Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with Clustering Analysis.

The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.

Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights.

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so-called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use USA county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis.

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3 years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata.

The spread of American Bullfrog has a significant impact on the surrounding ecosystem. It is important to study the mechanisms of their spreading so that proper mitigation can be applied when needed. This study analyzes data from national surveys on bullfrog distribution. We divided the data into 25 regional clusters. To assess the spread within each cluster, we constructed temporal sequences of spatial distribution using the agglomerative clustering method. We employed Elementary Cellular Automata (ECA) to identify rules governing the changes in spatial patterns. Each cell in the ECA grid represents either the presence or absence of bullfrogs based on observations. For each cluster, we counted the number of presence location in the sequence to quantify spreading intensity. We used a Convolutional Neural Network (CNN) to learn the ECA rules and predict future spreading intensity by estimating the expected number of presence locations over 400 simulated generations. We incorporated environmental factors by obtaining habitat suitability maps using Maxent. We multiplied spreading intensity by habitat suitability to create an overall assessment of bullfrog invasion risk. We estimated the relative spreading assessment and classified it into four categories: rapidly spreading, slowly spreading, stable populations, and declining populations.

Age- and sex-related changes in motor functions: a comprehensive assessment and component analysis.

Age-related motor impairments often cause caregiver dependency or even hospitalization. However, comprehensive investigations of the different motor abilities and the changes thereof across the adult lifespan remain sparse. We, therefore, extensively assessed essential basic and complex motor functions in 444 healthy adults covering a wide age range (range 21 to 88 years). Basic motor functions, here defined as simple isolated single or repetitive movements in one direction, were assessed by means of maximum grip strength (GS) and maximum finger-tapping frequency (FTF). Complex motor functions, comprising composite sequential movements involving both proximal and distal joints/muscle groups, were evaluated with the Action Research Arm Test (ARAT), the Jebsen-Taylor Hand Function Test (JTT), and the Purdue Pegboard Test. Men achieved higher scores than women concerning GS and FTF, whereas women stacked more pins per time than men during the Purdue Pegboard Test. There was no significant sex effect regarding JTT. We observed a significant but task-specific reduction of basic and complex motor performance scores across the adult lifespan. Linear regression analyses significantly predicted the participants' ages based on motor performance scores (R2 = 0.502). Of note, the ratio between the left- and right-hand performance remained stable across ages for all tests. Principal Component Analysis (PCA) revealed three motor components across all tests that represented dexterity, force, and speed. These components were consistently present in young (21-40 years), middle-aged (41-60 years), and older (61-88 years) adults, as well as in women and men. Based on the three motor components, K-means clustering analysis differentiated high- and low-performing participants across the adult life span. The rich motor data set of 444 healthy participants revealed age- and sex-dependent changes in essential basic and complex motor functions. Notably, the comprehensive assessment allowed for generating robust motor components across the adult lifespan. Our data may serve as a reference for future studies of healthy subjects and patients with motor deficits. Moreover, these findings emphasize the importance of comprehensively assessing different motor functions, including dexterity, force, and speed, to characterize human motor abilities and their age-related decline.

K-Means clustering of dermatology journals: comparing the distribution of "free-to-publish" and "pay-to-publish" models.

This study investigates the impact of Free-to-Publish (F2P) versus Pay-to-Publish (P2P) models in dermatology journals, focusing on their differences in terms of journal metrics, Article Processing Charges (APCs), and Open Access (OA) status. Utilizing k-means clustering, the research evaluates dermatology journals based on SCImago Journal Rankings (SJR), H-Index, and Impact Factor (IF), and examines the correlation between these metrics, APCs, and OA status (Full or Hybrid). Data from the SCImago Journal Rank and Journal Citation Report databases were used, and metrics from 106 journals were normalized and grouped into three tiers.The study reveals a higher proportion of F2P journals, especially in higher-tier journals, indicating a preference for quality-driven research acceptance. Conversely, a rising proportion of P2P journals in lower tiers suggests potential bias towards the ability to pay. This disparity poses challenges for researchers from less-funded institutions or those early in their careers. The study also finds significant differences in APCs between F2P and P2P journals, with hybrid OA being more common in F2P.Conclusively, the study highlights the disparities in dermatology journals between F2P and P2P models and underscores the need for further research into authorship demographics and institutional affiliations in these journals. It also establishes the effectiveness of k-means clustering as a standardized method for assessing journal quality, which can reduce reliance on potentially biased individual metrics.

Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges.

OBJECTIVES: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data-driven approach for bone quality classification. MATERIALS AND METHODS: Twenty-six expert clinicians were asked to classify 110 CBCT cross-sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1). Intra- and inter-examiner agreement analyses were performed using Cohen's kappa coefficient (CK) and Fleiss' kappa coefficient (FK), respectively. Additionally, radiomic features extraction was performed from 3D edentulous ridge blocks derived from the same 110 CBCTs, and unsupervised clustering using 3 different clustering methods was used to identify patterns in the obtained data. RESULTS: Intra-examiner agreement between T0 and T1 was weak (CK 0.515). Inter-examiner agreement at both time points was minimal (FK at T0: 0.273; FK at T1: 0.243). The three different unsupervised clustering methods based on radiomic features aggregated the 110 CBCTs in three groups in the same way. CONCLUSIONS: The results showed low agreement among clinicians when using L&Z classification, indicating that the system may not be as reliable as previously thought. The present study suggests the possible application of a reproducible data-driven approach based on radiomics for the classification of edentulous alveolar ridges, with potential implications for improving clinical outcomes. Further research is needed to determine the clinical significance of these findings and to develop more standardized and accurate methods for assessing bone quality of edentulous alveolar ridges.

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China.

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

Clustering regions with dynamic time warping to model obesity prevalence disparities in the United States.

Current methods for clustering adult obesity prevalence by state focus on creating a single map of obesity prevalence for a given year in the United States. Comparing these maps for different years may limit our understanding of the progression of state and regional obesity prevalence over time for the purpose of developing targeted regional health policies. In this application note, we adopt the non-parametric Dynamic Time Warping method for clustering longitudinal time series of obesity prevalence by state. This method captures the lead and lag relationship between the time series as part of the temporal alignment, allowing us to produce a single map that captures the regional and temporal clusters of obesity prevalence from 1990 to 2019 in the United States. We identify six regions of obesity prevalence in the United States and forecast future estimates of obesity prevalence based on ARIMA models.

Finding neural correlates of depersonalisation/derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores.

Mental health disorders are typically diagnosed based on subjective reports (e.g., through questionnaires) followed by clinical interviews to evaluate the self-reported symptoms. Therefore, considering the interconnected nature of psychiatric disorders, their accurate diagnosis is a real challenge without indicators of underlying physiological dysfunction. Depersonalisation/derealisation disorder (DPD) is an example of dissociative disorder affecting 1-2 % of the population. DPD is characterised mainly by persistent disembodiment, detachment from surroundings, and feelings of emotional numbness, which can significantly impact patients' quality of life. The underlying neural correlates of DPD have been investigated for years to understand and help with a more accurate and in-time diagnosis of the disorder. However, in terms of EEG studies, which hold great importance due to their convenient and inexpensive nature, the literature has often been based on hypotheses proposed by experts in the field, which require prior knowledge of the disorder. In addition, participants' labelling in research experiments is often derived from the outcome of the Cambridge Depersonalisation Scale (CDS), a subjective assessment to quantify the level of depersonalisation/derealisation, the threshold and reliability of which might be challenged. As a result, we aimed to propose a novel end-to-end EEG processing pipeline based on deep neural networks for DPD biomarker discovery, which requires no prior handcrafted labelled data. Alternatively, it can assimilate knowledge from clinical outcomes like CDS as well as data-driven patterns that differentiate individual brain responses. In addition, the structure of the proposed model targets the uncertainty in CDS scores by using them as prior information only to guide the unsupervised learning task in a multi-task learning scenario. A comprehensive evaluation has been done to confirm the significance of the proposed deep structure, including new ways of network visualisation to investigate spectral, spatial, and temporal information derived in the learning process. We argued that the proposed EEG analytics could also be applied to investigate other psychological and mental disorders currently indicated on the basis of clinical assessment scores. The code to reproduce the results presented in this paper is openly accessible at https://github.com/AbbasSalami/DPD_Analysis.

Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals.

BACKGROUND: The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. METHODS: Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. RESULTS: Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. DISCUSSION: Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening.

A novel integrated Urban flood risk assessment approach coupling GeoDetector-Dematel and clustering method.

Urban flood risk assessment plays a crucial role in disaster prevention and mitigation. A scientifically accurate assessment and risk stratification method are of paramount importance for effective flood risk management. This study aims to propose a comprehensive urban flood risk assessment approach by coupling GeoDetector-Dematel and Clustering Method to enhance the accuracy of urban flood risk evaluation. Based on simulation results from hydraulic models and existing literature, the research established a set of urban flood risk assessment indicators comprising 10 metrics across two dimensions: hazard factors and vulnerability factors, among which vulnerability factors include exposure factors, sensitivity factors, and adaptability factors. Subsequently, the research introduced the GeoDetector-Dematel method to determine indicator weights, significantly enhancing the scientific rigor and precision of weight calculation. Finally, the research employed the K-means clustering method to risk zonation, providing a more scientifically rational depiction of the spatial distribution of urban flood risks. This novel comprehensive urban flood risk assessment method was applied in the Fangzhuang area of Beijing. The results demonstrated that this integrated approach effectively enhances the accuracy of urban flood risk assessment. In conclusion, this research offers a new methodology for urban flood risk assessment and contributes to decision-making in disaster prevention and control measures.

An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market.

CO2 emissions play a crucial role in international politics. Countries enter into agreements to reduce the amount of pollution emitted into the atmosphere. Energy generation is one of the main contributors to pollution and is generally considered the main cause of climate change. Despite the interest in reducing CO2 emissions, few studies have focused on investigating energy pricing technologies. This article analyzes the technologies used to meet the demand for electricity from 2016 to 2021. The analysis is based on data provided by the Spanish Electricity System regulator, using statistical and clustering techniques. The objective is to establish the relationship between the level of pollution of electricity generation technologies and the hourly price and demand. Overall, the results suggest that there are two distinct periods with respect to the technologies used in the studied years, with a trend toward the use of cleaner technologies and a decrease in power generation using fossil fuels. It is also surprising that in the years 2016 to 2018, the most polluting technologies offered the cheapest prices.

Analyzing Multiple Social Determinants of Health Using Different Clustering Methods.

Social determinants of health (SDoH) have become an increasingly important area to acknowledge and address in healthcare; however, dealing with these measures in outcomes research can be challenging due to the inherent collinearity of these factors. Here we discuss our experience utilizing three statistical methods-exploratory factor analysis (FA), hierarchical clustering, and latent class analysis (LCA)-to analyze data collected using an electronic medical record social risk screener called Protocol for Responding to and Assessing Patient Assets, Risks, and Experience (PRAPARE). The PRAPARE tool is a standardized instrument designed to collect patient-reported data on SDoH factors, such as income, education, housing, and access to care. A total of 2380 patients had complete PRAPARE and neighborhood-level data for analysis. We identified a total of three composite SDoH clusters using FA, along with four clusters identified through hierarchical clustering, and four latent classes of patients using LCA. Our results highlight how different approaches can be used to handle SDoH, as well as how to select a method based on the intended outcome of the researcher. Additionally, our study shows the usefulness of employing multiple statistical methods to analyze complex SDoH gathered using social risk screeners such as the PRAPARE tool.

TreeKDE: clustering multivariate data based on decision tree and using one-dimensional kernel density estimation.

In this paper, we present an algorithm for clustering multidimensional data, which we named TreeKDE. It is based on a tree structure decision associated with the optimization of the one-dimensional kernel density estimator function constructed from the orthogonal projections of the data on the coordinate axes. Among the main features of the proposed algorithm, we highlight the automatic determination of the number of clusters and their insertion in a rectangular region. Comparative numerical experiments are presented to illustrate the performance of the proposed algorithm and the results indicate that the TreeKDE is efficient and competitive when compared to other algorithms from the literature. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field, which can be used as a basis for its improvement, and also for the development of new clustering algorithms based on the association between decision tree and kernel density estimator.

Spatial and Temporal Drug Usage Patterns in Wastewater Correlate with Socioeconomic and Demographic Indicators in Southern Nevada.

Evaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40×10-10) and opioids, particularly norfentanyl (pFDR =1.66×10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64×10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.

Comprehensive assessment of the combined impact of dyslipidemia and inflammation on chronic kidney disease development: A prospective cohort study.

BACKGROUND: There remains a limited comprehensive understanding of how dyslipidemia and chronic inflammation collectively contribute to the development of chronic kidney disease (CKD). OBJECTIVE: We aimed to identify clusters of individuals with five variables, including lipid profiles and C-reactive protein (CRP) levels, and to assess whether the clusters were associated with incident CKD risk. METHODS: We used the Korean Genome and Epidemiology Study-Ansan and Ansung data. K-means clustering analysis was performed to identify distinct clusters based on total cholesterol, triglyceride, non-high-density lipoprotein (HDL)-C, HDL-C, and CRP levels. Cox proportional hazards models were used to examine the association between incident CKD risk and the different clusters. RESULTS: During the mean 10-year follow-up period, CKD developed in 1,645 participants (690 men and 955 women) among a total of 8,053 participants with a mean age of 51.8 years. Four distinct clusters were identified: C1, low cholesterol group (LC); C2, high-density lipoprotein cholesterol group (HC); C3, insulin resistance and inflammation group (IIC); and C4, dyslipidemia and inflammation group (DIC). Cluster 4 had a significantly higher risk of incident CKD compared to clusters 2 (hazard ratio (HR) 1.455 [95% confidence interval (CI) 1.234-1.715]; p < 0.001) and cluster 1 (HR 1.264 [95% CI 1.067-1.498]; p = 0.007) after adjusting for confounders. Cluster 3 had a significantly higher risk of incident CKD compared to clusters 2 and 1. CONCLUSION: Clusters 4 and 3 had higher risk of incident CKD compared to clusters 2 and 1. The combination of dyslipidemia with inflammation or insulin resistance with inflammation appears to be pivotal in the development of incident CKD.

Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties.

COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.

Validation of an early vascular aging construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study.

BACKGROUND: Cardiovascular diseases (CVDs) remain a major global health concern, necessitating advanced risk assessment beyond traditional factors. Early vascular aging (EVA), characterized by accelerated vascular changes, has gained importance in cardiovascular risk assessment. METHODS: The EVasCu study in Spain examined 390 healthy participants using noninvasive measurements. A construct of four variables (Pulse Pressure, Pulse Wave Velocity, Glycated Hemoglobin, Advanced Glycation End Products) was used for clustering. K-means clustering with principal component analysis revealed two clusters, healthy vascular aging (HVA) and early vascular aging (EVA). External validation variables included sociodemographic, adiposity, glycemic, inflammatory, lipid profile, vascular, and blood pressure factors. RESULTS: EVA cluster participants were older and exhibited higher adiposity, poorer glycemic control, dyslipidemia, altered vascular properties, and higher blood pressure. Significant differences were observed for age, smoking status, body mass index, waist circumference, fat percentage, glucose, insulin, C-reactive protein, diabetes prevalence, lipid profiles, arterial stiffness, and blood pressure levels. These findings demonstrate the association between traditional cardiovascular risk factors and EVA. CONCLUSIONS: This study validates a clustering model for EVA and highlights its association with established risk factors. EVA assessment can be integrated into clinical practice, allowing early intervention and personalized cardiovascular risk management.