We utilized data from the UK Biobank study (Application Number 145937), a large-scale prospective cohort that enrolled over 500,000 participants aged 37 to 73 years from 22 assessment centers across the United Kingdom between 2006 and 2010. Participants provided extensive health-related information through a touchscreen questionnaire, covering demographics, socio-economic status, lifestyle factors, and health conditions. The study’s design and data collection procedures have been thoroughly detailed in prior publications (11).

The cohort initially comprised 370,880 participants with complete data on MetS components at baseline. Following the exclusion of individuals with a history of AA or connective tissue disease at baseline, as well as those with missing data on the INFLA component or other covariates, the final analysis included 312,505 participants ( Supplementary Figure 1 ).

MetS was defined according to the International Diabetes Federation (IDF) criteria as the presence of three or more of the following components: central obesity, hypertension, hyperglycemia, hypertriglyceridemia, and dyslipidemia characterized by low levels of high-density lipoprotein cholesterol (HDL-C) (12). Central obesity was determined using waist circumference thresholds of ≥88 cm for women and ≥102 cm for men. Blood pressure was assessed using two measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP), with the average values calculated. Hypertension was defined as SBP ≥ 130 mmHg, DBP ≥ 85 mmHg, or a history of antihypertensive medication use. For hyperglycemia, glycated hemoglobin (HbA1c) was used as a more stable metric due to limited fasting glucose measurements among participants. Impaired glucose regulation was identified as HbA1c ≥ 42.0 mmol/mol or a history of glucose-lowering medication. Hypertriglyceridemia was defined as triglyceride levels ≥ 1.70 mmol/L or a history of triglyceride-lowering medication. Reduced HDL-C levels were defined as <1.03 mmol/L for men and <1.29 mmol/L for women, or a history of cholesterol-lowering medication use.

To explore the role of inflammation in the association between MetS and AA incidence, we analyzed various inflammatory markers, including WBC count, platelet count, CRP levels, neutrophil count, lymphocyte count, and the neutrophil-to-lymphocyte ratio (NLR). Additionally, we calculated the INFLA score as a comprehensive measure of individual inflammatory status. Based on prior research, the INFLA score, strongly associated with multiple diseases (13, 14), integrates four inflammatory indicators: CRP, WBC count, platelet count, and NLR.

To calculate the INFLA score, each inflammatory marker was natural log-transformed. Biomarker levels within the highest decile (7th to 10th) were assigned scores ranging from +1 to +4, while those within the lowest decile (1st to 4th) were assigned scores ranging from −4 to −1.The resulting INFLA score ranged from -16 to +16, with higher scores reflecting elevated levels of low-grade inflammation (15). As part of a sensitivity analysis, we also developed a weighted INFLA score, assigning weights to each inflammatory marker according to its relative association with the outcome event.

Covariates encompassed demographics, baseline medical history, and lifestyle factors. Demographic variables included age, sex, education level, self-reported race, employment status, and body mass index (BMI). Baseline medical history covered the presence of chronic respiratory disease, chronic kidney disease, chronic liver disease, and cardiovascular disease.

Lifestyle factors were assessed based on six components aligned with World Health Organization guidelines: dietary habits, sleep patterns (categorized as healthy, moderate, or unhealthy), physical activity levels (high, moderate, or low), sedentary behavior (low, moderate, or high), and smoking and alcohol consumption history. Detailed information on the lifestyle assessments is provided in ( Supplementary Tables 1 , 2 ).

The primary outcome for this study was AA (International Classification of Diseases, 10th Revision [ICD-10] I71.1–I71.9). Cases of AA were ascertained through death registries, primary care records, hospital admission data, and self-reported diagnoses. The date of AA onset was defined as the date of the first reported diagnosis. Follow-up duration was calculated from the baseline assessment (2006–2010) to the earliest occurrence of AA diagnosis, death, loss to follow-up, or the end of the follow-up period, whichever occurred first.

Baseline characteristics of participants were summarized as means with standard deviations (SD) for continuous variables and as proportions for categorical variables. Statistical comparisons for continuous variables were conducted using analysis of variance (ANOVA), while the χ² test was employed to evaluate differences in categorical variables.

Kaplan-Meier curves were generated to estimate the cumulative incidence of AA in relation to MetS and its components. Cox proportional hazards regression models were applied to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of AA associated with MetS, its individual components, and the INFLA score. Three multivariable adjustment models were developed. Model 1 adjusted for age and gender. Model 2 included additional adjustments for education, self-reported ethnicity, the Thomson Deprivation Index, employment status, BMI, and medical comorbidities such as a history of cardiovascular disease, chronic respiratory disease, chronic kidney disease, or chronic liver disease. Model 3 further incorporated adjustments for personal lifestyle factors, including diet, sleep patterns, sedentary behavior, physical activity levels, and smoking and alcohol consumption.

The two-stage regression method for survival data, as proposed by VanderWeele (16), was applied to perform mediation analysis, assessing the relationship between MetS and its components (exposures) and AA (outcome) mediated through the INFLA score and its components (mediators). To enhance the stability and reliability of the results, a resampling procedure with 500 iterations was utilized. After adjusting for covariates in Model 3, restricted cubic splines (RCS) were employed to investigate the relationships between MetS components, the INFLA score, and its individual components with the risk of AA.

Stratified analyses were conducted based on MetS status to examine the associations between MetS, INFLA scores, and the development of AA. Participants with low INFLA scores were used as the reference group. Both additive and multiplicative interactions were quantified in this analysis. HRs with 95% CIs were calculated to evaluate interactions on the multiplicative scale, while relative excess risk due to interaction (RERI) with corresponding 95% CIs was used to assess interactions on the additive scale.

To evaluate the joint association, participants were divided into six groups based on the combination of MetS presence and INFLA scores. The reference group included individuals without MetS and with lower INFLA scores. HRs for AA occurrence were calculated for each group. Additionally, subgroup analyses were performed to examine potential differences, stratifying participants by sex (male and female), age (≥60 years and <60 years), BMI (normal BMI: 18.5–24.9 and abnormal BMI), smoking status, and the presence of hypertension. Interactions between these subgroups and the potential mediating effect of INFLA were also analyzed.

In this study, several sensitivity analyses were conducted to evaluate the robustness and consistency of the models. First, adjustments were made for individual inflammatory markers instead of the INFLA score. Second, weighted INFLA scores were constructed to explore the associations between specific inflammatory markers and outcomes. Third, individuals with other medical conditions (cardiovascular disease, chronic liver disease, chronic kidney disease, chronic respiratory disease) were excluded to examine the influence of comorbidities on the results. Fourth, events occurring within the first three years of follow-up were excluded to mitigate potential bias from early events. Fifth, multiple imputation was used to address missing covariate data and assess the impact of incomplete variables on the findings. Finally, to ensure comparability between participants with differing confounding factors, 1:1 propensity score matching (PSM) was performed based on demographic characteristics, lifestyle factors, and comorbidities. A stringent caliper width of 0.1 standard deviations of the propensity score was applied to optimize matching precision.

All statistical analyses were conducted using R software version 4.4.1 (R Foundation for Statistical Computing). P-values were two-sided, and a threshold of P < 0.05 was used to determine statistical significance.

The baseline characteristics of the participants are presented in Table 1 . The study included 312,505 individuals with a mean (SD) age of 56.4 (8.1) years. Of these, 147,087 (47.1%) were male, 299,065 (95.7%) were white, and 90,362 (28.9%) had MetS. Participants with MetS, compared to those without, were more likely to be male, have lower levels of education, adopt less healthy lifestyles, and report a greater prevalence of other medical conditions.

As illustrated by the Kaplan-Meier curves in Figure 1 , MetS and its individual components were significantly associated with the cumulative incidence of AA. Moreover, a dose-dependent relationship was evident, with the cumulative incidence of AA increasing markedly as the number of MetS components rose.

During a mean follow-up period of 14.6 years, 2,382 cases of AA were identified among the 312,505 participants. The associations between MetS, its components, the INFLA score, and AA incidence are summarized in Table 2 ; Supplementary Table 3 . In the fully adjusted model, which accounted for the INFLA score and other covariates, MetS was significantly associated with an increased risk of AA (HR: 1.27; 95% CI: 1.16–1.39). When analyzed as a linear variable, each additional MetS component was associated with a 16% higher risk of AA (HR: 1.16; 95% CI: 1.11–1.21). Among the five MetS components, hypertension (HR: 1.25; 95% CI: 1.10–1.43), central obesity (HR: 1.29; 95% CI: 1.16–1.45), hypertriglyceridemia (HR: 1.13; 95% CI: 1.04–1.23), and dyslipidemia (HR: 1.38; 95% CI: 1.26–1.51) were significantly associated with an increased risk of AA. However, hyperglycemia (HR: 0.93; 95% CI: 0.82–1.06) did not demonstrate a significant association after adjusting for all covariates. The results demonstrated robust consistency across all sensitivity analyses, including those incorporating weighted INFLA scores and excluding events occurring within the first three years of follow-up. The propensity score-matched dataset characteristics are presented in Supplementary Figure 2 . Notably, the primary findings remained consistent even after propensity score matching, as shown in Supplementary Table 4 .

The mediating effect of inflammation-related markers on the association between MetS and AA is presented in Supplementary Figure 3 . CRP, WBC, platelet count, lymphocyte count, neutrophil count, and the INFLA score were all identified as significant mediators. The respective mediating proportions were 3.9%, 11.4%, 1.7%, 2.8%, 8.9%, and 6.7%. Additionally, the mediating effect of the INFLA score on the relationship between individual MetS components and AA is detailed in Supplementary Figure 4 . Notably, the INFLA score significantly mediated the associations between central obesity (3.9%), hypertriglyceridemia (13.1%), dyslipidemia (1.9%), and hypertension (8.6%) with AA occurrence, while no significant mediating effect was observed for hyperglycemia.

We used restricted cubic splines to illustrate the dose-response relationships between MetS-related metabolic markers and the risk of AA. As shown in Supplementary Figure 5 , significant nonlinear associations were observed for diastolic blood pressure, HbA1c, triglycerides, and HDL-C with AA risk. Specifically, the risk of AA increased substantially with higher diastolic blood pressure and triglyceride levels. Conversely, higher levels of HbA1c and HDL-C were associated with a reduced AA risk, with the protective effect of HDL-C plateauing at approximately 2 mmol/L. Additionally, we analyzed the dose-response relationships between inflammatory markers, MetS, and AA risk ( Supplementary Figure 6 ). Significant nonlinear associations were observed for CRP, WBC, and platelet counts, with AA risk increasing markedly as levels of these inflammatory indicators rose. Finally, we evaluated the relationship between the INFLA score and AA incidence ( Figure 2A ). A linear association was observed, indicating that the risk of AA increased significantly with higher INFLA scores. Compared with individuals without MetS, those with MetS demonstrated a consistently higher risk of AA across all levels of the INFLA score.

The joint association of MetS and INFLA scores with AA incidence is illustrated in Figure 2B . Compared to non-MetS participants with low INFLA scores, individuals with both MetS and high INFLA scores demonstrated a significantly elevated HR of 1.68 (95% CI: 1.45–1.95).

Within the MetS population, participants with high INFLA scores exhibited an approximately 27% increased risk of developing AA compared to those with low INFLA scores (HR: 1.27; 95% CI: 1.08–1.49), as shown in Supplementary Figure 7 . However, no significant interactions were observed between INFLA scores and MetS on either the multiplicative or additive scale concerning AA incidence (P > 0.05).

The results of the subgroup analysis stratified by sex, age, BMI, smoking status, and the presence of hypertension are presented in Supplementary Table 5 . The mediating effect of the INFLA score on the association between MetS and AA was significant across all subgroups. The mediating proportion was highest in women (approximately 9.9%) and lowest in non-smokers (approximately 3.3%). Despite these variations in mediating effects, no significant interactions were identified within any of the subgroups.