Associations between pan-immune-inflammation value and cardiovascular disease: a cross-sectional study

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Its prognostic value has been extensively validated in a variety of clinical contexts. However, its specific role in cardiovascular disease (CVD) is still not fully understood and requires additional research. Methods This study examines the relationship between PIV and CVD prevalence using information from the National Health and Nutrition Examination Survey (NHANES).This analysis included 49305 adults from NHANES 2003–2023. Logistic regression analyses were used to assess the correlation between CVD prevalence and PIV in all participants.Piecewise linear regression analyses were additionally employed to explore the correlation between PIV and CVD.Subgroup analyses were performed to further clarify the effects of other covariates on the associations. Results This study recruited a total of 49,305 adults.Elevated PIV levels were significantly associated with increased CVD prevalence (P < 0.001). In fully adjusted model, individuals in the highest Ln-PIV quartile had a 27% higher odds of CVD prevalence compared to those in the lowest quartile [OR=1.27,95 %CI(1.16，1.39), P <0.01]. Smoothed curve fitting and threshold effect analyses also showed a positive association between Ln-PIV and CVD, with an inflection point for threshold and saturation effects of 5.49. Ln-PIV was positively associated with the likelihood of developing CVD when Ln-PIV > 5.49 [OR 1.23 , 95 %CI(1.06, 1.41) , P <0.01]. The results of subgroup analyses and interaction tests indicated that BMI status had a significant effect on the relationship between PIV and CVD (P < 0.05). Conclusion Our study reveals a positive association between PIV levels and CVD, suggesting that higher PIV levels are associated with an increased likelihood of developing CVD. pan-immune-inflammation value Cardiovascular disease NHANES Figures Figure 1 Figure 2 Figure 3 Introduction As the population ages, the incidence and mortality associated with CVD continue to rise, making it a significant public health concern . While cardiovascular disease (CVD) accounted for nearly one-third of global deaths in 2021[1] ,emerging evidence indicates an escalating burden. According to the 2023 American Heart Association (AHA) report[2], global CVD mortality in 2020 increased by 18.7% compared to 2010. Thus, given the profound impact of CVD on public health and the global economy, enhancing capabilities for its early identification and prediction is imperative. Although traditional risk factors—such as diabetes, hypertension, and hyperlipidemia—are strong predictors of cardiovascular risk [3-5], they do not fully account for the observed variability in CVD risk. Emerging evidence indicates [6-8]that activation of inflammatory pathways and nutritional imbalances jointly drive the progression of atherosclerosis and contribute to CVD development. The Pan-Inflammatory Value (PIV) is a biomarker used to evaluate a patient's immune and inflammatory status, incorporating the counts of neutrophils, platelets, monocytes, and lymphocytes. Previous studies have demonstrated that PIV is linked to the prognosis of various immune- and inflammation-related conditions, including breast cancer[9], anti-neutrophil cytoplasmic antibody-associated vasculitis [10], and ST-segment elevation myocardial infarction [11]. However, there is limited research investigating the relationship between PIV and CVD in the broader adult population in the United States. This study aims to investigate the association between PIV and CVD prevalence using nationally representative data from the National Health and Nutrition Examination Survey (NHANES). By evaluating the potential of PIV as a novel predictive biomarker, we seek to provide scientific insights for developing more effective early prevention strategies against CVD. Methods Study Design and Population The National Health and Nutrition Examination Survey (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), constitutes a collection of surveys that are designed to record and evaluate the health and nutritional status of the U.S. population. In this study, an initial cohort of 98551 participants recruited from 2003 to 2023, was identified. During data wrangling and screening, 49247 participants were excluded due to missing or incomplete AAC and PIV data. Consequently, the study encompassed 49305 participants aged ≥20 years ( Fig 1 ). Definition of the PIV and CVD The calculation of PIV is as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L) [12].Ln-PIV represents the logarithmic transformation of PIV, used to address the skewed distribution of PIV.CVD status was ascertained through physician-diagnosed medical conditions using a structured questionnaire during an individual interview . Participants were classified as having CVD if they self-reported any of the following: coronary heart disease (CHD), myocardial infarction (MI), congestive heart failure (CHF), angina pectoris, or stroke.[13, 14] Definition of Covariates This study examined a comprehensive set of variables and clinical conditions. Educational attainment was categorized into three levels: less than high school, high school or equivalent, and college or higher. Body mass index (BMI) classifications included normal (<25 kg/m²), overweight (25-29.9 kg/m²), and obese (≥30 kg/m²). Alcohol consumption was defined heavy drinking (≥4 drinks/day for men; ≥3 for women), moderate drinking (≤3 drinks/day for men; ≤2 for women), and low intake (<3 drinks/day for men; <2 for women)[15]. Smoking status comprised former smokers (≥100 lifetime cigarettes, currently abstinent), current smokers (≥100 lifetime cigarettes, currently smoking daily/occasionally), and never smokers (200 mg/dL, triglycerides ≥150 mg/dL, HDL <40 mg/dL (men) or <50 mg/dL (women), or LDL ≥130 mg/dL . [16, 17]. Hypertension was defined as mean systolic BP ≥140 mmHg, diastolic BP ≥90 mmHg, antihypertensive medication use, or documented clinical history. Diabetes mellitus diagnosis required fasting glucose ≥126 mg/dL, HbA₁c ≥6.5%, or current insulin/oral hypoglycemic agent use. Statistical analyses Continuous variables were presented as means with 95% confidence intervals (CI), and categorical variables as proportions with 95% CI. To examine baseline characteristics, individuals were grouped into quartiles of PIV. Weighted generalized linear models were applied to assess the relationship between PIV and CVD. Model 1 was unadjusted, while Model 2 adjusted for demographic factors including sex, age, and ethnicity. Model 3 included additional adjustments for BMI, PIR, education level, smoking habits, alcohol consumption, hypertension, hyperlipidemia, and diabetes. Restricted cubic splines were used to explore nonlinear associations and investigate threshold effects, including inflection points, in the relationship between PIV and CVD.Additionally, we performed a weighted multivariable regression subgroup analysis after adjusting for all covariates to examine the interactions between variables such as gender, race, education level, BMI, diabetes, smoking status, hypertension, hyperlipidmia and alcohol. The results were visualized using a forest plot.Interaction p-values assessed the consistency of treatment effects across subgroups. A non-significant p-value indicated consistent effects for the overall population, while a significant p-value suggested potential outcome differences in specific subgroups.All statistical analyses were carried out using Empower Stats (v4.2) and R software (version 4.3.2), with statistical significance determined by a two-sided P-value of < 0.05. Results Basic characteristics of participants This study included a total of 49,305 participants, with an average age of 50.25 years, comprising 23,611 men (47.89%) and 25,694 women (52.11%). There were 5,162 participants (10.47%)with CVD and 44,143 participants (89.53%) without CVD. Compared to participants without CVD, those with CVD were older, had a lower PIR, and were more likely to be male, non-Hispanic White, have education beyond high school, be obese (BMI ≥ 30), have a history of smoking, drink alcohol moderately, and have hypertension and hyperlipidemia. Additionally, the PIV in the CVD group was significantly higher than that in the non-CVD group (all p < 0.0001) (Table 1) Characteristics Total(n=49305) Non-CVD(n=44143) CVD(n=5162) P-value Age 50.25 ± 17.92 48.30 ± 17.44 66.89 ± 12.54 <0.001 Gender <0.001 Male 23611 (47.89%) 20758 (47.02%) 2853 (55.27%) Female 25694 (52.11%) 23385 (52.98%) 2309 (44.73%) Race <0.001 Mexican American 7254 (14.71%) 6787 (15.37%) 468 (9.07%) Non-Hispanic Black 4563 (9.25%) 4200 (9.51%) 363 (7.03%) Non-Hispanic White 22109 (44.84%) 19217 (43.53%) 2892 (56.02%) Other Hispanic 10052 (20.39%) 8988 (20.36%) 1064 (20.61%) Other Race 5327 (10.80%) 4952 (11.22%) 375 (7.26%) PIR 2.44 ± 1.61 2.48 ± 1.62 2.15 ± 1.45 <0.001 PIV 318.29 ± 277.20 310.84 ± 265.75 381.99 ± 354.27 <0.001 Ln (PIV) 5.52 ± 0.69 5.50 ± 0.69 5.67 ± 0.73 <0.001 Education <0.001 High School 26476 (53.70%) 24227 (54.88%) 2249 (43.57%) BMI <0.001 Normal(=25,=30) 18658 (37.84%) 16363 (37.07%) 2295 (44.46%) Diabetes <0.001 No 40029 (81.19%) 36873 (83.53%) 3156 (61.14%) Yes 9276 (18.81%) 7270 (16.47%) 2006 (38.86%) Smoke <0.001 Never 27402 (55.58%) 25362 (57.45%) 2040 (39.52%) Former 12138 (24.62%) 10044 (22.75%) 2094 (40.57%) Now 9765 (19.81%) 8737 (19.79%) 1028 (19.91%) Hypertension <0.001 No 26711 (54.18%) 25865 (58.59%) 846 (16.39%) Yes 22594 (45.82%) 18278 (41.41%) 4316 (83.61%) Hyperlipidemia 0.77 No 8928 (18.11%) 8001 (18.13%) 927 (17.96%) Yes 40377 (81.89%) 36142 (81.87%) 4235 (82.04%) Alcohol <0.001 Heavy 12286 (24.92%) 11576 (26.22%) 710 (13.75%) Moderate 9236 (18.73%) 8585 (19.45%) 651 (12.61%) Mild 27783 (56.35%) 23982 (54.33%) 3801 (73.63%) Table 1 Continuous variables were presented as means (standard error). Categorical variables were expressed as counts (percentages).PIR, family income-to-poverty ratio, PIV,pan-immune-inflammation value; BMI, body mass index; CVD, cardiovascular disease. Association of the PIV with CVD The outcomes of the regression analysis with multiple variables among PIV and CVD is exposed in( Table 2). In Model 1, covariates are not accounted for. Model 2 adjusts for sex, age and race ,while all covariates are included in Model 3. PIV showed a positive correlation with CVD.Following complete adjustment for all covariates, the positive relationship still remained [OR=1.17 ,95 %CI (1.11,1.22), P <0.01].With 100-unit increase in PIV was associated with a 17% increase in the risk of CVD.To further investigate the relationship between PIV and CVD, we grouped PIV based on quartile groups. With Quartile 1 as the baseline group, we performed a fully adjusted regression analysis for PIV quartiles.A significantly higher OR was observed for Quartile 4 in comparison with Quartile 1[OR=1.27 ,95 %CI (1.16, 1.39), P <0.05]. This suggests that 27% higher odds of CVD prevalence in Q4 compared to Q1.This suggests that an increase in PIV may be associated with an elevated risk of developing CVD. Variables Model 1 Model 2 Model 3 Ln-transformed 1.43 (1.37, 1.49) <0.01 1.29 (1.24, 1.35) <0.01 1.17 (1.11, 1.22) <0.01 PIV Quartiles Quartile 1 1.00 1 1 Quartile 2 1.11 (1.02, 1.22) 0.02 1.07 (0.98, 1.18) 0.15 1.01 (0.92, 1.11) 0.85 Quartile 3 1.31 (1.20, 1.43) <0.01 1.23(1.12, 1.35) <0.01 1.10 (1.00, 1.21) 0.05 Quartile 4 1.84 (1.70, 2.00) <0.01 1.54 (1.41, 1.69) <0.01 1.27 (1.16, 1.39) <0.01 Table 2 The association between weighted PIV and CVD. Crude model (Model 1): no covariate was adjusted. Partially adjusted model (Model 2): sex, age and race were adjusted. Fully adjusted model (Model 3):BMI, PIR, education level, smoking habits, alcohol consumption, hypertension, hyperlipidemia, and diabetes were adjusted PIV pan-immune inflammation value, BMI, body mass index; CVD, cardiovascular disease. In addition, the application of curve smoothing demonstrated the nonlinear association between Ln-PIV and the prevalence of CVD, as shown in (Fig 2). Then, threshold analysis then revealed a saturation effect of PIV at the inflection point of 5.49. A positive association between PIV and the prevalence of CVD was observed when NPAR > 5.49 (OR = 1.23, 95% CI: 1.06, 1.41, P < 0.005); whereas, we did not find a statistically significant relationship, when PIV< 5.49 (OR = 1.04, 95% CI: 0.94, 1.14, P = 0.4498) (Table 3). Ln-PIV Adjusted OR (95% CI) P Fitting by the standard linear model 1.17(1.11, 1.22) <0.0001 Fitting by the two-piecewise linear model Inflection point(K) 5.49 <K 1.04 (0.94, 1.14) 0.4498 ≥K 1.23(1.06, 1.41) 0.0048 Log likehood ratio test 0.005 Table 3 Linear regression analysis of PIV and CVD prevalence after adjusting for all covariates. The results of PIV, and CVD prevalence were expressed as Adjusted OR (95% CI). CVD, cardiovascular disease; PIV, pan-immune-inflammation value; OR, odds ratio; 95% CI;95% confidence interval. Subgroup investigation of the association between the PIV and CVD We performed a stratified analysis of the association between PIV and CVD prevalence. Subgroup analysis and interaction tests were conducted after adjusting for all covariates to further validate the consistency of the association between PIV and CVD and to identify potential differences in specific subgroups. The results indicated that after stratifying participants by gender, race, education level, diabetes, smoking, hypertension, hyperlipidemia, and alcohol consumption, the association between PIV and CVD prevalence remained consistent (interaction p > 0.05). However, a significant interaction was found between PIV and BMI. The association between PIV and CVD prevalence showed a stronger correlation in individuals classified as underweight (BMI) (OR = 1.36, 95% CI: 1.25-1.48)(Fig 3) . Discussion Utilizing data from 49,305 NHANES participants (2003–2023), this study revealed three principal findings: 1) A significant positive association existed between higher PIV quartiles and increased CVD prevalence, demonstrating a dose-response relationship; 2) A nonlinear U-shaped association was identified, with significantly elevated CVD risk observed in specific PIV ranges; 3) Subgroup analyses indicated BMI-dependent effect modification—the association was substantially stronger in underweight individuals (OR = 1.36, 95% CI: 1.25–1.48) compared to other BMI categories. Critically, the PIV-CVD relationship remained robust across all demographic strata after comprehensive adjustment for lifestyle factors and comorbidities. These results establish PIV as a clinically actionable integrative biomarker for CVD risk stratification, early detection, and precision prevention in high-risk cohorts. To our knowledge, this is the first study to examine the relationship between CVD prevalence and PIV. PIV integrates key peripheral immune cell types—neutrophils, platelets, monocytes, and lymphocytes—potentially serving as a composite marker of systemic inflammatory status.PIV was initially established in 2020 as a prognostic biomarker for metastatic colorectal cancer[ 12 ]. Subsequent studies have confirmed its significant prognostic value across multiple malignancies [ 18 , 19 ]. In recent years, research focus on PIV has expanded to cardiovascular disease (CVD) investigations[ 20 , 21 ]. Early evidence demonstrates significant associations between PIV and both all-cause and cardiovascular mortality in hypertensive populations[ 22 ]. However, its impact on CVD prevalence within this cohort remains uninvestigated. Mechanistic insights indicate that immune cells play a pivotal regulatory role in inflammatory cardiovascular diseases. Chronic inflammation serves as a critical pathological basis contributing to elevated CVD morbidity and mortality, with atherosclerosis and insulin resistance constituting core mechanisms driving CVD progression:Neutrophils drive disease advancement through participation in the full pathological continuum of atherosclerosis[ 23 ]—during the plaque initiation phase, they promote platelet adhesion, activate endothelial cells and macrophages, and release pro-inflammatory cytokines (IL-1β,IL-18) to enhance monocyte recruitment [ 24 – 26 ]; at terminal stages, they trigger severe complications including plaque rupture, intraplaque hemorrhage, and acute vascular occlusion[ 27 ].Concurrently, insulin resistance exacerbates neutrophil infiltration and macrophage proliferation via lipotoxic effects. Released inflammatory mediators activate vascular smooth muscle and endothelial cells, synergistically accelerating atherogenesis [ 28 ]. Risk factors such as obesity and diabetes amplify cardiovascular disease risk through enhanced inflammatory responses.[ 25 , 29 ] Nonetheless, little study has examined the association between PIV and CVD among the broader populace. Our results demonstrate that the association between CVD prevalence and increasing PIV is stable across various subgroup analyses, indicating that these factors do not influence the observed association (P > 0.05). In contrast, results from the BMI subgroup suggests that the factor may affects the PIV-CVD association.A low-BMI poverty ratio was significantly associated with higher CVD prevalence. No significant association was found for the middle-BMI or high-BMI group.While obesity or high BMI is generally associated with an elevated risk of cardiovascular events (including mortality) in the general population, a study of 236 studies involving 10 million participants, primarily from Western countries, found a strong correlation between coronary mortality and BMI. The lowest risk was observed in individuals with a BMI between 20–22.5 kg/m², with the risk approximately doubling for every 10 kg/m² increase. The excess mortality associated with overweight and obesity is largely attributed to the negative effects of obesity on blood pressure, blood lipids, and diabetes, all of which contribute to an increased risk of vascular and renal diseases[ 30 ]. Although high BMI, especially obesity, is widely recognized as a risk factor for cardiovascular disease, excessively low BMI may also elevate CVD risk. A meta-analysis of 89 coronary artery disease (CAD) patients showed that, compared to those with normal weight, underweight patients had significantly higher mortality risks in both short- and long-term follow-ups, while overweight/obese patients had lower mortality risks [ 31 ]. The exact mechanisms underlying the increased mortality associated with underweight are not fully understood, but potential contributing factors include poor lifestyle, malnutrition, underlying diseases, and behaviors such as smoking and alcohol consumption, particularly in males. Limitations This study has several limitations. First, it is constrained by the cross-sectional design, meaning that the relationship between PIV and CVD can only be interpreted as a correlation, rather than establishing causality. Second, all baseline data were collected from a single blood sample and self-reported questionnaires, but patient data may change over time during long-term follow-up. Lastly, the use of a public database imposes certain limitations, including missing data on factors such as detailed information on autoimmune diseases and the use of anti-inflammatory medications. Conclusion In U.S. adults, Elevated PIV levels are associated with increased CVD prevalence. Further prospective studies are needed to explore the causal relationship underlying this association. Abbreviations PIV pan-immune-inflammation value CVD cardiovascular disease Declarations Ethics approval and consent to participate The studies involving humans were approved by the National Center for Health Statistics Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. Consent for publication Written informed consent was obtained from the patient and her families for the use of the information and accompanying images in this study. Competing interests The authors declare no competing interests. Funding This work was supported by the Health Commission of Hunan Province(202204023391) Author Contribution RLL: Formal analysis, Investigation, Writing – original draft.ZRW and XL : Formal analysis, Investigation, Writing – original draft. QA and HY: Methodology, Writing – review & editing. QW: Formal analysis, Methodology, Writing – review & editing. Acknowledgements We thank all the doctors and nurses in the heart and thoracic surgery Department of the Second Xiangya Hospital of Central South University for their professional assistance. Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8036412","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578295907,"identity":"7e9d640b-42ab-4d65-be86-bf9c45fc58e0","order_by":0,"name":"renlang liu","email":"","orcid":"","institution":"The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"renlang","middleName":"","lastName":"liu","suffix":""},{"id":578295908,"identity":"d6541488-0934-4dd0-8009-ec58e0767bd6","order_by":1,"name":"Ling Xie","email":"","orcid":"","institution":"School of Public Health, Nanchang 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University","correspondingAuthor":false,"prefix":"","firstName":"qi","middleName":"","lastName":"ai","suffix":""},{"id":578295913,"identity":"44b7c062-be0d-4fd6-9e19-f0585c34bd41","order_by":5,"name":"qin wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3OsQrCMBCA4SuBuJx7iugzBAoVoeirKIVMDh3dFAS7iK6KPkTFwTVwg0sfoOIiFDo5CO5qcRRp6+aQnxsukA8OwGT60zSMRKsXTt4Pa1KNxJ4jUf9AwJqpQST6FYlM/B0FnKyDnaZ3BK8ZaZZdiokKaIXEOhvlNhCUE2nelkXETYaSUBCH85AzBBpEGrkoJ/nAKWb5Yc+qpK+ETBDyw3Q56cVZQKg9ac+Va2+l76yJu4XEDv39vf4Q42WN0tt11G0ujtOskOTJj52V/P8gJpPJZPrSC4WFSj6XNCeSAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"qin","middleName":"","lastName":"wu","suffix":""}],"badges":[],"createdAt":"2025-11-05 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1","display":"","copyAsset":false,"role":"figure","size":47667,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants selection from NHANES (2003-2023) A total of 98551 participants were enrolled initially. 49247 participants were excluded due to missing or incomplete CVD and PIV data. 49305 participants were finally enrolled in the study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036412/v1/0c57bce3affff6ab8497c0be.jpg"},{"id":101206298,"identity":"281a5a0e-4129-49db-8602-779aea362c09","added_by":"auto","created_at":"2026-01-27 09:55:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5059,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of Ln(PIV) with the likelihood of CVD using a restricted spline regression model. PIV, pan-immune-inflammation value; CVD, cardiovascular disease.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036412/v1/ffca5ecd8ae528178a474eee.jpg"},{"id":101170312,"identity":"8310c488-fb18-48c6-b425-9358f04c96f7","added_by":"auto","created_at":"2026-01-27 00:02:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62327,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the associations between PIV and CVD prevalence.PIV, pan-immune-inflammation value; BMI, body mass index; CVD, cardiovascular disease; OR, odds ratio; 95% CI: 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036412/v1/1b99eae65031465b26799040.jpg"},{"id":101208576,"identity":"2398aa33-60c4-4228-acdf-6ffb97ba620c","added_by":"auto","created_at":"2026-01-27 10:10:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":821932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8036412/v1/d7488d4d-d564-41a9-865d-88b65eda403c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between pan-immune-inflammation value and cardiovascular disease: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the population ages, the incidence and mortality associated with CVD continue to rise, making it a significant public health concern . While cardiovascular disease (CVD) accounted for nearly one-third of global deaths in 2021[1] ,emerging evidence indicates an escalating burden. According to the 2023 American Heart Association (AHA) report[2], global CVD mortality in 2020 increased by 18.7% compared to 2010. Thus, given the profound impact of CVD on public health and the global economy, enhancing capabilities for its early identification and prediction is imperative.\u003c/p\u003e\n\u003cp\u003eAlthough traditional risk factors\u0026mdash;such as diabetes, hypertension, and hyperlipidemia\u0026mdash;are strong predictors of cardiovascular risk [3-5], they do not fully account for the observed variability in CVD risk. Emerging evidence indicates [6-8]that activation of inflammatory pathways and nutritional imbalances jointly drive the progression of atherosclerosis and contribute to CVD development.\u003c/p\u003e\n\u003cp\u003eThe Pan-Inflammatory Value (PIV) is a biomarker used to evaluate a patient\u0026apos;s immune and inflammatory status, incorporating the counts of neutrophils, platelets, monocytes, and lymphocytes. Previous studies have demonstrated that PIV is linked to the prognosis of various immune- and inflammation-related conditions, including breast cancer[9], anti-neutrophil cytoplasmic antibody-associated vasculitis [10], and ST-segment elevation myocardial infarction [11]. However, there is limited research investigating the relationship between PIV and CVD in the broader adult population in the United States.\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the association between PIV and CVD prevalence using nationally representative data from the National Health and Nutrition Examination Survey (NHANES). By evaluating the potential of PIV as a novel predictive biomarker, we seek to provide scientific insights for developing more effective early prevention strategies against CVD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), constitutes a collection of surveys that are designed to record and evaluate the health and nutritional status of the U.S. population.\u003c/p\u003e\n\u003cp\u003eIn this study, an initial cohort of 98551 participants recruited from 2003 to 2023, was identified. During data wrangling and screening, 49247 participants were excluded due to missing or incomplete AAC and PIV data. Consequently, the study encompassed 49305 participants aged \u0026ge;20 years (\u0026ensp;Fig\u0026ensp;1\u0026ensp;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of the PIV and CVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe calculation of PIV is as follows: neutrophil count (109/L)\u0026nbsp;\u0026times;\u0026nbsp;platelet count (109/L)\u0026nbsp;\u0026times;\u0026nbsp;monocyte count (109/L)/lymphocyte count (109/L) [12].Ln-PIV represents the logarithmic transformation of PIV, used to address the skewed distribution of PIV.CVD status was ascertained through physician-diagnosed medical conditions using a structured questionnaire during an individual interview . Participants were classified as having CVD if they self-reported any of the following: coronary heart disease (CHD), myocardial infarction (MI), congestive heart failure (CHF), angina pectoris, or stroke.[13, 14]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined a comprehensive set of variables and clinical conditions. Educational attainment was categorized into three levels: less than high school, high school or equivalent, and college or higher. Body mass index (BMI) classifications included normal (\u0026lt;25 kg/m\u0026sup2;), overweight (25-29.9 kg/m\u0026sup2;), and obese (\u0026ge;30 kg/m\u0026sup2;). Alcohol consumption was defined heavy drinking (\u0026ge;4 drinks/day for men; \u0026ge;3 for women), moderate drinking (\u0026le;3 drinks/day for men; \u0026le;2 for women), and low intake (\u0026lt;3 drinks/day for men; \u0026lt;2 for women)[15]. Smoking status comprised former smokers (\u0026ge;100 lifetime cigarettes, currently abstinent), current smokers (\u0026ge;100 lifetime cigarettes, currently smoking daily/occasionally), and never smokers (\u0026lt;100 lifetime cigarettes). Hyperlipidemia required meeting \u0026ge;1 criterion: total cholesterol \u0026gt;200 mg/dL, triglycerides \u0026ge;150 mg/dL, HDL \u0026lt;40 mg/dL (men) or \u0026lt;50 mg/dL (women), or LDL \u0026ge;130 mg/dL\u003cu\u003e.\u0026nbsp;\u003c/u\u003e[16, 17]. Hypertension was defined as mean systolic BP \u0026ge;140 mmHg, diastolic BP \u0026ge;90 mmHg, antihypertensive medication use, or documented clinical history. Diabetes mellitus diagnosis required fasting glucose \u0026ge;126 mg/dL, HbA₁c \u0026ge;6.5%, or current insulin/oral hypoglycemic agent use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were presented as means with 95% confidence intervals (CI), and categorical variables as proportions with 95% CI. To examine baseline characteristics, individuals were grouped into quartiles of PIV. Weighted generalized linear models were applied to assess the relationship between PIV and CVD. Model 1 was unadjusted, while Model 2 adjusted for demographic factors including sex, age, and ethnicity. Model 3 included additional adjustments for BMI, PIR, education level, smoking habits, alcohol consumption, hypertension, hyperlipidemia, and diabetes. Restricted cubic splines were used to explore nonlinear associations and investigate threshold effects, including inflection points, in the relationship between PIV and CVD.Additionally, we performed a weighted multivariable regression subgroup analysis after adjusting for all covariates to examine the interactions between variables such as gender, race, education level, BMI, diabetes, smoking status, hypertension, hyperlipidmia and alcohol. The results were visualized using a forest plot.Interaction p-values assessed the consistency of treatment effects across subgroups. A non-significant p-value indicated consistent effects for the overall population, while a significant p-value suggested potential outcome differences in specific subgroups.All statistical analyses were carried out using Empower Stats (v4.2) and R software (version 4.3.2), with statistical significance determined by a two-sided P-value of \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a total of 49,305 participants, with an average age of 50.25 years, comprising 23,611 men (47.89%) and 25,694 women (52.11%). There were 5,162 participants \u0026nbsp;(10.47%)with CVD and 44,143 participants (89.53%) without CVD. Compared to participants without CVD, those with CVD were older, had a lower PIR, and were more likely to be male, non-Hispanic White, have education beyond high school, be obese (BMI \u0026ge; 30), have a history of smoking, drink alcohol moderately, and have hypertension and hyperlipidemia. Additionally, the PIV in the CVD group was significantly higher than that in the non-CVD group (all p \u0026lt; 0.0001) (Table 1)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(n=49305)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-CVD(n=44143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD(n=5162)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e50.25 \u0026plusmn; 17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e48.30 \u0026plusmn; 17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e66.89 \u0026plusmn; 12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e23611 (47.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e20758 (47.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2853 (55.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25694 (52.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e23385 (52.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2309 (44.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7254 (14.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e6787 (15.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e468 (9.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4563 (9.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4200 (9.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e363 (7.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e22109 (44.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e19217 (43.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2892 (56.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e10052 (20.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e8988 (20.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1064 (20.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e5327 (10.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e4952 (11.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e375 (7.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.44 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e2.48 \u0026plusmn; 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.15 \u0026plusmn; 1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e318.29 \u0026plusmn; 277.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e310.84 \u0026plusmn; 265.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e381.99 \u0026plusmn; 354.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn\u003c/strong\u003e(PIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e5.52 \u0026plusmn; 0.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e5.50 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.67 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026lt;High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11527 (23.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e9927 (22.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1600 (31.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11302 (22.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e9989 (22.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1313 (25.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026gt;High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e26476 (53.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e24227 (54.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2249 (43.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNormal(\u0026lt;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e14537 (29.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e13269 (30.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1268 (24.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eOverweight(\u0026gt;=25,\u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e16110 (32.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e14511 (32.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1599 (30.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eObese(\u0026gt;=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e18658 (37.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e16363 (37.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2295 (44.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e40029 (81.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e36873 (83.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3156 (61.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9276 (18.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e7270 (16.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2006 (38.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e27402 (55.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e25362 (57.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2040 (39.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e12138 (24.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e10044 (22.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2094 (40.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9765 (19.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e8737 (19.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1028 (19.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e26711 (54.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e25865 (58.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e846 (16.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e22594 (45.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e18278 (41.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4316 (83.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e8928 (18.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e8001 (18.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e927 (17.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e40377 (81.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e36142 (81.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4235 (82.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e12286 (24.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e11576 (26.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e710 (13.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9236 (18.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e8585 (19.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e651 (12.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e27783 (56.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e23982 (54.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3801 (73.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Continuous variables were presented as means (standard error). Categorical variables were expressed as counts (percentages).PIR, family income-to-poverty ratio, PIV,pan-immune-inflammation value; BMI, body mass index; CVD, cardiovascular disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of the PIV with CVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcomes of the regression analysis with multiple variables among PIV and CVD is exposed in( Table 2). In Model 1, covariates are not accounted for. Model 2 adjusts for sex, age and race ,while all covariates are included in Model 3. PIV showed a positive correlation with CVD.Following complete adjustment for all covariates, the positive relationship still remained \u0026nbsp;[OR=1.17 ,95 %CI (1.11,1.22), P \u0026lt;0.01].With 100-unit increase in PIV was associated with a 17% increase in the risk of CVD.To further investigate the relationship between PIV and CVD, we grouped PIV based on quartile groups. With Quartile 1 as the baseline group, we performed a fully adjusted regression analysis for PIV quartiles.A significantly higher OR was observed for Quartile 4 in comparison with Quartile 1[OR=1.27 ,95 %CI (1.16, 1.39), P \u0026lt;0.05]. This suggests that 27% higher odds of CVD prevalence in Q4 compared to Q1.This suggests that an increase in PIV may be associated with an elevated risk of developing CVD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLn-transformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43 (1.37, 1.49) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29 (1.24, 1.35) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (1.11, 1.22) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePIV Quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.11 (1.02, 1.22) \u0026nbsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e1.07 (0.98, 1.18) \u0026nbsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1.01 (0.92, 1.11) \u0026nbsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.31 (1.20, 1.43) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e1.23(1.12, 1.35) \u0026nbsp;\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1.10 (1.00, 1.21) \u0026nbsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1.84 (1.70, 2.00) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003e1.54 (1.41, 1.69) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1.27 (1.16, 1.39) \u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe association between weighted PIV and CVD.\u003c/p\u003e\n\u003cp\u003eCrude model (Model 1): no covariate was adjusted.\u003c/p\u003e\n\u003cp\u003ePartially adjusted model (Model 2): sex, age and race were adjusted.\u003c/p\u003e\n\u003cp\u003eFully adjusted model (Model 3):BMI, PIR, education level, smoking habits, alcohol consumption, hypertension, hyperlipidemia, and diabetes were adjusted\u003c/p\u003e\n\u003cp\u003ePIV pan-immune inflammation value, BMI, body mass index; CVD, cardiovascular disease.\u003c/p\u003e\n\u003cp\u003eIn addition, the application of curve smoothing demonstrated the nonlinear association between Ln-PIV and the prevalence of CVD, as shown in (Fig 2). Then, threshold analysis then revealed a saturation effect of PIV at the inflection point of 5.49. A positive association between PIV and the prevalence of CVD was observed when NPAR\u0026thinsp;\u0026gt;\u0026thinsp;5.49 (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.06, 1.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.005); whereas, we did not find a statistically significant relationship, when PIV\u0026lt;\u0026thinsp;5.49 (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.94, 1.14, P\u0026thinsp;=\u0026thinsp;0.4498) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLn-PIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted OR (95% CI) P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFitting by the standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17(1.11, 1.22) \u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFitting by the two-piecewise linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInflection point(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.94, 1.14) 0.4498\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.23(1.06, 1.41) 0.0048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog likehood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Linear regression analysis of PIV and CVD prevalence after adjusting for all covariates.\u003c/p\u003e\n\u003cp\u003eThe results of PIV, and CVD prevalence were expressed as Adjusted OR (95% CI).\u003c/p\u003e\n\u003cp\u003eCVD, cardiovascular disease; PIV, pan-immune-inflammation value; OR, odds ratio; 95% CI;95% confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup investigation of the association between the PIV and CVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a stratified analysis of the association between PIV and CVD prevalence. Subgroup analysis and interaction tests were conducted after adjusting for all covariates to further validate the consistency of the association between PIV and CVD and to identify potential differences in specific subgroups. The results indicated that after stratifying participants by gender, race, education level, diabetes, smoking, hypertension, hyperlipidemia, and alcohol consumption, the association between PIV and CVD prevalence remained consistent (interaction p \u0026gt; 0.05). However, a significant interaction was found between PIV and BMI. The association between PIV and CVD prevalence showed a stronger correlation in individuals classified as underweight (BMI) (OR = 1.36, 95% CI: 1.25-1.48)(Fig 3) .\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUtilizing data from 49,305 NHANES participants (2003\u0026ndash;2023), this study revealed three principal findings: 1) A significant positive association existed between higher PIV quartiles and increased CVD prevalence, demonstrating a dose-response relationship; 2) A nonlinear U-shaped association was identified, with significantly elevated CVD risk observed in specific PIV ranges; 3) Subgroup analyses indicated BMI-dependent effect modification\u0026mdash;the association was substantially stronger in underweight individuals (OR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.25\u0026ndash;1.48) compared to other BMI categories. Critically, the PIV-CVD relationship remained robust across all demographic strata after comprehensive adjustment for lifestyle factors and comorbidities. These results establish PIV as a clinically actionable integrative biomarker for CVD risk stratification, early detection, and precision prevention in high-risk cohorts.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to examine the relationship between CVD prevalence and PIV. PIV integrates key peripheral immune cell types\u0026mdash;neutrophils, platelets, monocytes, and lymphocytes\u0026mdash;potentially serving as a composite marker of systemic inflammatory status.PIV was initially established in 2020 as a prognostic biomarker for metastatic colorectal cancer[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Subsequent studies have confirmed its significant prognostic value across multiple malignancies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In recent years, research focus on PIV has expanded to cardiovascular disease (CVD) investigations[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Early evidence demonstrates significant associations between PIV and both all-cause and cardiovascular mortality in hypertensive populations[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, its impact on CVD prevalence within this cohort remains uninvestigated.\u003c/p\u003e \u003cp\u003eMechanistic insights indicate that immune cells play a pivotal regulatory role in inflammatory cardiovascular diseases. Chronic inflammation serves as a critical pathological basis contributing to elevated CVD morbidity and mortality, with atherosclerosis and insulin resistance constituting core mechanisms driving CVD progression:Neutrophils drive disease advancement through participation in the full pathological continuum of atherosclerosis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u0026mdash;during the plaque initiation phase, they promote platelet adhesion, activate endothelial cells and macrophages, and release pro-inflammatory cytokines (IL-1β,IL-18) to enhance monocyte recruitment [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; at terminal stages, they trigger severe complications including plaque rupture, intraplaque hemorrhage, and acute vascular occlusion[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Concurrently, insulin resistance exacerbates neutrophil infiltration and macrophage proliferation via lipotoxic effects. Released inflammatory mediators activate vascular smooth muscle and endothelial cells, synergistically accelerating atherogenesis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Risk factors such as obesity and diabetes amplify cardiovascular disease risk through enhanced inflammatory responses.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNonetheless, little study has examined the association between PIV and CVD among the broader populace. Our results demonstrate that the association between CVD prevalence and increasing PIV is stable across various subgroup analyses, indicating that these factors do not influence the observed association (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, results from the BMI subgroup suggests that the factor may affects the PIV-CVD association.A low-BMI poverty ratio was significantly associated with higher CVD prevalence. No significant association was found for the middle-BMI or high-BMI group.While obesity or high BMI is generally associated with an elevated risk of cardiovascular events (including mortality) in the general population, a study of 236 studies involving 10\u0026nbsp;million participants, primarily from Western countries, found a strong correlation between coronary mortality and BMI. The lowest risk was observed in individuals with a BMI between 20\u0026ndash;22.5 kg/m\u0026sup2;, with the risk approximately doubling for every 10 kg/m\u0026sup2; increase. The excess mortality associated with overweight and obesity is largely attributed to the negative effects of obesity on blood pressure, blood lipids, and diabetes, all of which contribute to an increased risk of vascular and renal diseases[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although high BMI, especially obesity, is widely recognized as a risk factor for cardiovascular disease, excessively low BMI may also elevate CVD risk. A meta-analysis of 89 coronary artery disease (CAD) patients showed that, compared to those with normal weight, underweight patients had significantly higher mortality risks in both short- and long-term follow-ups, while overweight/obese patients had lower mortality risks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The exact mechanisms underlying the increased mortality associated with underweight are not fully understood, but potential contributing factors include poor lifestyle, malnutrition, underlying diseases, and behaviors such as smoking and alcohol consumption, particularly in males.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it is constrained by the cross-sectional design, meaning that the relationship between PIV and CVD can only be interpreted as a correlation, rather than establishing causality. Second, all baseline data were collected from a single blood sample and self-reported questionnaires, but patient data may change over time during long-term follow-up. Lastly, the use of a public database imposes certain limitations, including missing data on factors such as detailed information on autoimmune diseases and the use of anti-inflammatory medications.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn U.S. adults, Elevated PIV levels are associated with increased CVD prevalence. Further prospective studies are needed to explore the causal relationship underlying this association.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epan-immune-inflammation value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe studies involving humans were approved by the National Center for Health Statistics Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e Written informed consent was obtained from the patient and her families for the use of the information and accompanying images in this study.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Health Commission of Hunan Province(202204023391)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRLL: Formal analysis, Investigation, Writing \u0026ndash; original draft.ZRW and XL : Formal analysis, Investigation, Writing \u0026ndash; original draft. QA and HY: Methodology, Writing \u0026ndash; review \u0026amp; editing. QW: Formal analysis, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank all the doctors and nurses in the heart and thoracic surgery Department of the Second Xiangya Hospital of Central South University for their professional assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal burden. of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Volume 396. Lancet; 2020. pp. 1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge M-P, Thacker EL, Virani SS, Voeks JH, Wang N-Y, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association, Circulation, 147 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuchs FD, Whelton PK. High Blood Pressure and Cardiovascular Disease, Hypertension, 75 (2020) 285\u0026ndash;292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlovaci D, Fan W, Wong ND. Epidemiology of Diabetes Mellitus and Cardiovascular Disease. Curr Cardiol Rep. 2019;21:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandesara PB, Virani SS, Fazio S, Shapiro MD. The Forgotten Lipids: Triglycerides, Remnant Cholesterol, and Atherosclerotic Cardiovascular Disease Risk. Endocr Rev. 2019;40:537\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorth BJ, Sinclair DA. The intersection between aging and cardiovascular disease. Circ Res. 2012;110:1097\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Backer G. Epidemiology and prevention of cardiovascular disease: Quo vadis? Eur J Prev Cardiol. 2017;24:768\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivim\u0026auml;ki M, Steptoe A. Effects of stress on the development and progression of cardiovascular disease. Nat Rev Cardiol. 2018;15:215\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin F, Zhang L-P, Xie S-Y, Huang H-Y, Chen X-Y, Jiang T-C, Guo L, Lin H-X. Pan-Immune-Inflammation Value: A New Prognostic Index in Operative Breast Cancer. Front Oncol. 2022;12:830138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee LE, Ahn SS, Pyo JY, Song JJ, Park Y-B, Lee S-W. Pan-immune-inflammation value at diagnosis independently predicts all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis. Clin Exp Rheumatol. 2021;39(Suppl 129):88\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurat B, Murat S, Ozgeyik M, Bilgin M. Comparison of pan-immune-inflammation value with other inflammation markers of long-term survival after ST-segment elevation myocardial infarction. 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Heart. 2015;101:1631\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pan-immune-inflammation value, Cardiovascular disease, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-8036412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pan-immune-inflammation value (PIV) is a novel biomarker that provides a quantitative measure of the overall immune-inflammatory state in the body. Its prognostic value has been extensively validated in a variety of clinical contexts. However, its specific role in cardiovascular disease (CVD) is still not fully understood and requires additional research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines the relationship between PIV and CVD prevalence using information from the National Health and Nutrition Examination Survey (NHANES).This analysis included 49305 adults from NHANES 2003–2023. Logistic regression analyses were used to assess the correlation between CVD prevalence and PIV in all participants.Piecewise linear regression analyses were additionally employed to explore the correlation between PIV and CVD.Subgroup analyses were performed to further clarify the effects of other covariates on the associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study recruited a total of 49,305 adults.Elevated PIV levels were significantly associated with increased CVD prevalence (P \u0026lt; 0.001). In fully adjusted model, individuals in the highest Ln-PIV quartile had a 27% higher odds of CVD prevalence compared to those in the lowest quartile [OR=1.27,95 %CI(1.16，1.39), P \u0026lt;0.01]. Smoothed curve fitting and threshold effect analyses also showed a positive association between Ln-PIV and CVD, with an inflection point for threshold and saturation effects of 5.49. Ln-PIV was positively associated with the likelihood of developing CVD when Ln-PIV \u0026gt; 5.49 [OR 1.23 , 95 %CI(1.06, 1.41) , P \u0026lt;0.01]. The results of subgroup analyses and interaction tests indicated that BMI status had a significant effect on the relationship between PIV and CVD (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study reveals a positive association between PIV levels and CVD, suggesting that higher PIV levels are associated with an increased likelihood of developing CVD.\u003c/p\u003e","manuscriptTitle":"Associations between pan-immune-inflammation value and cardiovascular disease: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 00:02:04","doi":"10.21203/rs.3.rs-8036412/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"215667557133989966391899309679992044203","date":"2026-02-02T13:46:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T16:45:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T12:53:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T12:52:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2025-11-05T08:52:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17d3648f-cf6e-4ed8-8f3e-75df9eb1b14f","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T00:02:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 00:02:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036412","identity":"rs-8036412","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}