Association of Retinal Microvascular Abnormalities with All-Cause and Specific-Cause Mortality Among U.S. Adults

Association of Retinal Microvascular Abnormalities with All-Cause and Specific-Cause Mortality Among U.S. Adults | 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 of Retinal Microvascular Abnormalities with All-Cause and Specific-Cause Mortality Among U.S. Adults Xiaoyun Chen, Hongyu Si, Yihang Fu, Weimin Yang, Yan Luo, Wei Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3929807/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Retinal microvascular abnormalities (RMA) reflect cumulative microvascular damage from systemic diseases and aging. However, little is known about the association between RMA and long-term survival outcomes. This study aimed to examine the relationships between RMA and the risk of all-cause and specific-cause mortality among U.S. adults. Methods: Individuals aged > 40 years were included from the U.S. National Health and Nutrition Examination Survey, 2005-2008. RMA and its subtypes, including retinopathy, arteriovenous nicking (AVN), focal arteriolar narrowing (FAN) and Hollenhorst plaque (HP), were manually graded from retinal photographs. Associations between RMA and the risk of all-cause and cause-specific mortality were examined with Cox regression analysis. Results: This cohort study of 5775 adults included 2881 women (weighted proportion, 52.6%) and 2894 men (weighted, 47.4%), with a weighted mean (SE) age of 56.6 (0.4) years. RMA were present in 1251 participants (weighted, 17.9%), of whom 710 (weighted, 9.8%) had retinopathy, 635 (weighted, 9.3%) had AVN, 64 (weighted, 1.0%) had FAN, and 21 (weighted, 0.3%) had HP. During a median of 12.2 years (range, 0.1-15.0 years) of follow-up, 1488 deaths occurred, including 452 associated with cardiovascular disease (CVD), 341 associated with cancer, and 695 associated with other causes. After adjusting confounding factors, the presence of any RMA and retinopathy at baseline was associated with higher risk of all-cause mortality (HR, 1.26; 95%CI, 1.07-1.47; HR, 1.36; 95%CI, 1.09-1.71, respectively), CVD mortality (HR, 1.36; 95%CI, 1.06-1.73; HR, 1.53; 95%CI, 1.04-2.26, respectively) and other-cause mortality (HR, 1.33; 95%CI, 1.06-1.67; HR, 1.55; 95%CI, 1.20-2.01, respectively). Additionally, FAN was significantly associated with an increased risk of other-cause mortality (HR, 2.06; 95%CI, 1.16-3.65). Although AVN was not associated with mortality in the whole population, it was significantly related to higher risks of all-cause and CVD death in those with obesity (HR, 1.68; 95%CI, 1.12-2.52; HR, 1.96; 95%CI, 1.23-3.13, respectively). Conclusions: This study revealed that the presence of RMA is independently associated with greater risks of all-cause, CVD and other-cause mortality in adults aged 40 years or older. National Health and Nutrition Examination Survey Retinal microvascular abnormality Mortality Figures Figure 1 Introduction The retinal arteriole has similar anatomical and physiological features to cerebral and coronary circulation. Given that retinal vessels can be observed easily and noninvasively, they can be used to monitor microvascular health status in vivo . Retinal microvascular abnormalities (RMA), including retinopathy, generalized or focal arteriolar narrowing (FAN), arteriovenous nicking (AVN) and Hollenhorst plaque (HP), are common in older persons, even in those without diabetes [ 1 – 3 ]. These findings reflect cumulative vascular damage from hypertension, aging, and other biological processes and have been hypothesized to be useful markers of cardiovascular diseases (CVD). Accumulating evidence has shown that RMA is positively associated with the risk of stroke, coronary heart disease, and cerebrovascular disease [ 4 – 10 ]. Specifically, retinopathy is related to elevated risks of coronary heart disease, stroke, carotid artery plaque, subclinical cerebral white matter lesions and cerebral atrophy, independent of traditional cerebrovascular risk factors [ 1 ]. Generalized arteriolar narrowing and AVN are known as irreversible long-term microvascular markers of cumulative hypertensive damage [ 11 ]. This existing evidence suggests that the RMA can provide important information about concurrent CVD and cerebrovascular disease status and predict the risk of related events. Hence, it is interesting to investigate the association between RMA and long-term health outcomes in the general population. To date, only a few prospective, population-based studies have evaluated the correlation between RMA and all-cause or CVD mortality. A large epidemiologic study revealed that retinopathy was independently associated with cardiovascular mortality, but the cause of death was not strictly validated [ 12 ]. Moreover, whether confounding factors, including smoking, weight status, and history of other diseases, could modify such associations remains unclear. The National Health and Nutrition Examination Survey (NHANES) is a periodic population-based study that provides detailed and validated information on demographic, comorbidity, and health-related behaviours. Linking death information obtained from the National Death Index, it offered an ideal opportunity to examine the association of eye diseases with mortality outcomes in adults. In this study, we investigated the associations of RMA, as measured by retinal photography, with all-cause and cause-specific mortality using the latest mortality data in NHANES. Methods Study Population NHANES is a nationally representative health survey in which a complex, multistage probability sampling method is used to represent the U.S. national, civilian, noninstitutionalized population. The National Centre for Health Statistics (NCHS) ethics review board approved the protocols, and assured the study conducted adhering to the statement of the Helsinki Declaration [ 13 ]. Written informed consent was obtained from each participant. The detailed methodology and data files used are publicly available online [ 14 ]. Briefly, each participant completed an in-home interview and a study visit at a mobile examination centre (MEC) to collect demographic, physical examination and laboratory sample data. In this study, we included participants aged ≥ 40 years from the NHANES, 2005–2008, when retinal photographs were taken to these targeted population. A total of 6797 participants were eligible for the inclusion criteria. Of them, 1022 were excluded due to missing retinal photographs (n = 969), ungradable images (n = 52), or missing mortality data (n = 1). The data for 5775 participants were included in the final analyses (eFigure S1 in the Supplement). Assessment of Retinal Microvascular Abnormalities For eligible participants, two non-mydriatic 45-degree retinal images centred on the fovea and the optic disc for each eye were taken with the Canon Non-Mydriatic Retinal Camera CR6-45NM (Canon, Tokyo, Japan). Digital images were transferred to the University of Wisconsin Ocular Epidemiologic Reading Centre, Madison, for manual grading according to a standardized grading protocol [ 15 ]. All images were graded by a preliminary grader and a detail grader. Controversial grading was reassessed by a third grader. If two of the three graders disagreed, the image was assessed by an adjudicator to obtain a final decision. RMA was recorded based on the worse of the 2 eyes. Retinopathy was defined as the presence of any of the following retinal abnormities: retinal microaneurysms, hard exudates, soft exudates, haemorrhages, intraretinal microvascular abnormalities, non-proliferative diabetic retinopathy (DR) or proliferative DR. AVN was defined as the presence of a pinching of the vein at an artery crossing. FAN was defined as the presence of focal pinching or narrowing of the arteriole. HP was defined as the presence of an embolus at the bifurcation of retinal arterioles. Any RMA was defined as the presence of retinopathy, AVN, FAN or HP. Ascertainment of Mortality Outcomes Data for mortality were obtained by linking the NHANES database with the National Death Index from the survey date through December 31, 2019 [ 16 ]. Causes of death were defined according to the International Classification of Diseases, Tenth Revision (ICD-10). All-cause mortality was defined as death for any reason. CVD mortality was defined as ICD-10 codes I00 to I09, I11, I13, I20 to I51, and I60 to I69. Cancer mortality was defined by the codes C00 to C97. Deaths not attributed to CVD or cancer were considered as other-cause mortality. The follow-up duration was calculated from the baseline to the date of death or December 31, 2019, whenever came first. Assessment of Covariates A standardised questionnaire was used to gather information about sociodemographic variables, including age, sex, race, education, and the family income to poverty ratio (IPR). Race and ethnicity were determined based on self-report (non-Hispanic White, non-Hispanic Black, Mexican American, and other race/ethnical groups). Education level was grouped into three categories: 1) less than high school, 2) high school or equivalent, and 3) greater than high school. Family IPR was divided into 3 categories: less than 1.30, 1.30 to 3.49, and 3.5 or higher. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared and was divided into 3 groups: normal or underweight (less than 25.0 kg/m 2 ), overweight (25.0 to 30.0 kg/m 2 ), and obese (greater than 30.0 kg/m 2 ). Smoking status was classified as never smoker, former smoker, and current smoker. Alcohol consumption was divided into never drinker, former drinker, and current drinker. Diabetes was determined by a self-reported physician diagnosis of diabetes, or use of glucose lowering medications or insulin, or fasting plasma glucose level of at least 126 mg/dL, or haemoglobin A1c (HbA1c) level of at least 6.5%. Hypertension was determined by systolic blood pressure ≥ 130 mmHg, or diastolic blood pressure ≥ 80 mmHg based on the mean value of 3 measurements, or self-reported history of hypertension, or use of blood pressure-lowering medicines [ 17 ]. Hypercholesterolemia was determined by a total cholesterol concentration of 240 mg/dL (6.2 mmol/L) or higher, or use of lipid-lowering medications. Self-reported general health condition was dichotomized as 1) excellent to good, and 2) fair or poor. Congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, and stroke were determined based on self-reported diagnoses. Statistical Analysis In accordance with the NHANES analytic guidelines [ 18 ], sampling weights, strata, and primary sampling units were applied in all analyses to account for the unequal probability of selection, oversampling of certain subpopulations, and nonresponse adjustment. We used a 4-year sample weight for combined analyses of the two NHANES cycles. No imputation for missing values was performed because the missing data rate was low for all covariates (< 10%). We reported categorical variables as unweighted numbers and weighted percentages and continuous variables as weighted means and standard errors (SEs). Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs for the associations between RMA and mortality outcomes. Schoenfeld residuals were used to test the proportional hazards assumption, and no violation was found (eFigure S2 in the Supplement). In the fully adjusted model, we adjusted for age, sex, race/ethnicity, educational level, marital status, family IPR, BMI, smoking status, alcohol consumption, general health condition, diabetes, hypertension, hypercholesteremia, history of congestive heart failure, coronary heart disease, angina, heart attack, and stroke. Stratified analyses and interaction analyses were performed to determine whether the associations differed by age, sex, race/ethnicity, weight status, smoking status, alcohol consumption, hypertension, diabetes, hypercholesterolemia, or history of CVD. A series of sensitivity analyses were also conducted. (1) Participants who died within 2 years of follow-up were excluded to minimise potential reverse causation bias. (2) We repeated the main analyses excluding participants with a history of CVD or malignancy at baseline. (3) By using the propensity score matching method, we repeated the main analysis by matching individuals with balanced confounders, including age, sex, race/ethnicity, education, marital status, family IPR, BMI, smoking status, drinking status, diabetes and hypertension. Balance statistics of the covariates before and after matching were illustrated using a Love plot (eFigure S3 in the Supplement). (4) Finally, we changed the definition of any RMA by excluding Hollenhorst plaque given its low prevalence. Analyses were conducted with R (version 4.2.3) [ 19 ], and the NHANES survey design was accounted for using the Survey package [ 20 ]. Propensity score matching was performed using the MatchIt package [ 21 ]. A 2-sided P threshold of < 0.05 was used for all analyses. Results This cohort study of 5775 adults aged 40 years and older included 2881 women (weighted proportion, 52.6%) and 2894 men (weighted, 47.4%), with a weighted mean (SE) age of 56.6 (0.4) years; 3105 participants (weighted, 77.1%) were of non-Hispanic white ancestry, 1190 (weighted, 9.7%) of non-Hispanic black ancestry, 890 (weighted, 5.4%) of Mexican American ancestry, and 590 (weighted, 7.8%) of other racial/ethnical ancestry. At baseline, any RMA was present in 1251 participants (weighted, 17.9%), of whom 710 (weighted, 9.8%) had retinopathy, 635 (weighted, 9.3%) had AVN, 64 (weighted, 1.0%) had FAN, and 21 (weighted, 0.3%) had HP. For 82.9% of the subjects, AVN and retinopathy were mutually exclusive (eFigure S4 in the Supplement). During 65 205 person-years of observation (median follow-up, 12.2 years [range, 0.1–15.0]), 1488 deaths occurred, including 452 associated with CVD, 341 with cancer, and 695 with other causes. The baseline demographic characteristics, health-related behaviours, and general health comorbidities of the participants overall and by RMA status were presented in Table 1 . Participants with RMA tended to be older (mean [SE] age, 61.6 [0.5] vs 55.6 [0.4] years), less educated (< high school, 451 [24.0%] vs 1256 [16.9%]), to have less family IPR ( ≥ 3.5, 318 [39.2%] vs 1610 [52.6%]), to have poor general health condition (430 [27.6%] vs 1058 [17.0%]), to have higher prevalence rates of hypertension (1001 [76.5%] vs 2872 [60.0%]), diabetes (481 [31.5%] vs 741 [11.9%]), hypercholesterolemia (581 [45.9%] vs 1785 [39.6%]), history of coronary heart disease (stroke, 111 [7.8%] vs 201 [3.5%]), congestive heart failure (103 [6.5%] vs 173 [2.8%]), and heart attack (136 [8.7%] vs 237 [4.2%]). Other characteristics were comparable between the groups with and without RMA. Participants with FAN and HP were more likely to be women (for FAN, 37 [65.1%] vs 2821 [52.4%]; for HP, 13 [61.1%] vs 2862 [52.6%]). Participants with any RMA or its subtypes invariably had higher prevalence rates of medical comorbidities, including hypertension, diabetes, hypercholesteremia, congestive heart failure, coronary heart disease, angina, heart attack and stroke (Table 1 ). Table 1 Baseline Characteristics of Participants With or Without Retinal Microvascular Abnormalities Characteristic a Total No RMA Presence of RMA Any RMA b Retinopathy AVN FAN HP Participants, No. 5775 4524 1251 710 635 64 21 Age, mean (SE), y 56.6 (0.4) 55.6 (0.4) 61.6 (0.5) 60.1 (0.8) 63.1 (0.6) 73.1 (1.8) 67.7 (2.7) Sex Male 2894 (47.4) 2237 (46.5) 657 (51.6) 389 (55.0) 329 (49.8) 27 (34.9) 8 (38.9) Female 2881 (52.6) 2287 (53.5) 594 (48.4) 321 (45.0) 306 (50.2) 37 (65.1) 13 (61.1) Race and ethnicity Non-Hispanic White 3105 (77.1) 2470 (77.5) 635 (75.0) 298 (67.8) 367 (81.0) 59 (97.0) 13 (85.1) Non-Hispanic Black 1190 (9.7) 897 (9.2) 293 (11.9) 211 (15.8) 116 (9.0) 0 (0) 7 (13.6) Mexican American 890 (5.4) 684 (5.3) 206 (6.1) 127 (7.2) 101 (5.6) 4 (2.2) 0 (0) Other c 590 (7.8) 473 (8.0) 117 (7.1) 74 (9.2) 51 (4.5) 1 (0.8) 1 (1.3) Educational level <High school 1707 (18.2) 1256 (16.9) 451 (24.0) 277 (25.8) 214 (22.2) 24 (33.1) 9 (41.0) High school 1437 (26.5) 1130 (26.3) 307 (27.3) 178 (29.9) 156 (26.0) 19 (33.5) 5 (25.8) Some college or more 2629 (55.4) 2137 (56.8) 492 (48.7) 255 (44.2) 264 (51.8) 20 (33.4) 7 (33.2) Marital status Married or living with partner 3662 (68.8) 2896 (69.3) 766 (66.7) 443 (67.6) 382 (66.3) 35 (61.2) 13 (69.6) Widowed, divorced, separated, or never married 2110 (31.2) 1626 (30.7) 484 33.3) 267 (32.4) 252 (33.7) 28 (38.8) 8 (30.4) Family IPR d <1.3 1374 (15.5) 1043 (14.9) 331 (17.9) 184 (17.3) 178 (18.7) 15 (18.9) 7 (42.3) 1.3–3.5 2066 (34.3) 1564 (32.4) 502 (42.9) 299 (45.9) 247 (41.4) 30 (56.6) 10 (52.3) ≥ 3.5 1928 (50.3) 1610 (52.6) 318 (39.2) 175 (44.2) 159 (40.0) 12 (24.4) 1 (5.4) BMI, kg/m 2 <25.0 1490 (27.8) 1225 (29.0) 265 (22.4) 132 (21.1) 136 (22.0) 22 (37.1) 7 (24.4) 25.0-29.9 2058 (35.4) 1603 (35.1) 455 (36.7) 264 (36.3) 222 (36.4) 31 (45.6) 5 (46.4) ≥ 30.0 2181 (36.8) 1660 (35.9) 521 (41.0) 310 (42.6) 271 (41.5) 10 (17.3) 9 (29.3) Smoking status Non-smoker 2717 (48.2) 2147 (49.1) 570 (44.4) 340 (47.6) 267 (38.9) 25 (41.8) 4 (14.6) Past smoker 1892 (31.3) 1465 (30.9) 427 (33.1) 233 (29.2) 225 (36.8) 26 (39.4) 7 (31.5) Current smoker 1162 (20.5) 909 (20.1) 253 (22.5) 137 (23.2) 142 (24.3) 12 (17.3) 10 (53.9) Alcohol consumption Never drinker 790 (12.9) 600 (12.2) 190 (15.9) 119 (20.1) 82 (11.9) 17 (27.0) 2 (5.3) Former drinker 609 (10.6) 446 (9.8) 163 (14.3) 103 (16.9) 73 (11.6) 6 (10.5) 4 (25.2) Current drinker 3371 (76.6) 2723 (78.0) 648 (69.8) 334 (63.0) 358 (76.5) 28 (62.5) 13 (69.4) General health condition Excellent to good 4160 (81.1) 3365 (83.0) 795 (72.4) 419 (67.8) 418 (73.3) 45 (72.5) 10 (54.2) Fair or poor 1488 (18.9) 1058 (17.0) 430 (27.6) 275 (32.2) 204 (26.7) 17 (27.5) 11 (45.8) Medical comorbidities Hypertension 3873 (62.9) 2872 (60.0) 1001 (76.5) 564 (74.6) 524 (80.3) 58 (92.2) 20 (95.7) Diabetes 1222 (15.4) 741 (11.9) 481 (31.5) 363 (41.8) 171 (22.7) 11 (15.3) 6 (26.9) Hypercholesteremia 2366 (40.7) 1785 (39.6) 581 (45.9) 342 (47.6) 294 (46.1) 29 (44.3) 13 (76.7) Congestive heart failure 276 (3.4) 173 (2.8) 103 (6.5) 73 (8.3) 45 (5.8) 6 (9.0) 2 (12.2) Coronary heart disease 342 (4.9) 218 (4.0) 124 (9.2) 76 (9.6) 58 (8.9) 8 (11.0) 3 (12.3) Angina/angina pectoris 240 (3.4) 157 (2.8) 83 (5.9) 52 (6.1) 38 (6.0) 2 (3.1) 3 (10.4) Heart attack 373 (5.0) 237 (4.2) 136 (8.7) 82 (8.5) 68 (9.0) 9 (11.9) 4 (16.0) Stroke 312 (4.2) 201 (3.5) 111 (7.8) 81 (9.9) 51 (7.5) 9 (12.0) 3 (6.5) Abbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; FAN, focal arteriolar narrowing; HP, Hollenhorst plaque; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); IPR, income to poverty ratio; NHANES, National Health and Nutrition Examination Survey. a The number of participants was unweighted. All means and SEs for continuous variables and percentages for categorical variables were weighted considering the complex sampling design of the NHANES. b Any RMA was defined as the presence of retinopathy, arteriovenous nicking, focal arteriolar narrowing, or Hollenhorst plaque in the worse eye. c Categorized based on self-reports from the NHANES interview. d IPR is categorized according to the eligibility of the Supplemental Nutrition Assistance Program (formerly the Food Stamp Program). Association between RMA and Mortality The associations of RMA with all-cause and cause-specific mortality are shown in Table 2 . Participants with any RMA were at a higher risk of death during the follow-up period, with death from all-cause in 38.5% (unweighted, 482 of 1251 vs. 1006 of 4524 [22.2%]) and from CVD in 13.7% (unweighted, 172 of 1251 vs. 280 of 4524 [6.2%]) of all individuals. Kaplan-Meier curves showed that participants with any RMA or retinopathy had significantly greater all-cause and CVD mortality as compared to counterparts without retinal lesions (eFigure S5 and S6 in the Supplement) after multivariate adjustment. The multivariate Cox regression model showed that any RMA present at baseline was associated with a higher risk of all-cause mortality (HR, 1.26; 95%CI, 1.07–1.47), CVD mortality (HR, 1.36; 95%CI, 1.06–1.73) and other-cause mortality (HR, 1.33; 95%CI, 1.06–1.67). Retinopathy was also significantly associated with a higher risk of all-cause mortality (HR, 1.36; 95%CI, 1.09–1.71), CVD mortality (HR, 1.53; 95%CI, 1.04–2.26), and other-cause mortality (HR, 1.55; 95%CI, 1.20–2.01). In addition, FAN was significantly associated with an increased risk of other-cause mortality (HR, 2.06; 95%CI, 1.16–3.65), but it did not increase the risk of all-cause or CVD mortality. Neither any RMA nor its subtype was associated with cancer mortality. No significant associations were found between AVN or HP and all-cause or cause-specific mortality after multiple adjustments. We investigated the dose‒response relationship between eye laterality and the risk of mortality. There was a significant dose‒response relationship between the number of eyes affected by RMA and the risk of all-cause, CVD and other mortality (all P for trend <0.05) but not for cancer mortality ( P for trend =0.946; Fig. 1 ). Table 2 Associations of Retinal Microvascular Abnormality and its Subtypes With All-Cause and Cause-Specific Mortality Hazard Ratio (95% CI) Any RMA Retinopathy AVN FAN HP Model Absent Present Absent Present Absent Present Absent Present Absent Present All-cause mortality Deaths, No./total No. 1006/4524 482/1251 1174/4993 275/710 1233/5125 247/635 1423/5674 44/64 1464/5742 17/21 Model 1 a 1 [Ref] 1.42 (1.28–1.58) 1 [Ref] 1.71 (1.44–2.02) 1 [Ref] 1.19 (1.04–1.35) 1 [Ref] 1.25 (0.93–1.68) 1 [Ref] 2.78(1.66–4.68) Model 2 b 1 [Ref] 1.42 (1.22–1.65) 1 [Ref] 1.69 (1.39–2.05) 1 [Ref] 1.18 (0.95–1.46) 1 [Ref] 1.30 (0.77–2.22) 1 [Ref] 1.88 (1.21–2.92) Model 3 c 1 [Ref] 1.26 (1.07–1.47) 1 [Ref] 1.36 (1.09–1.71) 1 [Ref] 1.13 (0.90–1.41) 1 [Ref] 1.37 (0.90–2.10) 1 [Ref] 1.57 (0.73–3.38) CVD mortality Deaths, No./total No. 280/4524 172/1251 338/4993 102/710 359/5125 91/635 432/5674 12/64 444/5742 6/21 Model 1 a 1 [Ref] 1.68 (1.39–2.05) 1 [Ref] 2.19 (1.71–2.82) 1 [Ref] 1.32 (0.98–1.77) 1 [Ref] 0.88 (0.52–1.48) 1 [Ref] 2.97 (1.35–6.53) Model 2 b 1 [Ref] 1.58 (1.27–1.98) 1 [Ref] 1.91 (1.37–2.66) 1 [Ref] 1.32 (0.97–1.79) 1 [Ref] 1.07 (0.61–1.87) 1 [Ref] 2.17 (0.99–4.73) Model 3 c 1 [Ref] 1.36 (1.06–1.73) 1 [Ref] 1.53 (1.04–2.26) 1 [Ref] 1.20 (0.82–1.74) 1 [Ref] 1.05 (0.62–1.78) 1 [Ref] 1.74 (0.51–5.93) Cancer mortality Deaths, No./total No. 249/4524 92/1251 285/4993 44/710 290/5125 51/635 334/5674 5/64 334/5742 4/21 Model 1 a 1 [Ref] 1.07 (0.82–1.38) 1 [Ref] 0.97 (0.65–1.50) 1 [Ref] 1.05 (0.77–1.43) 1 [Ref] 0.65 (0.31–1.38) 1 [Ref] 4.25 (1.04–17.3) Model 2 b 1 [Ref] 1.05 (0.75–1.47) 1 [Ref] 0.85 (0.49–1.49) 1 [Ref] 1.24 (0.85–1.81) 1 [Ref] 0.43 (0.10–1.79) 1 [Ref] 1.76 (0.48–6.40) Model 3 c 1 [Ref] 1.01 (0.67–1.52) 1 [Ref] 0.79 (0.42–1.49) 1 [Ref] 1.21 (0.79–1.87) 1 [Ref] 0.44 (0.11–1.72) 1 [Ref] 1.64 (0.37–7.23) Other mortality Deaths, No./total No. 477/4524 218/1251 551/4993 129/710 584/5125 105/635 657/5674 27/64 686/5742 7/21 Model 1 a 1 [Ref] 1.44 (1.22–1.70) 1 [Ref] 1.78 (1.46–2.17) 1 [Ref] 1.16 (0.91–1.50) 1 [Ref] 1.71 (1.15–2.54) 1 [Ref] 2.03 (0.94–4.36) Model 2 b 1 [Ref] 1.49(1.18–1.89) 1 [Ref] 1.96 (1.56–2.48) 1 [Ref] 1.06 (0.76–1.50) 1 [Ref] 1.82 (0.91–3.64) 1 [Ref] 1.77 (0.77–4.09) Model 3 c 1 [Ref] 1.33 (1.06–1.67) 1 [Ref] 1.55(1.20–2.01) 1 [Ref] 1.05 (0.79–1.39) 1 [Ref] 2.06 (1.16–3.65) 1 [Ref] 1.57 (0.60–4.08) Abbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; FAN, focal arteriolar narrowing; HP, Hollenhorst plaque; CI, confidence interval; Ref, reference. a Adjusted for age (continuous), sex (male or female), and race (non-Hispanic White, non-Hispanic Black, Mexican American, or other). b Further adjusted for educational level (< high school, high school, or ≥ some college), marital status (married/living with partner or widowed/divorced/separated/never married), family income level (IPR < 1.3, 1.3–3.5, or ≥ 3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: <25.0, 25.0-29.9, or ≥ 30.0), smoking status (never, past, or current), and alcohol consumption (never, former, or current). c Further adjusted for general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), history of congestive heart failure (yes or no), coronary heart disease (yes or no), angina/angina pectoris (yes or no), heart attack (yes or no), and stroke (yes or no). Stratified Analyses for Association of RMA with All-Cause Mortality In terms of any RMA, we found significant interactions between obesity, history of CVD and any RMA with the risk of all-cause mortality ( P = 0.026 and P = 0.014 for interactions, respectively) after multiple adjustment (Table 3 ). For the subgroup of obese individuals (BMI ≥ 30 kg/m 2 ), the HR of all-cause mortality was 1.68 (95% CI, 1.19–2.39) for those with any RMA compared with that of the subgroup without obesity (BMI < 30 kg/m 2 ; HR, 1.14; 95% CI, 0.96–1.35). In the subgroup without a history of CVD, the HR of all-cause mortality was 1.47 (95% CI, 1.25–1.73) for individuals with any RMA, while the risk was not increased for those with a history of CVD (HR, 1.06; 95% CI, 0.78–1.45). Similar patterns of interactions between obesity, history of CVD and AVN were found for the risk of all-cause mortality (Table 3 ). For retinopathy, we only found interaction between the history of CVD and retinopathy with the risk of all-cause mortality. The HRs were 1.72 (95% CI, 1.38–2.15) and 1.06 (95% CI, 0.78–1.45) for the subgroups without and with a history of CVD, respectively. No significant interactions were found between any RMA, retinopathy, AVN or any other strata variable and the risk of all-cause mortality (Table 3 ). Table 3 Stratified Analyses for the Association between Retinal Microvascular Abnormalities and All-Cause Mortality a Variable Any RMA, HR (95% CI) Retinopathy, HR (95% CI) AVN, HR (95% CI) Absent Present P Value for Interaction Absent Present P Value for Interaction Absent Present P Value for Interaction Age 0.184 0.347 0.216 <60 y 1 [Ref] 1.39 (0.90–2.13) 1 [Ref] 1.26 (0.82–1.93) 1 [Ref] 1.35 (0.69–2.65) ≥ 60 y 1 [Ref] 1.25 (1.06–1.47) 1 [Ref] 1.41 (1.11–1.79) 1 [Ref] 1.09 (0.86–1.38) Sex 0.284 0.854 0.856 Male 1 [Ref] 1.37 (1.09–1.71) 1 [Ref] 1.36 (0.98–1.90) 1 [Ref] 1.16 (0.91–1.49) Female 1 [Ref] 1.13 (0.91–1.41) 1 [Ref] 1.35 (1.07–1.72) 1 [Ref] 1.12 (0.79–1.57) Race/ethnicity 0.680 0.779 0.725 Non-Hispanic White 1 [Ref] 1.26 (1.04–1.52) 1 [Ref] 1.41 (1.07–1.86) 1 [Ref] 1.11 (0.86–1.43) Non-White b 1 [Ref] 1.19 (0.89–1.59) 1 [Ref] 1.29 (0.92–1.82) 1 [Ref] 1.12 (0.79–1.59) Weight status 0.026 0.148 0.002 BMI < 30 1 [Ref] 1.14 (0.96–1.35) 1 [Ref] 1.25 (0.96–1.64) 1 [Ref] 0.96 (0.79–1.18) BMI ≥ 30 1 [Ref] 1.68 (1.19–2.39) 1 [Ref] 1.70 (1.14–2.54) 1 [Ref] 1.68 (1.12–2.52) Smoking status 0.323 0.229 0.339 Never 1 [Ref] 1.10 (0.79–1.53) 1 [Ref] 1.18 (0.79–1.75) 1 [Ref] 0.95 (0.67–1.35) Past or current 1 [Ref] 1.38 (1.17–1.62) 1 [Ref] 1.51 (1.13–2.03) 1 [Ref] 1.27 (0.99–1.64) Alcohol consumption 0.695 0.547 0.338 Never 1 [Ref] 1.42 (0.98–2.08) 1 [Ref] 1.64 (1.10–2.43) 1 [Ref] 1.11 (0.80–1.54) Past or current 1 [Ref] 1.26 (1.06–1.49) 1 [Ref] 1.41 (1.09–1.83) 1 [Ref] 1.17 (0.92–1.49) Hypertension 0.709 0.528 0.852 No 1 [Ref] 1.09 (0.66–1.80) 1 [Ref] 1.46 (0.76–2.83) 1 [Ref] 1.03 (0.57–1.87) Yes 1 [Ref] 1.31 (1.11–1.54) 1 [Ref] 1.37 (1.09–1.71) 1 [Ref] 1.15 (0.91–1.45) Diabetes 0.806 0.804 0.507 No 1 [Ref] 1.22 (1.03–1.45) 1 [Ref] 1.32 (1.01–1.73) 1 [Ref] 1.08 (0.86–1.35) Yes 1 [Ref] 1.38 (1.03–1.84) 1 [Ref] 1.55 (1.10–2.18) 1 [Ref] 1.24 (0.83–1.87) Hypercholesterolemia 0.641 0.790 0.045 No 1 [Ref] 1.16 (0.95–1.42) 1 [Ref] 1.37 (1.02–1.85) 1 [Ref] 1.01 (0.77–1.34) Yes 1 [Ref] 1.39 (1.11–1.74) 1 [Ref] 1.35 (1.01–1.81) 1 [Ref] 1.42 (1.04–1.94) History of CVD c .014 .013 .014 No 1 [Ref] 1.47 (1.25–1.73) 1 [Ref] 1.72 (1.38–2.15) 1 [Ref] 1.25 (1.00-1.55) Yes 1 [Ref] 1.06 (0.78–1.45) 1 [Ref] 1.06 (0.78–1.45) 1 [Ref] 1.06 (0.78–1.45) Abbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CVD, cardiovascular disease; HR, hazard ratio; Ref, reference. a Analyses were adjusted for age (continuous), sex (male or female), race (non-Hispanic White, non-Hispanic Black, Mexican American, or other), educational level (< high school, high school, or ≥ some college), marital status (married/living with partner, or widowed/divorced/separated/never married), family income level (IPR < 1.3, 1.3–3.5, or ≥ 3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: <25.0, 25.0-29.9, or ≥ 30.0), smoking status (never, past, or current), alcohol consumption (never, former, or current), general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), histories of congestive heart failure (yes or no), coronary heart disease (yes or no), angina (yes or no), heart attack (yes or no), and stroke (yes or no). Stratification was not conducted for focal arteriolar narrowing or Hollenhorst plaque due to insufficient samples for subgroup analyses. b Non-white includes non-Hispanic Black, Mexican American, and other races and ethnicities. c A history of CVD was defined as a self-reported history of having a previous physician diagnosis of congestive heart failure, coronary heart disease, angina, heart attack, or stroke. Stratified Analyses for the Association of RMA with CVD Mortality For CVD mortality, stratified analyses showed significant interaction between any RMA and smoking status with the risk of CVD mortality ( P = 0.015 for interaction, Table 4 ). For the subgroup of patients who were past or current smokers, the HR of CVD mortality was 1.82 (95% CI, 1.26–2.62), and for the subgroup of non-smokers, the HR of CVD mortality was 0.79 (95% CI, 0.43–1.46). Similar interaction was found between retinopathy and smoking status with the risk of CVD mortality after multiple adjustment ( P = 0.011 for interaction, Table 4 ). The interaction between AVN and obesity with the risk of CVD mortality was significant ( P = 0.035 for interaction), showing HR = 1.96 (95% CI, 1.23–3.13) in the obesity subgroup and HR = 0.99 (95% CI, 0.64–1.55) in the non-obesity subgroup (Table 4 ). No other significant interactions were found between RMA and strata factors in our analyses. Table 4 Stratified Analyses for the Association between Retinal Microvascular Abnormalities and CVD Mortality a Variable Any RMA, HR (95% CI) Retinopathy, HR (95% CI) AVN, HR (95% CI) Absent Present P Value for Interaction Absent Present P Value for Interaction Absent Present P Value for Interaction Age 0.824 0.478 0.904 <60 y 1 [Ref] 1.07 (0.46–2.50) 1 [Ref] 1.42 (0.46–4.39) 1 [Ref] 0.78 (0.30–2.06) ≥ 60 y 1 [Ref] 1.38 (1.04–1.83) 1 [Ref] 1.50 (0.96–2.36) 1 [Ref] 1.25 (0.86–1.82) Sex 0.545 0.538 0.338 Male 1 [Ref] 1.61 (1.04–2.47) 1 [Ref] 1.87 (1.20–2.92) 1 [Ref] 1.06 (0.64–1.75) Female 1 [Ref] 1.20 (0.74–1.94) 1 [Ref] 1.25 (0.65–2.41) 1 [Ref] 1.57 (0.86–2.86) Race/ethnicity 0.697 0.567 0.752 Non-Hispanic White 1 [Ref] 1.40 (1.01–1.94) 1 [Ref] 1.69 (1.00-2.86) 1 [Ref] 1.16 (0.75–1.78) Non-White b 1 [Ref] 1.09 (0.63–1.88) 1 [Ref] 1.10 (0.63–1.93) 1 [Ref] 1.25 (0.77–2.03) Weight status 0.329 0.826 0.035 BMI < 30 1 [Ref] 1.27 (0.93–1.73) 1 [Ref] 1.55 (0.97–2.46) 1 [Ref] 0.99 (0.64–1.55) BMI ≥ 30 1 [Ref] 1.82 (1.26–2.62) 1 [Ref] 1.73 (1.07–2.81) 1 [Ref] 1.96 (1.23–3.13) Smoking status 0.015 .011 0.157 Never 1 [Ref] 0.79 (0.43–1.46) 1 [Ref] 0.84 (0.36–1.93) 1 [Ref] 0.89 (0.52–1.52) Past or current 1 [Ref] 1.90 (1.43–2.51) 1 [Ref] 2.24 (1.47–3.41) 1 [Ref] 1.46 (0.93–2.28) Alcohol consumption 0.481 0.279 0.462 Never 1 [Ref] 1.53 (0.86–2.71) 1 [Ref] 1.79 (1.00-3.18) 1 [Ref] 1.30 (0.60–2.81) Past or current 1 [Ref] 1.40 (1.07–1.83) 1 [Ref] 1.66 (1.09–2.53) 1 [Ref] 1.25 (0.88–1.78) Hypertension 0.688 0.401 0.265 No 1 [Ref] 1.66 (0.74–3.73) 1 [Ref] 0.86 (0.39–1.94) 1 [Ref] 2.26 (0.94–5.43) Yes 1 [Ref] 1.38 (1.05–1.81) 1 [Ref] 1.62 (1.06–2.46) 1 [Ref] 1.14 (0.75–1.72) Diabetes 0.753 0.694 0.721 No 1 [Ref] 1.24 (0.89–1.72) 1 [Ref] 1.36 (0.88–2.09) 1 [Ref] 1.16 (0.76–1.76) Yes 1 [Ref] 1.52 (0.88–2.64) 1 [Ref] 1.80 (0.90–3.59) 1 [Ref] 1.43 (0.72–2.86) Hypercholesterolemia 0.297 0.990 0.080 No 1 [Ref] 1.28 (0.90–1.83) 1 [Ref] 1.61 (0.99–2.61) 1 [Ref] 0.99 (0.59–1.66) Yes 1 [Ref] 1.47 (1.04–2.07) 1 [Ref] 1.37 (0.74–2.52) 1 [Ref] 1.69 (1.08–2.64) History of CVD c 0.296 0.296 0.296 No 1 [Ref] 1.53 (1.20–1.95) 1 [Ref] 1.88 (1.23–2.86) 1 [Ref] 1.38 (0.95–2.01) Yes 1 [Ref] 1.14 (0.70–1.86) 1 [Ref] 1.14 (0.70–1.86) 1 [Ref] 1.14 (0.70–1.86) Abbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval; Ref, reference. a Analyses were adjusted for age (continuous), sex (male or female), and race (non-Hispanic White, non-Hispanic Black, Mexican American, or other), educational level (< high school, high school, or ≥ some college), marital status (married/living with partner, or widowed/divorced/separated/never married), family income level (IPR < 1.3, 1.3–3.5, or ≥ 3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: <25.0, 25.0-29.9, or ≥ 30.0), smoking status (never, past, or current), alcohol consumption (never, former, or current), general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), histories of congestive heart failure (yes or no), coronary heart disease (yes or no), angina (yes or no), heart attack (yes or no), and stroke (yes or no). Stratification was not conducted for focal arteriolar narrowing or Hollenhorst plaque due to insufficient samples for their subgroup analyses. b Non-white includes non-Hispanic Black, Mexican American, and other races and ethnicities. c A history of CVD was defined as a self-reported history of having a previous physician diagnosis of congestive heart failure, coronary heart disease, angina, heart attack, or stroke. Sensitivity Analyses Sensitivity analyses demonstrated robustness of the results of our main analyses. The associations did not significantly differ when excluding the participants who died within 2 years of follow-up (eTable 1 in the Supplement), excluding the participants who had a history of CVD at baseline (eTable 2 in the Supplement), excluding the participants with malignancy at baseline (eTable 3 in the Supplement), performing the main analyses by matching subjects through a propensity score matching strategy (eTable 4 in the Supplement), or by changing the definition of any RMA through excluding Hollenhorst plaque (eTable 5 in the Supplement). Discussion In this large, population-based cohort study, we found that any RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality in U.S. adults aged 40 years and older. FAN was also significantly associated with an increased risk of other-cause mortality. Stratified analyses further indicated that the association between RMA and all-cause mortality were stronger among individuals with obesity and without a history of CVD, and the association with CVD mortality was stronger in former smokers than in current smokers. Although AVN was not associated with all-cause or other specific mortality in the whole population, it was significantly associated with a greater risk of all-cause and CVD death in individuals with obesity. Retinal photography is a simple, non-invasive approach to evaluate the microvascular health status in vivo . Extensive studies have suggested that the RMA is positively associated with the risk of CVD [ 1 , 22 , 23 ] and cerebrovascular diseases [ 12 , 24 – 27 ]. Associations between RMA and CVD mortality risk have also been identified in previous studies [ 7 , 12 ]. Witt et al reported that an increased risk of ischaemic heart disease death was associated with the arteriolar diameter using the Beaver Dam Eye population-based Study [ 7 ]. Moreover, retinopathy was identified to be associated with an increased risk of long-term CVD mortality in the Beaver Dam Eye population-based Study [ 12 ]. Toshimi et al. also reported that mild hypertensive retinopathy is a risk factor for CVD mortality independent of cardiovascular risk factors in Japanese people with and without hypertension [ 28 ]. However, there are no population-based data on the relationships between other RMA subtypes and all-cause mortality or specific mortality outcomes. In this population-based cohort study, we found that any RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality in older people. Interestingly, we further found that the associations of any RMA and retinopathy with all-cause mortality were stronger among individuals without a history of CVD. A possible explanation is that people without a history of CVD may be more prone to ignore microvascular damage, while people with a history of CVD manage their disease through medication use, diet control and lifestyle changes, resulting in a lower risk of mortality. Arteriolar narrowing and AVN represent microvascular changes occurring in the early phase of hypertension [ 29 ]. These abnormalities are related not only to concurrent blood pressure but also to past blood pressure measured 3 to 6 years previously, suggesting that these abnormalities are persistent markers of arteriolar damage from hypertension [ 30 ]. A previous study reported that AVN was strongly associated with a self-reported diagnosis of coronary heart disease in a population-based, cross-sectional study of 6267 participants aged ≥ 50 years in rural southwestern Harbin, China [ 31 ]. Witt et al. reported that generalized arteriolar narrowing was associated with increased stroke mortality [ 7 ]. However, Wong et al. demonstrated that the association between arteriolar narrowing or AVN and CVD mortality was present only in younger persons aged 43 to 74 years but was absent or even inverse in older persons aged 75 to 84 years [ 12 ]. In the present study, we did not find a significant association between FAN or AVN and all-cause or any specific mortality in the general population. These findings are not consistent with those of previous studies. We speculate that the method used for acquiring retinal images, the differences in definitions and numbers of confounders and the length of the follow-up period may explain the varied results of these studies. In this cohort, AVN was significantly associated with a higher risk of all-cause and CVD death in individuals with obesity. Thus, our study highlights the importance of weight control, especially in those with existing microvascular damage. HP (also known as retinal emboli) is an infrequent finding with an incidence ranging from 0.3–2.9% in the elderly population [ 32 – 38 ]. Several prospective studies have suggested that retinal emboli are associated with increased risks of stroke and stroke mortality, independent of conventional risk factors [ 33 , 38 , 39 ]. Retinal emboli occur more than 10 times more frequently in acute stroke patients than in large population-based studies [ 40 ]. The presence of asymptomatic retinal emboli indicates a 12% risk of a cerebrovascular event when compared to patients with no plaques seen on fundoscopy [ 41 ]. Thus, identifying this subgroup of patients and treating them early can reduce cerebrovascular and cardiovascular risks. Pooled data analysis from the Beaver Dam Eye Study (BDES) and the Blue Mountains Eye Study (BMES) showed that the presence of asymptomatic retinal emboli predicted a modestly increased risk of long-term, all-cause mortality independent of age, sex, and vascular risk factors. This increased all-cause mortality risk appeared to be partially driven by a higher risk of stroke-related mortality in persons with retinal emboli [ 42 ]. However, they did not find a significant association between retinal emboli and cardiovascular mortality [ 42 ]. In the present study, we found a low incidence rate (0.3%) of HP, but we did not find an association between HP and the risk of all-cause or any specific mortality. The main explanation for the inconsistency may be the low prevalence of HP in our study due to the methodological differences in retinal photography. Only two fields of fundus photos were taken per eye in the present study, whereas three or even seven fields per eye were taken in others. To our knowledge, the present study is the largest investigation of the associations of RMA with long-term all-cause and specific-cause mortality, considering a multitude of potential confounding factors. In addition, the present analysis is based on a nationally representative sample of U.S. adults, standardised objective methods for RMA assessment, and complete death records (only 1 missing). However, several limitations must be considered. First, RMA were only assessed at baseline, which may not accurately reflect long-term status and dynamic changes. Second, health behaviour and comorbidities collected at baseline may change over time, this may attenuate the true association between RMA and mortality. Third, although we adjusted for a comprehensive set of confounding factors, residual or unknown confounders could not be entirely excluded. Finally, due to the nature of the observational study design, our findings cannot be used for inference of causality. Conclusions In this population-based cohort study of a nationally representative sample of older people in the U.S., we found that RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality. In addition, AVN was also significantly associated with higher risks of all-cause and CVD death in individuals with obesity. The findings of our study suggest that routine retinal photography may be useful in the non-invasive assessment of concurrent cardiovascular and cerebrovascular disease status and can predict future risk of adverse health events. Abbreviations AVN arteriovenous nicking BMI body mass index CI confidence interval CVD cardiovascular disease DR diabetic retinopathy FAN focal arteriolar narrowing HP Hollenhorst plaque HR hazard ratio IPR income to poverty ratio MEC mobile examination centre NCHS National Centre for Health Statistics NHANES National Health and Nutrition Examination Survey RMA retinal microvascular abnormality STROBE Strengthening the Reporting of Observational Studies in Epidemiology Declarations Funding: This study was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515012192, 2023A1515030108). The funding organization had no role in the design or conduct of this research. Competing Interests: No conflicting relationship exists for any author. Acknowledgements We thank all the participants and staff involved in the National Health and Nutrition Examination Survey for their invaluable contributions. Data availability The NHANES data used in this are accessible through visiting https://www.cdc.gov/nchs/nhanes/index.htm. Author Contributions: Dr. Wei Xiao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design : Wei Xiao, Yan Luo. Acquisition, analysis, or interpretation of data : All authors. Drafting of the manuscript : Xiaoyun Chen and Wei Xiao. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis : Wei Xiao, Hongyu Si, Yihang Fu, Weimin Yang. Obtained funding : Wei Xiao, Xiaoyun Chen. Administrative, technical, or material support : Wei Xiao. Supervision : Wei Xiao, Xiaoyun Chen, Yan Luo. 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NHANES digital grading protocol. https://www.cdc.gov/nchs/data/nhanes/nhanes_05_06/NHANES_ophthamology_digital_grading_protocol.pdf . Accessed June 18, 2023. NCHS Data Linked to NDI Mortality Files. https://www.cdc.gov/nchs/data-linkage/mortality.htm . Accessed 18 June 2023. Whelton PK, Carey RM, Aronow WS, Casey DE Jr., Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2018;138(17):e426-e483. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, Curtin LR. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2 2013(161):1–24. The R Project for Statistical Computing. https://www.r-project.org/ . Accessed June 18, 2023. survey. : Analysis of Complex Survey Samples. https://cran.r-project.org/web/packages/survey/index.html . Accessed June 18, 2023. MatchIt. Nonparametric Preprocessing for Parametric Causal Inference. https://cran.r-project.org/web/packages/MatchIt/index.html . Accessed June 18, 2023. Li J, Kokubo Y, Arafa A, Sheerah HA, Watanabe M, Nakao YM, Honda-Kohmo K, Kashima R, Sakai Y, Watanabe E, et al. Mild Hypertensive Retinopathy and Risk of Cardiovascular Disease: The Suita Study. J Atheroscler Thromb. 2022;29(11):1663–71. Liew G, Xie J, Nguyen H, Keay L, Kamran Ikram M, McGeechan K, Klein BE, Jin Wang J, Mitchell P, Klaver CC, et al. Hypertensive retinopathy and cardiovascular disease risk: 6 population-based cohorts meta-analysis. Int J Cardiol Cardiovasc Risk Prev. 2023;17:200180. Wong TY, Klein R, Sharrett AR, Nieto FJ, Boland LL, Couper DJ, Mosley TH, Klein BE, Hubbard LD, Szklo M. Retinal microvascular abnormalities and cognitive impairment in middle-aged persons: the Atherosclerosis Risk in Communities Study. Stroke. 2002;33(6):1487–92. Wong TY, Klein R, Sharrett AR, Couper DJ, Klein BE, Liao DP, Hubbard LD, Mosley TH, Study AIARiC. Cerebral white matter lesions, retinopathy, and incident clinical stroke. JAMA. 2002;288(1):67–74. Wong TY, Mosley TH Jr., Klein R, Klein BE, Sharrett AR, Couper DJ, Hubbard LD. Atherosclerosis Risk in Communities S. Retinal microvascular changes and MRI signs of cerebral atrophy in healthy, middle-aged people. Neurology. 2003;61(6):806–11. Wong TY, Klein R, Couper DJ, Cooper LS, Shahar E, Hubbard LD, Wofford MR, Sharrett AR. Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet. 2001;358(9288):1134–40. Sairenchi T, Iso H, Yamagishi K, Irie F, Okubo Y, Gunji J, Muto T, Ota H. Mild retinopathy is a risk factor for cardiovascular mortality in Japanese with and without hypertension: the Ibaraki Prefectural Health Study. Circulation. 2011;124(23):2502–11. Garner A, Ashton N, Tripathi R, Kohner EM, Bulpitt CJ, Dollery CT. Pathogenesis of hypertensive retinopathy. An experimental study in the monkey. Br J Ophthalmol. 1975;59(1):3–44. Leung H, Wang JJ, Rochtchina E, Wong TY, Klein R, Mitchell P. Impact of current and past blood pressure on retinal arteriolar diameter in an older population. J Hypertens. 2004;22(8):1543–9. Wang J, Leng F, Li Z, Tang X, Qian H, Li X, Zhang Y, Chen X, Du H, Liu P. Retinal vascular abnormalities and their associations with cardiovascular and cerebrovascular diseases: a Study in rural southwestern Harbin, China. BMC Ophthalmol. 2020;20(1):136. Cugati S, Wang JJ, Rochtchina E, Mitchell P. Ten-year incidence of retinal emboli in an older population. Stroke. 2006;37(3):908–10. Wong TY, Klein R. Retinal arteriolar emboli: epidemiology and risk of stroke. Curr Opin Ophthalmol. 2002;13(3):142–6. Cheung N, Lim L, Wang JJ, Islam FM, Mitchell P, Saw SM, Aung T, Wong TY. Prevalence and risk factors of retinal arteriolar emboli: the Singapore Malay Eye Study. Am J Ophthalmol. 2008;146(4):620–4. Cheung N, Teo K, Zhao W, Wang JJ, Neelam K, Tan NYQ, Mitchell P, Cheng CY, Wong TY. Prevalence and Associations of Retinal Emboli With Ethnicity, Stroke, and Renal Disease in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Disease Study. JAMA Ophthalmol. 2017;135(10):1023–8. Hoki SL, Varma R, Lai MY, Azen SP, Klein R. Los Angeles Latino Eye Study G. Prevalence and associations of asymptomatic retinal emboli in Latinos: the Los Angeles Latino Eye Study (LALES). Am J Ophthalmol. 2008;145(1):143–8. Ahmmed AA, Carey PE, Steel DH, Sandinha T. Assessing Patients with Asymptomatic Retinal Emboli Detected at Retinal Screening. Ophthalmol Ther. 2016;5(2):175–82. Klein R, Klein BE, Jensen SC, Moss SE, Meuer SM. Retinal emboli and stroke: the Beaver Dam Eye Study. Arch Ophthalmol. 1999;117(8):1063–8. Klein R, Klein BE, Moss SE, Meuer SM. Retinal emboli and cardiovascular disease: the Beaver Dam Eye Study. Trans Am Ophthalmol Soc. 2003;101:173–80. discussion 180 – 172. Egan RA, Lutsep HL. Prevalence of Retinal Emboli and Acute Retinal Artery Occlusion in Acute Ischemic Stroke. J Stroke Cerebrovasc Dis. 2020;29(2):104446. Ghoneim BM, Westby D, Elsharkawi M, Said M, Walsh SR. Systematic review of the relationship between Hollenhorst plaques and cerebrovascular events. Vascular 2023:17085381231163339. Wang JJ, Cugati S, Knudtson MD, Rochtchina E, Klein R, Klein BE, Wong TY, Mitchell P. Retinal arteriolar emboli and long-term mortality: pooled data analysis from two older populations. Stroke. 2006;37(7):1833–6. Additional Declarations No competing interests reported. Supplementary Files eFigureS1.pdf eFigureS2.pdf eFigureS3.pdf eFigureS4.pdf eFigureS5.pdf eFigureS6.pdf eTable1.pdf eTable2.pdf eTable3.pdf eTable4.pdf eTable5.pdf Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 13 Jun, 2024 Editor invited by journal 29 Feb, 2024 Editor assigned by journal 29 Feb, 2024 Submission checks completed at journal 16 Feb, 2024 First submitted to journal 04 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3929807","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273189264,"identity":"deda3ce6-ce7b-4f85-b6de-9c2d4d907d18","order_by":0,"name":"Xiaoyun Chen","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Chen","suffix":""},{"id":273189265,"identity":"50eb7130-6716-4c8c-b3de-bea47e5bf9a3","order_by":1,"name":"Hongyu Si","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Si","suffix":""},{"id":273189266,"identity":"becf69e0-ec89-44ed-b451-14ccb6dcfeb1","order_by":2,"name":"Yihang Fu","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Yihang","middleName":"","lastName":"Fu","suffix":""},{"id":273189267,"identity":"0dab11c6-da4d-459c-827e-c241d2f846c3","order_by":3,"name":"Weimin Yang","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Yang","suffix":""},{"id":273189268,"identity":"55fad252-2ec8-40fd-a7a3-5fb8b95384b5","order_by":4,"name":"Yan Luo","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Luo","suffix":""},{"id":273189269,"identity":"8639af5e-7c86-4d48-9e61-59e83c62d667","order_by":5,"name":"Wei Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZhBR8V+OgYGx8QBMUIKwljPMxkAtDURqAQHGNubEBiBNnBaD48wPH/OwsaWvbT8MtOXPYXuDA8wHb/Mw2OXh0iLZzGZszMPDk7vtTGLDAca2w4kbDrAlW/MwJBfj0sLPzGAmnSMhkbvtAEhLw+EEgwM8ZtI8DAfATsUG2JjZv0nnGBikm51/CHMY/ze8WviZgWbmJCQkmN0A2sLAdphxwwEeNrxaJJt5io3/HDhguO0G0JbEtvTEmYfZjC3nGCTj1GJw/vjGhzP/HZA3O5/+8MGHP9b2fMebH954U2GHUwsqSGBohkauAVHqwaCOeKWjYBSMglEwYgAAMFBZflWHD3EAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2024-02-05 03:59:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3929807/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3929807/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-21117-0","type":"published","date":"2024-12-23T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51332754,"identity":"5c77e469-b0ef-4a5d-acd0-abce930ce42c","added_by":"auto","created_at":"2024-02-19 18:00:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203575,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of adjusted hazard ratios for all-cause and cause-specific mortality according to laterality of the eye affected by RMA. Data are presented as HRs and 95%CIs for unilateral and bilateral RMA vs no RMA for all-cause, CVD, cancer, and other-cause mortality. The adjustments included age, sex, race/ethnicity, education, marital status, family income level, body mass index, smoking status, alcohol consumption, general health condition, diabetes, hypertension, hypercholesteremia, history of congestive heart failure, coronary heart disease, angina, heart attack, and stroke.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/5165477415132f3a0db1e378.png"},{"id":72641128,"identity":"68f4541a-f7f5-4e7e-9b9a-a10adcf0932a","added_by":"auto","created_at":"2024-12-30 16:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1677748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/2b75618b-fe9b-466a-9d8e-ec265d755d3a.pdf"},{"id":51332772,"identity":"42c5c67b-14b7-4139-9bd3-69dfeb18daaf","added_by":"auto","created_at":"2024-02-19 18:00:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":372253,"visible":true,"origin":"","legend":"","description":"","filename":"eFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/596cad2605ffa891fc75e2b2.pdf"},{"id":51332768,"identity":"0fe25282-e348-474d-b431-b9227697c9e3","added_by":"auto","created_at":"2024-02-19 18:00:37","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":650846,"visible":true,"origin":"","legend":"","description":"","filename":"eFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/a7d7b6790a633b4fd2cf5efa.pdf"},{"id":51332778,"identity":"e064d9b8-9857-4564-ae45-e797a3093370","added_by":"auto","created_at":"2024-02-19 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18:00:31","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":248084,"visible":true,"origin":"","legend":"","description":"","filename":"eFigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/7779ad76bf5919dfdbf5a6a2.pdf"},{"id":51332760,"identity":"ab842c00-b9d9-4343-ae68-5f34cfb99b3c","added_by":"auto","created_at":"2024-02-19 18:00:34","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":235374,"visible":true,"origin":"","legend":"","description":"","filename":"eFigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/136c4b1d119a5005a63e713a.pdf"},{"id":51332763,"identity":"18f704cc-2e36-4558-85b2-482dd673246f","added_by":"auto","created_at":"2024-02-19 18:00:35","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":140509,"visible":true,"origin":"","legend":"","description":"","filename":"eTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/12c56bd1ed71db6d1a334ff8.pdf"},{"id":51332745,"identity":"6a15e254-3022-4409-91d3-a3c8bab42c70","added_by":"auto","created_at":"2024-02-19 18:00:31","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":140202,"visible":true,"origin":"","legend":"","description":"","filename":"eTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/a501318be65921a704551c24.pdf"},{"id":51332738,"identity":"6c744b2e-5be6-451d-86e1-3181b6d7d6bd","added_by":"auto","created_at":"2024-02-19 18:00:29","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":139829,"visible":true,"origin":"","legend":"","description":"","filename":"eTable3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/96fd0dbf30406365480a3bed.pdf"},{"id":51332751,"identity":"6e32464e-c9e7-4b7c-89f3-90efe0ab5123","added_by":"auto","created_at":"2024-02-19 18:00:33","extension":"pdf","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":123904,"visible":true,"origin":"","legend":"","description":"","filename":"eTable4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/470125d52368944b186da34c.pdf"},{"id":51332739,"identity":"8fac3a42-f678-4fdb-a1a7-92cf03a3383f","added_by":"auto","created_at":"2024-02-19 18:00:29","extension":"pdf","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":130953,"visible":true,"origin":"","legend":"","description":"","filename":"eTable5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929807/v1/3d915f9839e0704e5499b51d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Retinal Microvascular Abnormalities with All-Cause and Specific-Cause Mortality Among U.S. Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe retinal arteriole has similar anatomical and physiological features to cerebral and coronary circulation. Given that retinal vessels can be observed easily and noninvasively, they can be used to monitor microvascular health status \u003cem\u003ein vivo\u003c/em\u003e. Retinal microvascular abnormalities (RMA), including retinopathy, generalized or focal arteriolar narrowing (FAN), arteriovenous nicking (AVN) and Hollenhorst plaque (HP), are common in older persons, even in those without diabetes [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These findings reflect cumulative vascular damage from hypertension, aging, and other biological processes and have been hypothesized to be useful markers of cardiovascular diseases (CVD). Accumulating evidence has shown that RMA is positively associated with the risk of stroke, coronary heart disease, and cerebrovascular disease [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Specifically, retinopathy is related to elevated risks of coronary heart disease, stroke, carotid artery plaque, subclinical cerebral white matter lesions and cerebral atrophy, independent of traditional cerebrovascular risk factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Generalized arteriolar narrowing and AVN are known as irreversible long-term microvascular markers of cumulative hypertensive damage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This existing evidence suggests that the RMA can provide important information about concurrent CVD and cerebrovascular disease status and predict the risk of related events. Hence, it is interesting to investigate the association between RMA and long-term health outcomes in the general population. To date, only a few prospective, population-based studies have evaluated the correlation between RMA and all-cause or CVD mortality. A large epidemiologic study revealed that retinopathy was independently associated with cardiovascular mortality, but the cause of death was not strictly validated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, whether confounding factors, including smoking, weight status, and history of other diseases, could modify such associations remains unclear.\u003c/p\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a periodic population-based study that provides detailed and validated information on demographic, comorbidity, and health-related behaviours. Linking death information obtained from the National Death Index, it offered an ideal opportunity to examine the association of eye diseases with mortality outcomes in adults. In this study, we investigated the associations of RMA, as measured by retinal photography, with all-cause and cause-specific mortality using the latest mortality data in NHANES.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eNHANES is a nationally representative health survey in which a complex, multistage probability sampling method is used to represent the U.S. national, civilian, noninstitutionalized population. The National Centre for Health Statistics (NCHS) ethics review board approved the protocols, and assured the study conducted adhering to the statement of the Helsinki Declaration [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Written informed consent was obtained from each participant. The detailed methodology and data files used are publicly available online [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, each participant completed an in-home interview and a study visit at a mobile examination centre (MEC) to collect demographic, physical examination and laboratory sample data.\u003c/p\u003e \u003cp\u003eIn this study, we included participants aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;40 years from the NHANES, 2005\u0026ndash;2008, when retinal photographs were taken to these targeted population. A total of 6797 participants were eligible for the inclusion criteria. Of them, 1022 were excluded due to missing retinal photographs (n\u0026thinsp;=\u0026thinsp;969), ungradable images (n\u0026thinsp;=\u0026thinsp;52), or missing mortality data (n\u0026thinsp;=\u0026thinsp;1). The data for 5775 participants were included in the final analyses (eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supplement).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Retinal Microvascular Abnormalities\u003c/h2\u003e \u003cp\u003eFor eligible participants, two non-mydriatic 45-degree retinal images centred on the fovea and the optic disc for each eye were taken with the Canon Non-Mydriatic Retinal Camera CR6-45NM (Canon, Tokyo, Japan). Digital images were transferred to the University of Wisconsin Ocular Epidemiologic Reading Centre, Madison, for manual grading according to a standardized grading protocol [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. All images were graded by a preliminary grader and a detail grader. Controversial grading was reassessed by a third grader. If two of the three graders disagreed, the image was assessed by an adjudicator to obtain a final decision. RMA was recorded based on the worse of the 2 eyes.\u003c/p\u003e \u003cp\u003eRetinopathy was defined as the presence of any of the following retinal abnormities: retinal microaneurysms, hard exudates, soft exudates, haemorrhages, intraretinal microvascular abnormalities, non-proliferative diabetic retinopathy (DR) or proliferative DR. AVN was defined as the presence of a pinching of the vein at an artery crossing. FAN was defined as the presence of focal pinching or narrowing of the arteriole. HP was defined as the presence of an embolus at the bifurcation of retinal arterioles. Any RMA was defined as the presence of retinopathy, AVN, FAN or HP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAscertainment of Mortality Outcomes\u003c/h2\u003e \u003cp\u003eData for mortality were obtained by linking the NHANES database with the National Death Index from the survey date through December 31, 2019 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Causes of death were defined according to \u003cem\u003ethe International Classification of Diseases, Tenth Revision\u003c/em\u003e (ICD-10). All-cause mortality was defined as death for any reason. CVD mortality was defined as ICD-10 codes I00 to I09, I11, I13, I20 to I51, and I60 to I69. Cancer mortality was defined by the codes C00 to C97. Deaths not attributed to CVD or cancer were considered as other-cause mortality. The follow-up duration was calculated from the baseline to the date of death or December 31, 2019, whenever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Covariates\u003c/h2\u003e \u003cp\u003eA standardised questionnaire was used to gather information about sociodemographic variables, including age, sex, race, education, and the family income to poverty ratio (IPR). Race and ethnicity were determined based on self-report (non-Hispanic White, non-Hispanic Black, Mexican American, and other race/ethnical groups). Education level was grouped into three categories: 1) less than high school, 2) high school or equivalent, and 3) greater than high school. Family IPR was divided into 3 categories: less than 1.30, 1.30 to 3.49, and 3.5 or higher. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared and was divided into 3 groups: normal or underweight (less than 25.0 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0 to 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (greater than 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e). Smoking status was classified as never smoker, former smoker, and current smoker. Alcohol consumption was divided into never drinker, former drinker, and current drinker. Diabetes was determined by a self-reported physician diagnosis of diabetes, or use of glucose lowering medications or insulin, or fasting plasma glucose level of at least 126 mg/dL, or haemoglobin A1c (HbA1c) level of at least 6.5%. Hypertension was determined by systolic blood pressure\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;130 mmHg, or diastolic blood pressure\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;80 mmHg based on the mean value of 3 measurements, or self-reported history of hypertension, or use of blood pressure-lowering medicines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Hypercholesterolemia was determined by a total cholesterol concentration of 240 mg/dL (6.2 mmol/L) or higher, or use of lipid-lowering medications. Self-reported general health condition was dichotomized as 1) excellent to good, and 2) fair or poor. Congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, and stroke were determined based on self-reported diagnoses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn accordance with the NHANES analytic guidelines [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], sampling weights, strata, and primary sampling units were applied in all analyses to account for the unequal probability of selection, oversampling of certain subpopulations, and nonresponse adjustment. We used a 4-year sample weight for combined analyses of the two NHANES cycles. No imputation for missing values was performed because the missing data rate was low for all covariates (\u0026lt;\u0026thinsp;10%). We reported categorical variables as unweighted numbers and weighted percentages and continuous variables as weighted means and standard errors (SEs). Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs for the associations between RMA and mortality outcomes. Schoenfeld residuals were used to test the proportional hazards assumption, and no violation was found (eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e in the Supplement). In the fully adjusted model, we adjusted for age, sex, race/ethnicity, educational level, marital status, family IPR, BMI, smoking status, alcohol consumption, general health condition, diabetes, hypertension, hypercholesteremia, history of congestive heart failure, coronary heart disease, angina, heart attack, and stroke.\u003c/p\u003e \u003cp\u003eStratified analyses and interaction analyses were performed to determine whether the associations differed by age, sex, race/ethnicity, weight status, smoking status, alcohol consumption, hypertension, diabetes, hypercholesterolemia, or history of CVD.\u003c/p\u003e \u003cp\u003eA series of sensitivity analyses were also conducted. (1) Participants who died within 2 years of follow-up were excluded to minimise potential reverse causation bias. (2) We repeated the main analyses excluding participants with a history of CVD or malignancy at baseline. (3) By using the propensity score matching method, we repeated the main analysis by matching individuals with balanced confounders, including age, sex, race/ethnicity, education, marital status, family IPR, BMI, smoking status, drinking status, diabetes and hypertension. Balance statistics of the covariates before and after matching were illustrated using a Love plot (eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e in the Supplement). (4) Finally, we changed the definition of any RMA by excluding Hollenhorst plaque given its low prevalence.\u003c/p\u003e \u003cp\u003eAnalyses were conducted with R (version 4.2.3) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and the NHANES survey design was accounted for using the Survey package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Propensity score matching was performed using the MatchIt package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A 2-sided \u003cem\u003eP\u003c/em\u003e threshold of \u0026lt;\u0026thinsp;0.05 was used for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis cohort study of 5775 adults aged 40 years and older included 2881 women (weighted proportion, 52.6%) and 2894 men (weighted, 47.4%), with a weighted mean (SE) age of 56.6 (0.4) years; 3105 participants (weighted, 77.1%) were of non-Hispanic white ancestry, 1190 (weighted, 9.7%) of non-Hispanic black ancestry, 890 (weighted, 5.4%) of Mexican American ancestry, and 590 (weighted, 7.8%) of other racial/ethnical ancestry. At baseline, any RMA was present in 1251 participants (weighted, 17.9%), of whom 710 (weighted, 9.8%) had retinopathy, 635 (weighted, 9.3%) had AVN, 64 (weighted, 1.0%) had FAN, and 21 (weighted, 0.3%) had HP. For 82.9% of the subjects, AVN and retinopathy were mutually exclusive (eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e in the Supplement). During 65 205 person-years of observation (median follow-up, 12.2 years [range, 0.1\u0026ndash;15.0]), 1488 deaths occurred, including 452 associated with CVD, 341 with cancer, and 695 with other causes.\u003c/p\u003e \u003cp\u003eThe baseline demographic characteristics, health-related behaviours, and general health comorbidities of the participants overall and by RMA status were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants with RMA tended to be older (mean [SE] age, 61.6 [0.5] vs 55.6 [0.4] years), less educated (\u0026lt;\u0026thinsp;high school, 451 [24.0%] vs 1256 [16.9%]), to have less family IPR (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3.5, 318 [39.2%] vs 1610 [52.6%]), to have poor general health condition (430 [27.6%] vs 1058 [17.0%]), to have higher prevalence rates of hypertension (1001 [76.5%] vs 2872 [60.0%]), diabetes (481 [31.5%] vs 741 [11.9%]), hypercholesterolemia (581 [45.9%] vs 1785 [39.6%]), history of coronary heart disease (stroke, 111 [7.8%] vs 201 [3.5%]), congestive heart failure (103 [6.5%] vs 173 [2.8%]), and heart attack (136 [8.7%] vs 237 [4.2%]). Other characteristics were comparable between the groups with and without RMA. Participants with FAN and HP were more likely to be women (for FAN, 37 [65.1%] vs 2821 [52.4%]; for HP, 13 [61.1%] vs 2862 [52.6%]). Participants with any RMA or its subtypes invariably had higher prevalence rates of medical comorbidities, including hypertension, diabetes, hypercholesteremia, congestive heart failure, coronary heart disease, angina, heart attack and stroke (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Participants With or Without Retinal Microvascular Abnormalities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo RMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003ePresence of RMA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAny RMA \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetinopathy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants, No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SE), y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.6 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.6 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.1 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.7 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e2894 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2237 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e389 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e329 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (38.9)\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\u003e2881 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2287 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e594 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e306 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (61.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace and ethnicity\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3105 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2470 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e635 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e298 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e367 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59 (97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (85.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1190 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e897 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e890 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e684 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1707 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1256 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e451 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e277 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e214 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 (41.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1437 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1130 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2629 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2137 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e492 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e264 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (33.2)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3662 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2896 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e766 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e443 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e382 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (69.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed, divorced, separated, or never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2110 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1626 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484 33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e252 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily IPR \u003csup\u003ed\u003c/sup\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1374 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1043 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2066 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1564 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e502 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e299 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e247 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (52.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1928 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1610 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1490 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1225 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2058 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1603 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e264 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e222 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (46.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2181 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1660 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking 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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2717 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2147 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e570 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1892 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1465 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e225 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1162 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e909 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (53.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e790 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e609 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3371 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2723 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e648 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e358 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (69.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral health condition\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent to good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4160 (81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3365 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e795 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e419 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e418 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (54.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair or poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1488 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1058 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11 (45.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical comorbidities\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3873 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2872 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1001 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e564 (74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e524 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (95.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1222 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e741 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e481 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesteremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2366 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1785 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e294 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (76.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (12.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngina/angina pectoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart attack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; FAN, focal arteriolar narrowing; HP, Hollenhorst plaque; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); IPR, income to poverty ratio; NHANES, National Health and Nutrition Examination Survey.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e The number of participants was unweighted. All means and SEs for continuous variables and percentages for categorical variables were weighted considering the complex sampling design of the NHANES.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Any RMA was defined as the presence of retinopathy, arteriovenous nicking, focal arteriolar narrowing, or Hollenhorst plaque in the worse eye.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Categorized based on self-reports from the NHANES interview.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ed\u003c/sup\u003e IPR is categorized according to the eligibility of the Supplemental Nutrition Assistance Program (formerly the Food Stamp Program).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between RMA and Mortality\u003c/h2\u003e \u003cp\u003eThe associations of RMA with all-cause and cause-specific mortality are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Participants with any RMA were at a higher risk of death during the follow-up period, with death from all-cause in 38.5% (unweighted, 482 of 1251 vs. 1006 of 4524 [22.2%]) and from CVD in 13.7% (unweighted, 172 of 1251 vs. 280 of 4524 [6.2%]) of all individuals. Kaplan-Meier curves showed that participants with any RMA or retinopathy had significantly greater all-cause and CVD mortality as compared to counterparts without retinal lesions (eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e and S6 in the Supplement) after multivariate adjustment. The multivariate Cox regression model showed that any RMA present at baseline was associated with a higher risk of all-cause mortality (HR, 1.26; 95%CI, 1.07\u0026ndash;1.47), CVD mortality (HR, 1.36; 95%CI, 1.06\u0026ndash;1.73) and other-cause mortality (HR, 1.33; 95%CI, 1.06\u0026ndash;1.67). Retinopathy was also significantly associated with a higher risk of all-cause mortality (HR, 1.36; 95%CI, 1.09\u0026ndash;1.71), CVD mortality (HR, 1.53; 95%CI, 1.04\u0026ndash;2.26), and other-cause mortality (HR, 1.55; 95%CI, 1.20\u0026ndash;2.01). In addition, FAN was significantly associated with an increased risk of other-cause mortality (HR, 2.06; 95%CI, 1.16\u0026ndash;3.65), but it did not increase the risk of all-cause or CVD mortality. Neither any RMA nor its subtype was associated with cancer mortality. No significant associations were found between AVN or HP and all-cause or cause-specific mortality after multiple adjustments. We investigated the dose‒response relationship between eye laterality and the risk of mortality. There was a significant dose‒response relationship between the number of eyes affected by RMA and the risk of all-cause, CVD and other mortality (all \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efor trend\u003c/em\u003e\u003c/sub\u003e \u0026lt;0.05) but not for cancer mortality (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efor trend\u003c/em\u003e\u003c/sub\u003e =0.946; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eAssociations of Retinal Microvascular Abnormality and its Subtypes With All-Cause and Cause-Specific Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAny RMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRetinopathy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAVN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eFAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths, No./total No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1006/4524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e482/1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1174/4993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275/710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1233/5125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e247/635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1423/5674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1464/5742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17/21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.28\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71 (1.44\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19 (1.04\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.25 (0.93\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.78(1.66\u0026ndash;4.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.22\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69 (1.39\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18 (0.95\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30 (0.77\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.88 (1.21\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.07\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36 (1.09\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13 (0.90\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37 (0.90\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.57 (0.73\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths, No./total No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280/4524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172/1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338/4993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102/710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e359/5125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91/635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e432/5674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e444/5742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6/21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.39\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19 (1.71\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32 (0.98\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88 (0.52\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.97 (1.35\u0026ndash;6.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.27\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91 (1.37\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32 (0.97\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07 (0.61\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.17 (0.99\u0026ndash;4.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.06\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53 (1.04\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20 (0.82\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (0.62\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.74 (0.51\u0026ndash;5.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths, No./total No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249/4524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92/1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285/4993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44/710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e290/5125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51/635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e334/5674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e334/5742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4/21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.82\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.65\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (0.77\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.65 (0.31\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.25 (1.04\u0026ndash;17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.75\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.49\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24 (0.85\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.43 (0.10\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.76 (0.48\u0026ndash;6.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.67\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.42\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21 (0.79\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.44 (0.11\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.64 (0.37\u0026ndash;7.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths, No./total No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477/4524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218/1251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551/4993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129/710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e584/5125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105/635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e657/5674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e686/5742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7/21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.22\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78 (1.46\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16 (0.91\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71 (1.15\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.03 (0.94\u0026ndash;4.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49(1.18\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96 (1.56\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06 (0.76\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.82 (0.91\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.77 (0.77\u0026ndash;4.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.06\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55(1.20\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (0.79\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.06 (1.16\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.57 (0.60\u0026ndash;4.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; FAN, focal arteriolar narrowing; HP, Hollenhorst plaque; CI, confidence interval; Ref, reference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for age (continuous), sex (male or female), and race (non-Hispanic White, non-Hispanic Black, Mexican American, or other).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e Further adjusted for educational level (\u0026lt;\u0026thinsp;high school, high school, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;some college), marital status (married/living with partner or widowed/divorced/separated/never married), family income level (IPR\u0026thinsp;\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: \u0026lt;25.0, 25.0-29.9, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30.0), smoking status (never, past, or current), and alcohol consumption (never, former, or current).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003e Further adjusted for general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), history of congestive heart failure (yes or no), coronary heart disease (yes or no), angina/angina pectoris (yes or no), heart attack (yes or no), and stroke (yes or no).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analyses for Association of RMA with All-Cause Mortality\u003c/h2\u003e \u003cp\u003eIn terms of any RMA, we found significant interactions between obesity, history of CVD and any RMA with the risk of all-cause mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014 for interactions, respectively) after multiple adjustment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the subgroup of obese individuals (BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), the HR of all-cause mortality was 1.68 (95% CI, 1.19\u0026ndash;2.39) for those with any RMA compared with that of the subgroup without obesity (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e; HR, 1.14; 95% CI, 0.96\u0026ndash;1.35). In the subgroup without a history of CVD, the HR of all-cause mortality was 1.47 (95% CI, 1.25\u0026ndash;1.73) for individuals with any RMA, while the risk was not increased for those with a history of CVD (HR, 1.06; 95% CI, 0.78\u0026ndash;1.45). Similar patterns of interactions between obesity, history of CVD and AVN were found for the risk of all-cause mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For retinopathy, we only found interaction between the history of CVD and retinopathy with the risk of all-cause mortality. The HRs were 1.72 (95% CI, 1.38\u0026ndash;2.15) and 1.06 (95% CI, 0.78\u0026ndash;1.45) for the subgroups without and with a history of CVD, respectively. No significant interactions were found between any RMA, retinopathy, AVN or any other strata variable and the risk of all-cause mortality (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\u003eStratified Analyses for the Association between Retinal Microvascular Abnormalities and All-Cause Mortality \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAny RMA, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRetinopathy, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAVN, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.184\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39 (0.90\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26 (0.82\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.35 (0.69\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;60 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.06\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41 (1.11\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.09 (0.86\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.09\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.36 (0.98\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.16 (0.91\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.91\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35 (1.07\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.12 (0.79\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.04\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41 (1.07\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.11 (0.86\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.89\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.29 (0.92\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.12 (0.79\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.96\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25 (0.96\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96 (0.79\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.19\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.70 (1.14\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.68 (1.12\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.79\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18 (0.79\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95 (0.67\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast or current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.17\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.51 (1.13\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.27 (0.99\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.695\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42 (0.98\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.64 (1.10\u0026ndash;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.11 (0.80\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast or current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.06\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41 (1.09\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.17 (0.92\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.66\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46 (0.76\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.03 (0.57\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31 (1.11\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37 (1.09\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.15 (0.91\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.806\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.03\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32 (1.01\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.08 (0.86\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.03\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55 (1.10\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.24 (0.83\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.641\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.95\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37 (1.02\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.01 (0.77\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.11\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35 (1.01\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.42 (1.04\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of CVD\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\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 \u003cp\u003e.014\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.25\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.38\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25 (1.00-1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.78\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06 (0.78\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.06 (0.78\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CVD, cardiovascular disease; HR, hazard ratio; Ref, reference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Analyses were adjusted for age (continuous), sex (male or female), race (non-Hispanic White, non-Hispanic Black, Mexican American, or other), educational level (\u0026lt;\u0026thinsp;high school, high school, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;some college), marital status (married/living with partner, or widowed/divorced/separated/never married), family income level (IPR\u0026thinsp;\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: \u0026lt;25.0, 25.0-29.9, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30.0), smoking status (never, past, or current), alcohol consumption (never, former, or current), general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), histories of congestive heart failure (yes or no), coronary heart disease (yes or no), angina (yes or no), heart attack (yes or no), and stroke (yes or no). Stratification was not conducted for focal arteriolar narrowing or Hollenhorst plaque due to insufficient samples for subgroup analyses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e Non-white includes non-Hispanic Black, Mexican American, and other races and ethnicities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003e A history of CVD was defined as a self-reported history of having a previous physician diagnosis of congestive heart failure, coronary heart disease, angina, heart attack, or stroke.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analyses for the Association of RMA with CVD Mortality\u003c/h2\u003e \u003cp\u003eFor CVD mortality, stratified analyses showed significant interaction between any RMA and smoking status with the risk of CVD mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015 for interaction, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the subgroup of patients who were past or current smokers, the HR of CVD mortality was 1.82 (95% CI, 1.26\u0026ndash;2.62), and for the subgroup of non-smokers, the HR of CVD mortality was 0.79 (95% CI, 0.43\u0026ndash;1.46). Similar interaction was found between retinopathy and smoking status with the risk of CVD mortality after multiple adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011 for interaction, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The interaction between AVN and obesity with the risk of CVD mortality was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035 for interaction), showing HR\u0026thinsp;=\u0026thinsp;1.96 (95% CI, 1.23\u0026ndash;3.13) in the obesity subgroup and HR\u0026thinsp;=\u0026thinsp;0.99 (95% CI, 0.64\u0026ndash;1.55) in the non-obesity subgroup (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No other significant interactions were found between RMA and strata factors in our analyses.\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\u003eStratified Analyses for the Association between Retinal Microvascular Abnormalities and CVD Mortality \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAny RMA, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRetinopathy, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAVN, HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value for Interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.824\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.46\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42 (0.46\u0026ndash;4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.78 (0.30\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;60 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.04\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.50 (0.96\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25 (0.86\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61 (1.04\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.20\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.06 (0.64\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.74\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25 (0.65\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.57 (0.86\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.01\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.69 (1.00-2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.16 (0.75\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.63\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.63\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25 (0.77\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27 (0.93\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55 (0.97\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99 (0.64\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.26\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.73 (1.07\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.96 (1.23\u0026ndash;3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.43\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84 (0.36\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89 (0.52\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast or current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.43\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.24 (1.47\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.46 (0.93\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.53 (0.86\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.79 (1.00-3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.30 (0.60\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast or current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.07\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66 (1.09\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25 (0.88\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66 (0.74\u0026ndash;3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86 (0.39\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.26 (0.94\u0026ndash;5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.05\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.62 (1.06\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.14 (0.75\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.753\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24 (0.89\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.36 (0.88\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.16 (0.76\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52 (0.88\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.80 (0.90\u0026ndash;3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.43 (0.72\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.90\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61 (0.99\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99 (0.59\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.04\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37 (0.74\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.69 (1.08\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of CVD\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.296\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 \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.53 (1.20\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.88 (1.23\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.38 (0.95\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.70\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.14 (0.70\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 [Ref]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.14 (0.70\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: RMA, retinal microvascular abnormality; AVN, arteriovenous nicking; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval; Ref, reference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Analyses were adjusted for age (continuous), sex (male or female), and race (non-Hispanic White, non-Hispanic Black, Mexican American, or other), educational level (\u0026lt;\u0026thinsp;high school, high school, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;some college), marital status (married/living with partner, or widowed/divorced/separated/never married), family income level (IPR\u0026thinsp;\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3.5), body mass index (calculated as weight in kilograms divided by height in meters squared: \u0026lt;25.0, 25.0-29.9, or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30.0), smoking status (never, past, or current), alcohol consumption (never, former, or current), general health condition (excellent to good, or fair/poor), diabetes (yes or no), hypertension (yes or no), hypercholesteremia (yes or no), histories of congestive heart failure (yes or no), coronary heart disease (yes or no), angina (yes or no), heart attack (yes or no), and stroke (yes or no). Stratification was not conducted for focal arteriolar narrowing or Hollenhorst plaque due to insufficient samples for their subgroup analyses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e Non-white includes non-Hispanic Black, Mexican American, and other races and ethnicities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003e A history of CVD was defined as a self-reported history of having a previous physician diagnosis of congestive heart failure, coronary heart disease, angina, heart attack, or stroke.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses demonstrated robustness of the results of our main analyses. The associations did not significantly differ when excluding the participants who died within 2 years of follow-up (eTable 1 in the Supplement), excluding the participants who had a history of CVD at baseline (eTable 2 in the Supplement), excluding the participants with malignancy at baseline (eTable 3 in the Supplement), performing the main analyses by matching subjects through a propensity score matching strategy (eTable 4 in the Supplement), or by changing the definition of any RMA through excluding Hollenhorst plaque (eTable 5 in the Supplement).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, population-based cohort study, we found that any RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality in U.S. adults aged 40 years and older. FAN was also significantly associated with an increased risk of other-cause mortality. Stratified analyses further indicated that the association between RMA and all-cause mortality were stronger among individuals with obesity and without a history of CVD, and the association with CVD mortality was stronger in former smokers than in current smokers. Although AVN was not associated with all-cause or other specific mortality in the whole population, it was significantly associated with a greater risk of all-cause and CVD death in individuals with obesity.\u003c/p\u003e \u003cp\u003eRetinal photography is a simple, non-invasive approach to evaluate the microvascular health status \u003cem\u003ein vivo\u003c/em\u003e. Extensive studies have suggested that the RMA is positively associated with the risk of CVD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and cerebrovascular diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Associations between RMA and CVD mortality risk have also been identified in previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Witt et al reported that an increased risk of ischaemic heart disease death was associated with the arteriolar diameter using the Beaver Dam Eye population-based Study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, retinopathy was identified to be associated with an increased risk of long-term CVD mortality in the Beaver Dam Eye population-based Study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Toshimi et al. also reported that mild hypertensive retinopathy is a risk factor for CVD mortality independent of cardiovascular risk factors in Japanese people with and without hypertension [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, there are no population-based data on the relationships between other RMA subtypes and all-cause mortality or specific mortality outcomes. In this population-based cohort study, we found that any RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality in older people. Interestingly, we further found that the associations of any RMA and retinopathy with all-cause mortality were stronger among individuals without a history of CVD. A possible explanation is that people without a history of CVD may be more prone to ignore microvascular damage, while people with a history of CVD manage their disease through medication use, diet control and lifestyle changes, resulting in a lower risk of mortality.\u003c/p\u003e \u003cp\u003eArteriolar narrowing and AVN represent microvascular changes occurring in the early phase of hypertension [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These abnormalities are related not only to concurrent blood pressure but also to past blood pressure measured 3 to 6 years previously, suggesting that these abnormalities are persistent markers of arteriolar damage from hypertension [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A previous study reported that AVN was strongly associated with a self-reported diagnosis of coronary heart disease in a population-based, cross-sectional study of 6267 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years in rural southwestern Harbin, China [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Witt et al. reported that generalized arteriolar narrowing was associated with increased stroke mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, Wong et al. demonstrated that the association between arteriolar narrowing or AVN and CVD mortality was present only in younger persons aged 43 to 74 years but was absent or even inverse in older persons aged 75 to 84 years [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the present study, we did not find a significant association between FAN or AVN and all-cause or any specific mortality in the general population. These findings are not consistent with those of previous studies. We speculate that the method used for acquiring retinal images, the differences in definitions and numbers of confounders and the length of the follow-up period may explain the varied results of these studies. In this cohort, AVN was significantly associated with a higher risk of all-cause and CVD death in individuals with obesity. Thus, our study highlights the importance of weight control, especially in those with existing microvascular damage.\u003c/p\u003e \u003cp\u003eHP (also known as retinal emboli) is an infrequent finding with an incidence ranging from 0.3\u0026ndash;2.9% in the elderly population [\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several prospective studies have suggested that retinal emboli are associated with increased risks of stroke and stroke mortality, independent of conventional risk factors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Retinal emboli occur more than 10 times more frequently in acute stroke patients than in large population-based studies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The presence of asymptomatic retinal emboli indicates a 12% risk of a cerebrovascular event when compared to patients with no plaques seen on fundoscopy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Thus, identifying this subgroup of patients and treating them early can reduce cerebrovascular and cardiovascular risks. Pooled data analysis from the Beaver Dam Eye Study (BDES) and the Blue Mountains Eye Study (BMES) showed that the presence of asymptomatic retinal emboli predicted a modestly increased risk of long-term, all-cause mortality independent of age, sex, and vascular risk factors. This increased all-cause mortality risk appeared to be partially driven by a higher risk of stroke-related mortality in persons with retinal emboli [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, they did not find a significant association between retinal emboli and cardiovascular mortality [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the present study, we found a low incidence rate (0.3%) of HP, but we did not find an association between HP and the risk of all-cause or any specific mortality. The main explanation for the inconsistency may be the low prevalence of HP in our study due to the methodological differences in retinal photography. Only two fields of fundus photos were taken per eye in the present study, whereas three or even seven fields per eye were taken in others.\u003c/p\u003e \u003cp\u003eTo our knowledge, the present study is the largest investigation of the associations of RMA with long-term all-cause and specific-cause mortality, considering a multitude of potential confounding factors. In addition, the present analysis is based on a nationally representative sample of U.S. adults, standardised objective methods for RMA assessment, and complete death records (only 1 missing). However, several limitations must be considered. First, RMA were only assessed at baseline, which may not accurately reflect long-term status and dynamic changes. Second, health behaviour and comorbidities collected at baseline may change over time, this may attenuate the true association between RMA and mortality. Third, although we adjusted for a comprehensive set of confounding factors, residual or unknown confounders could not be entirely excluded. Finally, due to the nature of the observational study design, our findings cannot be used for inference of causality.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this population-based cohort study of a nationally representative sample of older people in the U.S., we found that RMA and retinopathy were significantly associated with increased risks of all-cause, CVD, and other-cause mortality. In addition, AVN was also significantly associated with higher risks of all-cause and CVD death in individuals with obesity. The findings of our study suggest that routine retinal photography may be useful in the non-invasive assessment of concurrent cardiovascular and cerebrovascular disease status and can predict future risk of adverse health events.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAVN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earteriovenous nicking\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\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\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetic retinopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efocal arteriolar narrowing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHollenhorst plaque\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\"\u003eIPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eincome to poverty ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emobile examination centre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Centre for Health Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eretinal microvascular abnormality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515012192, 2023A1515030108). The funding organization had no role in the design or conduct of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e No conflicting relationship exists for any author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants and staff involved in the National Health and Nutrition Examination Survey for their invaluable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES data used in this are accessible through visiting https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eDr. Wei Xiao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConcept and design\u003c/em\u003e: Wei Xiao, Yan Luo.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcquisition, analysis, or interpretation of data\u003c/em\u003e: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrafting of the manuscript\u003c/em\u003e: Xiaoyun Chen and Wei Xiao.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCritical revision of the manuscript for important intellectual content:\u003c/em\u003e All authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e: Wei Xiao, Hongyu Si, Yihang Fu, Weimin Yang.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eObtained funding\u003c/em\u003e: Wei Xiao, Xiaoyun Chen.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdministrative, technical, or material support\u003c/em\u003e: Wei Xiao.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupervision\u003c/em\u003e: Wei Xiao, Xiaoyun Chen, Yan Luo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWong TY, Klein R, Sharrett AR, Manolio TA, Hubbard LD, Marino EK, Kuller L, Burke G, Tracy RP, Polak JF, et al. 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Accessed 18 June 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhelton PK, Carey RM, Aronow WS, Casey DE Jr., Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2018;138(17):e426-e483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, Curtin LR. National health and nutrition examination survey: analytic guidelines, 1999\u0026ndash;2010. 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Nonparametric Preprocessing for Parametric Causal Inference. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/MatchIt/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/MatchIt/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed June 18, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Kokubo Y, Arafa A, Sheerah HA, Watanabe M, Nakao YM, Honda-Kohmo K, Kashima R, Sakai Y, Watanabe E, et al. Mild Hypertensive Retinopathy and Risk of Cardiovascular Disease: The Suita Study. J Atheroscler Thromb. 2022;29(11):1663\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiew G, Xie J, Nguyen H, Keay L, Kamran Ikram M, McGeechan K, Klein BE, Jin Wang J, Mitchell P, Klaver CC, et al. Hypertensive retinopathy and cardiovascular disease risk: 6 population-based cohorts meta-analysis. Int J Cardiol Cardiovasc Risk Prev. 2023;17:200180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong TY, Klein R, Sharrett AR, Nieto FJ, Boland LL, Couper DJ, Mosley TH, Klein BE, Hubbard LD, Szklo M. Retinal microvascular abnormalities and cognitive impairment in middle-aged persons: the Atherosclerosis Risk in Communities Study. Stroke. 2002;33(6):1487\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong TY, Klein R, Sharrett AR, Couper DJ, Klein BE, Liao DP, Hubbard LD, Mosley TH, Study AIARiC. Cerebral white matter lesions, retinopathy, and incident clinical stroke. JAMA. 2002;288(1):67\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong TY, Mosley TH Jr., Klein R, Klein BE, Sharrett AR, Couper DJ, Hubbard LD. Atherosclerosis Risk in Communities S. Retinal microvascular changes and MRI signs of cerebral atrophy in healthy, middle-aged people. Neurology. 2003;61(6):806\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong TY, Klein R, Couper DJ, Cooper LS, Shahar E, Hubbard LD, Wofford MR, Sharrett AR. Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet. 2001;358(9288):1134\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSairenchi T, Iso H, Yamagishi K, Irie F, Okubo Y, Gunji J, Muto T, Ota H. Mild retinopathy is a risk factor for cardiovascular mortality in Japanese with and without hypertension: the Ibaraki Prefectural Health Study. Circulation. 2011;124(23):2502\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarner A, Ashton N, Tripathi R, Kohner EM, Bulpitt CJ, Dollery CT. Pathogenesis of hypertensive retinopathy. An experimental study in the monkey. Br J Ophthalmol. 1975;59(1):3\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung H, Wang JJ, Rochtchina E, Wong TY, Klein R, Mitchell P. Impact of current and past blood pressure on retinal arteriolar diameter in an older population. J Hypertens. 2004;22(8):1543\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Leng F, Li Z, Tang X, Qian H, Li X, Zhang Y, Chen X, Du H, Liu P. Retinal vascular abnormalities and their associations with cardiovascular and cerebrovascular diseases: a Study in rural southwestern Harbin, China. BMC Ophthalmol. 2020;20(1):136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCugati S, Wang JJ, Rochtchina E, Mitchell P. Ten-year incidence of retinal emboli in an older population. Stroke. 2006;37(3):908\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong TY, Klein R. Retinal arteriolar emboli: epidemiology and risk of stroke. Curr Opin Ophthalmol. 2002;13(3):142\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung N, Lim L, Wang JJ, Islam FM, Mitchell P, Saw SM, Aung T, Wong TY. Prevalence and risk factors of retinal arteriolar emboli: the Singapore Malay Eye Study. Am J Ophthalmol. 2008;146(4):620\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung N, Teo K, Zhao W, Wang JJ, Neelam K, Tan NYQ, Mitchell P, Cheng CY, Wong TY. Prevalence and Associations of Retinal Emboli With Ethnicity, Stroke, and Renal Disease in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Disease Study. JAMA Ophthalmol. 2017;135(10):1023\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoki SL, Varma R, Lai MY, Azen SP, Klein R. Los Angeles Latino Eye Study G. Prevalence and associations of asymptomatic retinal emboli in Latinos: the Los Angeles Latino Eye Study (LALES). Am J Ophthalmol. 2008;145(1):143\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmmed AA, Carey PE, Steel DH, Sandinha T. Assessing Patients with Asymptomatic Retinal Emboli Detected at Retinal Screening. Ophthalmol Ther. 2016;5(2):175\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein R, Klein BE, Jensen SC, Moss SE, Meuer SM. Retinal emboli and stroke: the Beaver Dam Eye Study. Arch Ophthalmol. 1999;117(8):1063\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein R, Klein BE, Moss SE, Meuer SM. Retinal emboli and cardiovascular disease: the Beaver Dam Eye Study. Trans Am Ophthalmol Soc. 2003;101:173\u0026ndash;80. discussion 180\u0026thinsp;\u0026ndash;\u0026thinsp;172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgan RA, Lutsep HL. Prevalence of Retinal Emboli and Acute Retinal Artery Occlusion in Acute Ischemic Stroke. J Stroke Cerebrovasc Dis. 2020;29(2):104446.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhoneim BM, Westby D, Elsharkawi M, Said M, Walsh SR. Systematic review of the relationship between Hollenhorst plaques and cerebrovascular events. Vascular 2023:17085381231163339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang JJ, Cugati S, Knudtson MD, Rochtchina E, Klein R, Klein BE, Wong TY, Mitchell P. Retinal arteriolar emboli and long-term mortality: pooled data analysis from two older populations. Stroke. 2006;37(7):1833\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"National Health and Nutrition Examination Survey, Retinal microvascular abnormality, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-3929807/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3929807/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRetinal microvascular abnormalities (RMA) reflect cumulative microvascular damage from systemic diseases and aging. However, little is known about the association between RMA and long-term survival outcomes.\u003cstrong\u003e \u003c/strong\u003eThis study aimed to examine the relationships between RMA and the risk of all-cause and specific-cause mortality among U.S. adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Individuals aged \u003cu\u003e\u0026gt;\u003c/u\u003e40 years were included from the U.S. National Health and Nutrition Examination Survey, 2005-2008.\u003cstrong\u003e \u003c/strong\u003eRMA and its subtypes, including retinopathy, arteriovenous nicking (AVN), focal arteriolar narrowing (FAN) and Hollenhorst plaque (HP), were manually graded from retinal photographs. Associations between RMA and the risk of all-cause and cause-specific mortality were examined with Cox regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis cohort study of 5775 adults included 2881 women (weighted proportion, 52.6%) and 2894 men (weighted, 47.4%), with a weighted mean (SE) age of 56.6 (0.4) years. RMA were present in 1251 participants (weighted, 17.9%), of whom 710 (weighted, 9.8%) had retinopathy, 635 (weighted, 9.3%) had AVN, 64 (weighted, 1.0%) had FAN, and 21 (weighted, 0.3%) had HP. During a median of 12.2 years (range, 0.1-15.0 years) of follow-up, 1488 deaths occurred, including 452 associated with cardiovascular disease (CVD), 341 associated with cancer, and 695 associated with other causes. After adjusting confounding factors, the presence of any RMA and retinopathy at baseline was associated with higher risk of all-cause mortality (HR, 1.26; 95%CI, 1.07-1.47; HR, 1.36; 95%CI, 1.09-1.71, respectively), CVD mortality (HR, 1.36; 95%CI, 1.06-1.73; HR, 1.53; 95%CI, 1.04-2.26, respectively) and other-cause mortality (HR, 1.33; 95%CI, 1.06-1.67; HR, 1.55; 95%CI, 1.20-2.01, respectively). Additionally, FAN was significantly associated with an increased risk of other-cause mortality (HR, 2.06; 95%CI, 1.16-3.65). Although AVN was not associated with mortality in the whole population, it was significantly related to higher risks of all-cause and CVD death in those with obesity (HR, 1.68; 95%CI, 1.12-2.52; HR, 1.96; 95%CI, 1.23-3.13, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study revealed that the presence of RMA is independently associated with greater risks of all-cause, CVD and other-cause mortality in adults aged 40 years or older.\u003c/p\u003e","manuscriptTitle":"Association of Retinal Microvascular Abnormalities with All-Cause and Specific-Cause Mortality Among U.S. Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 17:58:39","doi":"10.21203/rs.3.rs-3929807/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-05T08:35:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T00:45:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-10-14T09:59:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-05T18:49:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56646056514110840675285259036516065174","date":"2024-09-16T15:50:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296663811915178553534005263883896133309","date":"2024-06-18T20:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-13T11:03:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-29T06:07:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-29T05:31:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-16T10:27:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-02-05T03:55:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1a4f995-91e5-43e2-acab-7d4dad53bb89","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:06:39+00:00","versionOfRecord":{"articleIdentity":"rs-3929807","link":"https://doi.org/10.1186/s12889-024-21117-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-23 15:57:21","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-02-19 17:58:39","video":"","vorDoi":"10.1186/s12889-024-21117-0","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21117-0","workflowStages":[]},"version":"v1","identity":"rs-3929807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3929807","identity":"rs-3929807","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}