a and Clathrin proteins reduced cellular uptake of MM-ZC and conclusively demonstrated the involvement of Clathrin-Rab5a pathways in MM-ZC endocytosis. Furthermore, both Rab5a and Clathrin reciprocally affected their association with MM-ZC when we depleted their proteins by siRNAs. Additionally, the loss of Rab5a decreased the Syntaxin-6 association with MMZC but not vice versa. Conversely, MM-ZC treatment enhanced the association between Clathrin and Rab5a.ConclusionOverall, the current study provides valuable insights into the cellular uptake and intracellular trafficking of MM-ZC in myeloma cells. Identifying these mechanisms and molecular players involved in MM-ZC uptake contributes to a better understanding of the delivery and potential applications of cell-specific Zip-Codes in gene delivery and drug targeting in cancer research.
| S-EPMC10920344 | biostudies-literature
Project description:Canonical fibroblast growth factors (FGFs) activate FGF receptors (FGFRs) through paracrine or autocrine mechanisms in a process that requires cooperation with heparan sulfate proteoglycans, which function as co-receptors for FGFR activation. By contrast, endocrine FGFs (FGF19, FGF21 and FGF23) are circulating hormones that regulate critical metabolic processes in a variety of tissues. FGF19 regulates bile acid synthesis and lipogenesis, whereas FGF21 stimulates insulin sensitivity, energy expenditure and weight loss. Endocrine FGFs signal through FGFRs in a manner that requires klothos, which are cell-surface proteins that possess tandem glycosidase domains. Here we describe the crystal structures of free and ligand-bound β-klotho extracellular regions that reveal the molecular mechanism that underlies the specificity of FGF21 towards β-klotho and demonstrate how the FGFR is activated in a klotho-dependent manner. β-Klotho serves as a primary 'zip code'-like receptor that acts as a targeting signal for FGF21, and FGFR functions as a catalytic subunit that mediates intracellular signalling. Our structures also show how the sugar-cutting enzyme glycosidase has evolved to become a specific receptor for hormones that regulate metabolic processes, including the lowering of blood sugar levels. Finally, we describe an agonistic variant of FGF21 with enhanced biological activity and present structural insights into the potential development of therapeutic agents for diseases linked to endocrine FGFs.
| S-EPMC6594174 | biostudies-literature
Project description:Pharmacogenetics-a branch of precision medicine-holds the promise of becoming a novel tool to reduce the social and healthcare burdens of cardiovascular disease (CVD) and coronary artery disease (CAD) in diabetes. The improvement in cardiovascular risk stratification resulting from adding genetic characteristics to clinical data has moved from the modest results obtained with genetic risk scores based on few genetic variants, to the progressively better performances of polygenic risk scores based on hundreds to millions of variants (CAD-PGRS). Similarly, over the past few years, the number of studies investigating the use of CAD-PGRS to identify different responses to cardio-preventive treatment has progressively increased, yielding striking results for lipid-lowering drugs such as proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and statins. The use of CAD-PGRS to stratify patients based on their likely response to diabetes-specific interventions has been less successful, but promising results have been obtained with regard to specific genetic variants modulating the effects of interventions such as intensive glycemic control and fenofibrate. The finding of diabetes-specific CAD-loci, such as GLUL, has also led to the identification of promising new targets that might hopefully result in the development of specific therapies to reduce CVD burden in patients with diabetes. As reported in consensus statements from international diabetes societies, some of these pharmacogenetic approaches are expected to be introduced in clinical practice over the next decade. For this to happen, in addition to continuing to improve and validate these tools, it will be necessary to educate physicians and patients about the opportunities and limits of pharmacogenetics, as summarized in this review.
| S-EPMC9504677 | biostudies-literature