Inflammatory responses are fundamental to maintaining health and protecting the body from external threats. Acute inflammation is a normal physiological reaction to infections, injuries, and other external stimuli, where the immune system is activated to eliminate pathogens and promote tissue repair (1). However, chronic inflammation has been strongly linked to the development of various diseases, including cardiovascular diseases, cancer, diabetes, and metabolic disorders (2–7). Persistent inflammatory responses can result in tissue damage, disrupt homeostasis, and accelerate disease onset and progression (6).

Immune-inflammatory biomarkers (IIBs), such as neutrophils (NEUs), lymphocytes (LYMs), monocytes (MONs), and platelets (PLTs), reflect the balance between the host’s immune and inflammatory states and are critical for assessing disease conditions. Several inflammatory indices derived from CBC parameters, such as the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SIRI), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII), are widely used for disease risk assessment and prognosis. Multiple studies have demonstrated that NLR, PLR, and LMR are effective predictors of disease progression and prognosis across diverse conditions, including cancer, cardiovascular diseases, and inflammatory disorders (8–13). Additionally, these indices have been employed to distinguish between different types of chronic inflammatory diseases, such as Crohn’s disease, further underscoring their broad clinical utility (14). Among these, SII has emerged as a valuable marker of inflammation, showing significant prognostic value in chronic conditions such as cancer and inflammatory diseases (8, 15, 16). Research conducted on general populations has also highlighted the potential of SII in assessing systemic inflammation (17, 18).

More recently, a novel and more comprehensive immune-inflammatory index, the Pan-Immune-Inflammation Value (PIV), has been developed. PIV integrates the counts of NEUs, PLTs, MONs, and LYMs, offering a more holistic assessment of the systemic immune and inflammatory status (19). Preliminary studies suggest that PIV has greater prognostic accuracy compared to traditional IIBs such as NLR and PLR, particularly in predicting outcomes for patients with cancers such as advanced colorectal cancer, hepatocellular carcinoma, and breast cancer (19–21). Although PIV has shown promise in predicting outcomes for cancer patients, its association with overall and cause-specific mortality in the general population remains understudied. Therefore, this study aims to evaluate the relationship between PIV and mortality rates in the U.S. population, with the goal of determining its potential as a prognostic marker and providing valuable insights to inform public health strategies.

This study investigated whether the PIV could predict long-term outcomes in a general population. Our results demonstrated that PIV is significantly associated with mortality across multiple causes in this population. A high PIV level was shown to be an independent risk factor for all-cause mortality and cause-specific mortality. Additionally, PIV exhibited a nonlinear relationship with all-cause, cardiovascular, and cancer mortality, while displaying a linear association with diabetes mortality.

The PIV is a novel biomarker derived from neutrophils, platelets, monocytes, and lymphocytes, providing an integrative view of a patient’s immune and inflammatory status. Originally studied in the context of metastatic colorectal cancer, PIV has shown superior prognostic power over traditional inflammatory markers, such as NLR and PLR (19). Its simplicity, along with its ability to combine multiple immune components into a single measure, makes PIV a valuable and non-invasive tool for assessing systemic inflammation across a variety of clinical settings.

PIV has been well-established as a prognostic marker in oncology, where elevated levels are associated with worse prognosis, rapid disease progression, and therapy resistance. Researches have shown that high PIV correlates with poor survival outcomes in multiple cancers, including pancreatic (28), colorectal (29, 30), lung (31, 32), ovarian (33) esophageal (34), and breast cancers (35, 36). In newly diagnosed glioblastoma multiforme (GBM), E. Topkan et al. reported a significant association between elevated PIV levels and shorter progression-free survival (PFS) and overall survival (OS) outcomes (37). Furthermore, dynamic changes in PIV during immune checkpoint inhibitor (ICI) treatment have been linked to patient outcomes in colorectal cancer, with higher PIV levels indicating poor response and survival (38) Additionally, PIV serves as an indicator of chemotherapy resistance; for instance, in breast cancer patients undergoing neoadjuvant chemotherapy, lower PIV levels have been associated with better responses and improved survival (35). Elevated PIV levels also predict enhanced tumor progression, aiding clinicians in tailoring treatment plans and identifying patients at higher risk of recurrence. PIV not only plays a critical role in prognostic assessment but also shows potential in tumor diagnosis and recurrence monitoring. Y.T. Yang et al. highlighted that PIV has high sensitivity and specificity for diagnosing brain tumors, particularly gliomas (39). In Merkel cell carcinoma (MCC), T. Gambichler’s study confirmed that PIV levels correlate with disease stage and are independent predictors of MCC recurrence (40).

Chronic inflammation is a central factor in cardiovascular diseases, and PIV provides a comprehensive measure of inflammatory burden in conditions such as ST-segment elevation myocardial infarction (STEMI) and hypertension. Elevated PIV levels are predictive of both short-term and long-term mortality following STEMI, underscoring its value in risk stratification (9). Among hypertensive patients, high PIV levels have been linked to increased cardiovascular mortality due to their role in promoting thrombosis and exacerbating atherosclerosis (41). PIV’s capacity to integrate immune and inflammatory markers makes it a valuable tool for tracking disease progression and tailoring interventions in cardiovascular care.

Beyond oncology and cardiovascular disease, PIV has shown promise across a broad spectrum of conditions. In autoimmune diseases like systemic lupus erythematosus (SLE), PIV levels are significantly elevated compared to healthy controls (42). This elevation captures both inflammatory activity and immune dysregulation, which are critical in autoimmune disease pathogenesis. In rheumatoid arthritis, where chronic inflammation drives joint damage and cardiovascular complications, PIV serves as a useful index of inflammatory burden (43) Additionally, PIV is a significant nonlinear predictor of 28-day and 90-day mortality in septic patients, with higher levels correlating with increased mortality risk beyond a specific threshold (44). In critically ill patients with non-traumatic subarachnoid hemorrhage (SAH), elevated admission PIV is independently associated with increased mortality across ICU, in-hospital, 30-day, 90-day, and 1-year outcomes (45). For patients with fatty liver disease (FLD), Pan and colleagues demonstrated that PIV, alongside the SII, is closely associated with all-cause mortality, particularly highlighting its link to cardiovascular mortality (46). Jiang and colleagues further showed that PIV, rather than SII, is associated with the prevalence of NAFLD and hepatic fibrosis, particularly in individuals under 60, positioning it as a valuable marker for liver health (47). In hypertensive patients, Long and colleagues identified elevated PIV as a significant predictor of sarcopenia, especially in those with coexisting diabetes (48). Guo and colleagues reported that PIV, along with SII and SIRI, is inversely associated with cognitive performance in older adults, suggesting its potential as a biomarker for cognitive decline (49). Qiu and colleagues found that higher PIV levels are associated with increased COPD prevalence and all-cause mortality, with nonlinear relationships displaying a J-shaped association for prevalence and a U-shaped association for mortality risk (50). In the study by Tang et al (24), elevated levels of NLR, MLR, PLR, SII, SIRI, and PIV were positively associated with frailty risk in middle-aged and older adults, while lower PLR levels were inversely related. In frail individuals, all six inflammatory markers were linked to increased all-cause mortality, with MLR exhibiting the strongest predictive value. Among pre-frail individuals, elevated NLR, MLR, SII, SIRI, and PIV, alongside increased neutrophil counts, were associated with higher mortality risk, whereas higher lymphocyte counts were protective. Notably, a U-shaped relationship between NLR, MLR, SIRI, and PIV with mortality was observed in pre-frail individuals, where excessively low or high levels increased mortality risk. The predictive superiority of MLR likely arises from its ability to reflect immune senescence, as elevated monocytes indicate systemic inflammation, and reduced lymphocytes represent immune dysfunction—both critical drivers of frailty progression and mortality. Our study aligns with the findings of Tang et al., demonstrating that MLR exhibits the highest predictive value for all-cause mortality risk. However, their research didn’t extend to the investigation of other cause-specific mortality risks.

Taken together, these findings underscore the versatility and clinical relevance of PIV across diverse medical conditions, including liver disease, sarcopenia, cognitive decline, respiratory diseases, autoimmune disorders, sepsis, and others. PIV’s ability to integrate systemic inflammation and immune dysregulation highlights its value as a robust biomarker for risk assessment and disease prognosis across various populations.

These findings underscore the versatility and clinical relevance of PIV across diverse medical conditions, including liver disease, sarcopenia, cognitive decline, respiratory diseases, autoimmune disorders, sepsis, and others. PIV’s ability to integrate systemic inflammation and immune dysregulation highlights its value as a robust biomarker for risk assessment and disease prognosis across various populations.

While the precise mechanisms underlying PIV’s prognostic value in various diseases remain uncertain, several explanations are emerging. Firstly, neutrophils, once considered straightforward immune defenders, are now understood to regulate diverse processes, including tissue repair, cancer progression, autoimmunity, and chronic inflammation. Low neutrophil levels can lead to severe immunodeficiency, while their excessive activation can damage host tissues (51). In cancer, neutrophils release VEGF, IL-6, and MMPs, which promote angiogenesis, tumor growth, and metastasis (52). However, they also suppress adaptive immunity by inhibiting T-cell activity through nitric oxide and reactive oxygen species (ROS), enabling tumor immune evasion (53). In ischemic heart failure, neutrophils initially assist in cardiac repair by initiating inflammation and clearing necrotic myocardial debris, but prolonged activation may lead to chronic inflammation, impairing cardiac function (54). Secondly, platelets are known for their complex roles in both physiological and pathological conditions. Beyond hemostasis and thrombosis, platelets regulate immune responses, chronic inflammation, and disease progression. In sterile inflammation (e.g., atherosclerosis), platelets bind damage-associated molecular patterns (DAMPs), activate signaling pathways such as MAPK and NF-κB, and release potent inflammatory mediators like HMGB1 (55, 56) Additionally, they interact with bacteria, initiate immune responses, and release inflammatory mediators through Toll-like receptors (TLRs), aiding in pathogen defense (55) Platelets also play a crucial role in cancer metastasis by cloaking circulating tumor cells, promoting endothelial adhesion, and facilitating tumor invasion and metastasis (57, 58). Thirdly, monocytes play central roles in immune defense and inflammation. Classical monocytes are recruited to infection and inflammation sites via the CCL2/CCR2 pathway, releasing cytokines like TNF-α and iNOS to kill pathogens and enhance adaptive immunity. while non-classical monocytes patrol the vascular endothelium to monitor for tissue injury via the CX3CL1/CX3CR1 axis. In conditions like atherosclerosis, monocytes differentiate into foam cells, sustaining chronic inflammation and plaque formation (59). In tumors, monocytes differentiate into tumor-associated macrophages (TAMs), which promote immunosuppression and angiogenesis, allowing tumor cells to evade immune surveillance (60). Lastly, lymphocytes are pivotal in immune surveillance and inflammation. In chronic inflammation, such as atherosclerosis, lymphocytes mediate immune responses against pathogens and contribute to tissue repair, though excessive activity can exacerbate inflammation and tissue damage (61–63). Within tumors, tumor-infiltrating lymphocytes (TILs) recognize and kill cancer cells, particularly in high mutation-load cancers (64, 65). Conversely, lymphopenia—a low lymphocyte count—is linked with poor outcomes, reflecting impaired immune competence and heightened disease susceptibility (66). Overall, these mechanisms highlight PIV’s potential to capture the complex interplay between immunity and inflammation across diverse diseases.

Our findings underscore a significant association between PIV and various mortality outcomes. We observed a dose-dependent increase in the risk of all-cause, cardiovascular, cancer, and diabetes-related mortality with elevated PIV levels. Even after adjusting for potential confounders, high PIV levels remained consistently associated with increased mortality risks. Kaplan-Meier survival curves further validated these disparities across PIV quartiles, demonstrating that individuals with higher PIV indices had markedly elevated long-term mortality risks. Notably, restricted cubic spline analysis revealed nonlinear dose-response relationships between PIV and all-cause, cardiovascular, and cancer mortality. Specifically, when PIV levels were below 254.07, no significant association with mortality risk was observed, but once this threshold was exceeded, the risks rose sharply. This threshold effect suggests that while low PIV levels may have minimal impact, elevated PIV could play a critical role in disease progression. Sensitivity and subgroup analyses reinforced the robustness of these associations, underscoring PIV’s potential as a reliable prognostic marker across diverse populations.

Additionally, our findings highlight the comparative predictive efficiency of PIV against other inflammatory markers in mortality risk assessment. ROC curve analyses demonstrated that PIV provides reasonable predictive performance for all-cause, cardiovascular, cancer, and diabetes-related mortality. Notably, PIV outperformed simpler markers such as PLR and SII, emphasizing its greater utility in reflecting systemic inflammatory responses. However, its predictive capability was surpassed by more comprehensive indices, including MLR, NLR, and SIRI, which likely integrate broader aspects of inflammatory and immune dynamics. For cancer mortality, PIV exhibited comparable performance to NLR and PLR but was inferior to MLR and SIRI, suggesting that composite indices may better capture the complexity of inflammation-driven processes in cancer progression. Similarly, for diabetes-related mortality, PIV demonstrated consistent and comparable predictive performance relative to other markers, further supporting its reliability in specific contexts. Taken together, these findings highlight PIV as a practical and accessible prognostic marker with considerable potential for mortality risk stratification. However, the superior performance of MLR, NLR, and SIRI indicates that combining PIV with complementary markers could enhance predictive accuracy. Future studies should prioritize investigating the synergistic use of PIV alongside other inflammatory indices to improve risk stratification and inform clinical decision-making across diverse populations and mortality outcomes.

In this study, we conducted a comprehensive comparison of baseline characteristics between participants who were excluded and those included in the final analysis ( Supplementary Table S5 ). Significant differences were observed in various demographic, clinical, and laboratory parameters, including age, race, educational attainment, family income-to-poverty ratio, smoking behavior, marital status, BMI, and laboratory measurements such as RBC count, lymphocyte count, platelet count, hemoglobin, ALT, TC, BUN, uric acid, creatinine, albumin, and HbA1c. Additionally, differences were notable in the prevalence of comorbidities, such as kidney disease, CHF, CHD, heart attack, stroke, and cancer, as well as in causes of mortality. Conversely, no significant differences were identified in gender, WBC count, neutrophil count, monocyte count, AST levels, or in the prevalence of angina pectoris, liver disease, hypertension, diabetes, and follow-up duration. Furthermore, inflammatory markers, including NLR, PLR, SII, and PIV, also showed no significant differences between the two groups. We recognize the potential for selection bias arising from these differences. To address this, we employed rigorous statistical adjustments, incorporating a variety of confounding variables into our analysis. Multiple models were constructed to validate the consistency and reliability of our findings, all of which demonstrated concordant trends. Despite the inherent limitations in sample selection, the robustness of our results underscores the credibility of our conclusions. This study provides a strong foundation for future research exploring the clinical relevance of PIV and related outcomes in diverse populations.

Our findings also have important clinical implications. First, as a composite biomarker derived from routine complete blood count (CBC) parameters, PIV is a cost-effective, readily available, and non-invasive marker that can be easily applied in daily clinical practice, including in primary care and resource-limited settings. Second, given its strong association with all-cause and cause-specific mortality, PIV may serve as an effective tool for early identification of individuals at high risk of adverse outcomes, who may benefit from targeted preventive interventions and more intensive clinical monitoring. For instance, individuals with elevated PIV levels could be prioritized for cardiovascular risk management, cancer screening, or metabolic evaluations. Third, since PIV reflects both innate and adaptive immune responses, as well as systemic inflammation, it offers a broader perspective on the overall immune-inflammatory status than traditional indices such as NLR or PLR, supporting its potential role in comprehensive risk stratification models. Furthermore, considering the dynamic nature of inflammation, repeated assessments of PIV over time may help monitor disease progression and evaluate treatment responses. Lastly, integrating PIV with other clinical information, including comorbidities, lifestyle factors, and biochemical markers, could improve personalized risk prediction and support clinical decision-making in preventive and therapeutic strategies. Further prospective and interventional studies are warranted to validate these clinical applications and to establish optimal PIV thresholds for risk stratification and clinical management.

Our study, leveraging a large cohort and extensive follow-up, provided valuable insights into the association between PIV and mortality outcomes, including all-cause, cardiovascular, cancer, and diabetes-related mortality in the general population. The use of restricted cubic spline models enabled us to explore nonlinear relationships between PIV and mortality, revealing nuanced dose-response patterns and potential threshold effects.

However, several limitations warrant discussion. This study is cross-sectional in design, which inherently limits its ability to establish causal relationships between PIV and mortality outcomes. While the observed associations provide valuable insights, the lack of longitudinal data prevents us from fully elucidating the temporal dynamics and causal pathways underlying these relationships. This limitation is particularly relevant given the multifactorial nature of inflammation and its interactions with mortality risks over time. First and foremost, PIV was measured only at baseline, which restricts our ability to capture dynamic changes in inflammatory status during follow-up. Inflammation is a highly variable and dynamic process, and the absence of longitudinal PIV measurements may obscure important temporal trends or fluctuations that could further clarify its association with mortality. For instance, repeated measures of PIV could reveal patterns of sustained inflammation or fluctuations that are more predictive of adverse outcomes. Future studies should consider incorporating multiple PIV assessments at different time points to better evaluate its trajectory and time-dependent predictive value. Second, baseline data on complications and lifestyle factors were self-reported, which introduces the potential for recall bias and inaccuracies in the data. This limitation may have impacted the reliability of certain variables, particularly those related to behavioral factors or self-perceived health conditions. Future research should prioritize the use of objective, validated measures and standardized data collection protocols to minimize these biases and enhance the reliability of findings. Third, while we adjusted for a wide range of potential confounders, there is always the possibility of residual confounding from unmeasured variables. Factors such as genetic predisposition, environmental exposures, access to healthcare, and specific treatments during follow-up may have influenced our results. Addressing these unmeasured variables in future research through more comprehensive data collection and advanced statistical techniques, such as causal inference models, will be critical. Finally, the generalizability of our findings is limited by the single-cohort design and population characteristics. The results may not fully reflect the diverse inflammatory and mortality profiles present across different regions or healthcare systems.

To address these limitations, future research should focus on several key areas. First, well-designed longitudinal cohort studies with repeated PIV measurements are warranted to capture the dynamic changes in inflammatory status over time and to better elucidate the temporal relationship between PIV fluctuations and mortality outcomes. These studies should explore whether persistent elevation or changes in PIV trajectories are more predictive of adverse outcomes compared to single baseline measurements. Second, further investigation is needed to determine optimal PIV cut-off values for risk stratification in diverse populations, considering differences in age, sex, ethnicity, and comorbid conditions, to enhance its clinical applicability. Third, mechanistic studies incorporating multi-omics approaches, including transcriptomics, proteomics, and metabolomics, could provide deeper insights into the biological pathways linking PIV with systemic inflammation and disease progression. Fourth, intervention-based studies, such as randomized controlled trials, should assess whether modulating systemic inflammation to reduce PIV levels can translate into improved clinical outcomes, thereby establishing PIV not only as a prognostic biomarker but also as a potential target for therapeutic interventions. Additionally, future studies should integrate PIV with advanced analytical techniques, including artificial intelligence and machine learning models, to develop robust, individualized prediction tools that can dynamically assess risk based on PIV trajectories and other clinical parameters. Finally, large multi-center and international studies are essential to validate the generalizability of PIV and to facilitate its integration into global clinical practice guidelines.

Despite these limitations, our study highlights the significant prognostic value of PIV as a biomarker for mortality risks. It provides a robust foundation for future investigations into inflammation-based risk stratification, paving the way for large-scale, longitudinal, and multi-center studies to further elucidate the clinical utility of PIV in predicting diverse mortality outcomes.

The PIV is a robust and versatile biomarker that integrates inflammation and immune status, providing valuable insights into disease progression, treatment response, and patient outcomes. Its prognostic utility has been demonstrated across various diseases, including cancer, cardiovascular conditions, autoimmune disorders, and infectious diseases. The individual contributions of neutrophils, platelets, monocytes, and lymphocytes reflect the intricate dynamics driving disease progression, underscoring the clinical relevance of PIV. As research advances, PIV holds substantial promise for personalized medicine, enabling clinicians to optimize treatment strategies, improve patient outcomes, and enhance healthcare delivery.