Principal Component Analysis Model Research Articles

Association between Heavy Metals and Trace Elements in Cancerous and Non-cancerous Tissues with the Risk of Colorectal Cancer Progression in Northwest China.

Alterations in heavy metals and trace element levels may be associated with various cancers. However, the role of this interaction in colorectal cancer (CRC) progression is unclear. In recent years, Principal Component Analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) models have provided new ideas for analyzing the effects of metal mixtures on CRC progression. Herein, we assessed the differences in the levels of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), selenium (Se), and zinc (Zn) in tumors and adjacent healthy tissues, to investigate the relationship between heavy metals/trace elements and CRC progression. Surgical samples of CRC and noncancerous tissues were collected, and trace metal levels were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression, PCA, and BKMR models were used to investigate the relationship between heavy metals and trace elements and the degree of tumor differentiation and lymph node metastasis in CRC. Cancer tissues showed lower As, Cd, Co, and Cr concentrations, and higher Se concentrations than healthy tissues (P < 0.05). In addition, CRC patients with poorly differentiated tumors and/or positive lymph node metastases had lower levels of Cd, Zn, Cu, and Se (P < 0.05). Logistic regression showed that single metal concentration was negatively correlated with CRC progression. PCA and BKMR models also showed that the metal mixture concentration was negatively correlated with CRC progression, with Cd contributing the most. Overall, changes in heavy metal and trace element levels may be related to the development of CRC; however, further mechanistic studies are required.

The Positive Association between Grit and Mental Toughness, Enhanced by a Minimum of 75 Minutes of Moderate-to-Vigorous Physical Activity, among US Students

Drawing from the 2015 Gucciardi et al.’s mental toughness (MT) framework, this study examines the association between grit and MT in US college students, while considering the moderating role of at least 75 min of moderate-to-vigorous physical activity (MVPA) based on recommendations from the American College of Sports Medicine. We administered the Grit-S Scale and the Mental Toughness Index in two samples of a total of 340 US undergraduate student-athletes and graduate students. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was employed to ensure internal validity, while statistical procedures including principal component analysis and regression models were utilized to analyze the collected data. A weighted component combining grit and the interaction between MVPA and grit significantly predicted MT, explaining 23% of its variability. Drawing from a specific conceptual framework, this study provides novel insights into the relationship between grit, engagement in at least 75 min of MVPA per week, and MT among US collegiate students. The findings support a positive association between grit, MVPA, and both MT and a specific component of MT, highlighting the significance of these factors in enhancing performance and suggesting potential implications for future research and practical applications in the field.

DIY google trends indicators in social sciences: A methodological note

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples’ search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Optimizing indoor air quality: Investigating particulate matter exposure in household kitchens and source identification

AbstractIndoor environments pose a significant health risk due to exposure to particulate matter (PM), leading to various diseases and premature deaths, and raising public concerns about indoor air quality. Household kitchens are a major contributor to indoor air pollution, emitting particulate matter of varying sizes during cooking, which disperses throughout the house. Understanding the extent of PM exposure and identifying its sources is crucial. This study assesses PM presence in Kuwaiti household kitchens, measuring concentrations of different‐sized particles (PM 0.3, 0.5, 1.0, 2.5, 5.0, and 10 μm) under various conditions. Measurements were taken for 24 h using a particle/mass monitor device, considering different ventilation scenarios, cooking oils, and methods. Spearman's rank correlation and indoor/outdoor (I/O) ratios investigate the correlation between indoor and outdoor PM exposure. Principal component analysis (PCA) models identify specific sources contributing to PM exposure. A questionnaire survey in Kuwaiti households determines primary kitchen sources during cooking. Results show that kitchens with ventilation systems consistently reduce PM concentrations. PM emissions during cooking vary based on factors like cooking method, energy type, cooking oil, food, additives, temperature, and ventilation. The cooking stove is the primary emission source, but other household activities contribute. This study enhances our understanding of influential factors affecting cooking emissions and their concentrations, contributing valuable insights into mitigating indoor air pollution.

PRELIMINARY MODEL OF POTENTIAL AREAS FOR RESTORATION FOR THE INTER-ANDEAN VALLEY OF CAUCA RIVER (COLOMBIA, SOUTH AMERICA) BASED ON HABITAT SUITABILITY MODELS

Tropical dry forests (TDF) are highly susceptible to land degradation. The inter-Andean Valley of the Cauca River (IVCR) has the most fragmented Colombian dry forests, and their restoration is essential. Here, potential areas for restoration were identified using a habitat suitability modeling (HSM) approach. TDF vascular plants and bioclimatic predictors were used. Species were selected based on threatened status, endemism, and priority level for conservation. Two sets of predictors were chosen using Variance Inflation Factor (VIF) and Principal Component Analysis (PCA). Then, with a maximum entropy algorithm, PCA and VIF models were projected for the selected species. These models were evaluated via true skill statistics (TSS) and area under the curve (AUC) statistical metrics. Models with good performance (TSS, AUC, standard deviation, variance) were ensembled, and a preliminary model where areas with suitable bioclimatic conditions for the selected species were generated. Results show that nearly 45 % of the IVCR has suitable conditions for the selected species. Although potential conflicts may arise in areas under permanent or semipermanent crops which represent more than 80 % of the IVCR, cropland mosaics, and natural and seminatural land covers might provide alternative solutions to reduce the land-use conflict. The potential areas for restoration identified in this study may provide a comprehensive framework for environmental impact and regional risk assessments related to the current land use and land cover change dynamics. Also, they may provide relevant information for designing landscape restoration programs as an adaptive strategy toward climate change.

Apple Varieties Classification Using Deep Features and Machine Learning

Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensing devices. Primarily, this study focused on classifying apple varieties using machine learning techniques. Firstly, to discern how different convolutional neural network (CNN) architectures handle different apple varieties, transfer learning approaches, using popular seven CNN architectures (VGG16, VGG19, InceptionV3, MobileNet, Xception, ResNet150V2 and DenseNet201), were adopted, taking advantage of the pre-trained models, and it was found that DenseNet201 had the highest (97.48%) classification accuracy. Secondly, using the DenseNet201, deep features were extracted and traditional Machine Learning (ML) models: support vector machine (SVM), multi-layer perceptron (MLP), random forest classifier (RFC) and K-nearest neighbor (KNN) were trained. It was observed that the classification accuracies were significantly improved and the best classification performance of 98.28% was obtained using SVM algorithms. Finally, the effect of dimensionality reduction in classification performance, deep features, principal component analysis (PCA) and ML models was investigated. MLP achieved an accuracy of 99.77%, outperforming SVM (99.08%), RFC (99.54%) and KNN (91.63%). Based on the performance measurement values obtained, our study achieved success in classifying apple varieties. Further investigation is needed to broaden the scope and usability of this technique, for an increased number of varieties, by increasing the size of the training data and the number of apple varieties.

Stratification of patients with idiopathic Rem Behavior Disease patients (iRBD) based on principal component analysis and multivariate machine learning models: an useful statistical tool for clinical decision making in Parkinson’s disease

Stratification of patients with idiopathic Rem Behavior Disease patients (iRBD) based on principal component analysis and multivariate machine learning models: an useful statistical tool for clinical decision making in Parkinson’s disease

Application of ATR-FTIR spectroscopy and chemometrics for the forensic discrimination of aged peripheral and menstrual bloodstains

Accurately distinguishing between peripheral and menstrual bloodstains at crime scenes is a major challenge in forensic practice. The time since deposition (TSD) of bloodstains at the crime scene also plays an essential role in determining the postmortem interval or injury time. In this study, we used attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometric methods to differentiate peripheral and menstrual bloodstains considering the TSD and menstrual phase.The results of the principal component analysis (PCA) model showed that the fresh peripheral and menstrual bloodstains were relatively easy to distinguish, while the aged samples (2 W, 4 W, and 6 W TSD) caused a certain level of spectral signature convergence in the spectral features. Subsequently, the partial least squares discriminant analysis (PLS-DA) model and the genetic algorithm combined with PLS-DA (GA-PLSDA) model were constructed considering the TSD.The accuracy of internal cross-validation and external validation of the PLS-DA model demonstrated internal and external classification abilities of 88.3 % and 90.0 % accuracy, respectively. Additionally, the results of GA-PLSDA model showed that the accuracy of external and internal validation was 89.2 % and 90.0 %, respectively.The PCA, PLS-DA, and GA-PLSDA models showed that the critical differences between peripheral and menstrual bloodstains were in protein, phosphoric acid and glucose content. This preliminary study suggests that ATR-FTIR spectroscopy combined with chemometrics may be a potential method to distinguish between peripheral and menstrual bloodstains using complex conditions and provides support for further studies.

Fault Diagnosis of Wastewater Treatment Processes Based on CPSO-DKPCA

The wastewater treatment process (WWTP) is one of the most common links in chemical plants. However, the testing for diagnosing faults in wastewater treatment plants is expensive and time-consuming. Due to strong nonlinearity and variable autocorrelation, traditional WWTP diagnostic methods based on principal component analysis (PCA) can lead to low fault detection rates (FDR) or difficulty in determining the root cause of faults. In this paper, an improved dynamic kernel principal component analysis (DKPCA) and Granger causality (GC) analysis model that uses chaotic particle swarm optimization (CPSO) to detect WWTP and locate the root causes of faults is proposed. First, a kernel function is introduced to map a nonlinear matrix to a linear space. Then, the training data are extended through a time lag constant to solve the problem of nonlinear and variable autocorrelation in WWTP. Moreover, a novel fault candidate variables selection method, together with GC, is introduced to locate the root variables of the fault. The CPSO algorithm is employed to optimize DKPCA's kernel function parameters, enhancing the accuracy of fault monitoring and diagnosis models. Compared with traditional methods, the proposed method has a better fault detection rate, achieving 95.83% and 93.33% fault detection rates in simulated and real WWTP, respectively.

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures

Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures.

Understanding the Determinants of Lane Inefficiency at Fully Actuated Intersections: An Empirical Analysis

As urban traffic challenges intensify, the growing interest for fully actuated control systems in intersection management is on the rise due to their capacity to adapt to dynamic traffic demands. These systems play a crucial role in sustainable traffic solutions, significantly reducing delays and emissions and enhancing overall system efficiency. The optimal performance of these systems relies on effectively facilitating vehicle discharge at the saturation flow rate throughout the green period. This study introduces a new parameter, lane inefficiency, evaluating vehicle discharge effectiveness by comparing saturation flow rate with instantaneous discharge for each green period. It provides a comprehensive assessment of green utilization for specific lanes. This study also explores the impact of signal control system parameters and traffic flow characteristics on lane inefficiency using principal component analysis (PCA) and multiple linear regression models. This approach holistically evaluates how both signal control system and traffic flow parameters collectively influence efficient green period utilization. The findings emphasize the impact of critical factors on lane inefficiency, including green time, the proportion of total unused green time to green time, total unused green time, the percentage of heavy vehicles in departing traffic, the ratio of effective green time to cycle time, the total time headways of the first four vehicles, and queue length. Decision makers need to pay due attention to these parameters to enhance intersection performance and foster a more sustainable urban transportation network.

Multi-layered physical factors govern mercury release from soil: Implications for predicting the environmental fate of mercury

Despite the recognised risks of human exposure to mercury (Hg), the drivers of gaseous elemental mercury (GEM) emissions from the soil remain understudied. In this study, we aimed to identify the environmental parameters that affect the GEM flux from soil and derive the correlations between environmental parameters and GEM flux. Principal component analysis (PCA), factor analysis (FA), and structural equation modelling (SEM) were performed on samples from forest and non-forest sites. The associated results revealed the impact of each environmental parameter on GEM flux, either due to the interaction between the parameters or as a coherent set of parameters. An introductory correlation matrix examining the relationship between two components showed a negative correlation between GEM flux and atmospheric pressure at the two sites, as well as strong correlations between atmospheric pressure and soil temperature. In cases of non-forest open sites with no trees, the PCA and FA results were consistent, indicating that atmospheric pressure, solar irradiance, and soil moisture—defined as primary causality—are largely independent drivers of GEM flux. In contrast, the PCA and FA results for the forest areas with high humidity, tree coverage, and shade were inconsistent, confirming the hypothesis that primary causality affects GEM flux rather than consequent parameters driven by primary causality, such as air and soil temperature and atmospheric humidity. The SEM results provided further evidence for primary and consequent causality as crucial drivers of the GEM flux. This study demonstrates the importance of key primary parameters, such as atmospheric pressure, solar irradiance, and soil moisture content, that can be used to predict mercury release from soils, as well as the importance of consequent parameters, such as air and soil temperature and atmospheric humidity. Monitoring the magnitude of these environmental parameters alone may facilitate the estimation of mercury release from soils and be useful for detailed modelling of soil–air Hg exchange.

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

Sensory-Guided Establishment of Sensory Lexicon and Investigation of Key Flavor Components for Goji Berry Pulp.

Many customers prefer goji berry pulp, well-known for its high nutritional content, over fresh goji berries. However, there is limited research on its sensory lexicon and distinctive flavor compounds. This study focused on developing a sensory lexicon for goji berry pulp and characterizing its aroma by sensory and instrumental analysis. Sensory characteristics of goji berry pulp were evaluated by our established lexicon. A total of 83 aromatic compounds in goji berry pulp were quantified using HS-SPME-GC-Orbitrap-MS. By employing OAV in combination, we identified 17 aroma-active compounds as the key ingredients in goji berry pulp. Then, we identified the potentially significant contributors to the aroma of goji berry pulp by combining principal component analysis and partial least squares regression (PLSR) models of aroma compounds and sensory attributes, which included 3-ethylphenol, methyl caprylate, 2-hydroxy-4-methyl ethyl valerate, benzeneacetic acid, ethyl ester, hexanal, (E,Z)-2,6-nonadienal, acetylpyrazine, butyric acid, 2-ethylhexanoic acid, 2-methyl-1-propanol, 1-pentanol, phenylethyl alcohol, and 2-nonanone. This study provides a theoretical basis for improving the quality control and processing technology of goji berry pulp.

Parking choice behavior of urban village residents considering parking risk: An integrated modeling approach

With rising car ownership, the demand for parking and land for parking is increasing rapidly. This strains urban land resources and accentuates the performance of urban villages, characterized by limited space, high building density, narrow roadways, and few parking spaces, making parking challenging. To address this, the study focuses on guiding residents' parking choices from a parking management perspective, aiming to reduce the demand for parking spots among village members. In April 2021, a survey was conducted in Shenzhen City, where urban villages are widely dispersed. The survey revealed that residents primarily rely on on-street parking within the village, off-street parking within the village, and on-street parking outside the village. Due to the unquantifiable nature of the latent variable of parking risk and the impact of individual heterogeneity, this paper adopts a discrete selection model that integrates principal component analysis and mixed logit model (PCA-ML). The model demonstrated a good fit, indicating that parking charges, search time, and walking distance to home have negative impacts on parking choices. In addition, street parking outside the village is positively influenced by the parking risk attitude variable, while village parking remains unaffected. This study assists authoritative departments in understanding the factors that influence parking management in urban villages, thereafter, adopting targeted guidance to direct residents in urban villages to park their vehicles, mitigating the current situation of parking difficulties in urban villages. Furthermore, it contributes to achieve equitable and sustainable development of urban villages.

A Study on the Enhancement of English Proficiency of Contemporary College Students by Intercultural Communication in the Background of the Internet

Abstract The process of English learning is closely related to the process of intercultural communication. This paper addresses the problem of quantifying the intercultural communication ability of college students, designs the dimensions of intercultural competence, establishes the factor indicators of intercultural competence, and constructs a fuzzy comprehensive evaluation method for the assessment of intercultural competence. A regression model is created to investigate the correlation between achievement and English proficiency. The nonparametric model of principal component analysis is used to optimize the problem of the stratification effect of general local linear regression, and the principal component-based local linear regression method (PC-LLR) is proposed. The second-year undergraduate students of different majors in X university were selected as the research subjects, and an overall assessment of intercultural communication competence was made to explore the differences among different student groups. Based on the correlation analysis between intercultural competence and English proficiency, PC-LLR regression analysis was launched. The Beta value interval of the regression was [0.0535, 5.0292], all of which were positive, and the four variables of knowledge, attitude, skill, and awareness had a positive predictive effect on the dependent variable. Intercultural knowledge has a relatively high Beta value, which makes it a strong predictor of English proficiency.

Automation Model Development for School Reaccreditation of Early Childhood Education

As stated in the Research and Development Objectives 2020-2024 by BSKAP, it was agreed on the need to implement a correct and credible quality monitoring and evaluation system. For Primary and Secondary Education, it is determined that 100% of education units will carry out Competency Assessment (AK) and Character Surveys (SK) starting in 2021 by first developing and preparing relevant measuring tools along with indicators of AK-SK readiness in the future, its implementation in 2021 while trying implement the overall accreditation process automation policy. Therefore, performance assessment in these units requires other proxies of learning indicators which are considered to have functions equivalent to competency assessments and character surveys. Instead of direct field visits, if correct, mathematical modeling can be performed to derive measurement proxies derived from the PPA or IPV variables or a combination of both. Automation modeling has been applied to approximately 5,000 school samples by applying three alternative methods, namely Principal Component Analysis (PCA), Partial Least Square (PLS) and Confirmatory Factor Analysis (CFA). PCA modeling was successfully used on 49 predictors without a response variable (Y); ii) PLS modeling was successfully applied to 49 predictors involving response variables; iii) CFA modeling has been successfully carried out on PPA and IPV one by one, because the combined modeling has not succeeded in producing an adequate model in the form of goodness of fit. Keywords: accreditation, assessments, early childhood education, statistical modeling

Implement an Intrusion Detection System Utilizing Machine Learning and Principal Component Analysis

The ever-evolving cybersecurity sector requires robust intrusion detection systems (IDS). Traditional rules-based measures are no longer sufficient due to the complexity of cyber threats, requiring new approaches. This study presents the architecture of an intrusion detection system combining machine learning and principal component analysis (PCA) to increase network security. A network traffic classification system was built and tested on the NSL-KDD dataset and used PCA for dimensionality reduction. The results were cross-validated to reduce overfitting and ensure generalizability of the model. Low-variance precision refers to the consistency of the cross-validation fold. The combination of PCA and machine learning models exceeds previous studies with an F1 score for the random forest model of over 99%. The study improves intrusion detection and network protection against cyber-attacks.

Interleukin-6, tumor necrosis factor-α, and high-sensitivity C-reactive protein for optimal immunometabolic profiling of the lifestyle-related cardiorenal risk.

The present study aimed to identify optimal inflammatory biomarkers involved in cardiorenal risk in response to major lifestyle factors. One hundred and twenty-nine adults aged 35-77 years participated voluntarily from 2017 to 2019 (Córdoba, Argentina) in a cross-sectional study to collect sociodemographic, clinical, and lifestyle data. Blood biomarkers (different cytokines, monocyte chemoattractant protein-1 [MCP-1], and high-sensitivity C-reactive protein [hs-CRP]) were measured using standard methods and then evaluated by principal component analysis and structural equation modeling (SEM) according to Mediterranean diet adherence, physical activity level, and waist circumference, while cardiorenal risk involved blood diastolic pressure, HDL-cholesterol, triacylglycerols, creatinine, and glycosylated hemoglobin. A principal component included TNF-α (tumor necrosis factor-alpha), IL-8 (interleukin-8), IL-6 (interleukin-6), hs-CRP, and MCP-1, with absolute rotated factor loadings >0.10. SEM showed that IL-6 (β=0.38, 95 % IC=0.08-0.68), hs-CRP (β=0.33, 95 % IC=0.17-0.48), and TNF-α (β=0.22, 95 % IC=0.11-0.32) were the mediators that better explained an inflammatory profile positively related to waist circumference (β=0.77, 95 % IC=0.61-0.94). Moreover, this profile was associated with an increased cardiorenal risk (β=0.78, 95 % IC=0.61-0.94), which was well-defined by the variable used. Immune mediators are key elements in profiling the cardiorenal risk associated with lifestyle factors, for which the combination of hs-CRP, IL-6, and TNF-α has emerged as a robust indicator. This work reaffirms the need for biomarker optimization for early diagnosis and risk assessment.