Association Between Novel Lipid and Obesity Indices with New-Onset Stroke: A Long-Term Study Based on the English Longitudinal Study of Ageing (ELSA)

Association Between Novel Lipid and Obesity Indices with New-Onset Stroke: A Long-Term Study Based on the English Longitudinal Study of Ageing (ELSA) | 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 Association Between Novel Lipid and Obesity Indices with New-Onset Stroke: A Long-Term Study Based on the English Longitudinal Study of Ageing (ELSA) Xinru Yu, Zhiyong Xiao, Han Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7831860/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Few studies investigated the association of newly occurring indexes of obesity and dyslipidemia including atherogenic index of plasma (AIP), body roundness index (BRI), waist-to-height ratio (WHTR) with new-onset stroke in the British population among older adults. Methods We utilized data from Wave 6 of the ELSA, with an initial pool of 4017 participants. New incidence of stroke was captured from self-report questionnaires at Waves 7 to 9 (2014–2019). Correlation detection applied multivariate logistic regression. Generalized additive models (GAMs), subgroup analysis and threshold analysis were designed for identifying nonlinear features or modifiers. Results One hundred and five subjects had experienced stroke (M: F = 3:1, aged 75.8 years) (2.6% of total subjects). Greater AIP, BRI, WHTR individually predicted an increase in risk of incidence of stroke [or 3.33 (95% CI 1.68 to 6.62) for AIP; or 1.13 (1.01 to 1.26) for BRI; or 18.03 (95% CI 1.38 to 234.89) for WHTR]. Quartiles were shown in trends between AIP( P -trend = 0.002), BRI ( P -trend = 0.056) and WHTR ( P -trend = 0.056). Stronger than old elderly. Effects were greater in < 65 years and females. The GAM shows evidence of non-linearity for AIP on stroke risk in younger and < 65 years groups. A threshold of -0.18 emerged. Results are robust under sensitivity analysis. Conclusion These newly occurrence parameters AIP, BRI, and WHTR can predict new-onset stroke non-linearly, showed stronger in those from younger elderly and females; they could help population stratify health risks, developing the approach treatment of metabolic and preventive in primary health care clinic settings of individuals. Atherogenic Index of Plasma Body Roundness Index Waist-to-Height Ratio Stroke ELSA Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Stroke serves as a pivotal agent in a prominent public health concern across the globe. It represents a primary contributor to deaths and economic hardship, and it is especially common in populations who are getting older and have higher metabolic risks( 1 ). Stroke is a principal cause of worldwide fatalities, leading to a worsening of cognitive decline, functional impairments, and need on long-term care, as shown in recent global assessments ( 2 ). Atherosclerotic processes culminate in ischemic or hemorrhagic symptoms; modifiable factors, such as central obesity and dyslipidemia, accelerate these processes( 3 ).In Western nations, despite advances in acute management such as reperfusion therapies( 4 ), global disparities in access to thrombolysis and thrombectomy-present even in high-income countries-highlight that treatment improvements alone cannot curb stroke incidence amid escalating metabolic burdens, necessitating the need for innovative risk stratification to reduce costs and enhance survivorship quality. Traditional risk models may fail to capture the subtle yet clinically relevant interactions between lipid profiles and adiposity, which recent studies( 5 ) indicate are crucial to stroke susceptibility. Moreover, population-specific disparities driven by socioeconomic, ethnic, and geographic factors further emphasize the need for refined risk assessment( 3 ). Emerging evidence indicates that composite lipid-associated indexes, including the atherogenic index of plasma, provide enhanced predictive value for stroke beyond conventional measures( 5 , 6 ). To lessen the increasing worldwide burden of stroke, integrated biomarkers could be useful in clinical practice by allowing for the early detection of individuals at high risk and the facilitation of proactive, individualized preventative efforts( 6 ). Emerging lipid-derived indices, such as AIP—computed using the log of the ratio between triglycerides and HDL-cholesterol—serve as effective indicators of atherogenic dyslipidemia( 7 , 8 ), reflecting small, dense low-density lipoprotein (sdLDL) subfractions that penetrate the arterial intima more readily and contribute to atherogenesis compared to isolated lipid measures( 9 ). Recent prospective investigations affirm AIP's superiority in prognosticating cerebrovascular risks, including stroke recurrence and severity, by reflecting systemic inflammation and endothelial dysfunction( 10 ). Paralleling this, anthropometric proxies like the Body Roundness Index (BRI), which incorporates height, weight, and waist circumference to quantify visceral fat accumulation( 11 ). Similarly, the WHtR excels in ascertaining central adiposity across diverse ethnicities, correlating strongly with cardiometabolic risk factors and showing superior or comparable predictive ability to BMI and waist circumference in meta-analyses of cardiometabolic outcomes( 12 ). Their accessibility, requiring only basic anthropometrics, positions them as pragmatic tools for clinical screening, yet gaps persist in understanding their longitudinal interplay with stroke, especially amid confounders like age, gender, and smoking/drinking status. Combining AIP with anthropometric indices may provide complementary insights into metabolic health, potentially enhancing risk prediction models beyond traditional assessments( 13 ). Despite the potential of AIP, BRI and WHtR as predictive biomarkers for cardiometabolic and cerebrovascular risks( 5 , 7 , 13 – 17 ), longitudinal studies examining their associations with stroke incidence in aging populations like those in ELSA remain absent. Since there is a lack of research on how these integrative indices can improve stroke risk stratification, this research aims to address that information shortfall by exploring the connections among these indicators and stroke occurrence in ELSA participants aged 50 and older, while accounting for key confounding factors. This underscores the need to carry out large-scale cohort studies for exploring their predictive value, making use of ELSA's strong framework to guide focused prevention efforts and tackle the growing worldwide burden of stroke. 2. Methods 2.1 Study Population and Data Acquisition ELSA data are publicly available; ethical approval was secured from the original study’s oversight body, the London Multicenter Research Ethics Committee. (11/SC/ 0374) ELSA gathers data from a variety of sources, including interviews, questionnaires, and clinical assessments, from English individuals living in the community who are 50 years old and older. The study is conducted every two years( 18 ). Using information from the UK Data Service, this study set exposures at Wave 6 (2012–2013) and used Waves 7–9 (2014–2019) to follow up on stroke outcomes prospectively. All subjects with full exposure and covariate data who were 50 years old or older were considered. The following were removed from the original pool of 10,601 Wave 6 participants: ( 1 ) 229 individuals under the age of 50, ( 2 ) 489 with prevalent stroke or missing stroke data, ( 3 ) 3,989 without AIP data, ( 4 ) 1,189 without BRI data, and ( 5 ) 33 without WHtR data. A total of 4,017 participants remained after 554, 565, and 536 individuals with missing stroke data in Waves 7–9, respectively, were removed from the analysis. Figure 1 shows the method for selecting participants, including the criteria for inclusion and exclusion as well as patterns of attrition ( Figure.1 ) 2.2 Exposures AIP = log(TG/HDL-C) ( 9 ) BRI = 364.2–365.5 × √(1 - (WC/(2π))² / (0.5 × height)²)( 11 ) WHtR = WC (cm) / height (cm) All indices were derived from Wave 6 (2012–2013) measurements, including blood draws and anthropometric assessments by trained nurses. To gain deeper insights into their associations with stroke, exposures were analyzed continuously and categorized into quartiles. 2.3 Stroke Stroke was assessed as new-onset stroke in this study. New-onset stroke was identified through self-reported in ELSA Waves 7–9 (2014–2019), among participants with no stroke history at Wave 6 (2012–2013) baseline, coded as a binary outcome (yes/no) based on ELSA’s standardized health questionnaire.( 19 , 20 ) 2.4 Covariates Covariates were chosen based on their well-documented links to stroke risk, gathered through standardized interviews and questionnaires from ELSA’s Wave 6 (2012–2013).Age was recorded continuously (years) and categorized (< 65 vs. ≥65 years) for stratification. Gender was coded as male or female. Education level was dichotomized as higher (college or above) versus lower (high school diploma or below). Marital status was grouped into married and other statuses. Smoking behavior was classified as current smoker (yes) or non-smoker (no), based on participants’self-reported cigarette use. Alcohol use was categorized as current drinker (yes) or non-drinker (no). All covariates were derived from ELSA’s structured assessments by trained interviewers, ensuring data reliability.( 18 , 21 – 23 ) 2.5 Statistical Analysis The baseline characteristics of the 4,017 participants were examined based on their stroke status (new-onset stroke versus no stroke). To compare continuous variables, t-tests were utilized, whereas chi-square tests were applied for categorical variable comparisons. We implemented multivariable logistic regression models featuring three levels of adjustment to evaluate the relationships between exposures (AIP, BRI and WHtR) and new-onset stroke occurrences. The initial level of adjustment was unadjusted, while the secondary level accounted for age, gender, education, marital status, smoking, and alcohol use( 24 ). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and exposures were analyzed both continuously and in quartiles to investigate dose-response patterns (P-trend). Subgroup analyses, categorized by age (< 65 vs. ≥65 years), sex, educational achievement, marital status, tobacco use, and alcohol consumption, assessed potential effect modification. To identify threshold effects, segmented regression models were utilized, pinpointing inflection points for AIP, BRI, and WHtR where the risk of stroke escalated. To capture potential non-linear associations, Generalized Additive Models (GAMs) employing a binomial distribution and logit link function were utilized, offering a sophisticated approach to analyze relationships that might not be purely linear. Sensitivity analyses evaluated robustness by excluding participants with missing covariates, ensuring findings remained consistent across different models. All statistical analyses were conducted using R software (version 4.5.1) and EmpowerStats (version 4.2), adhering to a two-tailed P-value threshold of less than 0.05 to determine statistical significance( 25 ). 3. Results 3.1 Baseline Characteristics Of the 4,017 individuals enrolled in the ELSA Wave 6 (2012–2013), 105 (2.6%) had a new-onset stroke (Group 1) during follow-up through Waves 7–9 (2014–2019), while 3,912 (97.4%) did not have a stroke (Group 0). Baseline traits, shown in Table 1 , showed that the groups had different profiles. People who had a new-onset stroke were older (mean age 71.04 ± 8.51 years vs. 65.32 ± 8.02 years; P < 0.001) and had higher levels of atherogenic and anthropometric indices, such as AIP (0.02 ± 0.31 vs. −0.09 ± 0.29; P < 0.001), BRI (5.55 ± 1.74 vs. 4.98 ± 1.75; P = 0.001) and WHtR (0.60 ± 0.08 vs. 0.57 ± 0.08; P < 0.001). The levels of schooling were different, with more people in Group 1 having only finished high school (66.67% vs. 54.01%; P = 0.010). Drinking habits were also different, with more people who didn't drink in Group 1 (15.24% vs. 9.00%; P = 0.029). No notable differences were observed across groups regarding marital status (P = 0.119), smoking status ( P = 0.101), or gender distribution (55.54% female overall; P = 0.145). Table 1 Characteristics of the participants at baseline (N = 4017). Variables Total (n = 4017) Without stroke (n = 3912) With stroke (n = 105) P Age, Mean ± SD 65.47 ± 8.08 65.32 ± 8.02 71.04 ± 8.51 < .001 AIP, Mean ± SD -0.08 ± 0.29 -0.09 ± 0.29 0.02 ± 0.31 < .001 BRI, Mean ± SD 5.00 ± 1.75 4.98 ± 1.75 5.55 ± 1.74 0.001 WHTR, Mean ± SD 0.58 ± 0.08 0.57 ± 0.08 0.60 ± 0.08 < .001 Gender, n (%) 0.145 Male 1786 (44.46) 1732 (44.27) 54 (51.43) Female 2231 (55.54) 2180 (55.73) 51 (48.57) Education, n (%) 0.010 College and above 1834 (45.66) 1799 (45.99) 35 (33.33) High school and below 2183 (54.34) 2113 (54.01) 70 (66.67) Marital status, n (%) 0.119 Married 1072 (26.69) 1037 (26.51) 35 (33.33) Others 2945 (73.31) 2875 (73.49) 70 (66.67) Smoke, n (%) 0.101 No 1612 (40.13) 1578 (40.34) 34 (32.38) Yes 2405 (59.87) 2334 (59.66) 71 (67.62) Drink, n (%) 0.029 No 368 (9.16) 352 (9.00) 16 (15.24) Yes 3649 (90.84) 3560 (91.00) 89 (84.76) SD: standard deviation 3.2 Models Associations Between Obesity-Related Indices and Stroke Risk The associations between AIP, BRI and WHTR with stroke risk were assessed using logistic regression models (Table 2 ). And the non-adjusted model included no covariates. Adjust I controlled for age and sex, while Adjust II further adjusted for age, sex, educational background, marital status, smoking, and drinking. Table 2 Relationship between AIP/BRI/WHTR and STROKE in diverse models Exposure Non-adjusted Adjust I Adjust II AIP 3.20 (1.71, 6.01) < 0.001 3.66 (1.86, 7.21) < 0.001 3.33 (1.68, 6.62) < 0.001 AIP quartile Q1 1 1 1 Q2 0.92 (0.47, 1.81) 0.807 0.84 (0.42, 1.66) 0.614 0.82 (0.41, 1.62) 0.565 Q3 1.76 (0.96, 3.23) 0.067 1.60 (0.87, 2.96) 0.131 1.54 (0.83, 2.85) 0.167 Q4 2.21 (1.23, 3.99) 0.008 2.17 (1.19, 3.97) 0.011 2.01 (1.10, 3.69) 0.024 P for trend < 0.001 < 0.001 0.002 BRI 1.18 (1.07, 1.30) 0.001 1.15 (1.04, 1.28) 0.010 1.13 (1.01, 1.26) 0.028 BRI quartile Q1 1 1 1 Q2 1.48 (0.80, 2.76) 0.215 1.21 (0.64, 2.27) 0.563 1.20 (0.64, 2.27) 0.566 Q3 1.18 (0.61, 2.27) 0.619 0.82 (0.42, 1.60) 0.555 0.79 (0.41, 1.55) 0.497 Q4 2.60 (1.47, 4.58) 0.001 1.96 (1.10, 3.50) 0.0227 1.82 (1.02, 3.27) 0.044 P for trend 0.001 0.026 0.056 WHTR 55.41 (5.32, 576.53) < 0.001 28.69 (2.26, 363.83) 0.010 18.03 (1.38, 234.89) 0.027 WHTR quartile Q1 1 1 1 Q2 1.48 (0.80, 2.76) 0.215 1.21 (0.64, 2.27) 0.563 1.20 (0.64, 2.27) 0.566 Q3 1.18 (0.61, 2.27) 0.619 0.82 (0.42, 1.60) 0.555 0.79 (0.41, 1.55) 0.497 Q4 2.60 (1.47, 4.58) 0.001 1.96 (1.10, 3.50) 0.023 1.82 (1.02, 3.27) 0.044 P for trend 0.001 0.026 0.056 Data in the table: β(95%CI) P value / OR (95%CI) P value Non-adjusted model adjust for: None Adjust I model adjust for: AGE; SEX Adjust II model adjust for: AGE; SEX; EDUCATIONAL BACKGROUND; MARITAL STATUS; SMOKE; DRINK AIP demonstrated the strongest positive association with stroke risk across models: non-adjusted OR 3.20 (95% CI 1.71–6.01, P < 0.001); Adjust I OR 3.66 (95% CI 1.86–7.21, P < 0.001); Adjust II OR 3.33 (95% CI 1.68–6.62, P < 0.001). In quartile analyses, Q4 versus Q1 showed an OR of 2.01 (95% CI 1.10–3.69, P = 0.024) in Adjust II, showing a notable pattern with a P-value for trend of 0.002. BRI and WHTR also exhibited positive associations, though attenuated after adjustments. For BRI, Adjust II OR was 1.13 (95% CI 1.01–1.26, P = 0.028), with Q4 versus Q1 OR 1.82 (95% CI 1.02–3.27, P = 0.044) and a borderline trend ( P for trend = 0.056). WHTR showed similar patterns: Adjust II OR 18.03 (95% CI 1.38–234.89, P = 0.027), Q4 versus Q1 OR 1.82 (95% CI 1.02–3.27, P = 0.044), and P for trend = 0.056. Overall, AIP and WHTR emerged as robust predictors, while BRI showed moderate effects. 3.3 Subgroup Analyses Subgroup analyses stratified by age, sex, educational background, marital status, tobacco use, and alcohol consumption assessed associations of AIP, BRI, and WHTR with stroke risk (Table 3 ). Table 3 subgroup analyses Subgroup AIP BRI WHTR Age Age below 65 4.40(1.54,12.58) 0.006 1.27(1.08,1.48) 0.003 402.42 (8.70,18622.25) 0.002 Age above 65 3.06(1.34, 6.95) 0.008 1.11(0.97, 1.26) 0.124 11.08 (0.50,243.44) 0.127 Gender Male 1.95(0.80,4.76) 0.143 1.15(0.98,1.34) 0.081 30.59 (0.75,1252.79) 0.071 Female 5.01(1.98,12.69) < 0.001 1.20(1.05,1.36) 0.006 79.08 (3.59,1740.00) 0.006 Education College and above 2.76(0.93,8.16) 0.067 1.27(1.07,1.51) 0.005 299.92 (4.98,18051.15) 0.006 High school and below 3.22(1.47,7.02) 0.003 1.11(0.98,1.25) 0.100 13.67 (0.75,248.98) 0.077 Marital status Married 6.36(2.11,19.16) 0.001 1.13(0.96,1.32) 0.151 18.53 (0.40,852.42) 0.135 Others 2.32(1.07,5.03) 0.032 1.20(1.07,1.36) 0.003 94.47 (4.98,1791.83) 0.003 Smoke No 2.05(0.63,6.60) 0.231 1.11(0.92,1.33) 0.284 12.85 (0.18,897.08) 0.239 Yes 3.65(1.72,7.75) < 0.001 1.21(1.07,1.36) 0.002 92.70 (5.41,1587.35) 0.002 Drink No 6.24(1.15,33.96) 0.034 1.22(0.97,1.52) 0.083 164.98 (0.64,42495.55) 0.071 Yes 2.76(1.39,5.48) 0.004 1.15(1.03,1.29) 0.011 33.05 (2.45,446.30) 0.008 Table 4 Evaluation of Threshold Effects between AIP and Stroke via Two-Segment Linear Regression Analysis Age =65 Model I Fitting by the standard linear model 3.11 (1.03, 9.36) 0.044 2.67 (1.15, 6.24) 0.023 Model II Inflection point (K) -0.55 -0.18 Segment effect 1 for K 3.62 (1.17, 11.22) 0.026 4.14 (1.33, 12.83) 0.014 Log likelihood ratio 0.334 0.279 Table 5 Evaluation of Threshold Effects between BRI and Stroke via Two-Segment Linear Regression Analysis Age =65 Model I Fitting by the standard linear model 1.21 (1.02, 1.42) 0.024 1.08 (0.95, 1.24) 0.237 Model II Inflection point (K) 7.62 8.18 Segment effect 1 for K 0.61 (0.26, 1.42) 0.252 0.67 (0.27, 1.67) 0.389 Log likelihood ratio 0.031 0.203 Table 6 Evaluation of Threshold Effects between WHtR and Stroke via Two-Segment Linear Regression Analysis AGE categorical =65 Model I Fitting by the standard linear model 121.10 (2.25, 6522.41) 0.018 6.60 (0.28, 155.97) 0.242 Model II Inflection point (K) 0.69 0.71 Segment effect 1 for K 0.00 (0.00, 23731.11) 0.267 0.00 (0.00, 2596754.12) 0.432 Log likelihood ratio 0.048 0.27 Stronger associations were observed in younger adults (< 65 years): AIP OR 4.40 (95% CI 1.54–12.58, P = 0.006), BRI OR 1.27 (95% CI 1.08–1.48, P = 0.003), and WHTR OR 402.42 (95% CI 8.70–18622.25, P = 0.002), compared to ≥ 65 years ( P -values 0.008, 0.1240, 0.1271, respectively). Females showed heightened risks: AIP OR 5.01 (95% CI 1.98–12.69, P < 0.001) and WHTR OR 79.08 (95% CI 3.59–1740.00, P = 0.006) versus males ( P = 0.143, 0.0709). For education, AIP was stronger in high school and below (OR 3.22, 95% CI 1.47–7.02, P = 0.003), while WHTR favored college and above (OR 299.92, 95% CI 4.98–18051.15, P = 0.006). Married individuals had elevated AIP (OR 6.36, 95% CI 2.11–19.16, P = 0.001) and WHTR (OR 18.53, 95% CI 0.40–852.42, P = 0.135) risks. Smokers and non-drinkers exhibited stronger effects: AIP OR 3.65 (95% CI 1.72–7.75, P < 0.001) and 6.24 (95% CI 1.15–33.96, P = 0.034), respectively; WHTR OR 92.70 (95% CI 5.41–1587.35, P = 0.002) in smokers. Overall, these findings underscore enhanced predictive value for AIP, BRI, and WHTR in younger adults, females, and select vulnerable subgroups, such as those with lower education or specific lifestyle factors. 3.4 Threshold Effect Analyses and Smooth Curve Fitting (GAMs) Generalized additive models (GAMs) revealed a non-linear association between the atherogenic index of plasma (AIP) and stroke risk in both age groups, with significant thresholds at -0.55 in < 65 years (OR: 3.11, 95% CI: 1.03–9.36, P = 0.044) and − 0.18 in ≥ 65 years (OR: 2.67, 95% CI: 1.15–6.24, P = 0.023). The body roundness index (BRI) showed a borderline non-linear trend in < 65 years, with a threshold at 7.62 (OR: 1.21, 95% CI: 1.02–1.42, P = 0.024), but no significant effect in ≥ 65 years. Waist-to-height ratio (WHtR) exhibited a borderline non-linear association in < 65 years, with a threshold at 0.69 (OR: 121.10, 95% CI: 2.25–6522.41, P = 0.018), but no significant effect in ≥ 65 years. 3.5 Sensitivity Analyses Excluding missing covariates yielded consistent ORs (AIP Adjust II OR 3.25 [1.62–6.52; P < 0.001]), affirming robustness. ( Supplementary material ) 4. Discussion 105 people out of 4017 people who participated had a stroke. These people were older (71.04 ± 8.51 years vs. 65.32 ± 8.02 years, P < 0.001) and had higher AIP (0.02 ± 0.31 vs. -0.09 ± 0.29, P < 0.001), BRI (5.55 ± 1.74 vs. 4.98 ± 1.75, P = 0.001) and WHtR (0.60 ± 0.08 vs. 0.57 ± 0.08, P < 0.001). Age and these other factors seem to be very important in raising the risk of having a stroke. Older age makes vascular stiffness and endothelial senescence worse by lowering the bioavailability of nitric oxide and breaking down elastin. This makes it easier for brain ischemia to happen( 26 – 28 ). Elevated AIP reflects atherogenic dyslipidemia, with small, dense LDL particles driving plaque formation( 29 , 30 ), while BRI and WHtR capture visceral adiposity( 31 ), which exacerbates inflammation( 32 ) and insulin resistance( 33 ), both linked to stroke pathogenesis( 34 ). Lower education and reduced alcohol consumption( 35 ) among stroke cases further suggest socioeconomic and lifestyle influences, consistent with the former study( 1 ). In adjusted models, AIP emerged as the strongest predictor (Adjust II OR = 3.33, 95% CI 1.68–6.62, P = 0.001; P -trend = 0.002), followed by WHtR (OR = 18.03, 95% CI 1.38–234.89, P = 0.027; P -trend = 0.056) and BRI (OR = 1.13, 95% CI 1.01–1.26, P = 0.028; P -trend = 0.056).The robust associations of AIP and WHtR, even after adjustment for covariates (including age, sex, educational attainment, marital status, tobacco use, and alcohol intake) ,highlight their independent contributions to stroke risk( 5 , 10 , 36 , 37 ). The significant P -trend for AIP (0.002) and borderline trends for WHtR and BRI (0.056) indicate dose-response relationships, reinforcing their utility as risk stratification tools. These findings extend prior ELSA-based research by Li et al. (2025)( 38 ), which linked AIP to cardiovascular disease, to stroke outcomes, emphasizing AIP’s role in atherogenesis. Subgroup analyses showed that there were stronger links in some groups. AIP’s effect was more pronounced in individuals aged < 65 years (OR = 4.40, P = 0.006 vs. 3.06, P = 0.008 for ≥ 65), females (OR = 5.01, P = 0.001 vs. 1.95, P = 0.143 for males), those with low education (OR = 4.01, P < 0.001 vs. 2.27, P = 0.119 for high), and non-smokers (OR = 3.46, P = 0.001 vs. 2.76, P = 0.152 for smokers). Based on these patterns, it looks like younger people, women, and people with lower incomes may be more likely to have dyslipidemia and an increased chance of stroke due to obesity. The fact that drinkers had a bigger effect on AIP (OR = 2.76, P = 0.004) than non-drinkers (OR = 6.24, P = 0.034) may be because alcohol has complex effects on lipid profiles( 39 ). These subgroup variations highlight the need for targeted interventions in high-risk populations. Using nonlinear risk trends reflected by generalized additive model (GAMs) for age-related thresholds for the risks was only shown for AIP (significant smooth terms) through the threshold test analysis, with threshold − 0.55 identified for younger participants (< 65 years old), meanwhile, value of threshold at -0.18 for participant aged 65 or over; risks increased after both of the thresholds, with ORs 3.11 and 2.67 ( P = 0.044, P = 0.023) accordingly. Such nonlinear patterns suggested AIP cut-off values would apply on young people who had sharpest increment of risk curves from that. WHtR almost nonlinearized in people younger than 65 years old showed very high odds ratio at K = 0.69 (OR = 121.10; P = 0.018) but just almost nonlinear for BRI with P = 0.071 approximately. With explained deviance within 0.0103% (from R^2 of GAM) = 0.001 ~ 0.0084, those risk indicators would play important role, complex interactions existed and change with age or some other known factors, nonlinear trend needs further study. Strengths and limitations This research exhibits various strengths. First, it stands out as one of the few studies that thoroughly investigates AIP, BRI and WHtR as indicators of stroke risk, with AIP and WHtR emerging as particularly robust biomarkers. Second, the use of longitudinal data from the ELSA cohort allowed for assessment of temporal and non-linear associations, with GAMs and threshold effect analyses providing nuanced insights into risk stratification across age and gender subgroups. Third, the representativeness of middle-aged and older British adults enhances its applicability to aging Western populations. Finally, the incorporation of novel indices beyond traditional tools such as the Framingham Risk Score highlights their potential for improving intermediate-risk assessment and guiding personalized prevention strategies( 40 ). However, certain limitations should be acknowledged. Being an observational study, the possibility of residual confounding persists, and critical variables such as diabetes, hypertension, dietary habits, and genetic influences were not included. Covariates were measured only at baseline, limiting assessment of temporal changes. Self-reported stroke outcomes may be affected by recall bias or misclassification due to the lack of objective confirmation. The relatively low event rate also reduces statistical power for subgroup analyses. Furthermore, the findings pertain specifically to British populations, necessitating validation within more ethnically diverse groups. 5. Conclusion AIP, BRI and WHTR predict new-onset stroke through lipid/obesity mechanisms; non-linear thresholds inform targeted prevention in high-risk groups. Abbreviations AIP Atherogenic Index of Plasma BRI Body Roundness Index CI Confidence Interval ELSA English Longitudinal Study of Ageing WHtR Waist-to-Height Ratio HR Hazard Ratio OR Odds Ratio GAM Generalized Additive Model Declarations Ethics approval and consent to participate The English Longitudinal Study of Ageing (ELSA) was approved by the London Multicentre Research Ethics Committee (MREC/01/2/91; 11/SC/0374). All participants provided informed consent for the original ELSA study. This secondary analysis used de-identified, publicly available data from the UK Data Service and does not constitute human subjects research, thus no additional ethical approval was required by the Institutional Review Board of Chengdu University of Traditional Chinese Medicine. The study adhered to the Declaration of Helsinki and STROBE guidelines for cohort studies. Availability of data and materials The datasets analyzed during the current study are publicly available from the UK Data Service repository under the ELSA dataset (DOI: 10.5255/UKDA-SN-5050-1). All data generated or analyzed are included in this published article and its supplementary information files. Raw data can be accessed upon registration with the UK Data Service. Competing interests The authors declare that they have no competing interests. Sources of funding This work was supported by the National Natural Science Foundation of China (82505302); the Natural Science Foundation of Sichuan Province (No. 2024NSFSC1861); the Technology Innovation R&D Project of Chengdu Science and Technology Bureau (No. 2024-YF05-00521-SN). Authors' contributions XRY conceived and designed the study. ZYX, HY, XRY conducted the research. XRY, ZYX wrote the manuscript. HY participated in funding acquisition. All authors read and approved the final manuscript. Acknowledgements We thank the participants and staff of the English Longitudinal Study of Ageing (ELSA) for their contributions. We also acknowledge the UK Data Service for providing access to the data. References Feigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021 Oct;20(10):795–820. 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Loneliness, social isolation and risk of cardiovascular disease in the English Longitudinal Study of Ageing. Eur J Prev Cardiol. 2018 Sept;25(13):1387–96. Yan H, Lang J, Li C, Eftekhariranjbar S, Jiang G, Lei J, et al. Cognitive frailty and cardiometabolic risk in middle-aged and older adults: evidence from the UK and China. Aging Clin Exp Res. 2025 Sept 4;37(1):269. Kunutsor SK, Bhattacharjee A, Jae SY, Laukkanen JA. A paradoxical association between a body shape index and cardiometabolic multimorbidity: findings from the english longitudinal study of ageing. Nutr Metab Cardiovasc Dis. 2025 May;104167. Song Y, Chang Z, Song C, Cui K, Yuan S, Qiao Z, et al. Association of sleep quality, its change and sleep duration with the risk of type 2 diabetes mellitus: findings from the english longitudinal study of ageing. Diabetes Metab Res Rev. 2023 Sept;39(6):e3669. Dong B, Chen Y, Yang X, Chen Z, Zhang H, Gao Y, et al. Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025 Apr 16;24(1):163. Jin Z, Sun W, Huang J, Zhou M, Zhang C, Zhao B, et al. Association between triglyceride glucose index and asthma exacerbation: a population-based study. Heart Lung J Crit Care. 2025;70:1–7. Gabriel-Salazar M, Morancho A, Rodriguez S, Buxó X, García-Rodríguez N, Colell G, et al. Importance of angiogenin and endothelial progenitor cells after rehabilitation both in ischemic stroke patients and in a mouse model of cerebral ischemia. Front Neurol. 2018;9:508. Reddin C, Hankey GJ, Ferguson J, Langhorne P, Oveisgharan S, Canavan M, et al. Influence of age on the association of vascular risk factors with acute stroke (INTERSTROKE): a case–control study. Lancet Healthy Longev. 2025 June;6(6):100709. Howard G, Banach M, Kissela B, Cushman M, Muntner P, Judd SE, et al. Age-related differences in the role of risk factors for ischemic stroke. Neurology. 2023 Apr 4;100(14):e1444–53. Liou L, Kaptoge S. Association of small, dense LDL-cholesterol concentration and lipoprotein particle characteristics with coronary heart disease: a systematic review and meta-analysis. Zirlik A, editor. PLOS One. 2020 Nov 9;15(11):e0241993. Stoicescu C, Vacarescu C, Cozma D. HDL function versus small dense LDL: cardiovascular benefits and implications. J Clin Med. 2025 July 12;14(14):4945. Zhou J, Yu W, Jiang G, Li H, Luo J, Li S, et al. Risk of gallstones increases with multiple dimensions of obesity indexes: a prospective study based on the UK biobank. Obes Facts. 2025 Mar 26;1–13. Shi C, Zhu L, Chen X, Gu N, Chen L, Zhu L, et al. IL-6 and TNF-α induced obesity-related inflammatory response through transcriptional regulation of miR-146b. J Interferon Cytokine Res Off J Int Soc Interferon Cytokine Res. 2014 May;34(5):342–8. Landolfo M, Spannella F, Giulietti F, Gezzi A, Biondini S, Fausti E, et al. Insulin resistance bio-anthropometric markers predict hypertension control in individuals without diabetes. Eur J Prev Cardiol. 2025 Aug 19;zwaf523. Huo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China health and retirement longitudinal study (CHARLS). Cardiovasc Diabetol. 2025 Mar 26;24(1):142. Qian N, Lu C, Wei T, Yang W, Han H, Wang M, et al. The global burden of stroke attributable to high alcohol use from 1990 to 2021: an analysis for the global burden of disease study 2021. PLOS One. 2025;20(7):e0328135. Liao J, Wang L, Duan L, Gong F, Zhu H, Pan H, et al. The association between waist circumference, weight-adjusted waist index, waist-to-height ratio and waist divided by height0.5 and the prevalence of cardiovascular diseases in patients with diabetes. BMC Public Health. 2025 Aug 19;25(1):2835. Wang X, Wen P, Liao Y, Wu T, Zeng L, Huang Y, et al. Association of atherogenic index of plasma and its modified indices with stroke risk in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: a longitudinal analysis based on CHARLS. Cardiovasc Diabetol. 2025 June 14;24(1):254. Li X, Lu L, Chen Y, Liu B, Liu B, Tian H, et al. Association of atherogenic index of plasma trajectory with the incidence of cardiovascular disease over a 12-year follow-up: findings from the ELSA cohort study. Cardiovasc Diabetol. 2025 Mar 19;24(1):124. Toubasi AA, Al-Sayegh TN. Alcohol use and types and ischemic stroke: a systematic review and meta-analysis. Eur Neurol. 2025 Aug 14;1–20. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the framingham heart study. Circulation. 2008 Feb 12;117(6):743–53. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":57022,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between AIP and Stroke stratified by age (a age\u0026lt;65; b age\u0026gt;=65)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7831860/v1/b4195d324b41df4441173a53.png"},{"id":94639028,"identity":"d548cf27-3e01-4be8-a1d8-7282ea1820a3","added_by":"auto","created_at":"2025-10-29 07:33:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52208,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between BRI and Stroke stratified by age (c age\u0026lt;65; d age\u0026gt;=65)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7831860/v1/1eb2e57965dd2d16a7d72f2a.png"},{"id":94639030,"identity":"61087577-e8e8-4929-836f-715f443bcc21","added_by":"auto","created_at":"2025-10-29 07:33:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56805,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between WHtR and Stroke stratified by age (e age\u0026lt;65; f age\u0026gt;=65)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7831860/v1/af70754c7cc7d096c80042fb.png"},{"id":95000527,"identity":"366e17d8-ccae-4214-9a42-0c48d0995079","added_by":"auto","created_at":"2025-11-03 08:59:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7831860/v1/a363ec93-d7ad-4bc1-8b7f-bea1b32565d6.pdf"},{"id":94639026,"identity":"b09fbece-d4fd-4185-9ca2-6b47310c9065","added_by":"auto","created_at":"2025-10-29 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Background","content":"\u003cp\u003eStroke serves as a pivotal agent in a prominent public health concern across the globe. It represents a primary contributor to deaths and economic hardship, and it is especially common in populations who are getting older and have higher metabolic risks(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Stroke is a principal cause of worldwide fatalities, leading to a worsening of cognitive decline, functional impairments, and need on long-term care, as shown in recent global assessments (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Atherosclerotic processes culminate in ischemic or hemorrhagic symptoms; modifiable factors, such as central obesity and dyslipidemia, accelerate these processes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).In Western nations, despite advances in acute management such as reperfusion therapies(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), global disparities in access to thrombolysis and thrombectomy-present even in high-income countries-highlight that treatment improvements alone cannot curb stroke incidence amid escalating metabolic burdens, necessitating the need for innovative risk stratification to reduce costs and enhance survivorship quality. Traditional risk models may fail to capture the subtle yet clinically relevant interactions between lipid profiles and adiposity, which recent studies(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) indicate are crucial to stroke susceptibility. Moreover, population-specific disparities driven by socioeconomic, ethnic, and geographic factors further emphasize the need for refined risk assessment(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Emerging evidence indicates that composite lipid-associated indexes, including the atherogenic index of plasma, provide enhanced predictive value for stroke beyond conventional measures(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). To lessen the increasing worldwide burden of stroke, integrated biomarkers could be useful in clinical practice by allowing for the early detection of individuals at high risk and the facilitation of proactive, individualized preventative efforts(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmerging lipid-derived indices, such as AIP\u0026mdash;computed using the log of the ratio between triglycerides and HDL-cholesterol\u0026mdash;serve as effective indicators of atherogenic dyslipidemia(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), reflecting small, dense low-density lipoprotein (sdLDL) subfractions that penetrate the arterial intima more readily and contribute to atherogenesis compared to isolated lipid measures(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Recent prospective investigations affirm AIP's superiority in prognosticating cerebrovascular risks, including stroke recurrence and severity, by reflecting systemic inflammation and endothelial dysfunction(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Paralleling this, anthropometric proxies like the Body Roundness Index (BRI), which incorporates height, weight, and waist circumference to quantify visceral fat accumulation(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, the WHtR excels in ascertaining central adiposity across diverse ethnicities, correlating strongly with cardiometabolic risk factors and showing superior or comparable predictive ability to BMI and waist circumference in meta-analyses of cardiometabolic outcomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Their accessibility, requiring only basic anthropometrics, positions them as pragmatic tools for clinical screening, yet gaps persist in understanding their longitudinal interplay with stroke, especially amid confounders like age, gender, and smoking/drinking status. Combining AIP with anthropometric indices may provide complementary insights into metabolic health, potentially enhancing risk prediction models beyond traditional assessments(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the potential of AIP, BRI and WHtR as predictive biomarkers for cardiometabolic and cerebrovascular risks(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), longitudinal studies examining their associations with stroke incidence in aging populations like those in ELSA remain absent. Since there is a lack of research on how these integrative indices can improve stroke risk stratification, this research aims to address that information shortfall by exploring the connections among these indicators and stroke occurrence in ELSA participants aged 50 and older, while accounting for key confounding factors. This underscores the need to carry out large-scale cohort studies for exploring their predictive value, making use of ELSA's strong framework to guide focused prevention efforts and tackle the growing worldwide burden of stroke.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population and Data Acquisition\u003c/h2\u003e\u003cp\u003e ELSA data are publicly available; ethical approval was secured from the original study\u0026rsquo;s oversight body, the London Multicenter Research Ethics Committee. (11/SC/ 0374)\u003c/p\u003e\u003cp\u003eELSA gathers data from a variety of sources, including interviews, questionnaires, and clinical assessments, from English individuals living in the community who are 50 years old and older. The study is conducted every two years(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Using information from the UK Data Service, this study set exposures at Wave 6 (2012\u0026ndash;2013) and used Waves 7\u0026ndash;9 (2014\u0026ndash;2019) to follow up on stroke outcomes prospectively. All subjects with full exposure and covariate data who were 50 years old or older were considered. The following were removed from the original pool of 10,601 Wave 6 participants: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 229 individuals under the age of 50, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) 489 with prevalent stroke or missing stroke data, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) 3,989 without AIP data, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) 1,189 without BRI data, and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) 33 without WHtR data. A total of 4,017 participants remained after 554, 565, and 536 individuals with missing stroke data in Waves 7\u0026ndash;9, respectively, were removed from the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the method for selecting participants, including the criteria for inclusion and exclusion as well as patterns of attrition (\u003cb\u003eFigure.1\u003c/b\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Exposures\u003c/h2\u003e\u003cp\u003eAIP\u0026thinsp;=\u0026thinsp;log(TG/HDL-C) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eBRI\u0026thinsp;=\u0026thinsp;364.2\u0026ndash;365.5 \u0026times; \u0026radic;(1 - (WC/(2π))\u0026sup2; / (0.5 \u0026times; height)\u0026sup2;)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWHtR\u0026thinsp;=\u0026thinsp;WC (cm) / height (cm)\u003c/p\u003e\u003cp\u003eAll indices were derived from Wave 6 (2012\u0026ndash;2013) measurements, including blood draws and anthropometric assessments by trained nurses. To gain deeper insights into their associations with stroke, exposures were analyzed continuously and categorized into quartiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Stroke\u003c/h2\u003e\u003cp\u003eStroke was assessed as new-onset stroke in this study. New-onset stroke was identified through self-reported in ELSA Waves 7\u0026ndash;9 (2014\u0026ndash;2019), among participants with no stroke history at Wave 6 (2012\u0026ndash;2013) baseline, coded as a binary outcome (yes/no) based on ELSA\u0026rsquo;s standardized health questionnaire.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Covariates\u003c/h2\u003e\u003cp\u003eCovariates were chosen based on their well-documented links to stroke risk, gathered through standardized interviews and questionnaires from ELSA\u0026rsquo;s Wave 6 (2012\u0026ndash;2013).Age was recorded continuously (years) and categorized (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years) for stratification. Gender was coded as male or female. Education level was dichotomized as higher (college or above) versus lower (high school diploma or below). Marital status was grouped into married and other statuses. Smoking behavior was classified as current smoker (yes) or non-smoker (no), based on participants\u0026rsquo;self-reported cigarette use. Alcohol use was categorized as current drinker (yes) or non-drinker (no). All covariates were derived from ELSA\u0026rsquo;s structured assessments by trained interviewers, ensuring data reliability.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003e The baseline characteristics of the 4,017 participants were examined based on their stroke status (new-onset stroke versus no stroke). To compare continuous variables, t-tests were utilized, whereas chi-square tests were applied for categorical variable comparisons. We implemented multivariable logistic regression models featuring three levels of adjustment to evaluate the relationships between exposures (AIP, BRI and WHtR) and new-onset stroke occurrences. The initial level of adjustment was unadjusted, while the secondary level accounted for age, gender, education, marital status, smoking, and alcohol use(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and exposures were analyzed both continuously and in quartiles to investigate dose-response patterns (P-trend). Subgroup analyses, categorized by age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), sex, educational achievement, marital status, tobacco use, and alcohol consumption, assessed potential effect modification. To identify threshold effects, segmented regression models were utilized, pinpointing inflection points for AIP, BRI, and WHtR where the risk of stroke escalated. To capture potential non-linear associations, Generalized Additive Models (GAMs) employing a binomial distribution and logit link function were utilized, offering a sophisticated approach to analyze relationships that might not be purely linear. Sensitivity analyses evaluated robustness by excluding participants with missing covariates, ensuring findings remained consistent across different models. All statistical analyses were conducted using R software (version 4.5.1) and EmpowerStats (version 4.2), adhering to a two-tailed P-value threshold of less than 0.05 to determine statistical significance(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eOf the 4,017 individuals enrolled in the ELSA Wave 6 (2012\u0026ndash;2013), 105 (2.6%) had a new-onset stroke (Group 1) during follow-up through Waves 7\u0026ndash;9 (2014\u0026ndash;2019), while 3,912 (97.4%) did not have a stroke (Group 0). Baseline traits, shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, showed that the groups had different profiles. People who had a new-onset stroke were older (mean age 71.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51 years vs. 65.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02 years; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had higher levels of atherogenic and anthropometric indices, such as AIP (0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 vs. \u0026minus;0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRI (5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 vs. 4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and WHtR (0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 vs. 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The levels of schooling were different, with more people in Group 1 having only finished high school (66.67% vs. 54.01%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). Drinking habits were also different, with more people who didn't drink in Group 1 (15.24% vs. 9.00%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). No notable differences were observed across groups regarding marital status (P\u0026thinsp;=\u0026thinsp;0.119), smoking status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.101), or gender distribution (55.54% female overall; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.145).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the participants at baseline (N\u0026thinsp;=\u0026thinsp;4017).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4017)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithout stroke\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3912)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWith stroke\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1786 (44.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1732 (44.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (51.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2231 (55.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2180 (55.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (48.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1834 (45.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1799 (45.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2183 (54.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2113 (54.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (66.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1072 (26.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1037 (26.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2945 (73.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2875 (73.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (66.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1612 (40.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1578 (40.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (32.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2405 (59.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2334 (59.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71 (67.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e368 (9.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352 (9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (15.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3649 (90.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3560 (91.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (84.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eSD: standard deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Models Associations Between Obesity-Related Indices and Stroke Risk\u003c/h2\u003e\u003cp\u003eThe associations between AIP, BRI and WHTR with stroke risk were assessed using logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). And the non-adjusted model included no covariates. Adjust I controlled for age and sex, while Adjust II further adjusted for age, sex, educational background, marital status, smoking, and drinking.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between AIP/BRI/WHTR and STROKE in diverse models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-adjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjust I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjust II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.20 (1.71, 6.01)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.66 (1.86, 7.21)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.33 (1.68, 6.62)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.47, 1.81) 0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84 (0.42, 1.66) 0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82 (0.41, 1.62) 0.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.76 (0.96, 3.23) 0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60 (0.87, 2.96) 0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.54 (0.83, 2.85) 0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21 (1.23, 3.99) 0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.17 (1.19, 3.97) 0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01 (1.10, 3.69) 0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 (1.07, 1.30) 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15 (1.04, 1.28) 0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13 (1.01, 1.26) 0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 (0.80, 2.76) 0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21 (0.64, 2.27) 0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.64, 2.27) 0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 (0.61, 2.27) 0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82 (0.42, 1.60) 0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 (0.41, 1.55) 0.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.60 (1.47, 4.58) 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.96 (1.10, 3.50) 0.0227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.82 (1.02, 3.27) 0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.41 (5.32, 576.53)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.69 (2.26, 363.83) 0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.03 (1.38, 234.89) 0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 (0.80, 2.76) 0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21 (0.64, 2.27) 0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.64, 2.27) 0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 (0.61, 2.27) 0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82 (0.42, 1.60) 0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 (0.41, 1.55) 0.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.60 (1.47, 4.58) 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.96 (1.10, 3.50) 0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.82 (1.02, 3.27) 0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eData in the table: β(95%CI) \u003cem\u003eP\u003c/em\u003e value / OR (95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNon-adjusted model adjust for: None\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAdjust I model adjust for: AGE; SEX\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAdjust II model adjust for: AGE; SEX; EDUCATIONAL BACKGROUND; MARITAL STATUS; SMOKE; DRINK\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAIP demonstrated the strongest positive association with stroke risk across models: non-adjusted OR 3.20 (95% CI 1.71\u0026ndash;6.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Adjust I OR 3.66 (95% CI 1.86\u0026ndash;7.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Adjust II OR 3.33 (95% CI 1.68\u0026ndash;6.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In quartile analyses, Q4 versus Q1 showed an OR of 2.01 (95% CI 1.10\u0026ndash;3.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) in Adjust II, showing a notable pattern with a P-value for trend of 0.002.\u003c/p\u003e\u003cp\u003eBRI and WHTR also exhibited positive associations, though attenuated after adjustments. For BRI, Adjust II OR was 1.13 (95% CI 1.01\u0026ndash;1.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), with Q4 versus Q1 OR 1.82 (95% CI 1.02\u0026ndash;3.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) and a borderline trend (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.056). WHTR showed similar patterns: Adjust II OR 18.03 (95% CI 1.38\u0026ndash;234.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), Q4 versus Q1 OR 1.82 (95% CI 1.02\u0026ndash;3.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), and \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.056.\u003c/p\u003e\u003cp\u003eOverall, AIP and WHTR emerged as robust predictors, while BRI showed moderate effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Subgroup Analyses\u003c/h2\u003e\u003cp\u003eSubgroup analyses stratified by age, sex, educational background, marital status, tobacco use, and alcohol consumption assessed associations of AIP, BRI, and WHTR with stroke risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003esubgroup analyses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHTR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge below 65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.40(1.54,12.58) 0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27(1.08,1.48) 0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e402.42\u003c/p\u003e\u003cp\u003e(8.70,18622.25) 0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge above 65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.06(1.34, 6.95) 0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11(0.97, 1.26) 0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.08\u003c/p\u003e\u003cp\u003e(0.50,243.44) 0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.95(0.80,4.76) 0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15(0.98,1.34) 0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.59 (0.75,1252.79) 0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.01(1.98,12.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20(1.05,1.36) 0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.08\u003c/p\u003e\u003cp\u003e(3.59,1740.00) 0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.76(0.93,8.16) 0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27(1.07,1.51) 0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e299.92\u003c/p\u003e\u003cp\u003e(4.98,18051.15) 0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.22(1.47,7.02) 0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11(0.98,1.25) 0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.67\u003c/p\u003e\u003cp\u003e(0.75,248.98) 0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.36(2.11,19.16) 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13(0.96,1.32) 0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.53\u003c/p\u003e\u003cp\u003e(0.40,852.42) 0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.32(1.07,5.03) 0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20(1.07,1.36) 0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.47\u003c/p\u003e\u003cp\u003e(4.98,1791.83) 0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.05(0.63,6.60) 0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11(0.92,1.33) 0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.85\u003c/p\u003e\u003cp\u003e(0.18,897.08) 0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.65(1.72,7.75)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21(1.07,1.36) 0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.70\u003c/p\u003e\u003cp\u003e(5.41,1587.35) 0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.24(1.15,33.96) 0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22(0.97,1.52) 0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164.98\u003c/p\u003e\u003cp\u003e(0.64,42495.55) 0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.76(1.39,5.48) 0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15(1.03,1.29) 0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.05\u003c/p\u003e\u003cp\u003e(2.45,446.30) 0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation of Threshold Effects between AIP and Stroke via Two-Segment Linear Regression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;=65\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by the standard linear model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.11 (1.03, 9.36) 0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67 (1.15, 6.24) 0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 1 for \u0026lt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00, 295.50) 0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.07, 7.95) 0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 2 for \u0026gt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.62 (1.17, 11.22) 0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.14 (1.33, 12.83) 0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation of Threshold Effects between BRI and Stroke via Two-Segment Linear Regression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;=65\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by the standard linear model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21 (1.02, 1.42) 0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08 (0.95, 1.24) 0.237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 1 for \u0026lt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 (1.14, 1.91) 0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15 (0.98, 1.36) 0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 2 for \u0026gt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61 (0.26, 1.42) 0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67 (0.27, 1.67) 0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation of Threshold Effects between WHtR and Stroke via Two-Segment Linear Regression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE categorical\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;=65\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by the standard linear model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.10 (2.25, 6522.41) 0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.60 (0.28, 155.97) 0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 1 for \u0026lt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5498.44 (16.52, 1830043.42) 0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.36 (0.45, 830.42) 0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSegment effect 2 for \u0026gt;\u0026thinsp;K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00, 23731.11) 0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00, 2596754.12) 0.432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStronger associations were observed in younger adults (\u0026lt;\u0026thinsp;65 years): AIP OR 4.40 (95% CI 1.54\u0026ndash;12.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), BRI OR 1.27 (95% CI 1.08\u0026ndash;1.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and WHTR OR 402.42 (95% CI 8.70\u0026ndash;18622.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), compared to \u0026ge;\u0026thinsp;65 years (\u003cem\u003eP\u003c/em\u003e-values 0.008, 0.1240, 0.1271, respectively). Females showed heightened risks: AIP OR 5.01 (95% CI 1.98\u0026ndash;12.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and WHTR OR 79.08 (95% CI 3.59\u0026ndash;1740.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) versus males (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.143, 0.0709). For education, AIP was stronger in high school and below (OR 3.22, 95% CI 1.47\u0026ndash;7.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), while WHTR favored college and above (OR 299.92, 95% CI 4.98\u0026ndash;18051.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Married individuals had elevated AIP (OR 6.36, 95% CI 2.11\u0026ndash;19.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and WHTR (OR 18.53, 95% CI 0.40\u0026ndash;852.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.135) risks. Smokers and non-drinkers exhibited stronger effects: AIP OR 3.65 (95% CI 1.72\u0026ndash;7.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 6.24 (95% CI 1.15\u0026ndash;33.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), respectively; WHTR OR 92.70 (95% CI 5.41\u0026ndash;1587.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) in smokers.\u003c/p\u003e\u003cp\u003eOverall, these findings underscore enhanced predictive value for AIP, BRI, and WHTR in younger adults, females, and select vulnerable subgroups, such as those with lower education or specific lifestyle factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Threshold Effect Analyses and Smooth Curve Fitting (GAMs)\u003c/h2\u003e\u003cp\u003eGeneralized additive models (GAMs) revealed a non-linear association between the atherogenic index of plasma (AIP) and stroke risk in both age groups, with significant thresholds at -0.55 in \u0026lt;\u0026thinsp;65 years (OR: 3.11, 95% CI: 1.03\u0026ndash;9.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) and \u0026minus;\u0026thinsp;0.18 in \u0026ge;\u0026thinsp;65 years (OR: 2.67, 95% CI: 1.15\u0026ndash;6.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). The body roundness index (BRI) showed a borderline non-linear trend in \u0026lt;\u0026thinsp;65 years, with a threshold at 7.62 (OR: 1.21, 95% CI: 1.02\u0026ndash;1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), but no significant effect in \u0026ge;\u0026thinsp;65 years. Waist-to-height ratio (WHtR) exhibited a borderline non-linear association in \u0026lt;\u0026thinsp;65 years, with a threshold at 0.69 (OR: 121.10, 95% CI: 2.25\u0026ndash;6522.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), but no significant effect in \u0026ge;\u0026thinsp;65 years.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Sensitivity Analyses\u003c/h2\u003e\u003cp\u003eExcluding missing covariates yielded consistent ORs (AIP Adjust II OR 3.25 [1.62\u0026ndash;6.52; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]), affirming robustness. (\u003cb\u003eSupplementary material\u003c/b\u003e)\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e105 people out of 4017 people who participated had a stroke. These people were older (71.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51 years vs. 65.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had higher AIP (0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 vs. -0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRI (5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 vs. 4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and WHtR (0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 vs. 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age and these other factors seem to be very important in raising the risk of having a stroke. Older age makes vascular stiffness and endothelial senescence worse by lowering the bioavailability of nitric oxide and breaking down elastin. This makes it easier for brain ischemia to happen(\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Elevated AIP reflects atherogenic dyslipidemia, with small, dense LDL particles driving plaque formation(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), while BRI and WHtR capture visceral adiposity(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), which exacerbates inflammation(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and insulin resistance(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), both linked to stroke pathogenesis(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Lower education and reduced alcohol consumption(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) among stroke cases further suggest socioeconomic and lifestyle influences, consistent with the former study(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn adjusted models, AIP emerged as the strongest predictor (Adjust II OR\u0026thinsp;=\u0026thinsp;3.33, 95% CI 1.68\u0026ndash;6.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; \u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.002), followed by WHtR (OR\u0026thinsp;=\u0026thinsp;18.03, 95% CI 1.38\u0026ndash;234.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027; \u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.056) and BRI (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI 1.01\u0026ndash;1.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028; \u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.056).The robust associations of AIP and WHtR, even after adjustment for covariates (including age, sex, educational attainment, marital status, tobacco use, and alcohol intake) ,highlight their independent contributions to stroke risk(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The significant \u003cem\u003eP\u003c/em\u003e-trend for AIP (0.002) and borderline trends for WHtR and BRI (0.056) indicate dose-response relationships, reinforcing their utility as risk stratification tools. These findings extend prior ELSA-based research by Li et al. (2025)(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), which linked AIP to cardiovascular disease, to stroke outcomes, emphasizing AIP\u0026rsquo;s role in atherogenesis.\u003c/p\u003e\u003cp\u003eSubgroup analyses showed that there were stronger links in some groups. AIP\u0026rsquo;s effect was more pronounced in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (OR\u0026thinsp;=\u0026thinsp;4.40, P\u0026thinsp;=\u0026thinsp;0.006 vs. 3.06, P\u0026thinsp;=\u0026thinsp;0.008 for \u0026ge;\u0026thinsp;65), females (OR\u0026thinsp;=\u0026thinsp;5.01, P\u0026thinsp;=\u0026thinsp;0.001 vs. 1.95, P\u0026thinsp;=\u0026thinsp;0.143 for males), those with low education (OR\u0026thinsp;=\u0026thinsp;4.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. 2.27, P\u0026thinsp;=\u0026thinsp;0.119 for high), and non-smokers (OR\u0026thinsp;=\u0026thinsp;3.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 vs. 2.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.152 for smokers). Based on these patterns, it looks like younger people, women, and people with lower incomes may be more likely to have dyslipidemia and an increased chance of stroke due to obesity. The fact that drinkers had a bigger effect on AIP (OR\u0026thinsp;=\u0026thinsp;2.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) than non-drinkers (OR\u0026thinsp;=\u0026thinsp;6.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) may be because alcohol has complex effects on lipid profiles(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These subgroup variations highlight the need for targeted interventions in high-risk populations.\u003c/p\u003e\u003cp\u003eUsing nonlinear risk trends reflected by generalized additive model (GAMs) for age-related thresholds for the risks was only shown for AIP (significant smooth terms) through the threshold test analysis, with threshold \u0026minus;\u0026thinsp;0.55 identified for younger participants (\u0026lt;\u0026thinsp;65 years old), meanwhile, value of threshold at -0.18 for participant aged 65 or over; risks increased after both of the thresholds, with ORs 3.11 and 2.67 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) accordingly. Such nonlinear patterns suggested AIP cut-off values would apply on young people who had sharpest increment of risk curves from that. WHtR almost nonlinearized in people younger than 65 years old showed very high odds ratio at K\u0026thinsp;=\u0026thinsp;0.69 (OR\u0026thinsp;=\u0026thinsp;121.10; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) but just almost nonlinear for BRI with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071 approximately. With explained deviance within 0.0103% (from R^2 of GAM)\u0026thinsp;=\u0026thinsp;0.001\u0026thinsp;~\u0026thinsp;0.0084, those risk indicators would play important role, complex interactions existed and change with age or some other known factors, nonlinear trend needs further study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research exhibits various strengths. First, it stands out as one of the few studies that thoroughly investigates AIP, BRI and WHtR as indicators of stroke risk, with AIP and WHtR emerging as particularly robust biomarkers. Second, the use of longitudinal data from the ELSA cohort allowed for assessment of temporal and non-linear associations, with GAMs and threshold effect analyses providing nuanced insights into risk stratification across age and gender subgroups. Third, the representativeness of middle-aged and older British adults enhances its applicability to aging Western populations. Finally, the incorporation of novel indices beyond traditional tools such as the Framingham Risk Score highlights their potential for improving intermediate-risk assessment and guiding personalized prevention strategies(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, certain limitations should be acknowledged. Being an observational study, the possibility of residual confounding persists, and critical variables such as diabetes, hypertension, dietary habits, and genetic influences were not included. Covariates were measured only at baseline, limiting assessment of temporal changes. Self-reported stroke outcomes may be affected by recall bias or misclassification due to the lack of objective confirmation. The relatively low event rate also reduces statistical power for subgroup analyses. Furthermore, the findings pertain specifically to British populations, necessitating validation within more ethnically diverse groups.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAIP, BRI and WHTR predict new-onset stroke through lipid/obesity mechanisms; non-linear thresholds inform targeted prevention in high-risk groups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtherogenic Index of Plasma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Roundness Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eELSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnglish Longitudinal Study of Ageing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWaist-to-Height Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGAM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeneralized Additive Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe English Longitudinal Study of Ageing (ELSA) was approved by the London Multicentre Research Ethics Committee (MREC/01/2/91; 11/SC/0374). All participants provided informed consent for the original ELSA study. This secondary analysis used de-identified, publicly available data from the UK Data Service and does not constitute human subjects research, thus no additional ethical approval was required by the Institutional Review Board of Chengdu University of Traditional Chinese Medicine. The study adhered to the Declaration of Helsinki and STROBE guidelines for cohort studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the UK Data Service repository under the ELSA dataset (DOI: 10.5255/UKDA-SN-5050-1). All data generated or analyzed are included in this published article and its supplementary information files. Raw data can be accessed upon registration with the UK Data Service.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eSources of funding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82505302); the Natural Science Foundation of Sichuan Province (No. 2024NSFSC1861); the Technology Innovation R\u0026amp;D Project of Chengdu Science and Technology Bureau (No. 2024-YF05-00521-SN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXRY conceived and designed the study. ZYX, HY, XRY conducted the research. XRY, ZYX wrote the manuscript. HY participated in funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and staff of the English Longitudinal Study of Ageing (ELSA) for their contributions. We also acknowledge the UK Data Service for providing access to the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021 Oct;20(10):795\u0026ndash;820.\u003c/li\u003e\n\u003cli\u003eTsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart disease and stroke statistics\u0026mdash;2023 update: a report from the american heart association. Circulation [Internet]. 2023 Feb 21 [cited 2025 Aug 21];147(8). 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Circulation. 2008 Feb 12;117(6):743\u0026ndash;53.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atherogenic Index of Plasma, Body Roundness Index, Waist-to-Height Ratio, Stroke, ELSA","lastPublishedDoi":"10.21203/rs.3.rs-7831860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7831860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFew studies investigated the association of newly occurring indexes of obesity and dyslipidemia including atherogenic index of plasma (AIP), body roundness index (BRI), waist-to-height ratio (WHTR) with new-onset stroke in the British population among older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe utilized data from Wave 6 of the ELSA, with an initial pool of 4017 participants. New incidence of stroke was captured from self-report questionnaires at Waves 7 to 9 (2014\u0026ndash;2019). Correlation detection applied multivariate logistic regression. Generalized additive models (GAMs), subgroup analysis and threshold analysis were designed for identifying nonlinear features or modifiers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOne hundred and five subjects had experienced stroke (M: F\u0026thinsp;=\u0026thinsp;3:1, aged 75.8 years) (2.6% of total subjects). Greater AIP, BRI, WHTR individually predicted an increase in risk of incidence of stroke [or 3.33 (95% CI 1.68 to 6.62) for AIP; or 1.13 (1.01 to 1.26) for BRI; or 18.03 (95% CI 1.38 to 234.89) for WHTR]. Quartiles were shown in trends between AIP(\u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.002), BRI (\u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.056) and WHTR (\u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;=\u0026thinsp;0.056). Stronger than old elderly. Effects were greater in \u0026lt;\u0026thinsp;65 years and females. The GAM shows evidence of non-linearity for AIP on stroke risk in younger and \u0026lt;\u0026thinsp;65 years groups. A threshold of -0.18 emerged. Results are robust under sensitivity analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese newly occurrence parameters AIP, BRI, and WHTR can predict new-onset stroke non-linearly, showed stronger in those from younger elderly and females; they could help population stratify health risks, developing the approach treatment of metabolic and preventive in primary health care clinic settings of individuals.\u003c/p\u003e","manuscriptTitle":"Association Between Novel Lipid and Obesity Indices with New-Onset Stroke: A Long-Term Study Based on the English Longitudinal Study of Ageing (ELSA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 07:33:35","doi":"10.21203/rs.3.rs-7831860/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4dafaebc-4ebc-4b17-aadc-3004ad121616","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T08:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 07:33:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7831860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7831860","identity":"rs-7831860","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}