Correlation Analysis Between Inflammatory Biomarkers and Dizziness: A Cross-Sectional Analysis Based on NHANES 1999-2004

Correlation Analysis Between Inflammatory Biomarkers and Dizziness: A Cross-Sectional Analysis Based on NHANES 1999-2004 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation Analysis Between Inflammatory Biomarkers and Dizziness: A Cross-Sectional Analysis Based on NHANES 1999-2004 Guangxin Hu, Wentao Hu, Xueying Wu, Jiaming Fu, Xinwu Liu, Junyao Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6863186/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Dizziness is a common symptom with diverse etiologies, and its prevalence increases with age. Emerging evidence indicates that systemic inflammatory processes might contribute to the development of dizziness. Nevertheless, the association between dizziness and integrated hematologic inflammatory markers has not been thoroughly investigated. Objective: This study aims to assess the relationship between inflammatory markers and dizziness. Methods: This population-based cross-sectional analysis utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2004. The study population consisted of 6,393 individuals aged over 40 years old. Seven inflammation-related biomarkers included monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio(PLR), and systemic immune-inflammation index (SII), which were computed from standard complete blood counts. Analytical approaches included weighted logistic regression, restricted cubic spline (RCS) models, threshold effect evaluation, subgroup stratification, and receiver operating characteristic (ROC) curve analyses to determine the relationship between these biomarkers and self-reported dizziness. Results: After adjusting for potential confounders, elevated levels of MLR, SIRI, AISI, and CAR were significantly associated with increased odds of dizziness. RCS and threshold effect analysis revealed a significant non-linear relationship between SIRI and dizziness, with a significant inflection point at 1.2526. Subgroup analyses indicated stronger associations among females, non-smokers, and individuals with higher educational attainment. Among all markers, SIRI demonstrated the highest area under the ROC curve (AUC = 0.5532), although overall predictive performance remained modest. Conclusion: Several CBC-derived inflammatory biomarkers (MLR, SIRI, and AISI) were independently associated with dizziness in a general adult population. These findings support the involvement of systemic inflammation in dizziness and suggest that such biomarkers may serve as adjunctive tools for risk identification. Further longitudinal studies are needed to clarify causality and underlying mechanisms. NHANES Dizziness inflammation monocyte-to-lymphocyte ratio systemic inflammation response index aggregate index of systemic inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Dizziness is one of the most common symptoms globally, with an estimated lifetime prevalence ranging from 15–35% in the general population, and its prevalence increases with advancing age [ 1 , 2 ]. Beyond its impact on daily functioning and quality of life, dizziness has been associated with heightened mortality risks linked to cardiovascular conditions and diabetes mellitus [ 3 , 4 ]. As the incidence of dizziness continues to rise, particularly among aging populations, it is imperative to identify reliable predictive biomarkers to facilitate early intervention and management. Dizziness is a multifactorial symptom that can result from a variety of conditions, including cerebrovascular disease, vestibular system diseases, multiple sclerosis, migraine, depression, anxiety, and adverse effects of medications [ 5 , 6 ]. Among the proposed mechanisms, inflammation has emerged as an important contributor to dizziness pathogenesis. For example, in patients with vestibular neuritis, viral or immune-mediated inflammation can directly impair vestibular structures, leading to symptoms such as dizziness and nausea [ 7 ]. Similarly, in systemic lupus erythematosus, immune complex deposition can damage the vestibular system, resulting in balance disorders [ 8 ]. These findings suggest that inflammatory processes may be centrally involved in both the onset and persistence of dizziness, positioning inflammatory biomarkers as valuable candidates for both diagnostic refinement and targeted interventions. In recent years, inflammatory biomarkers have gained increasing attention in clinical research. Composite hematologic indices such as the monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) which have been increasingly recognized as reliable surrogates for systemic inflammation and as predictors of diverse pathological outcomes [ 9 – 14 ]. SIRI has demonstrated significant associations with periodontitis in population-based analyses [ 15 ], while NLR, MLR, and PLR have been reported as prognostic markers in patients with advanced kidney disease [ 16 ], and CAR values have been previously linked to poor outcomes in neurological disorders such as stroke and cognitive decline [ 17 ]. Compared to single blood cell parameters, these composite indices provide a more integrated reflection of inflammatory status and have demonstrated superior predictive value in inflammatory and metabolic conditions [ 18 ]. Given their ability to capture systemic inflammatory burden, it is plausible that these indices may also serve as early indicators for inflammation-related dizziness. However, the potential link between dizziness and comprehensive blood count (CBC)-based inflammatory indices remains largely unexamined. To address this research gap, we performed a cross-sectional investigation utilizing data from the National Health and Nutrition Examination Survey (NHANES). The primary objective was to assess the association between dizziness and a range of inflammation-related hematological markers. Our study may support early identification and clinical evaluation of individuals at risk for dizziness. Materials and methods Data Source and Study Population This study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES, 1999–2004). NHANES employs a stratified multistage probability sampling design, covering non-hospitalized populations in the United States. The dataset includes demographic, dietary, physical examination, laboratory testing, and questionnaire information. The NHANES survey was approved by the Ethics Review Board of the National Center for Health Statistics, and all participants provided informed consent prior to enrollment. All NHANES data are publicly available on the website ( https://www.cdc.gov/nchs/nhanes ). Following a comprehensive screening process of the NHANES dataset, a cohort of 31,126 subjects spanning the years 1999 to 2004 was initially deemed eligible for inclusion in this investigative study. The analysis excluded individuals with missing data on dizziness symptoms (n = 21,176). In addition, participants with missing lymphocyte count (n = 1,255), serum albumin (n = 469), and C-reactive protein (CRP) (n = 1) were also removed. Pregnant individuals were excluded to avoid potential confounding (n = 7). Furthermore, we excluded participants with missing information on key covariates, including education level (n = 21), marital status (n = 281), poverty income ratio (PIR) (n = 749), drinking status (n = 384), Hypertension status (n = 38), diabetes status (n = 136), smoking status (n = 8), and body mass index (BMI) (n = 208). After implementing all exclusion criteria, the final analytic sample comprised 6,393 individuals. A detailed flow diagram of the participant selection process is provided in Fig. 1 . Study variables The main dependent variable in this analysis was dizziness, as assessed through participant self-report in the NHANES survey. Individuals were classified as experiencing dizziness based on their response (“yes” or “no”) to the question: “During the past 12 months, have you had a problem with dizziness, lightheadedness, feeling as if you are going to pass out or faint, unsteadiness or imbalance?” It is critically important to emphasize that participation in this survey was exclusively restricted to individuals aged over 40 years old. This age-based inclusion criterion was intentionally implemented to align with the research objectives of investigating adult-specific health patterns, thereby ensuring biological homogeneity and epidemiological relevance in the analytical cohort. The main independent variables were inflammation-related biomarkers calculated from complete blood count and serum biochemical measurements. The CBC parameters (neutrophil count, lymphocyte count, monocyte count and platelet count) were measured using the Beckman Coulter automated hematology analyzer, which applies impedance-based techniques for cell counting and sizing. For serum albumin, values were obtained using the bromocresol purple dye-binding method, as part of the NHANES Standard Biochemistry Profile. All blood samples were drawn during morning sessions following an overnight fast to minimize variability in biomarker levels. To capture the systemic inflammatory burden, the following composite indices were calculated: MLR = monocyte count / lymphocyte count SIRI = (neutrophil count × monocyte count) / lymphocyte count CAR = C-reactive protein / albumin AISI = (neutrophil count × platelet count × monocyte count) / lymphocyte count NLR = neutrophil count / lymphocyte count PLR = platelet count / lymphocyte count SII = (platelet count × neutrophil count) / lymphocyte count These markers, derived from absolute cell counts and biochemical parameters, provide comprehensive insight into the balance of immune cell subpopulations and the acute-phase inflammatory response. All measurements adhered to NHANES' rigorous quality control standards, which include standardized procedures, regularly calibrated instruments, and periodic proficiency testing to ensure data reliability and reproducibility. To explore potential dose–response relationships, all inflammatory biomarkers were treated as continuous variables and further stratified into quartiles. Selection of covariates Utilizing existing literature and clinical insights, we incorporated a range of covariates that could potentially influence the association between inflammation-related biomarkers and dizziness. The continuous variables included age, poverty-to-income ratio (PIR), and body mass index (BMI, kg/m²). The categorical variables included sex, race/ethnicity, education level, and marital status, smoking status (at least 100 cigarettes in a lifetime?), drinking status (had at least 12 alcohol drinks/1 yr?), diabetes history (have you been told by a doctor or health professional that you have diabetes?), hypertension history (have you been told by a doctor or health professional that you have hypertension, also called high blood pressure?). Statistical analysis To summarize participant characteristics, descriptive analyses were carried out, presenting categorical variables as frequencies and percentages, while continuous variables were expressed as means accompanied by standard errors (SE). Differences between groups were assessed using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical data. Given the stratified, multistage sampling framework employed in NHANES, all analyses incorporated appropriate sampling weights to correct for unequal selection probabilities, oversampling, and participant nonresponse. This weighting approach enhances the generalizability and precision of the results. To explore the association between inflammatory markers and dizziness, we constructed weighted binary logistic regression models, treating dizziness as the dependent variable. Inflammatory biomarkers including MLR, SIRI, AISI, CAR, NLR, PLR, and SII were included as independent variables. We developed three models: Model 1 provided unadjusted estimates; Model 2 adjusted for demographic variables including age, sex, and ethnicity; and Model 3 included additional adjustments for socioeconomic and health-related confounders, namely educational attainment, marital status, BMI, poverty income ratio (PIR), smoking habits, alcohol consumption, as well as diabetes and hypertension status. The results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Furthermore, restricted cubic spline (RCS) modeling and threshold analyses were employed to explore non-linear and dose-dependent associations between each biomarker and dizziness. Stratified analyses were also conducted to evaluate potential effect modification across subgroups. Statistical interaction was tested to assess heterogeneity among subgroups, and forest plots were created to visualize subgroup-specific ORs and confidence intervals. All statistical procedures were implemented using R (version 4.3.2) and EmpowerStats (version 4.2), with statistical significance defined as a two-sided p-value < 0.05. Results Baseline characteristics A total of 6,393 adult participants were included in the analysis, among whom 1,645 individuals (25.73%) reported experiencing dizziness. Table 1 summarizes the sociodemographic and behavioral attributes of the study population, categorized based on the presence or absence of dizziness. The prevalence of dizziness was notably higher among older, economically disadvantaged, and well-educated Non-Hispanic White females who were married and had a history of cigarette use, excessive alcohol intake, hypertension, or diabetes mellitus. Additionally, individuals in the dizziness group exhibited significantly increased mean values of several inflammation-related biomarkers, including MLR (0.31 ± 0.005), SIRI (1.41 ± 0.03), CAR (0.13 ± 0.01), AISI (380.50 ± 10.74), NLR (2.39 ± 0.04), and SII (635.19 ± 11.39). In contrast, no statistically significant difference was observed in PLR levels between the two groups (P = 0.84). Table 1 Baseline characteristics of the study population. Characteristics With dizziness (N = 1,645) Without dizziness (N = 4,748) P-value Age (year), mean ± SE 59.74 ± 0.46 55.53 ± 0.26 < 0.0001 PIR, mean ± SE 2.64 ± 0.08 3.43 ± 0.05 < 0.0001 Sex (n, %) < 0.0001 Male 634 (38.56%) 2,403 (50.61%) Female 1,011 (61.44%) 2,345 (49.39%) Race (n, %) 0.0431 Mexican American 68 (4.11%) 220 (4.63%) Other Hispanic 92 (5.60%) 201 (4.23%) Non-Hispanic White 1,271 (77.29%) 3,776 (79.52%) Non-Hispanic Black 133 (8.09%) 397 (8.36%) Other Race 81 (4.91%) 154 (3.25%) Education (n, %) < 0.0001 Less Than High School 438 (26.62%) 812 (17.11%) High School Diploma (including GED) 484 (29.40%) 1,181 (24.87%) More Than High School 723 (43.98%) 2,755 (58.02%) Marital Status (n, %) < 0.0001 Married 960 (58.38%) 3,319 (69.90%) Widowed 264 (16.06%) 373 (7.85%) Divorced 238 (14.45%) 533 (11.23%) Separated 44 (2.65%) 107 (2.25%) Never married 89 (5.41%) 262 (5.51%) Living with partner 50 (3.05%) 154 (3.26%) Smoking status (n, %) 0.0161 Yes 935 (56.84%) 2,490 (52.44%) No 710 (43.16%) 2,258 (47.56%) Drinking status (n, %) < 0.0001 Yes 1,034 (62.86%) 3,402 (71.66%) No 611 (37.14%) 1,346 (28.34%) Hypertension (n, %) < 0.0001 Yes 848 (51.52%) 1,607 (33.85%) No 797 (48.48%) 2,141 (66.15%) Diabetes (n, %) < 0.0001 Yes 269 (16.35%) 391 (8.23%) No 1,376 (83.65%) 4,357 (91.77%) BMI, mean ± SE 28.67 ± 0.22 28.58 ± 0.15 0.6925 MLR, mean ± SE 0.31 ± 0.005 0.29 ± 0.002 0.0032 SIRI, mean ± SE 1.41 ± 0.03 1.25 ± 0.01 < 0.0001 CAR, mean ± SE 0.13 ± 0.01 0.10 ± 0.003 < 0.0001 AISI, mean ± SE 380.50 ± 10.74 335.45 ± 4.60 0.0001 NLR, mean ± SE 2.39 ± 0.04 2.22 ± 0.02 0.0001 PLR, mean ± SE 142.99 ± 1.74 142.63 ± 1.22 0.8441 SII, mean ± SE 635.19 ± 11.39 589.33 ± 6.09 0.0004 Note: Values are weighted mean ± SE or weighted % (95% confidence interval). P values are weighted Table 1 (continued) Correlation between Inflammatory Biomarkers and Dizziness The partial correlation among inflammatory biomarkers and dizziness both in continuous and categorical analyses are illustrated in Table 2 . As the continuous analysis demonstrated, positive associations were consistently found between SIRI, MLR, AISI, NLR and dizziness in Models 1–3 (all P < 0.05). A strong association between the prevalence of dizziness and CAR was observed in Models 1 (OR = 1.8726, 95% CI: 1.3438–2.6096, P = 0.0006) and Model 2(OR = 1.6066, 95% CI: 1.1940–2.1618, P = 0.0034), however, this relationship diminished in Model 3 (P = 0.0689). Furthermore, there were no relationships between PLR, SII and dizziness in model 1 (P > 0.05), suggesting limited diagnostic utility in this context. Among all biomarkers analyzed, MLR emerged as the most robust predictor of dizziness in Model 3 (OR = 2.0474, 95% CI: 1.1886–3.5268, P = 0.0163). Table 2 Association between inflammatory biomarkers and dizziness. Exposure MODEL1 OR (95%CI) P-value MODEL2 OR (95%CI) P-value MODEL3 OR (95%CI) P-value MLR 2.4256 (1.4137, 4.1621) 0.0025 2.2053 (1.2614, 3.8555) 0.0086 2.0474 (1.1886, 3.5268) 0.0163 Q1 reference reference reference Q2 1.0247 (0.8327, 1.2610) 0.8187 1.1068 (0.8858, 1.3830) 0.3779 1.1526 (0.9380, 1.4164) 0.1903 Q3 1.0984 (0.9272, 1.3012) 0.2839 1.1709 (0.9775, 1.4026) 0.0956 1.1882 (0.9894, 1.4268) 0.0783 Q4 1.2948 (1.0493, 1.5979) 0.0206 1.3374 (1.0594, 1.6883) 0.0196 1.3464 (1.0786, 1.6807) 0.0153 p for trend p < 0.0109 0.0102 0.0112 SIRI 1.1989 (1.1163, 1.2876) < 0.0001 1.1851 (1.0984, 1.2787) 0.0001 1.1111 (1.0271, 1.2020) 0.0148 Q1 reference reference reference Q2 1.0829 (0.8759, 1.3389) 0.4660 1.1266 (0.9174, 1.3835) 0.2631 1.1137 (0.8893, 1.3947) 0.3582 Q3 1.3767 (1.1070, 1.7121) 0.0064 1.4215 (1.1349, 1.7806) 0.0042 1.3254 (1.0502, 1.6727) 0.0268 Q4 1.5579 (1.3033, 1.8622) < 0.0001 1.5820 (1.2983, 1.9276) 0.0001 1.3674 (1.1197, 1.6700) 0.0056 p for trend p < 0.0001 p < 0.0001 0.0018 CAR 1.8726 (1.3438, 2.6096) 0.0006 1.6066 (1.1940, 2.1618) 0.0034 1.2825 (0.9928, 1.6567) 0.0689 Q1 reference reference reference Q2 1.1359 (0.9195, 1.4034) 0.2442 1.0731 (0.8647, 1.3317) 0.5259 1.0466 (0.8447, 1.2968) 0.6808 Q3 1.2469 (1.0171, 1.5286) 0.0398 1.1180 (0.9137, 1.3680) 0.2860 1.0355 (0.8425, 1.2728) 0.7432 Q4 1.7096 (1.4225, 2.0545) < 0.0001 1.4709 (1.2163, 1.7787) 0.0003 1.2647 (1.0478, 1.5265) 0.0229 p for trend p < 0.0001 0.0003 0.0295 AISI 1.0005 (1.0003, 1.0007) < 0.0001 1.0004 (1.0002, 1.0007) 0.0005 1.0003 (1.0000, 1.0005) 0.0460 Q1 reference reference reference Q2 1.3522 (1.0676, 1.7126) 0.0164 1.3647 (1.0743, 1.7336) 0.0154 1.3005 (1.0229, 1.6534) 0.0432 Q3 1.2544 (1.0381, 1.5157) 0.0238 1.2637 (1.0243, 1.5591) 0.0357 1.1539 (0.9270, 1.4363) 0.2135 Q4 1.5645 (1.2836, 1.9068) 0.0001 1.5357 (1.2381, 1.9048) 0.0004 1.3101 (1.0541, 1.6283) 0.0235 p for trend 0.0001 0.0008 0.0564 NLR 1.1132 (1.0634, 1.1653) < 0.0001 1.0913 (1.0382, 1.1471) 0.0015 1.0619 (1.0059, 1.1210) 0.0397 Q1 reference reference reference Q2 1.0424 (0.8119, 1.3384) 0.7461 1.1122 (0.8719, 1.4187) 0.3978 1.1261 (0.8887, 1.4270) 0.3362 Q3 1.2636 (0.9983, 1.5993) 0.0586 1.2975 (1.0266, 1.6399) 0.0361 1.2853 (1.0106, 1.6348) 0.0529 Q4 1.2503 (1.0244, 1.5259) 0.0337 1.2394 (0.9992, 1.5374) 0.0589 1.1777 (0.9386, 1.4775) 0.1717 p for trend 0.0046 0.015 0.0787 PLR 1.0001 (0.9991, 1.0011) 0.8438 0.9996 (0.9986, 1.0006) 0.3999 1.0002 (0.9993, 1.0011) 0.6831 Q1 reference reference reference Q2 0.9615 (0.7840, 1.1792) 0.7081 0.9616 (0.7857, 1.1769) 0.7064 1.0444 (0.8599, 1.2686) 0.6654 Q3 0.8437 (0.6918, 1.0288) 0.1007 0.8298 (0.6714, 1.0257) 0.0933 0.9382 (0.7591, 1.1596) 0.5614 Q4 0.9796 (0.8091, 1.1861) 0.8338 0.9141 (0.7521, 1.1110) 0.3729 1.0661 (0.8915, 1.2748) 0.4904 p for trend 0.5797 0.2322 0.7743 SII 1.0003 (1.0002, 1.0004) 0.0002 1.0002 (1.0001, 1.0004) 0.0048 1.0001 (1.0000, 1.0003) 0.1071 Q1 reference reference reference Q2 0.9843 (0.7886, 1.2285) 0.8892 0.9920 (0.7843, 1.2548) 0.9471 0.9855 (0.7867, 1.2346) 0.9001 Q3 1.1725 (0.9596, 1.4326) 0.1273 1.1816 (0.9554, 1.4613) 0.1328 1.1698 (0.9514, 1.4383) 0.1511 Q4 1.1947 (0.9740, 1.4654) 0.0953 1.1428 (0.9196, 1.4202) 0.2368 1.0522 (0.8404, 1.3174) 0.6614 p for trend 0.0384 0.1155 0.396 Model 1: Non-adjusted model; Model 2: adjusted for: sex, age, race; Model 3 adjusted for: age, sex, race, education attainment, marital status, alcohol status, smoking status, poverty income ratio, BMI, Hypertension, diabetes. In the categorical analysis, using the lowest quartile (Q1) as the reference group, higher quartiles of several biomarkers were associated with increased dizziness risk. Significant associations were evident for SIRI in both Q3 and Q4, while elevated levels of MLR, CAR, and AISI in Q4 were also linked to greater odds of dizziness. Moreover, a clear dose–response pattern was observed, where increases in SIRI, MLR, and CAR levels corresponded with progressively higher odds ratios (P-values < 0.05). Specifically, in Model 3, Q4 of SIRI (OR = 1.3674, 95% CI: 1.1197–1.6700), MLR (OR = 1.3464, 95% CI: 1.0786–1.6807), CAR (OR = 1.2647, 95% CI: 1.0478–1.5265), LAP (OR = 1.5400, 95% CI: 1.1600–2.0500), and AISI (OR = 1.3101, 95% CI: 1.0541–1.6283) showed significant associations with dizziness when compared to the reference quartiles. These findings suggest that elevated levels of these inflammatory markers serve as independent predictors of dizziness. Interestingly, although the continuous analysis identified a statistically significant positive relationship between NLR and dizziness, this association did not persist in the categorical analysis. There has no significant differences were observed across quartiles in either Model 2 or Model 3. This inconsistency suggests that the link between NLR and dizziness may lack stability or robustness across different analytical approaches. To explore the relationship between inflammatory biomarkers and dizziness more comprehensively, restricted cubic spline (RCS) regression models were applied with full covariate adjustment. As shown in Fig. 2 , a significant positive linear association was observed between both SIRI and MLR levels and the prevalence of dizziness (p for overall effect 0.05), suggesting the statistical significance and stability of the inflammatory- dizziness association. To further explore this pattern, a threshold effect analysis was performed using a piecewise linear regression model (Table 3 ). A significant inflection point was identified at SIRI = 1.2526, corroborated by a log-likelihood ratio test (P = 0.038). Below this threshold (SIRI < 1.2526), SIRI was significantly associated with increased risk of dizziness (OR = 1.3903, 95% CI: 1.1061–1.7474, P = 0.0047), whereas the association was attenuated and non-significant above the threshold (OR = 1.0553, 95% CI: 0.9811–1.1353, P = 0.1343), indicating a potential saturation effect. No significant threshold effects were observed for other biomarkers. Table 3 Threshold effect analysis of inflammatory biomarkers and dizziness. Fitting by the 2-piecewise linear model Adjusted OR (95% CI) P-value Inflection point 1.2526 SIRI = 1.2526 1.0553 (0.9811, 1.1353) 0.1480 p for Log-likelihood ratio 0.0380 adjusted for: age, sex, race, education attainment, marital status, alcohol status, smoking status, poverty income ratio, BMI, Hypertension, diabetes. Subgroup and ROC analysis To investigate potential effect modifiers in the relationship between inflammatory biomarkers and dizziness, we conducted stratified analyses across key demographic and clinical variables, including sex, age, race/ethnicity, education level, marital status, BMI, smoking status, drinking status, and history of hypertension and diabetes (Fig. 3 ). Interaction terms were incorporated into the regression models to evaluate the statistical significance of effect modification. Notably, significant interactions were identified for CAR and smoking status, NLR and education level, and SII and education level (all P for interaction < 0.05), indicating that the strength of the associations between these inflammatory markers and dizziness may vary across subgroups defined by these characteristics. Further subgroup analyses revealed that several inflammatory markers demonstrated stronger positive associations with dizziness in specific populations. For instance: In participants with educational attainment beyond high school, the associations between dizziness and SIRI, AISI, NLR, and SII were more pronounced (P < 0.05). Among female participants, significant associations were observed for MLR, SIRI, AISI, and NLR (P < 0.05). Among non-smokers, stronger associations were found between dizziness and both CAR and AISI (P < 0.05). These findings suggest that demographic and lifestyle factors may modulate the relationship between systemic inflammation and dizziness, emphasizing the importance of considering subgroup characteristics when evaluating potential inflammatory risk factors. These results highlight that the link between systemic inflammation and dizziness may be influenced by specific sociodemographic and behavioral factors, underlining the necessity of incorporating subgroup-specific characteristics when assessing inflammatory biomarkers as potential predictors of dizziness. The diagnostic performances of the eight inflammatory biomarkers for dizziness were investigated using ROC curves. The ROC curve indicated that SIRI had comparable and highest diagnostic efficacy for dizziness. (AUC: 0.5532, 95% CI: 0.5371–0.569) (Fig. 4 ). Discussion This cross-sectional analysis represents the first investigation to assess the association between systemic inflammatory markers and the presence of dizziness. Our findings demonstrated that elevated levels of MLR, SIRI, and AISI were positively associated with dizziness, even after adjusting for a wide range of potential confounders. Among these markers, MLR exhibited the strongest and most consistent association, indicating its potential value in reflecting low-grade systemic inflammation involved in dizziness pathophysiology. Although CAR was initially associated with dizziness, this relationship diminished after controlling covariates, indicating a lack of stability in its predictive power. This variability may, in part, be attributed to findings from our interaction analyses, which revealed a statistically significant modifying effect of smoking status on the CAR–dizziness association. We also found a positive linear relationship between both SIRI and MLR and dizziness through RCS. In particular, the threshold analysis for SIRI revealed a significant inflection point at 1.2526, below which SIRI was strongly associated with dizziness. This suggests that low-to-moderate elevations in systemic inflammation may have a more pronounced effect on dizziness risk, whereas higher levels might reach a saturation point beyond which additional risk does not increase significantly. This nonlinear pattern suggesting that immune system activation may play a role in early stages of neurological or vestibular dysfunction, while chronic high-level inflammation might induce adaptation or compensatory mechanisms. Inflammatory biomarkers such as the MLR and the SIRI have been associated with neurological outcomes, particularly in stroke [ 19 ]. Inflammation also plays a role in the pathophysiology of dizziness, with oxidative stress and pro-inflammatory signaling pathways contributing to chronic vestibular symptoms [ 20 ]. Patients with vestibular dysfunction frequently exhibit signs of immune or systemic involvement, suggesting that dizziness may be influenced by underlying inflammatory processes [ 21 ]. Furthermore, vestibular balance disorders have been linked to bilateral otolith dysfunction, which has been associated with dizziness symptoms even in the absence of apparent central nervous system damage [ 22 ]. Central vestibular disorders, including those affecting the brainstem and cerebellum, are also recognized contributors to dizziness, and their clinical manifestations may overlap with those of systemic inflammation [ 23 ]. In addition, functional dizziness—an increasingly acknowledged condition in clinical practice—often arises from complex interactions between physiological and psychological factors, including inflammation and heightened autonomic reactivity [ 24 ]. Subgroup analyses offered deeper insights into the heterogeneity of the observed associations. Significant interaction effects were found between CAR and smoking status, as well as between NLR and SII and educational level. These results suggest that individuals with certain behavioral or socioeconomic characteristics (such as non-smokers and those with higher educational attainment) may be more susceptible to the effects of systemic inflammation on dizziness. Previous studies have demonstrated that systemic inflammation, as measured by CRP and other biomarkers, is strongly influenced by smoking status. Specifically, current smokers tend to exhibit higher levels of inflammation than non-smokers and former smokers [ 25 , 26 ]. This phenomenon occurs because dizziness in non-smokers is more likely to be exclusively mediated by inflammatory mechanisms (unconfounded by smoking-related factors), whereas in smokers, dizziness may arise from multifactorial etiologies (e.g., vasospasm, carbon monoxide toxicity) that diminish the relative contribution of inflammatory components [ 27 ]. Moreover, higher educational attainment has been associated with a stronger inflammatory response to psychological and physical stressors, possibly due to differences in lifestyle or health perception, which may increase their physiological sensitivity to systemic inflammation. Additionally, socioeconomic factors such as education level have been found to modulate inflammatory responses across the life course, with variable patterns depending on the biomarker and context, indicating that educational attainment interacts with stress and immune function in complex ways [ 28 ]. Our gender-stratified analysis revealed that MLR, SIRI, AISI and NLR were more predictive of dizziness among women. Research indicates that systemic inflammation levels such as CRP and IL-6 vary significantly with sex, with females often exhibiting elevated inflammatory markers that contribute to cognitive and neurological decline [ 29 ]. These sex-related differences in inflammatory profiles may partly stem from hormonal fluctuations, several studies demonstrate that estrogen may Toll-like receptor signaling pathways to amplify inflammatory responses [ 30 – 32 ]. From a clinical perspective, the ability of SIRI to differentiate dizziness was modest (AUC = 0.5532) [ 33 ]. This suboptimal performance may stem from potential model overfitting or variability in predictor stability. Moreover, the operational definition of dizziness in this analysis relied on self-reported data from the NHANES questionnaire, which may lack the diagnostic precision required for clinical classification. Nonetheless, when integrated into comprehensive, multimodal predictive frameworks, inflammatory biomarkers such as SIRI could still contribute to improving overall diagnostic accuracy. These indices have shown predictive value in various inflammatory conditions, including cardiovascular disease [ 34 ], sarcopenia[ 35 ], psoriatic disease [ 36 ], and fatty liver disease [ 37 ]. Specifically, SII and SIRI have been associated with increased mortality risk [ 38 ] and disease severity in infections and neurovascular events [ 39 ], while CAR and SII have been explored as biomarkers distinguishing causes of vertigo [ 40 ]. More broadly, the link between systemic inflammation and vestibular or balance-related symptoms has been supported by experimental and clinical research. Cytokines such as IL-6, TNF-α, and CRP have been shown to influence neurovascular integrity and vestibular afferent function. For instance, patients with Menière’s disease and vestibular migraine demonstrate elevated TNF-α and IL-6 levels compared to controls, indicating a role of inflammation in vestibular dysfunction [ 41 ]. Chronic inflammation may impair cerebral perfusion and promote oxidative stress, as evidenced by vascular vertigo patients exhibiting significantly elevated TNF-α and IL-6 levels along with reduced antioxidant capacity [ 42 ]. Furthermore, studies using in vitro blood-labyrinth barrier models have confirmed that cytokines like TNF-α increase inner ear vascular permeability and may contribute to dizziness-related pathology [ 43 ]. Inflammatory labyrinthitis, characterized by blood-labyrinth barrier impairment, further supports a direct inflammatory mechanism contributing to vestibular dysfunction [ 44 ]. Moreover, inflammation may exacerbate central perception of dizziness in individuals with heightened interoceptive or anxiety-related responses, as supported by findings that vestibular rehabilitation can modulate oxidative stress pathways and reduce dizziness symptoms through the anti-inflammatory SIRT1 axis [ 45 ]. Finally, studies in pediatric populations show that systemic inflammatory responses, such as elevated CRP, can coincide with vestibular neuronitis and vertigo during respiratory infections [ 46 ]. localized neuroinflammation may significantly contribute to the onset and persistence of dizziness. Experimental research has demonstrated that key pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α can be upregulated in the vestibular nuclei and surrounding brainstem regions following peripheral vestibular injury [ 47 ]. These mediators not only enhance glial activation and neuronal excitability but also interfere with synaptic plasticity, a key element in the process of central vestibular compensation [ 48 ]. Additionally, microglial activation plays a central role in modulating inflammation and neuronal plasticity within the vestibular nuclei, influencing the speed and completeness of compensation [ 49 ]. Furthermore, chronic low-grade inflammation may impair the physiological process of vestibular adaptation. Vestibular compensation relies on neuroplasticity, cerebellar recalibration, and multisensory integration. However, inflammation can compromise blood–brain barrier integrity and reduce neurotrophic factor signaling like BDNF, critical for synaptic recovery and balance function [ 50 ]. Structural brain plasticity in multisensory cortices has also been observed during vestibular recovery, underlining the role of inflammation in modulating central compensation [ 51 ]. Additionally, modulation of BDNF-TrkB signaling pathways in the medial vestibular nucleus enhances neuronal survival and functional recovery [ 52 ]. Conclusion Several CBC-derived inflammatory biomarkers (MLR, SIRI, and AISI) were independently associated with dizziness in a general adult population. These findings support the involvement of systemic inflammation in dizziness and suggest that such biomarkers may serve as adjunctive tools for risk identification. Further longitudinal studies are needed to clarify causality and underlying mechanisms. Abbreviations AISI aggregate index of systemic inflammation BMI body mass index CAR C-reactive protein-to-albumin ratio CBC comprehensive blood count CIs confidence intervals CRP C-reactive protein MLR monocyte-to-lymphocyte ratio NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination Survey NLR neutrophil-to-lymphocyte ratio ORs odds ratios PIR poverty income ratio PLR platelet-to-lymphocyte ratio RCS restricted cubic spline ROC receiver operating characteristic SE standard errors SII systemic immune-inflammation index SIRI systemic inflammation response index Declarations Acknowledgements We extend our gratitude to all volunteers who participated in the National Health and Nutrition Examination Survey (NHANES). We also acknowledge the National Center for Health Statistics for making the NHANES data publicly available for research purposes. Data availability The data supporting the findings of this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available dataset. The NHANES data can be accessed through the Centers for Disease Control and Prevention (CDC) website (https://wwwn.cdc.gov/Nchs/Nhanes/). Author contributions Guangxin Hu, Wentao Hu and Jiaming Fu designed and conceived this study. Jiaming Fu and Junyao Chen collected the data. Xinwu Liu, Wentao Hu and Xueying Wu analyzed the data. Guangxin Hu and Junyi Fu wroted the manuscript. Yunyun Liang and Junyi Fu revised the paper. Each author contributed important content during manuscript drafting or revision and accepts accountability for the overall work, and all the authors agreed on the final manuscript. Funding This work was supported by the Basic and Applied Basic Research Fund of Guangdong Province (2022A1515220024), the Basic and Applied Basic Research Fund of Guangzhou science project (2024A04J3630), Guangzhou Medical Key Discipline Construction Project (2025-2027). Ethics approval and consent to participate This study utilized data from the National Health and Nutrition Examination Survey (NHANES), which received approval from the Ethics Review Board of the National Center for Health Statistics (NCHS). Informed consent was obtained from all participants by the NCHS during the data collection phase. This study will be performed in line with the principles of the Declaration of Helsinki. The current study involved a secondary analysis of publicly available, de-identified data, for which no additional ethical approval was necessary. Consent for publication Not applicable. Conflict of interest The authors declare that there is no conflict of interest. References Kroenke K, Price RK. 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Life (Basel) 2024, 14. Shen D, Cai X, Hu J, Song S, Zhu Q, Ma H, Zhang Y, Ma R, Zhou P, Yang W, et al. Inflammatory Indices and MAFLD Prevalence in Hypertensive Patients: A Large-Scale Cross-Sectional Analysis from China. J Inflamm Res. 2025;18:1623–38. Xia Y, Xia C, Wu L, Li Z, Li H, Zhang J. Systemic Immune Inflammation Index (SII), System Inflammation Response Index (SIRI) and Risk of All-Cause Mortality and Cardiovascular Mortality: A 20-Year Follow-Up Cohort Study of 42,875 US Adults. J Clin Med 2023, 12. Fois A, Paliogiannis P, Scano V, Cau S, Babudieri S, Perra R, Ruzzittu G, Zinellu E, Pirina P, Carru C et al. The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients. Molecules 2020, 25. Şahin O, Güneş M, Dönmez R. C-reactive protein-to-albumin ratio and systemic immune-inflammatory index as potential markers in distinguishing acute cerebellar infarction from benign paroxysmal positional vertigo. Neurosciences (Riyadh). 2025;30:30–5. Monaghan NP, Shah S, Keith BA, Nguyen SA, Newton DA, Baatz JE, Wagner CL, Rizk HG. Proinflammatory Cytokine Profiles in Menière's Disease and Vestibular Migraine. Otol Neurotol. 2025;46:88–95. Qian SX, Gu JX, Guan QB, Zhang XL, Wang YP. Serum oxidative stress, inflammatory response and platelet activation in patients with vascular vertigo. J Biol Regul Homeost Agents. 2019;33:499–504. Sekulic M, Puche R, Bodmer D, Petkovic V. Human blood-labyrinth barrier model to study the effects of cytokines and inflammation. Front Mol Neurosci. 2023;16:1243370. Djian C, Champion K, Lai N, Drouet L, Amador Borrero B, Depond A, Mouly S, Jourdaine C, Herman P, Eliezer M et al. Infliximab for the Treatment of Inflammatory Labyrinthitis: A Retrospective Cohort Study. J Clin Med 2023, 12. Kao CL, Tsai KL, Cheng YY, Kuo CH, Lee SD, Chan RC. Vestibular rehabilitation ameliorates chronic dizziness through the SIRT1 axis. Front Aging Neurosci. 2014;6:27. Dzięciołowska-Baran EA, Gawlikowska-Sroka A. Vertigo with a Vestibular Dysfunction in Children During Respiratory Tract Infections. Adv Exp Med Biol. 2015;858:79–85. Liu F, Yuan H. Role of glia in neuropathic pain. Front Biosci (Landmark Ed). 2014;19:798–807. Rizzo FR, Musella A, De Vito F, Fresegna D, Bullitta S, Vanni V, Guadalupi L, Stampanoni Bassi M, Buttari F, Mandolesi G et al. Tumor Necrosis Factor and Interleukin-1β Modulate Synaptic Plasticity during Neuroinflammation. Neural Plast 2018, 2018:8430123. El Mahmoudi N, Marouane E, Rastoldo G, Pericat D, Watabe I, Lapotre A, Tonetto A, Chabbert C, Tighilet B. Microglial Dynamics Modulate Vestibular Compensation in a Rodent Model of Vestibulopathy and Condition the Expression of Plasticity Mechanisms in the Deafferented Vestibular Nuclei. Cells 2022, 11. Mao D, He Z, Xuan W, Deng J, Li W, Fang X, Li L, Zhang F. Effect and mechanism of BDNF/TrkB signaling on vestibular compensation. Bioengineered. 2021;12:11823–36. Dutia M. Mechanisms of vestibular compensation: recent advances. Curr Opin Otolaryngol Head Neck Surg. 2010;18:420. Zhou L, Zhou W, Zhang S, Liu B, Liang P, Zhou Y, Zhou T, Zhang K, Leng Y, Kong W. BDNF signaling in the rat cerebello-vestibular pathway during vestibular compensation: BDNF signaling in vestibular compensation. FEBS J 2015, 282. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6863186","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484785388,"identity":"93ade005-a0f1-42a4-9cfa-9b715e1afe0b","order_by":0,"name":"Guangxin Hu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangxin","middleName":"","lastName":"Hu","suffix":""},{"id":484785389,"identity":"947c0488-787b-4913-a595-bb2506a6981c","order_by":1,"name":"Wentao Hu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Hu","suffix":""},{"id":484785390,"identity":"369c90aa-4202-4b18-b75b-927ba02303ef","order_by":2,"name":"Xueying Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Wu","suffix":""},{"id":484785391,"identity":"0ebe26c1-3df8-4194-a378-66fd009f9c30","order_by":3,"name":"Jiaming Fu","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaming","middleName":"","lastName":"Fu","suffix":""},{"id":484785392,"identity":"a35b5d33-72f8-4c71-802c-d48c703b7346","order_by":4,"name":"Xinwu Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinwu","middleName":"","lastName":"Liu","suffix":""},{"id":484785393,"identity":"1fef65b6-21d5-465e-be27-ec2ffc13ed81","order_by":5,"name":"Junyao Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junyao","middleName":"","lastName":"Chen","suffix":""},{"id":484785396,"identity":"fe0147bd-98b9-45ed-8042-b53a44249160","order_by":6,"name":"Junyi Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBAC9gaGBCAlIcfAwAMWYGwgpIXnAESLMUlawCCxgXgtEgnPpAvbLNLn+589upmHwUZ2wwHmZw8IaEmTntkmkbvxwLm02zwMacYbDrCZG+DTYg/SwgvS0thjBtRyOHHDAR42CYK2ALWkGzbzgLT8J15LgjwbWMsBIrTwPEi25jknYbiBh8fs5hyDZOOZh9nM8Gthz0m8zVNWJy/ff8bsxpsKO9m+483P8GoBakpgYGRjYDA4AOKAgooZv3ogYAeq/cPAIN9AUOUoGAWjYBSMVAAAWWFDASfz6lgAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Fu","suffix":""},{"id":484785401,"identity":"65c856a3-3b5b-4aab-8d8d-b65cba70aa9e","order_by":7,"name":"Yunyun Liang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunyun","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-06-10 12:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6863186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6863186/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86778816,"identity":"8104b1cf-3020-4e06-9162-9a8c0c98c767","added_by":"auto","created_at":"2025-07-15 13:04:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":275585,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart showing the selection of study participants.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6863186/v1/7c8c7f9e07a22f8e1b678b31.png"},{"id":86779879,"identity":"09b16e90-bb8d-414b-b0d3-e01fbe34f122","added_by":"auto","created_at":"2025-07-15 13:12:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5480196,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines of l inflammatory biomarkers and dizziness.\u003c/p\u003e\n\u003cp\u003eNote: The red shaded areas represent the 95% confidence intervals. Adjusted for: age, sex, race, education attainment, marital status, alcohol status, smoking status, poverty income ratio, BMI, Hypertension, diabetes;\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6863186/v1/e73ad94ad3e425c1b1dec41e.png"},{"id":86779878,"identity":"06d4aac2-2a42-4c6d-86ec-b3d53b2bbf91","added_by":"auto","created_at":"2025-07-15 13:12:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1755071,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between inflammatory biomarkers and dizziness.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6863186/v1/7f9dfa37f44c6daf4fd012d5.png"},{"id":86778817,"identity":"79362b06-07f7-4c9a-affc-ab2abcc65c7e","added_by":"auto","created_at":"2025-07-15 13:04:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22423,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves and the AUC values of inflammatory biomarkers and dizziness.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6863186/v1/525da51e05e00cdd38884e09.png"},{"id":93374374,"identity":"75f9857c-27ba-4dee-a642-88b1dd400491","added_by":"auto","created_at":"2025-10-13 07:32:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6936290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863186/v1/a759fa9d-954c-4901-8750-fb6c9437b956.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Analysis Between Inflammatory Biomarkers and Dizziness: A Cross-Sectional Analysis Based on NHANES 1999-2004","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDizziness is one of the most common symptoms globally, with an estimated lifetime prevalence ranging from 15\u0026ndash;35% in the general population, and its prevalence increases with advancing age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its impact on daily functioning and quality of life, dizziness has been associated with heightened mortality risks linked to cardiovascular conditions and diabetes mellitus [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the incidence of dizziness continues to rise, particularly among aging populations, it is imperative to identify reliable predictive biomarkers to facilitate early intervention and management.\u003c/p\u003e\u003cp\u003eDizziness is a multifactorial symptom that can result from a variety of conditions, including cerebrovascular disease, vestibular system diseases, multiple sclerosis, migraine, depression, anxiety, and adverse effects of medications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among the proposed mechanisms, inflammation has emerged as an important contributor to dizziness pathogenesis. For example, in patients with vestibular neuritis, viral or immune-mediated inflammation can directly impair vestibular structures, leading to symptoms such as dizziness and nausea [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, in systemic lupus erythematosus, immune complex deposition can damage the vestibular system, resulting in balance disorders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These findings suggest that inflammatory processes may be centrally involved in both the onset and persistence of dizziness, positioning inflammatory biomarkers as valuable candidates for both diagnostic refinement and targeted interventions.\u003c/p\u003e\u003cp\u003eIn recent years, inflammatory biomarkers have gained increasing attention in clinical research. Composite hematologic indices such as the monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) which have been increasingly recognized as reliable surrogates for systemic inflammation and as predictors of diverse pathological outcomes [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SIRI has demonstrated significant associations with periodontitis in population-based analyses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while NLR, MLR, and PLR have been reported as prognostic markers in patients with advanced kidney disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and CAR values have been previously linked to poor outcomes in neurological disorders such as stroke and cognitive decline [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Compared to single blood cell parameters, these composite indices provide a more integrated reflection of inflammatory status and have demonstrated superior predictive value in inflammatory and metabolic conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given their ability to capture systemic inflammatory burden, it is plausible that these indices may also serve as early indicators for inflammation-related dizziness.\u003c/p\u003e\u003cp\u003eHowever, the potential link between dizziness and comprehensive blood count (CBC)-based inflammatory indices remains largely unexamined. To address this research gap, we performed a cross-sectional investigation utilizing data from the National Health and Nutrition Examination Survey (NHANES). The primary objective was to assess the association between dizziness and a range of inflammation-related hematological markers. Our study may support early identification and clinical evaluation of individuals at risk for dizziness.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source and Study Population\u003c/h2\u003e\u003cp\u003eThis study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES, 1999\u0026ndash;2004). NHANES employs a stratified multistage probability sampling design, covering non-hospitalized populations in the United States. The dataset includes demographic, dietary, physical examination, laboratory testing, and questionnaire information. The NHANES survey was approved by the Ethics Review Board of the National Center for Health Statistics, and all participants provided informed consent prior to enrollment. All NHANES data are publicly available on the website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFollowing a comprehensive screening process of the NHANES dataset, a cohort of 31,126 subjects spanning the years 1999 to 2004 was initially deemed eligible for inclusion in this investigative study. The analysis excluded individuals with missing data on dizziness symptoms (n\u0026thinsp;=\u0026thinsp;21,176). In addition, participants with missing lymphocyte count (n\u0026thinsp;=\u0026thinsp;1,255), serum albumin (n\u0026thinsp;=\u0026thinsp;469), and C-reactive protein (CRP) (n\u0026thinsp;=\u0026thinsp;1) were also removed. Pregnant individuals were excluded to avoid potential confounding (n\u0026thinsp;=\u0026thinsp;7). Furthermore, we excluded participants with missing information on key covariates, including education level (n\u0026thinsp;=\u0026thinsp;21), marital status (n\u0026thinsp;=\u0026thinsp;281), poverty income ratio (PIR) (n\u0026thinsp;=\u0026thinsp;749), drinking status (n\u0026thinsp;=\u0026thinsp;384), Hypertension status (n\u0026thinsp;=\u0026thinsp;38), diabetes status (n\u0026thinsp;=\u0026thinsp;136), smoking status (n\u0026thinsp;=\u0026thinsp;8), and body mass index (BMI) (n\u0026thinsp;=\u0026thinsp;208). After implementing all exclusion criteria, the final analytic sample comprised 6,393 individuals. A detailed flow diagram of the participant selection process is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eThe main dependent variable in this analysis was dizziness, as assessed through participant self-report in the NHANES survey. Individuals were classified as experiencing dizziness based on their response (\u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;) to the question: \u0026ldquo;During the past 12 months, have you had a problem with dizziness, lightheadedness, feeling as if you are going to pass out or faint, unsteadiness or imbalance?\u0026rdquo; It is critically important to emphasize that participation in this survey was exclusively restricted to individuals aged over 40 years old. This age-based inclusion criterion was intentionally implemented to align with the research objectives of investigating adult-specific health patterns, thereby ensuring biological homogeneity and epidemiological relevance in the analytical cohort.\u003c/p\u003e\u003cp\u003eThe main independent variables were inflammation-related biomarkers calculated from complete blood count and serum biochemical measurements. The CBC parameters (neutrophil count, lymphocyte count, monocyte count and platelet count) were measured using the Beckman Coulter automated hematology analyzer, which applies impedance-based techniques for cell counting and sizing. For serum albumin, values were obtained using the bromocresol purple dye-binding method, as part of the NHANES Standard Biochemistry Profile. All blood samples were drawn during morning sessions following an overnight fast to minimize variability in biomarker levels. To capture the systemic inflammatory burden, the following composite indices were calculated:\u003c/p\u003e\u003cp\u003eMLR\u0026thinsp;=\u0026thinsp;monocyte count / lymphocyte count\u003c/p\u003e\u003cp\u003eSIRI = (neutrophil count \u0026times; monocyte count) / lymphocyte count\u003c/p\u003e\u003cp\u003eCAR\u0026thinsp;=\u0026thinsp;C-reactive protein / albumin\u003c/p\u003e\u003cp\u003eAISI = (neutrophil count \u0026times; platelet count \u0026times; monocyte count) / lymphocyte count\u003c/p\u003e\u003cp\u003eNLR\u0026thinsp;=\u0026thinsp;neutrophil count / lymphocyte count\u003c/p\u003e\u003cp\u003ePLR\u0026thinsp;=\u0026thinsp;platelet count / lymphocyte count\u003c/p\u003e\u003cp\u003eSII = (platelet count \u0026times; neutrophil count) / lymphocyte count\u003c/p\u003e\u003cp\u003eThese markers, derived from absolute cell counts and biochemical parameters, provide comprehensive insight into the balance of immune cell subpopulations and the acute-phase inflammatory response. All measurements adhered to NHANES' rigorous quality control standards, which include standardized procedures, regularly calibrated instruments, and periodic proficiency testing to ensure data reliability and reproducibility. To explore potential dose\u0026ndash;response relationships, all inflammatory biomarkers were treated as continuous variables and further stratified into quartiles.\u003c/p\u003e\n\u003ch3\u003eSelection of covariates\u003c/h3\u003e\n\u003cp\u003eUtilizing existing literature and clinical insights, we incorporated a range of covariates that could potentially influence the association between inflammation-related biomarkers and dizziness. The continuous variables included age, poverty-to-income ratio (PIR), and body mass index (BMI, kg/m\u0026sup2;). The categorical variables included sex, race/ethnicity, education level, and marital status, smoking status (at least 100 cigarettes in a lifetime?), drinking status (had at least 12 alcohol drinks/1 yr?), diabetes history (have you been told by a doctor or health professional that you have diabetes?), hypertension history (have you been told by a doctor or health professional that you have hypertension, also called high blood pressure?).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo summarize participant characteristics, descriptive analyses were carried out, presenting categorical variables as frequencies and percentages, while continuous variables were expressed as means accompanied by standard errors (SE). Differences between groups were assessed using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical data. Given the stratified, multistage sampling framework employed in NHANES, all analyses incorporated appropriate sampling weights to correct for unequal selection probabilities, oversampling, and participant nonresponse. This weighting approach enhances the generalizability and precision of the results. To explore the association between inflammatory markers and dizziness, we constructed weighted binary logistic regression models, treating dizziness as the dependent variable. Inflammatory biomarkers including MLR, SIRI, AISI, CAR, NLR, PLR, and SII were included as independent variables. We developed three models: Model 1 provided unadjusted estimates; Model 2 adjusted for demographic variables including age, sex, and ethnicity; and Model 3 included additional adjustments for socioeconomic and health-related confounders, namely educational attainment, marital status, BMI, poverty income ratio (PIR), smoking habits, alcohol consumption, as well as diabetes and hypertension status. The results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Furthermore, restricted cubic spline (RCS) modeling and threshold analyses were employed to explore non-linear and dose-dependent associations between each biomarker and dizziness. Stratified analyses were also conducted to evaluate potential effect modification across subgroups. Statistical interaction was tested to assess heterogeneity among subgroups, and forest plots were created to visualize subgroup-specific ORs and confidence intervals. All statistical procedures were implemented using R (version 4.3.2) and EmpowerStats (version 4.2), with statistical significance defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eA total of 6,393 adult participants were included in the analysis, among whom 1,645 individuals (25.73%) reported experiencing dizziness. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the sociodemographic and behavioral attributes of the study population, categorized based on the presence or absence of dizziness. The prevalence of dizziness was notably higher among older, economically disadvantaged, and well-educated Non-Hispanic White females who were married and had a history of cigarette use, excessive alcohol intake, hypertension, or diabetes mellitus. Additionally, individuals in the dizziness group exhibited significantly increased mean values of several inflammation-related biomarkers, including MLR (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005), SIRI (1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03), CAR (0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01), AISI (380.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74), NLR (2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04), and SII (635.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39). In contrast, no statistically significant difference was observed in PLR levels between the two groups (P\u0026thinsp;=\u0026thinsp;0.84).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith dizziness\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1,645)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithout dizziness\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4,748)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e634 (38.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,403 (50.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,011 (61.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,345 (49.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (4.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220 (4.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (5.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201 (4.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,271 (77.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,776 (79.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (8.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e397 (8.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (4.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154 (3.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess Than High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e438 (26.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e812 (17.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School Diploma (including GED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e484 (29.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,181 (24.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore Than High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e723 (43.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,755 (58.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e960 (58.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,319 (69.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264 (16.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e373 (7.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238 (14.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e533 (11.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (2.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (2.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (5.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262 (5.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (3.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154 (3.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e935 (56.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,490 (52.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e710 (43.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,258 (47.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking status (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,034 (62.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,402 (71.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e611 (37.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,346 (28.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e848 (51.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,607 (33.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e797 (48.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,141 (66.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (16.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e391 (8.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,376 (83.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,357 (91.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e380.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e335.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e635.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e589.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values are weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE or weighted % (95% confidence interval). P values are weighted Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (continued)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation between Inflammatory Biomarkers and Dizziness\u003c/h3\u003e\n\u003cp\u003eThe partial correlation among inflammatory biomarkers and dizziness both in continuous and categorical analyses are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As the continuous analysis demonstrated, positive associations were consistently found between SIRI, MLR, AISI, NLR and dizziness in Models 1\u0026ndash;3 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A strong association between the prevalence of dizziness and CAR was observed in Models 1 (OR\u0026thinsp;=\u0026thinsp;1.8726, 95% CI: 1.3438\u0026ndash;2.6096, P\u0026thinsp;=\u0026thinsp;0.0006) and Model 2(OR\u0026thinsp;=\u0026thinsp;1.6066, 95% CI: 1.1940\u0026ndash;2.1618, P\u0026thinsp;=\u0026thinsp;0.0034), however, this relationship diminished in Model 3 (P\u0026thinsp;=\u0026thinsp;0.0689). Furthermore, there were no relationships between PLR, SII and dizziness in model 1 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting limited diagnostic utility in this context. Among all biomarkers analyzed, MLR emerged as the most robust predictor of dizziness in Model 3 (OR\u0026thinsp;=\u0026thinsp;2.0474, 95% CI: 1.1886\u0026ndash;3.5268, P\u0026thinsp;=\u0026thinsp;0.0163).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between inflammatory biomarkers and dizziness.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMODEL1\u003c/p\u003e\u003cp\u003eOR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMODEL2\u003c/p\u003e\u003cp\u003eOR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMODEL3\u003c/p\u003e\u003cp\u003eOR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4256 (1.4137, 4.1621) 0.0025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2053 (1.2614, 3.8555) 0.0086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0474 (1.1886, 3.5268) 0.0163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0247 (0.8327, 1.2610) 0.8187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1068 (0.8858, 1.3830) 0.3779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1526 (0.9380, 1.4164) 0.1903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0984 (0.9272, 1.3012) 0.2839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1709 (0.9775, 1.4026) 0.0956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1882 (0.9894, 1.4268) 0.0783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2948 (1.0493, 1.5979) 0.0206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3374 (1.0594, 1.6883) 0.0196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3464 (1.0786, 1.6807) 0.0153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1989 (1.1163, 1.2876)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1851 (1.0984, 1.2787) 0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1111 (1.0271, 1.2020) 0.0148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0829 (0.8759, 1.3389) 0.4660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1266 (0.9174, 1.3835) 0.2631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1137 (0.8893, 1.3947) 0.3582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3767 (1.1070, 1.7121) 0.0064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4215 (1.1349, 1.7806) 0.0042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3254 (1.0502, 1.6727) 0.0268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5579 (1.3033, 1.8622)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5820 (1.2983, 1.9276) 0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3674 (1.1197, 1.6700) 0.0056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8726 (1.3438, 2.6096) 0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6066 (1.1940, 2.1618) 0.0034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2825 (0.9928, 1.6567) 0.0689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1359 (0.9195, 1.4034) 0.2442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0731 (0.8647, 1.3317) 0.5259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0466 (0.8447, 1.2968) 0.6808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2469 (1.0171, 1.5286) 0.0398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1180 (0.9137, 1.3680) 0.2860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0355 (0.8425, 1.2728) 0.7432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7096 (1.4225, 2.0545)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4709 (1.2163, 1.7787) 0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2647 (1.0478, 1.5265) 0.0229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0005 (1.0003, 1.0007)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0004 (1.0002, 1.0007) 0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0003 (1.0000, 1.0005) 0.0460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3522 (1.0676, 1.7126) 0.0164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3647 (1.0743, 1.7336) 0.0154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3005 (1.0229, 1.6534) 0.0432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2544 (1.0381, 1.5157) 0.0238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2637 (1.0243, 1.5591) 0.0357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1539 (0.9270, 1.4363) 0.2135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5645 (1.2836, 1.9068) 0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5357 (1.2381, 1.9048) 0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3101 (1.0541, 1.6283) 0.0235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1132 (1.0634, 1.1653)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0913 (1.0382, 1.1471) 0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0619 (1.0059, 1.1210) 0.0397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0424 (0.8119, 1.3384) 0.7461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1122 (0.8719, 1.4187) 0.3978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1261 (0.8887, 1.4270) 0.3362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2636 (0.9983, 1.5993) 0.0586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2975 (1.0266, 1.6399) 0.0361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2853 (1.0106, 1.6348) 0.0529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2503 (1.0244, 1.5259) 0.0337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2394 (0.9992, 1.5374) 0.0589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1777 (0.9386, 1.4775) 0.1717\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0001 (0.9991, 1.0011) 0.8438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9996 (0.9986, 1.0006) 0.3999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0002 (0.9993, 1.0011) 0.6831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9615 (0.7840, 1.1792) 0.7081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9616 (0.7857, 1.1769) 0.7064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0444 (0.8599, 1.2686) 0.6654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8437 (0.6918, 1.0288) 0.1007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8298 (0.6714, 1.0257) 0.0933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9382 (0.7591, 1.1596) 0.5614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9796 (0.8091, 1.1861) 0.8338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9141 (0.7521, 1.1110) 0.3729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0661 (0.8915, 1.2748) 0.4904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0003 (1.0002, 1.0004) 0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0002 (1.0001, 1.0004) 0.0048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0001 (1.0000, 1.0003) 0.1071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9843 (0.7886, 1.2285) 0.8892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9920 (0.7843, 1.2548) 0.9471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9855 (0.7867, 1.2346) 0.9001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1725 (0.9596, 1.4326) 0.1273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1816 (0.9554, 1.4613) 0.1328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1698 (0.9514, 1.4383) 0.1511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1947 (0.9740, 1.4654) 0.0953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1428 (0.9196, 1.4202) 0.2368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0522 (0.8404, 1.3174) 0.6614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: Non-adjusted model;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for: sex, age, race;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3 adjusted for: age, sex, race, education attainment, marital status, alcohol status, smoking status, poverty income ratio, BMI, Hypertension, diabetes.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the categorical analysis, using the lowest quartile (Q1) as the reference group, higher quartiles of several biomarkers were associated with increased dizziness risk. Significant associations were evident for SIRI in both Q3 and Q4, while elevated levels of MLR, CAR, and AISI in Q4 were also linked to greater odds of dizziness. Moreover, a clear dose\u0026ndash;response pattern was observed, where increases in SIRI, MLR, and CAR levels corresponded with progressively higher odds ratios (P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, in Model 3, Q4 of SIRI (OR\u0026thinsp;=\u0026thinsp;1.3674, 95% CI: 1.1197\u0026ndash;1.6700), MLR (OR\u0026thinsp;=\u0026thinsp;1.3464, 95% CI: 1.0786\u0026ndash;1.6807), CAR (OR\u0026thinsp;=\u0026thinsp;1.2647, 95% CI: 1.0478\u0026ndash;1.5265), LAP (OR\u0026thinsp;=\u0026thinsp;1.5400, 95% CI: 1.1600\u0026ndash;2.0500), and AISI (OR\u0026thinsp;=\u0026thinsp;1.3101, 95% CI: 1.0541\u0026ndash;1.6283) showed significant associations with dizziness when compared to the reference quartiles. These findings suggest that elevated levels of these inflammatory markers serve as independent predictors of dizziness.\u003c/p\u003e\u003cp\u003eInterestingly, although the continuous analysis identified a statistically significant positive relationship between NLR and dizziness, this association did not persist in the categorical analysis. There has no significant differences were observed across quartiles in either Model 2 or Model 3. This inconsistency suggests that the link between NLR and dizziness may lack stability or robustness across different analytical approaches.\u003c/p\u003e\u003cp\u003eTo explore the relationship between inflammatory biomarkers and dizziness more comprehensively, restricted cubic spline (RCS) regression models were applied with full covariate adjustment. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a significant positive linear association was observed between both SIRI and MLR levels and the prevalence of dizziness (p for overall effect\u0026thinsp;\u0026lt;\u0026thinsp;0.05; p for nonlinearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting the statistical significance and stability of the inflammatory- dizziness association.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further explore this pattern, a threshold effect analysis was performed using a piecewise linear regression model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A significant inflection point was identified at SIRI\u0026thinsp;=\u0026thinsp;1.2526, corroborated by a log-likelihood ratio test (P\u0026thinsp;=\u0026thinsp;0.038). Below this threshold (SIRI\u0026thinsp;\u0026lt;\u0026thinsp;1.2526), SIRI was significantly associated with increased risk of dizziness (OR\u0026thinsp;=\u0026thinsp;1.3903, 95% CI: 1.1061\u0026ndash;1.7474, P\u0026thinsp;=\u0026thinsp;0.0047), whereas the association was attenuated and non-significant above the threshold (OR\u0026thinsp;=\u0026thinsp;1.0553, 95% CI: 0.9811\u0026ndash;1.1353, P\u0026thinsp;=\u0026thinsp;0.1343), indicating a potential saturation effect. No significant threshold effects were observed for other biomarkers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold effect analysis of inflammatory biomarkers and dizziness.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFitting by the 2-piecewise linear model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.2526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u0026thinsp;\u0026lt;\u0026thinsp;1.2526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.3903 (1.1061, 1.7474)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.2526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.0553 (0.9811, 1.1353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for Log-likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.0380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eadjusted for: age, sex, race, education attainment, marital status, alcohol status, smoking status, poverty income ratio, BMI, Hypertension, diabetes.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eSubgroup and ROC analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate potential effect modifiers in the relationship between inflammatory biomarkers and dizziness, we conducted stratified analyses across key demographic and clinical variables, including sex, age, race/ethnicity, education level, marital status, BMI, smoking status, drinking status, and history of hypertension and diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interaction terms were incorporated into the regression models to evaluate the statistical significance of effect modification. Notably, significant interactions were identified for CAR and smoking status, NLR and education level, and SII and education level (all P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the strength of the associations between these inflammatory markers and dizziness may vary across subgroups defined by these characteristics. Further subgroup analyses revealed that several inflammatory markers demonstrated stronger positive associations with dizziness in specific populations. For instance: In participants with educational attainment beyond high school, the associations between dizziness and SIRI, AISI, NLR, and SII were more pronounced (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among female participants, significant associations were observed for MLR, SIRI, AISI, and NLR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among non-smokers, stronger associations were found between dizziness and both CAR and AISI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that demographic and lifestyle factors may modulate the relationship between systemic inflammation and dizziness, emphasizing the importance of considering subgroup characteristics when evaluating potential inflammatory risk factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results highlight that the link between systemic inflammation and dizziness may be influenced by specific sociodemographic and behavioral factors, underlining the necessity of incorporating subgroup-specific characteristics when assessing inflammatory biomarkers as potential predictors of dizziness.\u003c/p\u003e\u003cp\u003eThe diagnostic performances of the eight inflammatory biomarkers for dizziness were investigated using ROC curves. The ROC curve indicated that SIRI had comparable and highest diagnostic efficacy for dizziness. (AUC: 0.5532, 95% CI: 0.5371\u0026ndash;0.569) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional analysis represents the first investigation to assess the association between systemic inflammatory markers and the presence of dizziness. Our findings demonstrated that elevated levels of MLR, SIRI, and AISI were positively associated with dizziness, even after adjusting for a wide range of potential confounders. Among these markers, MLR exhibited the strongest and most consistent association, indicating its potential value in reflecting low-grade systemic inflammation involved in dizziness pathophysiology. Although CAR was initially associated with dizziness, this relationship diminished after controlling covariates, indicating a lack of stability in its predictive power. This variability may, in part, be attributed to findings from our interaction analyses, which revealed a statistically significant modifying effect of smoking status on the CAR\u0026ndash;dizziness association.\u003c/p\u003e\u003cp\u003eWe also found a positive linear relationship between both SIRI and MLR and dizziness through RCS. In particular, the threshold analysis for SIRI revealed a significant inflection point at 1.2526, below which SIRI was strongly associated with dizziness. This suggests that low-to-moderate elevations in systemic inflammation may have a more pronounced effect on dizziness risk, whereas higher levels might reach a saturation point beyond which additional risk does not increase significantly. This nonlinear pattern suggesting that immune system activation may play a role in early stages of neurological or vestibular dysfunction, while chronic high-level inflammation might induce adaptation or compensatory mechanisms.\u003c/p\u003e\u003cp\u003eInflammatory biomarkers such as the MLR and the SIRI have been associated with neurological outcomes, particularly in stroke [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Inflammation also plays a role in the pathophysiology of dizziness, with oxidative stress and pro-inflammatory signaling pathways contributing to chronic vestibular symptoms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients with vestibular dysfunction frequently exhibit signs of immune or systemic involvement, suggesting that dizziness may be influenced by underlying inflammatory processes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, vestibular balance disorders have been linked to bilateral otolith dysfunction, which has been associated with dizziness symptoms even in the absence of apparent central nervous system damage [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Central vestibular disorders, including those affecting the brainstem and cerebellum, are also recognized contributors to dizziness, and their clinical manifestations may overlap with those of systemic inflammation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, functional dizziness\u0026mdash;an increasingly acknowledged condition in clinical practice\u0026mdash;often arises from complex interactions between physiological and psychological factors, including inflammation and heightened autonomic reactivity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubgroup analyses offered deeper insights into the heterogeneity of the observed associations. Significant interaction effects were found between CAR and smoking status, as well as between NLR and SII and educational level. These results suggest that individuals with certain behavioral or socioeconomic characteristics (such as non-smokers and those with higher educational attainment) may be more susceptible to the effects of systemic inflammation on dizziness. Previous studies have demonstrated that systemic inflammation, as measured by CRP and other biomarkers, is strongly influenced by smoking status. Specifically, current smokers tend to exhibit higher levels of inflammation than non-smokers and former smokers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This phenomenon occurs because dizziness in non-smokers is more likely to be exclusively mediated by inflammatory mechanisms (unconfounded by smoking-related factors), whereas in smokers, dizziness may arise from multifactorial etiologies (e.g., vasospasm, carbon monoxide toxicity) that diminish the relative contribution of inflammatory components [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, higher educational attainment has been associated with a stronger inflammatory response to psychological and physical stressors, possibly due to differences in lifestyle or health perception, which may increase their physiological sensitivity to systemic inflammation. Additionally, socioeconomic factors such as education level have been found to modulate inflammatory responses across the life course, with variable patterns depending on the biomarker and context, indicating that educational attainment interacts with stress and immune function in complex ways [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our gender-stratified analysis revealed that MLR, SIRI, AISI and NLR were more predictive of dizziness among women. Research indicates that systemic inflammation levels such as CRP and IL-6 vary significantly with sex, with females often exhibiting elevated inflammatory markers that contribute to cognitive and neurological decline [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These sex-related differences in inflammatory profiles may partly stem from hormonal fluctuations, several studies demonstrate that estrogen may Toll-like receptor signaling pathways to amplify inflammatory responses [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, the ability of SIRI to differentiate dizziness was modest (AUC\u0026thinsp;=\u0026thinsp;0.5532) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This suboptimal performance may stem from potential model overfitting or variability in predictor stability. Moreover, the operational definition of dizziness in this analysis relied on self-reported data from the NHANES questionnaire, which may lack the diagnostic precision required for clinical classification. Nonetheless, when integrated into comprehensive, multimodal predictive frameworks, inflammatory biomarkers such as SIRI could still contribute to improving overall diagnostic accuracy.\u003c/p\u003e\u003cp\u003eThese indices have shown predictive value in various inflammatory conditions, including cardiovascular disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], sarcopenia[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], psoriatic disease [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and fatty liver disease [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Specifically, SII and SIRI have been associated with increased mortality risk [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and disease severity in infections and neurovascular events [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], while CAR and SII have been explored as biomarkers distinguishing causes of vertigo [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore broadly, the link between systemic inflammation and vestibular or balance-related symptoms has been supported by experimental and clinical research. Cytokines such as IL-6, TNF-α, and CRP have been shown to influence neurovascular integrity and vestibular afferent function. For instance, patients with Meni\u0026egrave;re\u0026rsquo;s disease and vestibular migraine demonstrate elevated TNF-α and IL-6 levels compared to controls, indicating a role of inflammation in vestibular dysfunction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Chronic inflammation may impair cerebral perfusion and promote oxidative stress, as evidenced by vascular vertigo patients exhibiting significantly elevated TNF-α and IL-6 levels along with reduced antioxidant capacity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, studies using in vitro blood-labyrinth barrier models have confirmed that cytokines like TNF-α increase inner ear vascular permeability and may contribute to dizziness-related pathology [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Inflammatory labyrinthitis, characterized by blood-labyrinth barrier impairment, further supports a direct inflammatory mechanism contributing to vestibular dysfunction [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, inflammation may exacerbate central perception of dizziness in individuals with heightened interoceptive or anxiety-related responses, as supported by findings that vestibular rehabilitation can modulate oxidative stress pathways and reduce dizziness symptoms through the anti-inflammatory SIRT1 axis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Finally, studies in pediatric populations show that systemic inflammatory responses, such as elevated CRP, can coincide with vestibular neuronitis and vertigo during respiratory infections [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003elocalized neuroinflammation may significantly contribute to the onset and persistence of dizziness. Experimental research has demonstrated that key pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α can be upregulated in the vestibular nuclei and surrounding brainstem regions following peripheral vestibular injury [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These mediators not only enhance glial activation and neuronal excitability but also interfere with synaptic plasticity, a key element in the process of central vestibular compensation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, microglial activation plays a central role in modulating inflammation and neuronal plasticity within the vestibular nuclei, influencing the speed and completeness of compensation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, chronic low-grade inflammation may impair the physiological process of vestibular adaptation. Vestibular compensation relies on neuroplasticity, cerebellar recalibration, and multisensory integration. However, inflammation can compromise blood\u0026ndash;brain barrier integrity and reduce neurotrophic factor signaling like BDNF, critical for synaptic recovery and balance function [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Structural brain plasticity in multisensory cortices has also been observed during vestibular recovery, underlining the role of inflammation in modulating central compensation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, modulation of BDNF-TrkB signaling pathways in the medial vestibular nucleus enhances neuronal survival and functional recovery [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSeveral CBC-derived inflammatory biomarkers (MLR, SIRI, and AISI) were independently associated with dizziness in a general adult population. These findings support the involvement of systemic inflammation in dizziness and suggest that such biomarkers may serve as adjunctive tools for risk identification. Further longitudinal studies are needed to clarify causality and underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAISI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eaggregate index of systemic inflammation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein-to-albumin ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecomprehensive blood count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocyte-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Center for Health Statistics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eORs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eodds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epoverty income ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard errors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic immune-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic inflammation response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to all volunteers who participated in the National Health and Nutrition Examination Survey (NHANES). We also acknowledge the National Center for Health Statistics for making the NHANES data publicly available for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available dataset. The NHANES data can be accessed through the Centers for Disease Control and Prevention (CDC) website (https://wwwn.cdc.gov/Nchs/Nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangxin Hu, Wentao Hu and Jiaming Fu designed and conceived this study. Jiaming Fu and Junyao Chen collected the data. Xinwu Liu, Wentao Hu and Xueying Wu analyzed the data. Guangxin Hu and Junyi Fu wroted the manuscript. Yunyun Liang and Junyi Fu revised the paper. Each author contributed important content during manuscript drafting or revision and accepts accountability for the overall work, and all the authors agreed on the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Basic and Applied Basic Research Fund of Guangdong Province (2022A1515220024), the Basic and Applied Basic Research Fund of Guangzhou science project (2024A04J3630), Guangzhou Medical Key Discipline Construction Project (2025-2027).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey (NHANES), which received approval from the Ethics Review Board of the National Center for Health Statistics (NCHS). Informed consent was obtained from all participants by the NCHS during the data collection phase. This study will be performed in line with the principles of the Declaration of Helsinki. The current study involved a secondary analysis of publicly available, de-identified data, for which no additional ethical approval was necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKroenke K, Price RK. Symptoms in the community. Prevalence, classification, and psychiatric comorbidity. Arch Intern Med. 1993;153:2474\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeuhauser HK. The epidemiology of dizziness and vertigo. 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FEBS J 2015, 282.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NHANES, Dizziness, inflammation, monocyte-to-lymphocyte ratio, systemic inflammation response index, aggregate index of systemic inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6863186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDizziness is a common symptom with diverse etiologies, and its prevalence increases with age. Emerging evidence indicates that systemic inflammatory processes might contribute to the development of dizziness. Nevertheless, the association between dizziness and integrated hematologic inflammatory markers has not been thoroughly investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aims to assess the relationship between inflammatory markers and dizziness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This population-based cross-sectional analysis utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2004. The study population consisted of 6,393 individuals aged over 40 years old. Seven inflammation-related biomarkers included monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio(PLR), and systemic immune-inflammation index (SII), which were computed from standard complete blood counts. Analytical approaches included weighted logistic regression, restricted cubic spline (RCS) models, threshold effect evaluation, subgroup stratification, and receiver operating characteristic (ROC) curve analyses to determine the relationship between these biomarkers and self-reported dizziness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e After adjusting for potential confounders, elevated levels of MLR, SIRI, AISI, and CAR were significantly associated with increased odds of dizziness. RCS and threshold effect analysis revealed a significant non-linear relationship between SIRI and dizziness, with a significant inflection point at 1.2526. Subgroup analyses indicated stronger associations among females, non-smokers, and individuals with higher educational attainment. Among all markers, SIRI demonstrated the highest area under the ROC curve (AUC = 0.5532), although overall predictive performance remained modest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Several CBC-derived inflammatory biomarkers (MLR, SIRI, and AISI) were independently associated with dizziness in a general adult population. These findings support the involvement of systemic inflammation in dizziness and suggest that such biomarkers may serve as adjunctive tools for risk identification. Further longitudinal studies are needed to clarify causality and underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Correlation Analysis Between Inflammatory Biomarkers and Dizziness: A Cross-Sectional Analysis Based on NHANES 1999-2004","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:04:42","doi":"10.21203/rs.3.rs-6863186/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eabe1856-ca80-4366-864c-f7bff82212b4","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T07:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 13:04:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6863186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6863186","identity":"rs-6863186","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}