Independent and Inflammation-Linked Contribution of Fasting Plasma Glucose to Heart Failure Risk in Chronic Non-Rheumatic Aortic Regurgitation

Independent and Inflammation-Linked Contribution of Fasting Plasma Glucose to Heart Failure Risk in Chronic Non-Rheumatic Aortic Regurgitation | 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 Independent and Inflammation-Linked Contribution of Fasting Plasma Glucose to Heart Failure Risk in Chronic Non-Rheumatic Aortic Regurgitation Weichao Liu, Qian Zhao, Jing Tao, Peng Chao, Yaoguo Wang, Hui Peng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8300166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Heart failure (HF) frequently complicates chronic nonrheumatic aortic regurgitation (CNRAR), yet the prognostic impact of fasting plasma glucose (FPG) and its inflammatory mechanisms remain unclear. We assessed whether FPG predicts HF in CNRAR and quantified inflammation-mediated effects. Methods We analyzed 13,200 CNRAR patients from Northwest China. The endpoint was new-onset HF. Post-discharge FPG was measured before an endpoint. Multivariable Cox models, competing-risk analysis, restricted cubic splines (RCSs), sensitivity analyses, inverse probability of treatment weighting (IPTW) and mediation analysis were performed. In addition, 13,200 CNRAR patients were 1:1 propensity matched to 2,774,530 general population participants to estimate relative hazard ratios (RHRs). External validation included 418 CNRAR or mixed aortic stenosis-regurgitation cases from the UK Biobank. Results During 535-day median follow-up, higher FPG demonstrated a dose-risk association. Compared with normoglycemia, FPG 6.1–7.0 and 7.0–16.7 mmol/L were associated with 17.1% (HR 1.171, 95% CI 1.026–1.337) and 26.2% (HR 1.262, 95% CI 1.102–1.446) higher HF risk; each 1.0 mmol/L increase conferred a 4.0% higher risk. White blood cells (WBC) accounted for 13.26% of the mediated effect on heart failure. Compared with the general population, CNRAR showed greater vulnerability at 6.1–7.0 mmol/L (RHR 1.270, 95% CI 1.037–1.555), while RHR at ≥ 7.0 mmol/L was non-significant (RHR 1.076, 95% CI 0.901–1.284). In UK Biobank, FPG ≥ 6.1 mmol/L was associated with higher HF risk (HR 1.713, 95% CI 1.044–2.808). Conclusions Elevated FPG independently predicts HF in CNRAR, partially through inflammation, supporting intensified glycemic and inflammatory risk management. Chronic nonrheumatic aortic regurgitation Fasting plasma glucose Heart failure Chronic inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Clinical Perspective What Is New? 1. This is the first large-scale cohort study to investigate the impact of fasting plasma glucose (FPG) on the development of new-onset heart failure (HF) in patients with chronic non-rheumatic aortic regurgitation (CNRAR), with a focus on both independent and inflammation-mediated associations. 2. Elevated FPG (≥6.1 mmol/L) was associated with significantly increased risk of new-onset HF, with a dose-response relationship, showing that each 1.0 mmol/L increase in FPG corresponded to a 4% higher risk of HF. 3. Inflammation, as measured by white blood cell count, mediated 13.26% of the relationship between FPG and HF risk, emphasizing the role of metabolic and inflammatory pathways in heart failure development. What Are the Clinical Implications? 1. Elevated FPG levels may serve as an independent prognostic marker for HF risk in patients with CNRAR, offering clinicians a tool to better stratify patients at risk for new-onset HF. 2. Maintaining FPG levels below 6.1 mmol/L may be crucial in preventing HF in this high-risk population, suggesting that early intervention in glycemic control could potentially mitigate the progression to HF. 3. Given the substantial role of inflammation in mediating the FPG-HF relationship, integrating anti-inflammatory strategies alongside glycemic control may enhance the overall management and reduce the burden of HF in CNRAR patients. 1. Background There is considerable variation in the incidence of aortic regurgitation (AR) across countries, ranging from approximately 1.6% to 4.9% [ 1 , 2 ] . Global burden analyses have revealed a decline in rheumatic valvular heart disease but a steady rise in nonrheumatic valvular disorders, largely driven by population aging [ 3 ] . Nevertheless, epidemiological data specifically addressing CNRAR remain scarce. Progressive regurgitation causes chronic left ventricular volume overload, leading to chamber dilation, contractile dysfunction, and ultimately increased rates of HF and mortality, which together pose a substantial burden to families and healthcare systems [ 4 , 5 ] . Accumulating evidence indicates that elevated FPG is strongly associated with HF across diverse ethnic populations [ 6 – 8 ] . However, optimal glucose management targets for individuals with CNRAR are undefined. Neither the European Society of Cardiology (ESC) nor the American Heart Association (AHA) guidelines on valvular heart disease, HF, or diabetes provide specific recommendations for this subgroup [ 9 – 14 ] . This gap underscores the need for population-based evidence addressing the prognostic impact of FPG in CNRAR. To fill this evidence gap, we analyzed 13,200 patients with CNRAR from Northwest China and 418 CNRAR patients from the UK Biobank, all of whom were free of baseline HF. FPG levels were assessed from the first discharge to the subsequent hospitalization for incident HF, death, or until the study cutoff date. Using multivariate Cox regression, cumulative incidence curve, sensitivity and competing-risk analyses, gradient boosting machine (GBM)-assisted IPTW, RCSs, and mediation modeling with the WBC count as an inflammatory mediator, we quantified the dose-response relationship between FPG and HF risk and explored inflammation-mediated pathways. By providing large-scale, methodologically rigorous data on FPG-related prognosis in CNRAR, this study offers theoretical support for optimizing glucose and inflammation control in clinical management. To our knowledge, this is the first large-scale prospective cohort study to evaluate the independent and inflammation-mediated effects of FPG on CNRAR outcomes. 2. Methods This study was a retrospective analysis based on a prospectively followed cohort of patients with CNRAR in Northwest China between 2019 and 2023. All the data were obtained from the Health and Hygiene Commission of the Autonomous Region. Owing to the sensitive nature of these data, all the authors signed confidentiality agreements. The first and last authors take full responsibility for data integrity and analysis accuracy. Since this study was not a clinical trial but an observational study, the requirement for registration and written informed consent was waived. 2.1 Study Subjects We conducted a large-scale prospective cohort study involving 13,200 patients newly diagnosed with CNRAR in Northwest China between January 1, 2019, and December 31, 2023. For external validation, 418 participants were identified from the UK Biobank database who either self-reported CNRAR or were diagnosed with CNRAR or aortic stenosis combined with regurgitation at admission. The inclusion criteria were as follows: (1) a first-time hospitalization diagnosis of CNRAR without concomitant HF; and (2) routine physical examination data obtained from the first discharge until either readmission for new-onset HF, death, or the end of follow-up. The exclusion criteria were as follows: (1) concomitant HF on first admission; (2) rheumatic heart disease, congenital valvular malformation, infective endocarditis, aortic dissection, or acute myocardial infarction; (3) FPG < 3.9 mmol/L or ≥ 16.7 mmol/L; and (4) loss to follow-up or noncooperation during follow-up (Figure. 1 and Figure. S1). The place of Figure. 1 2.2 Study Cohort The diagnoses were primarily based on the International Classification of Diseases, 10th Revision (ICD-10) codes recorded in the discharge summaries. The ICD-10 codes for all disease classifications used in this study are listed in Table. S1. After applying all the inclusion and exclusion criteria, 13,200 patients with newly diagnosed CNRAR were enrolled. Participants were followed from their initial admission until readmission for new-onset HF, death, or the follow-up endpoint on December 31, 2023. Loss to follow-up was not specifically adjusted for. Nonetheless, because outcome status was obtained through linkage with the regional health information system, follow-up completeness was high and the impact of potential informative censoring is expected to be modest. In the external validation cohort, 418 UK Biobank participants with CNRAR were followed from enrollment until September 30, 2023. All individuals had at least one recorded FPG measurement between their first discharge and the occurrence of an endpoint event or the end of follow-up. In the Northwest China cohort, patients were categorized into three groups according to their FPG levels: 3.9–6.1mmol/L, 6.1–7.0 mmol/L and 7.0–16.7 mmol/L. In the external validation cohort from the UK Biobank, participants were classified into two groups: FPG < 6.1 mmol/L and FPG ≥ 6.1 mmol/L. 2.3 Outcomes Two primary outcomes were evaluated: readmission for new-onset HF and all-cause mortality. HF readmissions were identified from ICD-10 codes on the medical record homepage, whereas deaths were confirmed through the regional death registration system. The observation period continued until an outcome occurred or until December 31, 2023 (Northwest China cohort), or September 30, 2023 (UK Biobank cohort). 2.4 Statistical Analyses Continuous variables following a normal distribution were expressed as mean ± standard deviation (SD), whereas nonnormally distributed variables were summarized as median (interquartile range, IQR). Categorical variables were presented as percentages. Participants with missing FPG data were excluded. Those with FPG < 3.9 mmol/L (hypoglycemia) or ≥ 16.7 mmol/L (critical hyperglycemia) were also excluded. For other variables with ≤ 10% missing data, multiple imputation was performed: random forest imputation for continuous variables and logistic regression for categorical variables (Table. S2). Comparisons among groups were performed via one-way ANOVA for continuous variables and the chi-square test for categorical variables. The association between FPG and HF risk in CNRAR was evaluated via a multivariable Cox proportional hazards model adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, coronary atherosclerotic heart disease (CAD), hyperlipidemia, waist circumference, and body mass index (BMI). Cumulative incidence curves were plotted to visualize HF event distributions across FPG categories. To account for competing risks between HF and all-cause mortality, a Fine–Gray subdistribution hazard model was used. A four-knot RCS was applied to assess the potential nonlinear relationship between FPG and incident HF after adjusting for the same covariates. The sensitivity analyses included the following: 1. A Cox model adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, diabetes, hyperlipidemia, waist circumference and BMI; 2. A Cox model excluding diabetic patients, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, hyperlipidemia, waist circumference and BMI; 3. A Cox model excluding CAD, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, hyperlipidemia, waist circumference and BMI; 4. A Cox model excluding missing values, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, hyperlipidemia, waist circumference and BMI; 5. GBM-assisted IPTW based on covariates including age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, diabetes, CAD, hyperlipidemia, waist circumference, BMI and followed by weighted Cox regression with FPG as the independent variable. Subgroup and interaction analyses were conducted for age, gender, occupation, hypertension, hyperlipidemia, and BMI. After excluding participants with infectious and hematologic diseases (n = 3,773), mediation analysis was performed to examine whether the plasma WBC count mediated the relationship between FPG and HF events. To compare the risk of new-onset HF in the general population without CNRAR and the population with CNRAR at the same FPG levels, we conducted 1:1 propensity score matching between 2,774,530 individuals in the general population in Northwest China and individuals with CNRAR, matching for covariates including age, gender, education level, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, diabetes, hyperlipidemia, waist circumference, and BMI. We compared the RHR of the two cohorts at the same FPG level. All the statistical analyses were performed via R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria). 3. Results 3.1 Study Population A total of 13,200 patients with CNRAR were included in the present analysis in the Northwest China cohort. The overall median follow-up duration was 535 days, with median follow-up times of 529, 544, and 569 days in the three groups, respectively. The median age of the study population was 65 (57, 72) years, and patients in the 6.1–7.0 mmol/L group were the oldest in terms of the median age (66 (58, 73) years, p < 0.001). The proportion of participants aged ≥ 65 years was also highest in this group (56.3%). Men accounted for more than half of the study population (52.7%), with the greatest proportion observed in the 6.1–7.0 mmol/L group (55.7%, p = 0.042). With respect to socioeconomic characteristics, most participants had an education level below secondary school (81.9%), and the majority were engaged in physical labor (66.7%). Notably, the proportion of individuals engaged in mental work increased with higher FPG levels (11.7%, 12.8%, and 14.1%, respectively; p = 0.004). With respect to marital status, the proportion of widowed participants rose progressively with increasing FPG levels (10.4%, 11.6%, and 12.0%; p = 0.027). In terms of lifestyle factors, the majority of participants reported no regular physical activity (84.9%). Most participants were nonsmokers (94.1%) or non-drinkers (96.6%), although the prevalence of alcohol consumption slightly increased with increasing FPG levels (p = 0.009). With respect to comorbidities, the prevalence of hypertension (31.4%), diabetes (15.2%), and hyperlipidemia (40.0%) increased significantly across FPG categories (all p < 0.001). Waist circumference and BMI were also positively associated with higher FPG levels (both p < 0.001). In contrast, the prevalence of CAD did not differ significantly among the groups (p = 0.352) (Table. 1). The place of Table. 1 In the external validation cohort derived from the UK Biobank, 418 participants with CNRAR were included, of whom 372 (89.0%) had FPG < 6.1 mmol/L and 46 (11.0%) had FPG ≥ 6.1 mmol/L. The median age was 62 (56, 66) years, and no significant difference in age distribution was observed between the two FPG groups (p = 0.848). Men accounted for approximately two-thirds of the participants (63.4%), with similar proportions across FPG categories (p = 0.913). The prevalence of smoking and alcohol consumption did not differ significantly between the groups (both p > 0.05). However, participants with elevated FPG levels presented a greater prevalence of hypertension (65.2% vs. 47.8%; p = 0.039), diabetes (60.9% vs. 9.9%; p < 0.001), and CAD (37.0% vs. 20.2%; p = 0.016). The prevalence of hyperlipidemia tended to be greater in the elevated-FPG group (56.5% vs. 40.9%), although the difference was not statistically significant (p = 0.062). The median FPG level was 4.90 (4.59, 5.25) mmol/L in the lower-FPG group and 6.97 (6.39, 9.44) mmol/L in the higher-FPG group (p < 0.001). These findings demonstrate that, in both the Northwest China cohort and the UK Biobank cohort, higher FPG levels were consistently associated with an adverse cardiometabolic profile (Table. S3). 3.2 Association between FPG Levels and the Risk of Heart Failure During a median follow-up of 535 days, 2,019 out of 13,200 patients (15.3%) were readmitted due to new onset HF. The incidence of HF increased progressively with increasing FPG, with 14.27% in the 3.9–6.1 mmol/L group, 16.84% in the 6.1–7.0 mmol/L group, and 19.75% in the 7.0–16.7 mmol/L group. Cox regression analyses, revealed that elevated FPG was consistently associated with an increased risk of HF readmission across all the models. After adjustment for age and gender (Model 1), the hazard ratios (HRs) for the 6.1–7.0 mmol/L and 7.0–16.7 mmol/L groups were 1.189 (95% CI: 1.043–1.356, p = 0.010) and 1.374 (95% CI: 1.221–1.546, p < 0.001), respectively (P for trend < 0.001). When FPG was modeled as a continuous variable, each 1.0 mmol/L increase in FPG corresponded to a 5.5% higher risk of HF readmission (HR: 1.055; 95% CI: 1.033–1.078; p < 0.001). Further adjustment for age, gender, hypertension, CAD and hyperlipidemia (Model 2) slightly attenuated the associations, but both elevated FPG categories remained significant risk factors (HR: 1.182; 95% CI: 1.036–1.348 and HR: 1.372; 95% CI: 1.217–1.545, respectively; both p < 0.05). A 1.0 mmol/L increase in FPG was associated with a 5.6% higher risk of HF (HR: 1.056; 95% CI: 1.033–1.079; p < 0.001). In the fully adjusted model (Model 3), which additionally accounted for sociodemographic factors, lifestyle behaviors, waist circumference, and BMI, the results remained robust. Compared with those of the reference group (3.9–6.1 mmol/L), the HRs for the 6.1–7.0 mmol/L and 7.0–16.7 mmol/L categories were 1.181 (95% CI: 1.035–1.348, p = 0.019) and 1.342 (95% CI: 1.190–1.513; p < 0.001), respectively, with a significant linear trend (p < 0.001). Each 1.0 mmol/L increase in FPG was associated with a 5.3% higher risk of HF readmission (HR: 1.053; 95% CI: 1.030–1.076; p < 0.001) (Table. 2). The place of Table. 2 The robustness and generalizability of these associations were further evaluated in the external validation cohort from the UK Biobank, which included 418 participants with CNRAR. During follow-up, 113 participants (27.0%) experienced new-onset HF, with a markedly higher incidence among those with elevated FPG levels (47.8% vs. 24.5%). In the age- and gender-adjusted analyses (Model 1), participants with FPG ≥ 6.1 mmol/L had greater than twofold greater risk of HF than did those with FPG < 6.1 mmol/L (HR: 2.439; 95% CI: 1.529–3.891; p < 0.001). When FPG was analyzed as a continuous variable, each 1.0 mmol/L increase in FPG was associated with a 20.4% increase in HF risk (HR: 1.204; 95% CI: 1.115–1.302; p < 0.001). After additional adjustment for hypertension, CAD, and hyperlipidemia (Model 2), the association was slightly attenuated but remained significant (HR: 1.880; 95% CI: 1.158–3.050; p = 0.011), with a 13.5% increased risk per 1.0 mmol/L FPG increment (HR: 1.135; 95% CI: 1.046–1.231; p = 0.002). In the fully adjusted model (Model 3), which further accounted for smoking and alcohol consumption behaviors, elevated FPG remained an independent predictor of HF (HR: 1.713; 95% CI: 1.044–2.808; p = 0.033), with a per-unit increase in FPG associated with an 11.8% higher risk (HR: 1.118; 95% CI: 1.017–1.202; p = 0.010). Collectively, these consistent findings across both the primary and validation cohorts highlight that higher FPG levels independently predict an increased risk of HF among patients with CNRAR, underscoring the potential role of glycemic control in preventing adverse cardiac outcomes (Table. S4). 3.3 Sensitivity Analysis and Competitive Risk Analysis To further examine the robustness of the association between FPG and HF risk, a series of sensitivity analyses were performed. First, in the models adjusted for diabetes (sensitivity analysis 1), both the 6.1–7.0 mmol/L and the 7.0–16.7 mmol/L FPG groups presented a significantly greater risk of new-onset HF readmission than did the reference group (HR: 1.171; 95% CI: 1.026 − 1.337 and HR: 1.262; 95% CI: 1.102–1.446, respectively; p for trend < 0.001). Each 1.0 mmol/L increment in FPG corresponded to a 4.0% greater risk of HF (HR: 1.040; 95% CI: 1.014–1.066; P = 0.002). Excluding participants with diabetes (sensitivity analysis 2) yielded consistent results, with HRs of 1.158 (95% CI: 1.004–1.335; p = 0.044) for the 6.1–7.0 mmol/L group and 1.367 (95% CI: 1.160–1.611; p < 0.001) for the 7.0–16.7 mmol/L group. Similarly, each 1.0 mmol/L increase in FPG was associated with a 5.7% greater risk of HF (HR: 1.057; 95% CI: 1.020–1.095; p = 0.002). When patients with CAD were excluded (sensitivity analysis 3), the associations remained largely unchanged. The HRs for the 6.1–7.0 mmol/L and 7.0–16.7 mmol/L groups were 1.155 (95% CI: 1.004–1.328; p = 0.044) and 1.329 (95% CI: 1.172–1.508; p < 0.001), respectively, and a 1.0 mmol/L increase in FPG was linked to a 5.1% higher risk (HR: 1.051; 95% CI: 1.027–1.075; p < 0.001). When patients with missing values were excluded (sensitivity analysis 4), the associations remained stable. The HR for the 6.1–7.0 mmol/L group was 1.161 (95% CI: 1.003–1.343; p = 0.046), the HR for the 7.0–16.7 mmol/L group was 1.351 (95% CI: 1.184–1.542; p < 0.001), and a 1.0 mmol/L increase in FPG was linked to a 5.5% higher risk (HR: 1.055; 95% CI: 1.030–1.081; p < 0.001). Finally, the application of GBM machine learning-assisted IPTW to account for baseline covariates further confirmed the stability of the findings. Under the IPTW-Cox model, higher FPG remained independently associated with elevated HF risk, with HRs of 1.171 (95% CI: 1.022–1.341; p = 0.023) and 1.337 (95% CI: 1.161–1.541; p < 0.001) for the intermediate and highest FPG categories, respectively. Each 1.0 mmol/L increase in FPG was associated with a 4.7% increased risk of HF (HR: 1.047; 95% CI: 1.019–1.075; p < 0.001) (Table. 3, Table. S5 and Figure. S2). These consistent results across multiple analytic strategies highlight the robustness of the observed association between higher FPG and increased new-onset HF incidence in patients with CNRAR. The place of Table. 3 The cumulative event curve revealed that, compared with the group with 3.9 mmol/L ≤ FPG < 6.1 mmol/L, the HF events of the groups with 6.1 mmol/L ≤ FPG < 7.0 mmol/L and 7.0 mmol/L ≤ FPG < 16.7 mmol/L showed an increasing trend (p < 0.001) in sequence (Figure. 2(A-C)). The competitive risk curve with all-cause mortality as the competitive risk also revealed that as FPG levels increased, the cumulative events of readmission due to HF also increased (Figure. 2-D). The competitive risk model showed that compared with the FPG normal group, the subdistribution hazard ratios (SHRs) for the 6.1–7.0 mmol/L and 7.0–16.7 mmol/L groups were 1.175 (95% CI: 1.030–1.340, p = 0.017) and 1.334 (95% CI: 1.183–1.504, p < 0.001), respectively (P for trend < 0.001). When FPG was modeled as a continuous variable, each 1.0 mmol/L increase in FPG corresponded to a 5.2% higher risk of new-onset HF readmission (SHR: 1.039; 95% CI: 1.029–1.075; p < 0.001) (Table. S6). The Kaplan-Meier curves before and after IPTW revealed that as the level of FPG increased, the proportion of nonheart failure events decreased in sequence (Figure. 3). After adjusting for the covariates of age, gender, hypertension, CAD, hyperlipidemia, education, occupation, marriage, physical activity, smoking, alcohol consumption, waist circumference and BMI, a 4-node RCS revealed a nonlinear relationship between FPG levels and the incidence of new-onset HF events (Figure. 4A). The RCS curves, excluding those of patients with diabetes and CAD, also revealed a nonlinear relationship between FPG and new-onset HF events (Figure. 4B and Figure. 4C). The place of Figure. 2, Figure. 3 and Figure. 4 3.4 Interaction Analysis and Subgroup Analysis Interaction analysis revealed that there was no interaction effect between FPG level and age, gender, occupation, hypertension, hyperlipidemia or BMI on new-onset HF events. Subgroup analysis revealed that, compared with that in the normal FPG group, the risk of readmission due to new-onset HF was significantly greater in the 7.0–16.7 mmol/L FPG group regardless of (age < 65 years or ≥ 65 years), female or male sex, mental or physical labor, hypertension or hyperlipidemia, and a BMI < 24 kg/m 2 or ≥ 24 kg/m 2 , except for the subgroup with unknown occupation. In addition, our study revealed that in the subgroups of individuals aged < 65 years, women, mental workers, and patients without hypertension, those with hyperlipidemia, and a BMI < 24 kg/m 2 , when the FPG level ranged from 6.1–7.0mmol/L, the risk of readmission due to new-onset HF did not significantly increase, but once the FPG level exceeded 7.0 mmol/L, the risk of new-onset HF significantly increased (Figure. 5). The place of Figure. 5 3.5 Mediation Analysis After excluding patients with infectious and hematologic diseases, the mediation analysis revealed that the total effect of 7.0 ≤ FPG < 16.7 mmol/L on readmission due to new-onset HF was 4.0%, of which the direct effect of FPG was 3.48%, the mediation effect through WBC count was 0.52%, and the proportion of mediation was 13.26% (Figure. 6). The place of Figure. 6 3.6 Relative Hazard Ratios between the General Population and the CNRAR Population After performing 1:1 propensity score matching between individuals with CNRAR and the general population, a total of 13,199 matched participants were included for comparative analysis (Table. S7). In the general population cohort, individuals with FPG levels of 6.1–7.0 mmol/L presented no significant increase in the risk of new-onset HF readmission compared with those with normal FPG levels (3.9–6.0 mmol/L) (HR: 0.969; 95% CI: 0.831–1.131; p = 0.693). In contrast, among patients with CNRAR, the same FPG category was significantly associated with a greater risk of new-onset HF readmission (HR: 1.231; 95% CI: 1.080–1.403; p = 0.002), yielding an RHR of 1.270 (95% CI: 1.037–1.555; p = 0.021) compared with the general population. For individuals with FPG levels of 7.0–16.7 mmol/L, the risk of new-onset HF readmission was significantly elevated in both cohorts. The HRs were 1.312 (95% CI: 1.149–1.498; p < 0.001) in the general population and 1.411 (95% CI: 1.254–1.588; p < 0.001) in the CNRAR cohort. However, the between-group comparison yielded a nonsignificant RHR of 1.076 (95% CI: 0.901–1.284; p = 0.421), suggesting that while hyperglycemia was associated with an increased risk of HF across both cohorts, the excess risk conferred by elevated FPG did not differ significantly between CNRAR patients and the general population at higher glucose levels (Table. 4). The place of Table. 4 4. Discussion Clinical guidelines currently lack clear recommendations for FPG management in patients with CNRAR. In this large-scale prospective cohort involving 13,200 patients from Northwest China, 2,019 (15.3%) developed new-onset heart failure (HF) during a median follow-up of 535 days, and 803 (6.1%) experienced all-cause mortality. The principal finding of this study is that elevated FPG was independently associated with a higher risk of new-onset HF readmission in CNRAR. Specifically, compared with individuals with normal FPG levels, those with FPG 6.1–7.0 mmol/L had a 17.1% greater HF risk, and those with FPG 7.0–16.7 mmol/L had a 26.2% greater risk. Moreover, each 1.0 mmol/L increase in FPG corresponded to a 4.0% higher risk of new-onset HF readmission. These data suggest that maintaining FPG levels below 6.1 mmol/L may be an optimal threshold for reducing subsequent HF risk in this population. Because the primary objective of this study was to investigate the prognostic significance of post-discharge FPG levels in patients with established CNRAR, it was necessary to account for diabetes as a potential confounder. Therefore, multiple sensitivity analyses were conducted. The results of the diabetes-adjusted and diabetes-excluded Cox models consistently demonstrated that elevated FPG was associated with increased HF risk, independent of diabetes status. Specifically, among nondiabetic participants, the risk of new-onset HF readmission increased by 15.8% for FPG 6.1–7.0 mmol/L and by 36.7% for FPG 7.0–16.7 mmol/L, with a 5.7% higher risk per 1.0 mmol/L increase. These findings imply that the association between FPG and HF in CNRAR cannot be fully explained by overt diabetes but rather reflects broader metabolic and vascular abnormalities. Similarly, previous studies have shown that elevated FPG predicts cardiovascular disease (CVD) and HF events even among individuals without diabetes [ 15 – 20 ] , further supporting this observation. Given that CAD is a major determinant of HF and a possible confounder, we further excluded CAD patients. The positive association between FPG and HF persisted, with HRs increasing by 15.5% and 32.9% for the intermediate and high FPG groups, respectively, and by 5.1% per 1.0 mmol/L increase in FPG. To minimize potential bias from competing outcomes, all-cause mortality was incorporated as a competing event via the Fine–Gray model. The results again demonstrated consistent associations, with HF risk rising by 17.5% in the 6.1–7.0 mmol/L group, by 33.4% in the 7.0–16.7 mmol/L group, and by 5.2% for each 1.0 mmol/L increase in FPG. To further strengthen causal inference, we applied IPTW assisted by GBM algorithms, achieving optimal covariate balance. The IPTW-adjusted Cox analysis confirmed the robustness of these findings, showing a 17.1% and 33.7% higher risk of HF in the intermediate and high FPG groups, respectively, and a 4.7% higher risk per 1.0 mmol/L increase. Taken together, the consistency of the results across multiple analytic frameworks-including multivariable, sensitivity, competing-risk, and machine-learning-weighted models-strongly supports a robust and independent relationship between higher FPG and increased HF risk in patients with CNRAR. Previous reports have described a U-shaped association between FPG and all-cause mortality [ 21 , 22 ] , yet the relationship between FPG and HF has been less well characterized. Our RCS analyses revealed a nonlinear, U-shaped association between FPG and new-onset HF events, with both excessively low and high glucose levels associated with increased HF risk. However, the lower end of this curve did not reach statistical significance, likely due to the small number of individuals with hypoglycemia, resulting in limited statistical power. Subgroup analyses provided further insight into potential heterogeneity. The association between FPG and HF was particularly evident in younger (< 65 years), female, mentally active, normotensive, and lean (BMI < 24 kg/m²) patients. In these subgroups, HF risk remained stable at FPG levels of 6.1–7.0 mmol/L but rose sharply when FPG exceeded 7.0 mmol/L, suggesting a lower glycemic tolerance in these populations. These findings highlight the potential need for individualized glycemic management strategies tailored to patient characteristics and comorbidities. To explore the underlying mechanisms, mediation analysis was performed using the WBC count as an inflammatory mediator after excluding patients with infectious and hematologic disorders. Elevated FPG (7.0–16.7 mmol/L) had both direct and indirect effects on HF risk, with inflammation accounting for 13.26% of the total association. This finding aligns with experimental evidence suggesting that hyperglycemia and insulin resistance promote left ventricular hypertrophy, myocardial fibrosis, and diastolic dysfunction via oxidative stress, advanced glycation end-product accumulation, mitochondrial injury, and microvascular endothelial dysfunction, ultimately leading to HF [ 23 – 28 ] . Chronic low-grade inflammation appears to be a critical link between dysglycemia and myocardial remodeling in CNRAR. Importantly, our propensity score-matched comparative analysis between patients with CNRAR and the general population further demonstrated that the RHR for new-onset HF was significantly greater in the CNRAR cohort at moderate FPG elevations (6.1–7.0 mmol/L; RHR = 1.270, p = 0.021), indicating greater vulnerability to glycemic stress in this population. At higher FPG levels (≥ 7.0 mmol/L), both populations presented comparably increased new-onset HF risk, suggesting that once glucose exceeds the diabetic threshold, its adverse cardiovascular impact becomes universal. Collectively, these findings provide novel evidence that elevated FPG contributes to HF development in CNRAR patients both independently and via inflammatory pathways. From a clinical perspective, the results emphasize that maintaining FPG below 6.1 mmol/L may reduce HF risk in this high-risk population. Moreover, the integration of anti-inflammatory strategies alongside glucose control could represent a promising therapeutic approach. 5. Limitations This study has several limitations that warrant consideration. First, the cohort lacked systematically recorded echocardiographic grading of aortic regurgitation severity, an inherent constraint in large-scale real-world clinical databases. To address potential residual confounding, we implemented GBM–assisted IPTW to achieve robust covariate balance, performed competing-risk analyses, and conducted multiple sensitivity tests to evaluate result stability. Notably, the reproducibility of the primary findings in the UK Biobank–a demographically and structurally distinct population–further supports the external generalizability of our results. Second, residual confounding remains possible despite extensive covariate adjustment and the application of IPTW. Variables such as medication use (e.g., antidiabetic, antihypertensive, or lipid-lowering therapies), dietary habits, and other unmeasured lifestyle factors were not available and might have influenced both glycemic control and HF outcomes. Finally, although inflammatory mediation was statistically supported, mechanistic inference remains associative and should be interpreted within the bounds of non-interventional design. 6. Conclusion In patients with CNRAR, FPG levels exceeding 6.1 mmol/L were independently associated with a significantly increased risk of new-onset HF. This association persisted across multiple analytic approaches and was partly mediated through chronic inflammatory pathways. The excess risk was particularly evident among younger patients, women, individuals engaged in mental work, those without hypertension, and those with a BMI < 24 kg/m², suggesting a lower tolerance to glycemic elevation in these subgroups. These findings highlight the prognostic importance of even mild hyperglycemia in CNRAR and provide novel mechanistic evidence linking dysglycemia, inflammation, and valvular-associated HF. From a clinical perspective, maintaining FPG levels below 6.1 mmol/L and integrating strategies targeting both glucose and inflammation control may reduce subsequent HF risk. Future guidelines on valvular heart disease management should consider incorporating specific recommendations for glycemic assessment and intervention in this high-risk population. Abbreviations HF Heart failure FPG Fasting plasma glucose CNRAR Chronic nonrheumatic aortic regurgitation IPTW Inverse probability of treatment weighting RCS Restricted cubic spline RHR Relative hazard ratio AR Aortic regurgitation ESC European Society of Cardiology AHA American Heart Association GBM Gradient boosting machine IQR Interquartile range CAD Coronary atherosclerotic heart disease BMI Body mass index CVD Cardiovascular disease Declarations Ethics Approval and Consent to Participate This study was approved by the Ethics Committee of the People's Hospital of Xinjiang Uygur Autonomous Region (KY2023042009). Since this study was not a clinical trial but an observational study, the requirement for registration and written informed consent was waived. Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request and with appropriate institutional approvals. Publicly available datasets were analyzed in this study (UK Biobank, Application No. 674129). Competing Interests The authors declare that they have no conflicts of interest. Sources of Funding This study was supported by the National Natural Science Foundation of China (82260073), the Tianshan Talent Cultivation Program Project of Xinjiang Uygur Autonomous Region (2022TSYCLJ0028) and the Key Research and Development Program of the Autonomous Region (2022B03009-3). Author Contributions Weichao Liu contributed to the study design, data analysis and drafting of the manuscript. Qian Zhao, Jing Tao, Yaoguo Wang and Peng Chao contributed to the data collection. Hui Peng and Yining Yang reviewed and revised the manuscript. All the authors approved the final manuscript. Acknowledgments None. References Santangelo G, Bursi F, Faggiano A, Moscardelli S, Simeoli PS, Guazzi M, et al. The Global Burden of Valvular Heart Disease: From Clinical Epidemiology to Management. J Clin Med. 2023; 12(6): 2178. Li GX, Li T, Chen YL, Guo XF, Li Z, Zhou Y, et al. Associations between aortic regurgitation severity and risk of incident myocardial infarction and stroke among patients with degenerative aortic valve disease: insights from a large Chinese population-based cohort study. BMJ Open. 2021; 11(8): e046824. Liu CY, Li HC, Chen PF, Chen MJ, Zhao DM, Wang LQ. Global, Regional, and National Burden of Non-Rheumatic Valvular Heart Diseases in Women: A Systematic Analysis of Global Burden of Disease 1990-2021. Glob Heart. 2025; 20(1): 33. Chen QF, Shi SZ, Wang YF, Shi JJ, Liu CY, Xu TC, et al. Global, Regional, and National Burden of Valvular Heart Disease, 1990 to 2021. J Am Heart Assoc. 2024 Dec 17;13(24): e037991. Akinseye OA, Pathak A, Ibebuogu UN. Aortic Valve Regurgitation: A Comprehensive Review. Curr Probl Cardiol. 2018; 43(8): 315-334. Sinha A, Ning HY, Ahmad FS, Bancks MP, Carnethon MR, O , Brien MJ. Association of fasting glucose with lifetime risk of incident heart failure: the Lifetime Risk Pooling Project. Cardiovasc Diabetol. 2021; 20(1): 66. Echouffo-Tcheugui JB, Mwasongwe SE, Musani SK, Hall ME, Correa A, Hernandez AF. Dysglycemia and incident heart failure among blacks: The jackson heart study. Am Heart J. 2022; 245: 1-9. Filidei E, Caselli C, Menichetti L, Poli M, Petroni D, Guiducci L, et al. Long-term prognostic impact of fasting plasma glucose and myocardial flow reserve beyond other risk factors and heart disease phenotypes. Eur Heart J Imaging Methods Pract. 2024; 2(3): qyae070. 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Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025. Diabetes Care. 2025; 48(1 Suppl 1): S27-S49. Yang CD, Chen JW, Quan JW, Shu XY, Feng S, Aihemaiti M, et al. Long-term glycemic variability predicts compromised development of heart failure with improved ejection fraction: a cohort study. Front Endocrinol (Lausanne). 2023; 14: 1211954. Wang YY, Zhou JD, Qi WW, Zhang N, Tse G, Li GP, et al. Visit-to-Visit Variability in Fasting Blood Glucose Predicts the New-Onset Heart Failure: Results From Two Large Chinese Cohorts. Curr Probl Cardiol. 2023; 48(9): 101842. Martinez-Morata I, Domingo-Relloso A, Zhang Y, Fretts AM, Pichler G, Pinilla JMG, et al. Heart Failure Risk Prediction in a Population With a High Burden of Diabetes: Evidence From the Strong Heart Study. J Am Heart Assoc. 2024; 13(17): e033772. Valensi P. Evidence of a bi-directional relationship between heart failure and diabetes: a strategy for the detection of glucose abnormalities and diabetes prevention in patients with heart failure. Cardiovasc Diabetol. 2024; 23(1): 354. Hsu JC, Yang YY, Chuang SL, Lin LY. Long-Term Glycemic Variability Predicts Adverse Outcomes in Diabetic Heart Failure With Preserved Ejection Fraction. J Clin Endocrinol Metab. 2025; 110(7): 1929-1937. Levitan EB, Song YQ, Ford ES, Liu S. Is nondiabetic hyperglycemia a risk factor for cardiovascular disease? A meta-analysis of prospective studies. Arch Intern Med. 2004; 164(19): 2147-2155. Gao Q, Wang Q, Gan ZJ, Wang M, Lu DF, Zhan BD. Fasting plasma glucose levels are associated with all-cause and cancer mortality: A population-based retrospective cohort study. PLOS One. 2024; 19(11): e0311150. Chen XJ, Ding JC, Shi Z, Bai KZ, Shi SH, Tian QF. Association of longitudinal trajectories of fasting plasma glucose with all-cause and cardiovascular mortality among a Chinese older population: a retrospective cohort study. BMC Public Health. 2024; 24(1): 1335. Bellemare M, Bourcier L, Iglesies-Grau J, Boulet J, O , Meara E, Bouabdallaoui N. Mechanisms of diabetic cardiomyopathy: Focus on inflammation. Diabetes Obes Metab. 2025; 27(5): 2326-2338. Kaur N, Guan YS, Raja R, Ruiz-Velasco A, Liu W. Mechanisms and Therapeutic Prospects of Diabetic Cardiomyopathy Through the Inflammatory Response. Front Physiol. 2021; 12: 694864. Zhao L, Hu HR, Zhang L, Liu ZT, Huang YC, Liu Q. Inflammation in diabetes complications: molecular mechanisms and therapeutic interventions. Med Comm (2020). 2024; 5(4): e516. Athithan L, Gulsin GS, McCann GP, Levelt E. Diabetic cardiomyopathy: Pathophysiology, theories and evidence to date. World J Diabetes. 2019;10(10): 490-510. Sun JH, Zhou RL, Liu M, Zhang D. The role of myocardial fibrosis in the diabetic cardiomyopathy. Diabetol Metab Syndr. 2025; 17(1): 242. Theofilis P, Oikonomou E, Tsioufis K, Tousoulis D. Diabetes Mellitus and Heart Failure: Epidemiology, Pathophysiologic Mechanisms, and the Role of SGLT2 Inhibitors. Life (Basel). 2023; 13(2): 497. Additional Declarations No competing interests reported. Supplementary Files SuplimentMaterial20251207.docx Graphicalabstract.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Jan, 2026 Editor invited by journal 16 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Submission checks completed at journal 15 Dec, 2025 First submitted to journal 07 Dec, 2025 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. 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06:12:32","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159010,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/1f4f86c8f99e1da02e064a5e.html"},{"id":100011935,"identity":"d50ea1bf-3f17-491e-973a-caa2cb513167","added_by":"auto","created_at":"2026-01-12 06:12:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43150,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart of the Study Cohort and Analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/3d2f31454ebba9f4b291dad0.png"},{"id":100361142,"identity":"97bcf42d-de85-4888-a71e-f68c7c4442e6","added_by":"auto","created_at":"2026-01-16 07:44:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116697,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Event Curve of the Relationship between Fasting Plasma Glucose Levels and Heart Failure (A. Original Cumulative Event Curve B. Cumulative Event Curve Excluding Patients with Diabetes C. Cumulative Event Curve Excluding Patients with Coronary Atherosclerotic Heart Disease D. Competitive Risk Curve)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/3040da41611f70db6beaf104.png"},{"id":100362083,"identity":"388557a9-aedd-4813-a0d6-7d0a8f94da56","added_by":"auto","created_at":"2026-01-16 07:46:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104872,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of Nonheart Failure Events before and after IPTW (A. Nonheart Failure Events Before IPTW B. Nonheart Failure Events after IPTW)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/e42b39e072ae9d7f39ac412f.png"},{"id":100360968,"identity":"4edbf924-dad1-4e92-bf78-ee87cbfaa8ea","added_by":"auto","created_at":"2026-01-16 07:44:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45099,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Splines Revealed a Non-linear Relationship between FPG Levels and the Incidence of Heart Failure Events (A. Original RCS Curve B. RCS Curve Excluding Patients with Diabetes C. RCS Curve Excluding Patients with Coronary Atherosclerotic Heart Disease)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/20245d7998153d88ab73832f.png"},{"id":100011943,"identity":"10dcf0cc-48c5-48e2-a9bb-4680609bb094","added_by":"auto","created_at":"2026-01-12 06:12:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339363,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot for Interaction Analysis and Subgroup Analysis\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/f9e6aeb9d22e3c2f3bcbd2cd.png"},{"id":100011942,"identity":"1c41d8c7-7378-4d47-8cf9-e9e76db2e3d2","added_by":"auto","created_at":"2026-01-12 06:12:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":47999,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/fc7a7f6a1b47279d989726e8.png"},{"id":100381286,"identity":"e8a18a6c-8252-4440-b109-34b747282ff1","added_by":"auto","created_at":"2026-01-16 10:37:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/a83bcae3-a804-42db-8738-ce451bb438d9.pdf"},{"id":100362020,"identity":"9ca0f644-252b-44eb-aad7-09ea49bb782c","added_by":"auto","created_at":"2026-01-16 07:46:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57180,"visible":true,"origin":"","legend":"","description":"","filename":"SuplimentMaterial20251207.docx","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/5301931da606f659fc12ee5e.docx"},{"id":100011937,"identity":"5b0038b0-c041-4499-9ab5-58b3e57163c7","added_by":"auto","created_at":"2026-01-12 06:12:31","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":240079,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8300166/v1/72b05d2884bd72388accb59a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Independent and Inflammation-Linked Contribution of Fasting Plasma Glucose to Heart Failure Risk in Chronic Non-Rheumatic Aortic Regurgitation","fulltext":[{"header":"Clinical Perspective","content":"\u003cp\u003e\u003cstrong\u003eWhat Is New?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. This is the first large-scale cohort study to investigate the impact of fasting plasma glucose (FPG) on \u0026nbsp;the development of new-onset heart failure (HF) in patients with chronic non-rheumatic aortic regurgitation (CNRAR), with a focus on both independent and inflammation-mediated associations.\u003c/p\u003e\n\u003cp\u003e2. Elevated FPG (≥6.1 mmol/L) was associated with significantly increased risk of new-onset HF, with a dose-response relationship, showing that each 1.0 mmol/L increase in FPG corresponded to a 4% higher risk of HF.\u003c/p\u003e\n\u003cp\u003e3. Inflammation, as measured by white blood cell count, mediated 13.26% of the relationship between \u0026nbsp;FPG and HF risk, emphasizing the role of metabolic and inflammatory pathways in heart failure development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat Are the Clinical Implications?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Elevated FPG levels may serve as an independent prognostic marker for HF risk in patients with CNRAR, offering clinicians a tool to better stratify patients at risk for new-onset HF.\u003c/p\u003e\n\u003cp\u003e2. Maintaining FPG levels below 6.1 mmol/L may be crucial in preventing HF in this high-risk population, suggesting that early intervention in glycemic control could potentially mitigate the progression to HF.\u003c/p\u003e\n\u003cp\u003e3. Given the substantial role of inflammation in mediating the FPG-HF relationship, integrating anti-inflammatory strategies alongside glycemic control may enhance the overall management and reduce the burden of HF in CNRAR patients.\u003c/p\u003e"},{"header":"1. Background","content":"\u003cp\u003eThere is considerable variation in the incidence of aortic regurgitation (AR) across countries, ranging from approximately 1.6% to 4.9% \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Global burden analyses have revealed a decline in rheumatic valvular heart disease but a steady rise in nonrheumatic valvular disorders, largely driven by population aging \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, epidemiological data specifically addressing CNRAR remain scarce. Progressive regurgitation causes chronic left ventricular volume overload, leading to chamber dilation, contractile dysfunction, and ultimately increased rates of HF and mortality, which together pose a substantial burden to families and healthcare systems \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccumulating evidence indicates that elevated FPG is strongly associated with HF across diverse ethnic populations \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, optimal glucose management targets for individuals with CNRAR are undefined. Neither the European Society of Cardiology (ESC) nor the American Heart Association (AHA) guidelines on valvular heart disease, HF, or diabetes provide specific recommendations for this subgroup \u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This gap underscores the need for population-based evidence addressing the prognostic impact of FPG in CNRAR.\u003c/p\u003e \u003cp\u003eTo fill this evidence gap, we analyzed 13,200 patients with CNRAR from Northwest China and 418 CNRAR patients from the UK Biobank, all of whom were free of baseline HF. FPG levels were assessed from the first discharge to the subsequent hospitalization for incident HF, death, or until the study cutoff date. Using multivariate Cox regression, cumulative incidence curve, sensitivity and competing-risk analyses, gradient boosting machine (GBM)-assisted IPTW, RCSs, and mediation modeling with the WBC count as an inflammatory mediator, we quantified the dose-response relationship between FPG and HF risk and explored inflammation-mediated pathways. By providing large-scale, methodologically rigorous data on FPG-related prognosis in CNRAR, this study offers theoretical support for optimizing glucose and inflammation control in clinical management. To our knowledge, this is the first large-scale prospective cohort study to evaluate the independent and inflammation-mediated effects of FPG on CNRAR outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e This study was a retrospective analysis based on a prospectively followed cohort of patients with CNRAR in Northwest China between 2019 and 2023. All the data were obtained from the Health and Hygiene Commission of the Autonomous Region. Owing to the sensitive nature of these data, all the authors signed confidentiality agreements. The first and last authors take full responsibility for data integrity and analysis accuracy. Since this study was not a clinical trial but an observational study, the requirement for registration and written informed consent was waived.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Subjects\u003c/h2\u003e \u003cp\u003eWe conducted a large-scale prospective cohort study involving 13,200 patients newly diagnosed with CNRAR in Northwest China between January 1, 2019, and December 31, 2023. For external validation, 418 participants were identified from the UK Biobank database who either self-reported CNRAR or were diagnosed with CNRAR or aortic stenosis combined with regurgitation at admission.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) a first-time hospitalization diagnosis of CNRAR without concomitant HF; and (2) routine physical examination data obtained from the first discharge until either readmission for new-onset HF, death, or the end of follow-up. The exclusion criteria were as follows: (1) concomitant HF on first admission; (2) rheumatic heart disease, congenital valvular malformation, infective endocarditis, aortic dissection, or acute myocardial infarction; (3) FPG\u0026thinsp;\u0026lt;\u0026thinsp;3.9 mmol/L or \u0026ge;\u0026thinsp;16.7 mmol/L; and (4) loss to follow-up or noncooperation during follow-up (Figure. 1 and Figure. S1).\u003c/p\u003e \u003cp\u003eThe place of Figure. 1\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Cohort\u003c/h2\u003e \u003cp\u003eThe diagnoses were primarily based on the International Classification of Diseases, 10th Revision (ICD-10) codes recorded in the discharge summaries. The ICD-10 codes for all disease classifications used in this study are listed in Table. S1. After applying all the inclusion and exclusion criteria, 13,200 patients with newly diagnosed CNRAR were enrolled. Participants were followed from their initial admission until readmission for new-onset HF, death, or the follow-up endpoint on December 31, 2023. Loss to follow-up was not specifically adjusted for. Nonetheless, because outcome status was obtained through linkage with the regional health information system, follow-up completeness was high and the impact of potential informative censoring is expected to be modest.\u003c/p\u003e \u003cp\u003eIn the external validation cohort, 418 UK Biobank participants with CNRAR were followed from enrollment until September 30, 2023. All individuals had at least one recorded FPG measurement between their first discharge and the occurrence of an endpoint event or the end of follow-up.\u003c/p\u003e \u003cp\u003eIn the Northwest China cohort, patients were categorized into three groups according to their FPG levels: 3.9\u0026ndash;6.1mmol/L, 6.1\u0026ndash;7.0 mmol/L and 7.0\u0026ndash;16.7 mmol/L. In the external validation cohort from the UK Biobank, participants were classified into two groups: FPG\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L and FPG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e \u003cp\u003eTwo primary outcomes were evaluated: readmission for new-onset HF and all-cause mortality. HF readmissions were identified from ICD-10 codes on the medical record homepage, whereas deaths were confirmed through the regional death registration system. The observation period continued until an outcome occurred or until December 31, 2023 (Northwest China cohort), or September 30, 2023 (UK Biobank cohort).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analyses\u003c/h2\u003e \u003cp\u003eContinuous variables following a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas nonnormally distributed variables were summarized as median (interquartile range, IQR). Categorical variables were presented as percentages. Participants with missing FPG data were excluded. Those with FPG\u0026thinsp;\u0026lt;\u0026thinsp;3.9 mmol/L (hypoglycemia) or \u0026ge;\u0026thinsp;16.7 mmol/L (critical hyperglycemia) were also excluded. For other variables with \u0026le;\u0026thinsp;10% missing data, multiple imputation was performed: random forest imputation for continuous variables and logistic regression for categorical variables (Table. S2).\u003c/p\u003e \u003cp\u003eComparisons among groups were performed via one-way ANOVA for continuous variables and the chi-square test for categorical variables. The association between FPG and HF risk in CNRAR was evaluated via a multivariable Cox proportional hazards model adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, coronary atherosclerotic heart disease (CAD), hyperlipidemia, waist circumference, and body mass index (BMI). Cumulative incidence curves were plotted to visualize HF event distributions across FPG categories. To account for competing risks between HF and all-cause mortality, a Fine\u0026ndash;Gray subdistribution hazard model was used. A four-knot RCS was applied to assess the potential nonlinear relationship between FPG and incident HF after adjusting for the same covariates. The sensitivity analyses included the following: 1. A Cox model adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, diabetes, hyperlipidemia, waist circumference and BMI; 2. A Cox model excluding diabetic patients, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, hyperlipidemia, waist circumference and BMI; 3. A Cox model excluding CAD, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, hyperlipidemia, waist circumference and BMI; 4. A Cox model excluding missing values, and adjusted for age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, hyperlipidemia, waist circumference and BMI; 5. GBM-assisted IPTW based on covariates including age, gender, education, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, diabetes, CAD, hyperlipidemia, waist circumference, BMI and followed by weighted Cox regression with FPG as the independent variable. Subgroup and interaction analyses were conducted for age, gender, occupation, hypertension, hyperlipidemia, and BMI. After excluding participants with infectious and hematologic diseases (n\u0026thinsp;=\u0026thinsp;3,773), mediation analysis was performed to examine whether the plasma WBC count mediated the relationship between FPG and HF events. To compare the risk of new-onset HF in the general population without CNRAR and the population with CNRAR at the same FPG levels, we conducted 1:1 propensity score matching between 2,774,530 individuals in the general population in Northwest China and individuals with CNRAR, matching for covariates including age, gender, education level, occupation, marital status, physical activity, smoking, alcohol consumption, hypertension, CAD, diabetes, hyperlipidemia, waist circumference, and BMI. We compared the RHR of the two cohorts at the same FPG level. All the statistical analyses were performed via R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Population\u003c/h2\u003e \u003cp\u003eA total of 13,200 patients with CNRAR were included in the present analysis in the Northwest China cohort. The overall median follow-up duration was 535 days, with median follow-up times of 529, 544, and 569 days in the three groups, respectively. The median age of the study population was 65 (57, 72) years, and patients in the 6.1\u0026ndash;7.0 mmol/L group were the oldest in terms of the median age (66 (58, 73) years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of participants aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years was also highest in this group (56.3%). Men accounted for more than half of the study population (52.7%), with the greatest proportion observed in the 6.1\u0026ndash;7.0 mmol/L group (55.7%, p\u0026thinsp;=\u0026thinsp;0.042). With respect to socioeconomic characteristics, most participants had an education level below secondary school (81.9%), and the majority were engaged in physical labor (66.7%). Notably, the proportion of individuals engaged in mental work increased with higher FPG levels (11.7%, 12.8%, and 14.1%, respectively; p\u0026thinsp;=\u0026thinsp;0.004). With respect to marital status, the proportion of widowed participants rose progressively with increasing FPG levels (10.4%, 11.6%, and 12.0%; p\u0026thinsp;=\u0026thinsp;0.027). In terms of lifestyle factors, the majority of participants reported no regular physical activity (84.9%). Most participants were nonsmokers (94.1%) or non-drinkers (96.6%), although the prevalence of alcohol consumption slightly increased with increasing FPG levels (p\u0026thinsp;=\u0026thinsp;0.009). With respect to comorbidities, the prevalence of hypertension (31.4%), diabetes (15.2%), and hyperlipidemia (40.0%) increased significantly across FPG categories (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Waist circumference and BMI were also positively associated with higher FPG levels (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the prevalence of CAD did not differ significantly among the groups (p\u0026thinsp;=\u0026thinsp;0.352) (Table. 1).\u003c/p\u003e \u003cp\u003eThe place of Table. 1\u003c/p\u003e \u003cp\u003eIn the external validation cohort derived from the UK Biobank, 418 participants with CNRAR were included, of whom 372 (89.0%) had FPG\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L and 46 (11.0%) had FPG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L. The median age was 62 (56, 66) years, and no significant difference in age distribution was observed between the two FPG groups (p\u0026thinsp;=\u0026thinsp;0.848). Men accounted for approximately two-thirds of the participants (63.4%), with similar proportions across FPG categories (p\u0026thinsp;=\u0026thinsp;0.913). The prevalence of smoking and alcohol consumption did not differ significantly between the groups (both p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, participants with elevated FPG levels presented a greater prevalence of hypertension (65.2% vs. 47.8%; p\u0026thinsp;=\u0026thinsp;0.039), diabetes (60.9% vs. 9.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CAD (37.0% vs. 20.2%; p\u0026thinsp;=\u0026thinsp;0.016). The prevalence of hyperlipidemia tended to be greater in the elevated-FPG group (56.5% vs. 40.9%), although the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.062). The median FPG level was 4.90 (4.59, 5.25) mmol/L in the lower-FPG group and 6.97 (6.39, 9.44) mmol/L in the higher-FPG group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings demonstrate that, in both the Northwest China cohort and the UK Biobank cohort, higher FPG levels were consistently associated with an adverse cardiometabolic profile (Table. S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between FPG Levels and the Risk of Heart Failure\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 535 days, 2,019 out of 13,200 patients (15.3%) were readmitted due to new onset HF. The incidence of HF increased progressively with increasing FPG, with 14.27% in the 3.9\u0026ndash;6.1 mmol/L group, 16.84% in the 6.1\u0026ndash;7.0 mmol/L group, and 19.75% in the 7.0\u0026ndash;16.7 mmol/L group. Cox regression analyses, revealed that elevated FPG was consistently associated with an increased risk of HF readmission across all the models. After adjustment for age and gender (Model 1), the hazard ratios (HRs) for the 6.1\u0026ndash;7.0 mmol/L and 7.0\u0026ndash;16.7 mmol/L groups were 1.189 (95% CI: 1.043\u0026ndash;1.356, p\u0026thinsp;=\u0026thinsp;0.010) and 1.374 (95% CI: 1.221\u0026ndash;1.546, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When FPG was modeled as a continuous variable, each 1.0 mmol/L increase in FPG corresponded to a 5.5% higher risk of HF readmission (HR: 1.055; 95% CI: 1.033\u0026ndash;1.078; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further adjustment for age, gender, hypertension, CAD and hyperlipidemia (Model 2) slightly attenuated the associations, but both elevated FPG categories remained significant risk factors (HR: 1.182; 95% CI: 1.036\u0026ndash;1.348 and HR: 1.372; 95% CI: 1.217\u0026ndash;1.545, respectively; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A 1.0 mmol/L increase in FPG was associated with a 5.6% higher risk of HF (HR: 1.056; 95% CI: 1.033\u0026ndash;1.079; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted model (Model 3), which additionally accounted for sociodemographic factors, lifestyle behaviors, waist circumference, and BMI, the results remained robust. Compared with those of the reference group (3.9\u0026ndash;6.1 mmol/L), the HRs for the 6.1\u0026ndash;7.0 mmol/L and 7.0\u0026ndash;16.7 mmol/L categories were 1.181 (95% CI: 1.035\u0026ndash;1.348, p\u0026thinsp;=\u0026thinsp;0.019) and 1.342 (95% CI: 1.190\u0026ndash;1.513; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, with a significant linear trend (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each 1.0 mmol/L increase in FPG was associated with a 5.3% higher risk of HF readmission (HR: 1.053; 95% CI: 1.030\u0026ndash;1.076; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table. 2).\u003c/p\u003e \u003cp\u003eThe place of Table. 2\u003c/p\u003e \u003cp\u003eThe robustness and generalizability of these associations were further evaluated in the external validation cohort from the UK Biobank, which included 418 participants with CNRAR. During follow-up, 113 participants (27.0%) experienced new-onset HF, with a markedly higher incidence among those with elevated FPG levels (47.8% vs. 24.5%). In the age- and gender-adjusted analyses (Model 1), participants with FPG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L had greater than twofold greater risk of HF than did those with FPG\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L (HR: 2.439; 95% CI: 1.529\u0026ndash;3.891; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When FPG was analyzed as a continuous variable, each 1.0 mmol/L increase in FPG was associated with a 20.4% increase in HF risk (HR: 1.204; 95% CI: 1.115\u0026ndash;1.302; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After additional adjustment for hypertension, CAD, and hyperlipidemia (Model 2), the association was slightly attenuated but remained significant (HR: 1.880; 95% CI: 1.158\u0026ndash;3.050; p\u0026thinsp;=\u0026thinsp;0.011), with a 13.5% increased risk per 1.0 mmol/L FPG increment (HR: 1.135; 95% CI: 1.046\u0026ndash;1.231; p\u0026thinsp;=\u0026thinsp;0.002). In the fully adjusted model (Model 3), which further accounted for smoking and alcohol consumption behaviors, elevated FPG remained an independent predictor of HF (HR: 1.713; 95% CI: 1.044\u0026ndash;2.808; p\u0026thinsp;=\u0026thinsp;0.033), with a per-unit increase in FPG associated with an 11.8% higher risk (HR: 1.118; 95% CI: 1.017\u0026ndash;1.202; p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003cp\u003eCollectively, these consistent findings across both the primary and validation cohorts highlight that higher FPG levels independently predict an increased risk of HF among patients with CNRAR, underscoring the potential role of glycemic control in preventing adverse cardiac outcomes (Table. S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensitivity Analysis and Competitive Risk Analysis\u003c/h2\u003e \u003cp\u003eTo further examine the robustness of the association between FPG and HF risk, a series of sensitivity analyses were performed. First, in the models adjusted for diabetes (sensitivity analysis 1), both the 6.1\u0026ndash;7.0 mmol/L and the 7.0\u0026ndash;16.7 mmol/L FPG groups presented a significantly greater risk of new-onset HF readmission than did the reference group (HR: 1.171; 95% CI: 1.026 \u0026minus;\u0026thinsp;1.337 and HR: 1.262; 95% CI: 1.102\u0026ndash;1.446, respectively; p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each 1.0 mmol/L increment in FPG corresponded to a 4.0% greater risk of HF (HR: 1.040; 95% CI: 1.014\u0026ndash;1.066; P\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eExcluding participants with diabetes (sensitivity analysis 2) yielded consistent results, with HRs of 1.158 (95% CI: 1.004\u0026ndash;1.335; p\u0026thinsp;=\u0026thinsp;0.044) for the 6.1\u0026ndash;7.0 mmol/L group and 1.367 (95% CI: 1.160\u0026ndash;1.611; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for the 7.0\u0026ndash;16.7 mmol/L group. Similarly, each 1.0 mmol/L increase in FPG was associated with a 5.7% greater risk of HF (HR: 1.057; 95% CI: 1.020\u0026ndash;1.095; p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eWhen patients with CAD were excluded (sensitivity analysis 3), the associations remained largely unchanged. The HRs for the 6.1\u0026ndash;7.0 mmol/L and 7.0\u0026ndash;16.7 mmol/L groups were 1.155 (95% CI: 1.004\u0026ndash;1.328; p\u0026thinsp;=\u0026thinsp;0.044) and 1.329 (95% CI: 1.172\u0026ndash;1.508; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, and a 1.0 mmol/L increase in FPG was linked to a 5.1% higher risk (HR: 1.051; 95% CI: 1.027\u0026ndash;1.075; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWhen patients with missing values were excluded (sensitivity analysis 4), the associations remained stable. The HR for the 6.1\u0026ndash;7.0 mmol/L group was 1.161 (95% CI: 1.003\u0026ndash;1.343; p\u0026thinsp;=\u0026thinsp;0.046), the HR for the 7.0\u0026ndash;16.7 mmol/L group was 1.351 (95% CI: 1.184\u0026ndash;1.542; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a 1.0 mmol/L increase in FPG was linked to a 5.5% higher risk (HR: 1.055; 95% CI: 1.030\u0026ndash;1.081; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFinally, the application of GBM machine learning-assisted IPTW to account for baseline covariates further confirmed the stability of the findings. Under the IPTW-Cox model, higher FPG remained independently associated with elevated HF risk, with HRs of 1.171 (95% CI: 1.022\u0026ndash;1.341; p\u0026thinsp;=\u0026thinsp;0.023) and 1.337 (95% CI: 1.161\u0026ndash;1.541; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for the intermediate and highest FPG categories, respectively. Each 1.0 mmol/L increase in FPG was associated with a 4.7% increased risk of HF (HR: 1.047; 95% CI: 1.019\u0026ndash;1.075; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table. 3, Table. S5 and Figure. S2). These consistent results across multiple analytic strategies highlight the robustness of the observed association between higher FPG and increased new-onset HF incidence in patients with CNRAR.\u003c/p\u003e \u003cp\u003eThe place of Table. 3\u003c/p\u003e \u003cp\u003eThe cumulative event curve revealed that, compared with the group with 3.9 mmol/L\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L, the HF events of the groups with 6.1 mmol/L\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026lt;\u0026thinsp;7.0 mmol/L and 7.0 mmol/L\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026lt;\u0026thinsp;16.7 mmol/L showed an increasing trend (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in sequence (Figure. 2(A-C)). The competitive risk curve with all-cause mortality as the competitive risk also revealed that as FPG levels increased, the cumulative events of readmission due to HF also increased (Figure. 2-D). The competitive risk model showed that compared with the FPG normal group, the subdistribution hazard ratios (SHRs) for the 6.1\u0026ndash;7.0 mmol/L and 7.0\u0026ndash;16.7 mmol/L groups were 1.175 (95% CI: 1.030\u0026ndash;1.340, p\u0026thinsp;=\u0026thinsp;0.017) and 1.334 (95% CI: 1.183\u0026ndash;1.504, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When FPG was modeled as a continuous variable, each 1.0 mmol/L increase in FPG corresponded to a 5.2% higher risk of new-onset HF readmission (SHR: 1.039; 95% CI: 1.029\u0026ndash;1.075; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table. S6). The Kaplan-Meier curves before and after IPTW revealed that as the level of FPG increased, the proportion of nonheart failure events decreased in sequence (Figure. 3). After adjusting for the covariates of age, gender, hypertension, CAD, hyperlipidemia, education, occupation, marriage, physical activity, smoking, alcohol consumption, waist circumference and BMI, a 4-node RCS revealed a nonlinear relationship between FPG levels and the incidence of new-onset HF events (Figure. 4A). The RCS curves, excluding those of patients with diabetes and CAD, also revealed a nonlinear relationship between FPG and new-onset HF events (Figure. 4B and Figure. 4C).\u003c/p\u003e \u003cp\u003eThe place of Figure. 2, Figure. 3 and Figure. 4\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Interaction Analysis and Subgroup Analysis\u003c/h2\u003e \u003cp\u003eInteraction analysis revealed that there was no interaction effect between FPG level and age, gender, occupation, hypertension, hyperlipidemia or BMI on new-onset HF events. Subgroup analysis revealed that, compared with that in the normal FPG group, the risk of readmission due to new-onset HF was significantly greater in the 7.0\u0026ndash;16.7 mmol/L FPG group regardless of (age\u0026thinsp;\u0026lt;\u0026thinsp;65 years or \u0026ge;\u0026thinsp;65 years), female or male sex, mental or physical labor, hypertension or hyperlipidemia, and a BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026ge;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e, except for the subgroup with unknown occupation. In addition, our study revealed that in the subgroups of individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, women, mental workers, and patients without hypertension, those with hyperlipidemia, and a BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e, when the FPG level ranged from 6.1\u0026ndash;7.0mmol/L, the risk of readmission due to new-onset HF did not significantly increase, but once the FPG level exceeded 7.0 mmol/L, the risk of new-onset HF significantly increased (Figure. 5).\u003c/p\u003e \u003cp\u003eThe place of Figure. 5\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mediation Analysis\u003c/h2\u003e \u003cp\u003eAfter excluding patients with infectious and hematologic diseases, the mediation analysis revealed that the total effect of 7.0\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026lt;\u0026thinsp;16.7 mmol/L on readmission due to new-onset HF was 4.0%, of which the direct effect of FPG was 3.48%, the mediation effect through WBC count was 0.52%, and the proportion of mediation was 13.26% (Figure. 6).\u003c/p\u003e \u003cp\u003eThe place of Figure. 6\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Relative Hazard Ratios between the General Population and the CNRAR Population\u003c/h2\u003e \u003cp\u003eAfter performing 1:1 propensity score matching between individuals with CNRAR and the general population, a total of 13,199 matched participants were included for comparative analysis (Table. S7). In the general population cohort, individuals with FPG levels of 6.1\u0026ndash;7.0 mmol/L presented no significant increase in the risk of new-onset HF readmission compared with those with normal FPG levels (3.9\u0026ndash;6.0 mmol/L) (HR: 0.969; 95% CI: 0.831\u0026ndash;1.131; p\u0026thinsp;=\u0026thinsp;0.693). In contrast, among patients with CNRAR, the same FPG category was significantly associated with a greater risk of new-onset HF readmission (HR: 1.231; 95% CI: 1.080\u0026ndash;1.403; p\u0026thinsp;=\u0026thinsp;0.002), yielding an RHR of 1.270 (95% CI: 1.037\u0026ndash;1.555; p\u0026thinsp;=\u0026thinsp;0.021) compared with the general population.\u003c/p\u003e \u003cp\u003eFor individuals with FPG levels of 7.0\u0026ndash;16.7 mmol/L, the risk of new-onset HF readmission was significantly elevated in both cohorts. The HRs were 1.312 (95% CI: 1.149\u0026ndash;1.498; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the general population and 1.411 (95% CI: 1.254\u0026ndash;1.588; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the CNRAR cohort. However, the between-group comparison yielded a nonsignificant RHR of 1.076 (95% CI: 0.901\u0026ndash;1.284; p\u0026thinsp;=\u0026thinsp;0.421), suggesting that while hyperglycemia was associated with an increased risk of HF across both cohorts, the excess risk conferred by elevated FPG did not differ significantly between CNRAR patients and the general population at higher glucose levels (Table. 4).\u003c/p\u003e \u003cp\u003eThe place of Table. 4\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e Clinical guidelines currently lack clear recommendations for FPG management in patients with CNRAR. In this large-scale prospective cohort involving 13,200 patients from Northwest China, 2,019 (15.3%) developed new-onset heart failure (HF) during a median follow-up of 535 days, and 803 (6.1%) experienced all-cause mortality. The principal finding of this study is that elevated FPG was independently associated with a higher risk of new-onset HF readmission in CNRAR. Specifically, compared with individuals with normal FPG levels, those with FPG 6.1\u0026ndash;7.0 mmol/L had a 17.1% greater HF risk, and those with FPG 7.0\u0026ndash;16.7 mmol/L had a 26.2% greater risk. Moreover, each 1.0 mmol/L increase in FPG corresponded to a 4.0% higher risk of new-onset HF readmission. These data suggest that maintaining FPG levels below 6.1 mmol/L may be an optimal threshold for reducing subsequent HF risk in this population.\u003c/p\u003e \u003cp\u003eBecause the primary objective of this study was to investigate the prognostic significance of post-discharge FPG levels in patients with established CNRAR, it was necessary to account for diabetes as a potential confounder. Therefore, multiple sensitivity analyses were conducted. The results of the diabetes-adjusted and diabetes-excluded Cox models consistently demonstrated that elevated FPG was associated with increased HF risk, independent of diabetes status. Specifically, among nondiabetic participants, the risk of new-onset HF readmission increased by 15.8% for FPG 6.1\u0026ndash;7.0 mmol/L and by 36.7% for FPG 7.0\u0026ndash;16.7 mmol/L, with a 5.7% higher risk per 1.0 mmol/L increase. These findings imply that the association between FPG and HF in CNRAR cannot be fully explained by overt diabetes but rather reflects broader metabolic and vascular abnormalities. Similarly, previous studies have shown that elevated FPG predicts cardiovascular disease (CVD) and HF events even among individuals without diabetes \u003csup\u003e[\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, further supporting this observation.\u003c/p\u003e \u003cp\u003eGiven that CAD is a major determinant of HF and a possible confounder, we further excluded CAD patients. The positive association between FPG and HF persisted, with HRs increasing by 15.5% and 32.9% for the intermediate and high FPG groups, respectively, and by 5.1% per 1.0 mmol/L increase in FPG.\u003c/p\u003e \u003cp\u003eTo minimize potential bias from competing outcomes, all-cause mortality was incorporated as a competing event via the Fine\u0026ndash;Gray model. The results again demonstrated consistent associations, with HF risk rising by 17.5% in the 6.1\u0026ndash;7.0 mmol/L group, by 33.4% in the 7.0\u0026ndash;16.7 mmol/L group, and by 5.2% for each 1.0 mmol/L increase in FPG.\u003c/p\u003e \u003cp\u003eTo further strengthen causal inference, we applied IPTW assisted by GBM algorithms, achieving optimal covariate balance. The IPTW-adjusted Cox analysis confirmed the robustness of these findings, showing a 17.1% and 33.7% higher risk of HF in the intermediate and high FPG groups, respectively, and a 4.7% higher risk per 1.0 mmol/L increase. Taken together, the consistency of the results across multiple analytic frameworks-including multivariable, sensitivity, competing-risk, and machine-learning-weighted models-strongly supports a robust and independent relationship between higher FPG and increased HF risk in patients with CNRAR.\u003c/p\u003e \u003cp\u003ePrevious reports have described a U-shaped association between FPG and all-cause mortality \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, yet the relationship between FPG and HF has been less well characterized. Our RCS analyses revealed a nonlinear, U-shaped association between FPG and new-onset HF events, with both excessively low and high glucose levels associated with increased HF risk. However, the lower end of this curve did not reach statistical significance, likely due to the small number of individuals with hypoglycemia, resulting in limited statistical power.\u003c/p\u003e \u003cp\u003eSubgroup analyses provided further insight into potential heterogeneity. The association between FPG and HF was particularly evident in younger (\u0026lt;\u0026thinsp;65 years), female, mentally active, normotensive, and lean (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;) patients. In these subgroups, HF risk remained stable at FPG levels of 6.1\u0026ndash;7.0 mmol/L but rose sharply when FPG exceeded 7.0 mmol/L, suggesting a lower glycemic tolerance in these populations. These findings highlight the potential need for individualized glycemic management strategies tailored to patient characteristics and comorbidities.\u003c/p\u003e \u003cp\u003eTo explore the underlying mechanisms, mediation analysis was performed using the WBC count as an inflammatory mediator after excluding patients with infectious and hematologic disorders. Elevated FPG (7.0\u0026ndash;16.7 mmol/L) had both direct and indirect effects on HF risk, with inflammation accounting for 13.26% of the total association. This finding aligns with experimental evidence suggesting that hyperglycemia and insulin resistance promote left ventricular hypertrophy, myocardial fibrosis, and diastolic dysfunction via oxidative stress, advanced glycation end-product accumulation, mitochondrial injury, and microvascular endothelial dysfunction, ultimately leading to HF \u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Chronic low-grade inflammation appears to be a critical link between dysglycemia and myocardial remodeling in CNRAR.\u003c/p\u003e \u003cp\u003eImportantly, our propensity score-matched comparative analysis between patients with CNRAR and the general population further demonstrated that the RHR for new-onset HF was significantly greater in the CNRAR cohort at moderate FPG elevations (6.1\u0026ndash;7.0 mmol/L; RHR\u0026thinsp;=\u0026thinsp;1.270, p\u0026thinsp;=\u0026thinsp;0.021), indicating greater vulnerability to glycemic stress in this population. At higher FPG levels (\u0026ge;\u0026thinsp;7.0 mmol/L), both populations presented comparably increased new-onset HF risk, suggesting that once glucose exceeds the diabetic threshold, its adverse cardiovascular impact becomes universal.\u003c/p\u003e \u003cp\u003eCollectively, these findings provide novel evidence that elevated FPG contributes to HF development in CNRAR patients both independently and via inflammatory pathways. From a clinical perspective, the results emphasize that maintaining FPG below 6.1 mmol/L may reduce HF risk in this high-risk population. Moreover, the integration of anti-inflammatory strategies alongside glucose control could represent a promising therapeutic approach.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThis study has several limitations that warrant consideration. First, the cohort lacked systematically recorded echocardiographic grading of aortic regurgitation severity, an inherent constraint in large-scale real-world clinical databases. To address potential residual confounding, we implemented GBM\u0026ndash;assisted IPTW to achieve robust covariate balance, performed competing-risk analyses, and conducted multiple sensitivity tests to evaluate result stability. Notably, the reproducibility of the primary findings in the UK Biobank\u0026ndash;a demographically and structurally distinct population\u0026ndash;further supports the external generalizability of our results. Second, residual confounding remains possible despite extensive covariate adjustment and the application of IPTW. Variables such as medication use (e.g., antidiabetic, antihypertensive, or lipid-lowering therapies), dietary habits, and other unmeasured lifestyle factors were not available and might have influenced both glycemic control and HF outcomes. Finally, although inflammatory mediation was statistically supported, mechanistic inference remains associative and should be interpreted within the bounds of non-interventional design.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn patients with CNRAR, FPG levels exceeding 6.1 mmol/L were independently associated with a significantly increased risk of new-onset HF. This association persisted across multiple analytic approaches and was partly mediated through chronic inflammatory pathways. The excess risk was particularly evident among younger patients, women, individuals engaged in mental work, those without hypertension, and those with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;, suggesting a lower tolerance to glycemic elevation in these subgroups. These findings highlight the prognostic importance of even mild hyperglycemia in CNRAR and provide novel mechanistic evidence linking dysglycemia, inflammation, and valvular-associated HF. From a clinical perspective, maintaining FPG levels below 6.1 mmol/L and integrating strategies targeting both glucose and inflammation control may reduce subsequent HF risk. Future guidelines on valvular heart disease management should consider incorporating specific recommendations for glycemic assessment and intervention in this high-risk population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eHF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Heart failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Fasting plasma glucose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCNRAR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Chronic nonrheumatic aortic regurgitation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPTW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInverse probability of treatment weighting\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Restricted cubic spline\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRHR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative hazard ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Aortic regurgitation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eESC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; European Society of Cardiology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAHA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; American Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGBM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Gradient boosting machine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Interquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Coronary atherosclerotic heart disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Cardiovascular disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the People's Hospital of Xinjiang Uygur Autonomous Region (KY2023042009). Since this study was not a clinical trial but an observational study, the requirement for registration and written informed consent was waived.\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\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author upon reasonable request and with appropriate institutional approvals. Publicly available datasets were analyzed in this study (UK Biobank, Application No. 674129).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82260073), the Tianshan Talent Cultivation Program Project of Xinjiang Uygur Autonomous Region (2022TSYCLJ0028) and the Key Research and Development Program of the Autonomous Region (2022B03009-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeichao Liu contributed to the study design, data analysis and drafting of the manuscript. Qian Zhao, Jing Tao, Yaoguo Wang and Peng Chao contributed to the data collection. Hui Peng and Yining Yang reviewed and revised the manuscript. All the authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSantangelo G, Bursi F, Faggiano A, Moscardelli S, Simeoli PS, Guazzi M, et al. The Global Burden of Valvular Heart Disease: From Clinical Epidemiology to Management. J Clin Med. 2023; 12(6): 2178. \u003c/li\u003e\n\u003cli\u003eLi GX, Li T, Chen YL, Guo XF, Li Z, Zhou Y, et al. Associations between aortic regurgitation severity and risk of incident myocardial infarction and stroke among patients with degenerative aortic valve disease: insights from a large Chinese population-based cohort study. BMJ Open. 2021; 11(8): e046824. \u003c/li\u003e\n\u003cli\u003eLiu CY, Li HC, Chen PF, Chen MJ, Zhao DM, Wang LQ. 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Cardiovasc Diabetol. 2024; 23(1): 354.\u003c/li\u003e\n\u003cli\u003eHsu JC, Yang YY, Chuang SL, Lin LY. Long-Term Glycemic Variability Predicts Adverse Outcomes in Diabetic Heart Failure With Preserved Ejection Fraction. J Clin Endocrinol Metab. 2025; 110(7): 1929-1937. \u003c/li\u003e\n\u003cli\u003eLevitan EB, Song YQ, Ford ES, Liu S. Is nondiabetic hyperglycemia a risk factor for cardiovascular disease? A meta-analysis of prospective studies. Arch Intern Med. 2004; 164(19): 2147-2155.\u003c/li\u003e\n\u003cli\u003eGao Q, Wang Q, Gan ZJ, Wang M, Lu DF, Zhan BD. Fasting plasma glucose levels are associated with all-cause and cancer mortality: A population-based retrospective cohort study. PLOS One. 2024; 19(11): e0311150. \u003c/li\u003e\n\u003cli\u003eChen XJ, Ding JC, Shi Z, Bai KZ, Shi SH, Tian QF. Association of longitudinal trajectories of fasting plasma glucose with all-cause and cardiovascular mortality among a Chinese older population: a retrospective cohort study. 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Life (Basel). 2023; 13(2): 497. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic nonrheumatic aortic regurgitation, Fasting plasma glucose, Heart failure, Chronic inflammation","lastPublishedDoi":"10.21203/rs.3.rs-8300166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8300166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHeart failure (HF) frequently complicates chronic nonrheumatic aortic regurgitation (CNRAR), yet the prognostic impact of fasting plasma glucose (FPG) and its inflammatory mechanisms remain unclear. We assessed whether FPG predicts HF in CNRAR and quantified inflammation-mediated effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 13,200 CNRAR patients from Northwest China. The endpoint was new-onset HF. Post-discharge FPG was measured before an endpoint. Multivariable Cox models, competing-risk analysis, restricted cubic splines (RCSs), sensitivity analyses, inverse probability of treatment weighting (IPTW) and mediation analysis were performed. In addition, 13,200 CNRAR patients were 1:1 propensity matched to 2,774,530 general population participants to estimate relative hazard ratios (RHRs). External validation included 418 CNRAR or mixed aortic stenosis-regurgitation cases from the UK Biobank.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring 535-day median follow-up, higher FPG demonstrated a dose-risk association. Compared with normoglycemia, FPG 6.1\u0026ndash;7.0 and 7.0\u0026ndash;16.7 mmol/L were associated with 17.1% (HR 1.171, 95% CI 1.026\u0026ndash;1.337) and 26.2% (HR 1.262, 95% CI 1.102\u0026ndash;1.446) higher HF risk; each 1.0 mmol/L increase conferred a 4.0% higher risk. White blood cells (WBC) accounted for 13.26% of the mediated effect on heart failure. Compared with the general population, CNRAR showed greater vulnerability at 6.1\u0026ndash;7.0 mmol/L (RHR 1.270, 95% CI 1.037\u0026ndash;1.555), while RHR at \u0026ge;\u0026thinsp;7.0 mmol/L was non-significant (RHR 1.076, 95% CI 0.901\u0026ndash;1.284). In UK Biobank, FPG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L was associated with higher HF risk (HR 1.713, 95% CI 1.044\u0026ndash;2.808).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated FPG independently predicts HF in CNRAR, partially through inflammation, supporting intensified glycemic and inflammatory risk management.\u003c/p\u003e","manuscriptTitle":"Independent and Inflammation-Linked Contribution of Fasting Plasma Glucose to Heart Failure Risk in Chronic Non-Rheumatic Aortic Regurgitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:12:26","doi":"10.21203/rs.3.rs-8300166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-07T10:44:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T14:16:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T02:14:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T02:14:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-12-07T13:35:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b02a950-bb17-46e5-9556-ebd9feb4c8cf","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T06:12:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:12:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8300166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8300166","identity":"rs-8300166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}