Osteoporosis is the most prevalent systemic skeletal illness featuring reduced bone mineral density, bone micro-structure destruction as well as elevated likelihood of fracture (1). Investigations from pertinent institutes indicate that nearly 33% of women and 20% of men over 50 face the risk of developing osteoporosis globally (2). The rapid aging of populations across the world will likely lead to a substantial growth in osteoporosis prevalence, consequently raising fracture occurrence rates. Fragility fractures are a frequent complication and significant clinical outcome of osteoporosis usually arising from low-energy external stresses (3, 4). It can profoundly lower the quality of life and health by inducing considerable pain, disability, loss of independence, and potentially mortality in affected individuals (2). Therefore, the efficient and prompt prevention of osteoporosis and the associated fracture risk has emerged as an essential priority in contemporary public health (5). In contemporary clinical practice, the assessment of osteoporotic fracture risk primarily relies on bone mineral density measurements and predictive models such as FRAX (6). Traditional predictive approaches incorporate selected clinical risk factors but overlook systemic inflammation, a pivotal pathophysiological mechanism in osteoporosis development (7). In contrast, immune inflammation indexes derived from routine complete blood counts serve as dynamic indicators of the body’s inflammatory status. These biomarkers are cost-effective, readily accessible, and capable of providing real-time reflections of immune-inflammatory activity. By incorporating inflammation as an independent risk determinant, such indices can substantially augment existing assessment models, offering a novel biological perspective for identifying high-risk individuals who may otherwise be underestimated by the FRAX model.

Inflammation is strongly linked to the onset of varied chronic diseases arising from proinflammatory cytokine secretion and persistent immune system activation (8, 9). Related studies suggested that changed host immunological status is an important danger factor contributing to abnormalities in bone metabolism, with its indirect effects potentially causing persistent bone damage (10, 11). This possibly results from the immunological microenvironment, where various immune cells interact with osteoblasts to yield outcomes (12). The immune inflammation index is a composite metric derived from peripheral blood cell subset counts for thorough inflammatory response and immune status evaluation. It reflects the dynamic interplay between circulating immune cells and inflammatory markers. Commonly utilized indexes encompass the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), as well as systemic inflammation response index (SIRI), among others. In the domain of osteoporosis, an increasing number of studies on immune inflammatory indexes are being carried out, with the expectation of offering novel approaches for predicting fracture risk in affected populations. Fu et al. identified NLR, MLR, as well as SIRI as independent spinal compression fracture risk factors, with MLR demonstrating a comparatively high diagnostic capacity (13). Fang et al. proved SII as an effective risk predictor for postmenopausal osteoporosis evaluation and a reliable discriminator of osteoporotic fracture risk (14).

To date, many clinical studies have validated the prediction value of immune inflammatory indexes concerning fracture risk. However, the existing evidence remains limited, with insufficient sample sizes and a lack of comprehensive analyses. Notably, no systematic review has yet been published to elucidate the clinical significance of this topic. Therefore, the present study aims to systematically evaluate and meta-analyze the available clinical data to determine whether immune inflammatory indexes can serve as effective fracture risk predictors in the osteoporosis population. Moreover, this study endeavors to provide an updated theoretical foundation to support more accurate fracture prediction model development for clinical application.

An imbalance in bone metabolism may give rise to a range of metabolic bone disorders like osteoporosis (26, 27). Immune dysregulation and the activation of systemic inflammatory responses are pivotal in osteoporosis and fragility fractures. The inflammatory processes and immune disturbances disrupt bone homeostasis, promote osteoclastogenesis, and enhance osteoclast activity, thereby lowering bone mineral density and strength, ultimately markedly raising the likelihood of fracture in individuals with osteoporosis (10, 28, 29). Research on postmenopausal osteoporosis indicates that menopause induces alterations in the immunological and inflammatory milieu due to age, calcium depletion, and diminished estrogen levels, which profoundly impact the microscopic architecture of bones. Specifically, inflammatory cells within the bone marrow stimulate cytokine and chemokine secretion, causing osteoporosis and elevating the fracture risk in females (14, 30). In recent years, a variety of inflammation-related disorders like chronic obstructive pulmonary disease, ankylosing spondylitis, systemic lupus erythematosus, as well as osteoarthritis were identified as risk factors for osteoporosis and fragility fractures (31–33). Inflammation is now recognized not only as a central pathogenic mechanism in the progression of osteoporosis but also as an important predictor of disease onset and fracture risk (13). Plenty of biomarkers obtained from normal blood testing, including NLR, LMR, SII, PLR, and SIRI, can indicate systemic inflammation and immunological state. Given their accessibility, cost-effectiveness, and comprehensiveness, these routinely available blood-based markers hold promise for predicting both osteoporosis and fracture risk, warranting further exploration. These indexes have already demonstrated diagnostic and prognostic utility in oncological and autoimmune diseases (21). Building upon this theoretical foundation, our study seeks to first evaluate whether multiple immune inflammatory indexes reliably predict the fracture risk among the osteoporosis population, thereby unveiling how inflammation influences bone health and informing future research in this domain.

This meta-analysis encompassed 3,769 patients and endeavored to evaluate the predictive relevance of the immune inflammation index (NLR, LMR, SII, PLR, SIRI) regarding the probability of fracture incidence in osteoporotic individuals. Our research has demonstrated that NLR, LMR, and SII have considerable predictive value for fracture risk in osteoporotic individuals. Meanwhile, the results of categorical and continuous variables for NLR, LMR, and SII between fracture and non-fracture groups were statistically significant, corroborating the strong correlation between the three indexes and the likelihood of fracture occurrence. Although the PLR and SIRI demonstrated notable disparities between the fracture and non-fracture groups, a consistent association was not observed when analyzed as categorical variables. The sensitivity analysis demonstrated that the pooled effect estimates for several indices, including NLR, LMR, SII, and PLR, exhibited notable fluctuations when individual studies were sequentially excluded, suggesting a certain degree of instability in the aggregated data. Such instability may stem from the predominantly retrospective nature of the included studies, which introduces potential uncontrolled confounding biases (14, 19, 23); inconsistencies in the timing of immune inflammatory index assessments and the lack of standardized cut-off definitions across studies; as well as the limited sample sizes in some investigations, particularly regarding fracture event counts, which may result in insufficient statistical power. Furthermore, heterogeneity in the study populations (for example, differences in menopausal status) may compromise the reliability of the estimated associations. Accordingly, future studies should aim to standardize both the measurement protocols and reporting criteria for these indices, and employ more sophisticated multivariable adjustment models in statistical analyses to enhance the robustness and generalizability of the findings. The assessment of publication bias revealed evidence of bias for the predictive indices NLR, LMR, and SII in categorical analyses. This phenomenon may be attributable to the retrospective design of the included studies and their geographic concentration within Asian populations. Therefore, the strength of associations reported in the present study should be interpreted with caution, as they may reflect a combination of genuine effects and publication bias. Ensuring research integrity in this field will require the inclusion of unpublished negative results and the conduct of prospective investigations to achieve a more balanced and objective risk estimation.

In the analysis, three primary subgroups, study design, mean age, and study population were established to evaluate the study content and to examine the correlation between various immune inflammatory indexes and the risk of fracture development. It is important to note that the “unclassified osteoporosis” group refers to patients with primary osteoporosis who have not been clearly categorized as having either senile or postmenopausal osteoporosis. The investigation revealed a decrease in sensitivity to immune inflammation indexes among patients aged 65 and older. This diminished responsiveness may be attributed to the age-related decline in innate immune function, commonly referred to as immunosenescence, which impairs the capacity of older individuals to mount effective physiological responses to fracture-induced stress, thereby reducing the prognostic utility of these biomarkers (34, 35). Moreover, within the NLR subgroup, studies targeting specific postmenopausal osteoporosis populations demonstrated a higher sensitivity to disease incidence compared to those that did not stratify osteoporosis populations. The foregoing findings reflect the imperative for future research to prioritize osteoporosis patient stratification by sex and to conduct independent investigations into the dynamic changes in inflammatory and immunological status before and after fracture events. Additionally, the substantial heterogeneity observed in the subgroup analyses may be attributed to multiple factors. In addition to variations in study design and population characteristics, clinical differences, such as disparities in fracture outcomes (e.g., vertebral versus hip fractures) across studies, may have introduced confounding effects by directly influencing systemic inflammatory responses. Moreover, inconsistencies in osteoporosis treatment protocols among study groups may have masked the true impact of immune inflammation indexes. From a methodological perspective, variability in blood sample analyzers and differences in the timing of biomarker assessments could have further contributed to measurement inconsistencies. Collectively, these factors account for the observed variability in outcomes. Future studies should emphasize the use of comprehensive and standardized testing methodologies to better control for potential confounding variables.

In the skeletal system, chronic inflammation and immune system activation directly influence bone integrity and quality. Evidence from studies on postmenopausal osteoporosis suggests that estrogen deficiency induces a persistent pro-inflammatory state, significantly increasing the secretion of inflammatory mediators within the local microenvironment, including interleukins (ILs), tumor necrosis factor-alpha (TNF-α), IL-1β, as well as reactive oxygen species (ROS) (12, 36, 37). These mediators influence the equilibrium of bone metabolism, facilitating the onset of osteoporosis and fragility fractures. Innate immune cells are the main sources of pro-inflammation mediators (37). Macrophages, derived from the monocyte lineage, exhibit pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes based on the immunological milieu (38). Their considerable plasticity allows them to shift between M1 and M2 states in response to environmental cues (39). M1 macrophages serve as precursors to osteoclasts. Prior investigations have demonstrated that stimulation of macrophages with established M1 inducers leads to a downregulation of osteoinductive factors like morphogenetic proteins BMP-2 and BMP-6 (38). Another study involving ovariectomized osteoporotic C57BL/6 mice proved that M1 and M2 in the bone marrow differentiate into functional osteoclasts under the influence of RANKL, with M2 macrophages contributing more significantly to this differentiation. This process ultimately resulted in an elevated M1/M2 ratio (40). Neutrophils, produced by precursors in the bone marrow, participate in innate immune responses and are implicated in bone loss via chemokine secretion and osteoclastogenic cell recruitment (41). Previous research has demonstrated that neutrophils play a pivotal role in the pathogenesis of inflammatory diseases. Their involvement encompasses the secretion of proteolytic enzymes, the formation of neutrophil extracellular traps (NETs), and the modulation of other immune cell activities (42).

Additionally, evidence indicates a robust link between the elevation of neutrophils induced by RANKL and reduced bone density in inflammatory conditions (43). Monocytes act as progenitors to osteoclasts and modulate inflammatory responses by secreting cytokines like IL-1, TNF, CXCL10, as well as IL-15, thereby facilitating osteoclast-mediated bone resorption and bone metabolism regulation (37, 44)]. Lymphocytes, including T and B cells, interact with osteoblasts and osteoclasts both directly and indirectly across cells, thereby influencing bone homeostasis. T cells demonstrate opposing roles in bone remodeling; while quiescent Txcells confer protective effects by limiting osteoclast activity, activated T cells promote osteoclastogenesis (45). In chronic inflammatory situations, regulatory T cells (Tregs) inhibit the activity of helper T cells 17 (Th17) (45). Additionally, there is an increase in inflammatory cytokines, including NFATc1, TNF-α, and IL-1β, synthesized by T cells (46). These methods render T lymphocytes pivotal in inflammation-induced bone resorption. B cells contribute to bone control akin to T cells by generating bone marrow-derived OPG under physiological conditions to suppress osteoclast development. Findings show that B cells, triggered by estrogen deficiency and inflammatory states, facilitate bone resorption via G-CSF and RANKL production (47). Under conditions of inflammatory activation, platelets directly interact with circulating leukocytes, resulting in alterations in the surface expression of P-selectin and CD40L. Moreover, platelets form specialized aggregates with monocytes and neutrophils, thereby participating actively in immune and inflammatory responses (48, 49). A cross-sectional study by Zhang et al. demonstrated a negative association between platelet count and BMD, underscoring the necessity of continuous platelet count monitoring in patients with osteoporosis (50). In conclusion, this inflammatory milieu facilitates the activation and recruitment of multiple blood cell types, including neutrophils, monocytes, and platelets. Such phenomena are reflected by variations in immune inflammation indexes. The intricate mechanisms linking immune-mediated inflammation and osteoporosis require further investigation. The intercellular interactions involved remain incompletely characterized. The present findings are consistent with prior evidence, demonstrating associations between alterations in several immune inflammatory indexes and increased risk of osteoporosis and related fractures.

Despite its clinical utility, the present meta-analysis is subject to several limitations that warrant consideration. A notable limitation of the present study lies in the fact that all included literature originated from East Asia (predominantly China), thereby restricting the generalizability of the findings across different racial groups. Variations in bone metabolic characteristics, genetic backgrounds, and comorbidity profiles among diverse populations may substantially influence both the strength of associations and the optimal cut-off values between immune inflammation indexes and fracture risk. Consequently, caution should be exercised when extrapolating these results to non-Asian populations. Future large-scale, multicenter, prospective studies involving osteoporotic cohorts of diverse ethnicities, including Caucasian and African populations, are urgently needed to validate the universality of these immune inflammation indexes and to explore potential race-specific thresholds. Such studies would provide more reliable evidence for accurate global risk prediction. Furthermore, most of the included research employed retrospective designs, which ma.y not adequately control for selection bias and confounding variables, thereby potentially compromising the robustness of the conclusions. To overcome this methodological limitation, future investigations should prioritize prospective cohort studies. Such studies are pivotal for establishing temporal relationships between immune inflammation indexes and subsequent fracture events, thereby providing a firmer basis for causal inference. Once the predictive value of these indicators is confirmed, research should advance toward interventional trials to translate predictive insights into actionable strategies, ultimately supporting precision prevention and treatment of osteoporosis.

Most studies did not define specific cut-off values for the respective immune inflammation indexes, and among those that did, substantial variability was observed. This inconsistency hampers cross-study comparability and data synthesis, likely contributing to the considerable heterogeneity identified in our meta-analysis. The absence and variability of established thresholds pose significant challenges to transforming these indexes from investigational markers into clinically practical prognostic tools. Without large-scale, validated cut-off values demonstrating definitive diagnostic utility, clinicians cannot reliably identify high-risk patients or make informed clinical decisions based on these markers, as they would when interpreting T-scores in osteoporosis diagnosis. Therefore, concerted efforts are needed to establish standardized threshold systems with strong discriminatory capacity to ensure research consistency and clinical applicability. These thresholds should be stratified by sex, age, and osteoporosis subtype to enhance the precision and relevance of risk assessment and clinical decision-making.

Additionally, the present study suggests that immune inflammation indexes may complement the traditional FRAX model in risk prediction. Whereas FRAX relies primarily on conventional clinical risk factors, inflammatory markers dynamically reflect underlying biological processes directly related to bone resorption. Integrating immune inflammation indexes into existing assessment frameworks may aid in identifying “hidden” high-risk individuals characterized by persistently elevated inflammation despite moderate FRAX scores, thus enabling more refined risk stratification. Future studies should focus on elucidating the biological mechanisms linking immune inflammation indexes with osteoporosis, developing innovative composite predictive models incorporating inflammatory biomarkers, and exploring early intervention strategies for patients with elevated inflammation levels.

The immune inflammation indexes, NLR, LMR, and SII, exhibit considerable potential in predicting fracture risk among individuals with osteoporosis. Meanwhile, given the limited number of studies evaluating the SIRI, only two in total, its predictive potential requires further validation through additional high-quality research. Subgroup analysis indicates that the prediction performance of these indexes may vary according to the specific type of osteoporosis. Given the methodological limitations, including the predominance of retrospective designs, small sample sizes, geographic homogeneity, and possible instability and publication bias, additional international, multicenter, and prospective studies are urgently required. Such efforts will be essential to validate these indexes as reliable tools for fracture risk prediction, with the ultimate goal of facilitating early identification and timely intervention for high-risk individuals.