The association between educational attainment of a social network and cardiac biomarkers: analysis of sociocentric data from Korean older adults

The association between educational attainment of a social network and cardiac biomarkers: analysis of sociocentric data from Korean older 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 Article The association between educational attainment of a social network and cardiac biomarkers: analysis of sociocentric data from Korean older adults Ekaterina Baldina, Sung-Ha Lee, Yeong-Ran Park, Hyeon Chang Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6729552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The link between education and recovery from cardiovascular diseases is well-established, yet the relationship between the educational attainment of social network members and the development of these conditions remains underexplored. This study examines how the educational composition of social ties correlates with cardiac biomarkers. This study utilizes longitudinal observational data from the Korean Social Life, Health, and Aging Project (KSHAP), tracking a prospective cohort of 709 older adults aged 60–95 years over eight years, with surveys conducted in 2011, 2016, and 2019. We analyzed a sociocentric network encompassing an entire village and measured three cardiac biomarkers: BNP, Troponin, and NT-proBNP. Fixed-effects models were employed to reduce unobserved heterogeneity by assessing within-individual changes. The educational attainment of rural older adults, who typically possess only basic education, shows no association with levels of cardiac biomarkers. In contrast, a higher number of college-educated individuals within one’s social network is negatively associated with all measured biomarkers (BNP: β = -0.23, 95% CI = -0.35 to -0.11; Troponin: β = -0.64, 95% CI = -1.01 to -0.26; NT-proBNP: β = -0.54, 95% CI = -0.92 to -0.15). The educational attainment of social networks, rather than one’s own, is consistently linked to cardiac biomarkers among less-educated older adults. Enriching social environments with college-educated ties may offer valuable information and stimulation for individuals with basic education, informing effective community-level strategies to prevent cardiac diseases. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Medical research Health sciences/Risk factors cardiac biomarkers social networks education educational composition older adults CVD Figures Figure 1 Introduction Individuals with more education and higher-level degrees tend to have better physical functioning [1], higher self-reported health [2], fewer medical conditions [3, 4], a lower risk of mortality [5-7], and less probability of subclinical cardiovascular disease than those with lower levels of education [8-11]. Also, higher levels of education are associated with reduced levels of coronary heart disease risk factors like smoking, blood pressure, weight, and cholesterol levels, and a lower risk of cardiovascular mortality [8, 12]. Older persons in the lowest educational and income groups had 41% and 50% higher risk, respectively, for subclinical cardiovascular disease [13]. Education may help us to prevent CVDs by increasing the tendency for healthier behaviours and decisions related to health. If one’s educational benefit is so evident, having highly educated friends also must be beneficial. Proper and timely information, motivation, or attitudes for preventing CVDs can be constantly provided in daily life, even without awareness by friends or members of social networks. Also, if people need various advice regarding CVDs, they usually reach out to their friends first, not doctors. For example, people with larger and more supportive social networks, on average, experience better recovery after cardiovascular disease [14], less risk of stroke and cardiovascular disease incidence [15], and less risk of death from coronary heart disease [16]. Lack of support, social isolation, being unmarried, or not having a confidant is associated with poor prognosis and higher mortality after the incidence of coronary heart disease or other cardiac events [17]. However, little attention has been paid to the educational attainment level of friends or social ties above and beyond the size of the supportive social networks. A few exceptions include three recent studies about the educational attainment of network members and health outcomes [18-20]. Hernandez, Pullen [19] demonstrate that higher levels of social capital, measured as the proportion of college-educated network members, were associated with a higher probability of being vaccinated. Halpern-Manners and colleagues (2022) find that spouse education was beneficially related to one’s health net of the individual’s education. Brooks (2024) focuses on pathways between social networks and health, showing that a higher average education level of network members was associated with self-rated good physical health. Furthermore, many studies have confirmed the relationships between social networks and the recovery from CVDs [17, 21-25]. In contrast, the evidence on the association of social networks with the incidence and development of cardiovascular disease is inconclusive at best [17]. Our study explores the educational composition of social networks measured by the number of college-educated ties and examines its relationship with cardiovascular disease incidence-related biomarkers. We use three blood biomarkers to examine and define cardiac health conditions: Brain Natriuretic Peptide (BNP), Troponin, and N-Terminal -proBrain Natriuretic Peptide (NT-proBNP) [26]. Troponin is a protein produced in cardiac muscle. Recent studies suggest the importance of Troponin as a marker of chronic subclinical myocardial damage in asymptomatic populations with no history of cardiovascular disease [27]. BNP and NT-proBNP, markers of cardiac overload, are secreted when the myocardial wall experiences stress and are usually used to diagnose heart failure [28]. A handful of studies explored the relationships between levels of education and cardiac biomarkers. One study reported that individuals with low educational attainment had a 36% higher chance of elevated Troponin levels [29]. We provide further evidence of the relationships between cardiac biomarkers and the educational attainment of social networks. To pursue this, we use 8-year follow-up cohort data, including a sociocentric (global) social network of one entire village from the Korean Social Life, Health, and Aging Project (KSHAP) [30]. Since our study comprises relatively healthy older adults, we have too few cases to analyze the incidence of CVD. Therefore, we focus on the incidence-related cardiac biomarkers instead. We hypothesize that securing highly educated friends helps older adults avoid or delay cardiac incidence. Results The mean age at the baseline is 71.70 years (SD 7.28 years). About 58.6% of participants are women, 75.0% live with a spouse, and 15% have an education equal to and higher than high school (Table 1). 62% of respondents reported having an annual income of less than $10,000. Around 6% of respondents reported having at least one college-educated social network member. The average number of college-educated network members was 0.08 (SD=0.30) for friends and 0.13 (SD = 0.41) for friends of their friends (friends of social distance two). At the baseline, participants have average levels of BNP equalled 32.95 pg/ml (SD = 18.87), Troponin equalled 275.77 pg/ml (SD=457.81), and NT-proBNP equalled 36.84 pg/ml (SD=63.42). We use widely accepted thresholds for each biomarker to assess the proportion of high-risk participants in our study. We find that only twelve respondents have a BNP level higher than the normal range (>=100 pg/ml), and 21.2% of respondents have a Troponin level higher than the normal range (>=400 pg/ml). However, nobody has an NT-proBNP level higher than the normal range (>=900 pg/ml among 50~75 years respondents and >=1800 pg/ml among 75 years and higher respondents). The observational nature of the data from healthy older adults who could comply with the face-to-face surveys and clinical assessment protocols may explain the relatively low prevalence of high-risk CVD sample participants. [Table 1] First, we examine the relationship between the respondents’ own education and cardiac biomarkers (Table 2 Model 1). We measure individual education levels to indicate whether a respondent reported high school or higher education. The analysis does not identify any statistically significant association between the respondent’s education level and levels of biomarkers (BNP: β=0.03, p=0.835, Troponin: β=-0.06, p=0.893, NT-proBNP: β=-0.15, p=0.768). We repeat analyses to check if significant associations are observed for up to college-educated respondents (2.96% of the sample), but these are also insignificant (results are not shown). [Table 2] Next, we examine the impact of social network educational composition. For directly connected friends (Table 2 Model 2), coefficients were marginally significant for log(BNP) and for log(Troponin) (BNP: β=-0.18, p = 0.067; Troponin: β=-0.54, p = 0.067) and statistically significant for log(NT-proBNP) (β=-0.69, p =0.021). All coefficients were negative, meaning that an increase in the number of college-educated friends was associated with a decrease in the levels of cardiac biomarkers. Further, we examine the relationships between educational composition friends of social distance up to two and biomarker levels (Model 3). We found similar negative relationships and slightly higher magnitude in coefficients for BNP and Troponin, suggesting that having not only direct but also indirect college-educated network members is negatively associated with the levels of cardiac biomarkers. For each additional college-educated network member among friends of friends, respondents had lower levels of every biomarker (BNP: β=-0.23, p = 0.000, Troponin: β=-0.64, p= 0.001, NT-proBNP: β=-0.54, p = 0.006). To put numbers in perspective, we calculated average predicted cardiac biomarkers values for respondents with zero and three college-educated friends of friends. For reference, the BNP values greater than 100 pg/ml are considered critical and used to diagnose heart failure. On average, having zero college-educated network members results in a level of BNP equal to 29.91, while having three college-educated friends of friends yields a number of 14.95 pg/ml. We further examine whether an increase in social distance remains associated with cardiac biomarkers. Previous studies have confirmed that interpersonal similarities and diffusion effects operate only up to a social distance of four. This has been observed in loneliness [31], obesity [32], smoking [33], and depression.[34] To assess this, we calculate the social network’s educational composition at distances of three and four and conduct analyses to determine the extent to which social distance is associated with cardiac biomarkers (Table S1). Our findings indicate that the social network’s educational composition at distances of three and four loses statistical significance in relation to cardiac biomarkers. Even if individuals have more college-educated network members at a social distance of three or four, it may not be practical to receive meaningful support from people at this level of social distance (i.e., friends of friends’ friends). Discussion The primary objective of this study is to examine the association between the educational attainment of social ties among older adults and their cardiac biomarkers. Our findings reveal that (1) participants’ own educational level is not associated with cardiac biomarkers; (2) an increase in the number of college-educated individuals within one’s social network is consistently linked to a statistically significant decrease in cardiac biomarkers; and (3) the educational attainment of social networks is associated with cardiac biomarkers only up to a social distance of two—that is, the educational level of friends (social distance of one) or friends of friends (social distance of two). The educational composition of social networks demonstrates a robust association with cardiac biomarkers. As the number of college-educated individuals in one’s social discussion network increases, levels of cardiac biomarkers decrease. Access to more individuals with tertiary education may enhance health information access, improve health literacy and numeracy, and provide an advantage in obtaining health-related information through social networks over time [35, 36] Our findings underscore the role of education within social networks as a critical mechanism for disseminating health-related knowledge, consistent with prior research on health information transmission through network resources [18, 37]. For instance, studies have shown that greater educational attainment among social network members correlates with increased medical help-seeking, resource diversity, and financial support during the COVID-19 pandemic [38, 39]. Similarly, we find that the health of our sample respondents is associated with the number of college-educated individuals in their social networks. Beyond information, these educated network members may exert health-related influences and pressures, encouraging adjustments in beliefs, attitudes, or behaviors. Additional analyses revealed no association between the number of college-educated network members residing outside the village and cardiac biomarkers, suggesting that resource proximity significantly shapes health-related benefits. This may stem from distant network members providing less timely or contextually relevant information and influence. For instance, respondents’ children in urban areas may lack insight into rural health challenges, such as farming’s physical demands, health-related lifestyles, or limited healthcare access. Moreover, physical distance could reduce communication frequency and timely resource exchange, despite the perceived importance of these ties. This finding refines existing theories by highlighting the spatial and contextual factors influencing educational benefits, indicating that mechanisms from prior research vary with the geographic and social proximity of network ties. Prior literature has established that an individual’s educational attainment contributes to cardiac health [29, 40-44]. However, our study reveals no such association within this specific population, potentially due to the relatively homogeneous low educational background of the study population. Given that the residents of traditional Korean rural townships have, on average, very low educational attainment with minimal variation, the effect of an individual’s education may be difficult to detect. In fact, only 2.9% of participants in the KSHAP sample have a college degree. This study utilizes sociocentric data from KSHAP, allowing us to examine the educational attainment of participants’ social ties and their association with cardiac biomarkers. However, this very strength also limits the generalizability of our findings beyond the context of remote rural villages. Despite the limitations regarding generalizability, our findings hold significant practical implications for the early prevention of cardiac diseases among older adults in remote rural areas. Our results suggest that a low educational composition within one’s social network can be considered a significant risk factor for the development of cardiac diseases. The study provides a foundation for early screening of individuals with disadvantaged social network compositions and highlights the importance of targeted interventions to prevent the progression of cardiovascular conditions. Notably, we find that the estimated effect of social network education composition (β = -0.54) on NT-proBNP is comparable to, or even greater than, the effects found in previous studies. For example, Wassberg et al. reported that financial stress and feelings of depression contribute to an increase in NT-proBNP levels of 0.22 and 0.17 points, respectively[45]. These findings emphasize the potential role of social network education composition as a key factor in cardiovascular risk assessment and intervention strategies. Methods Study design and participants We use a part of the prospective cohort data obtained from the Korean Social Life, Health and Aging Project (KSHAP), which was collected across three waves: wave 1 (2011), wave 4 (2015-2016), and wave 5 (2018-2019) [30, 46]. Older adults (60 years or older in 2011) and their spouses were eligible to participate in the face-to-face survey and clinical assessment. Sociocentric (global) network data of one entire village were collected during the survey, and biomarkers were collected during the clinical examination [30]. Sociocentric network data aim to include the social ties of everybody in a given target population, not just the sample of the population of interest. Given that the 83.6% response rate is the lowest during the waves, we are confident our data captures the global social networks of the entire village well. This socicentric data enables us to examine the educational level of the social ties up to many social distances: in other words, we can probe the role of the educational level of friends, friends of friends, friends of friends of friends, and further. Township K is a typical rural Korean village where farming is the primary industry. With the aid of the public officers of township K and a pilot study, 860 people aged 60 or older and their spouses were identified as the KSHAP population. The face-to-face survey was completed with 814 out of the 860 target residents during wave 1, with a response rate of 94.7%. The final sample consists of 709 individuals after excluding participants with missing covariates, respondents younger than 60 years old, with diagnosed cardiovascular diseases, and those whose social network size equalled zero. The respondents were educated on the nature of the survey and informed written consent was obtained from all subjects and/or their legal guardian(s) before survey completion. The institutional review board approved this study of Yonsei University and all research was performed in accordance with relevant guidelines/regulations (YUIRB-2011-012-01 in 2011; 7001988-201806-HRBR-244-04 in 2016; 7001988-201812-HR-505-02 in 2018). Measures We use Troponin, BNP, and NT-proBNP biomarkers collected during health assessments in 2011, 2015-2016, and 2018-2019. Blood samples were collected from the antecubital vein after an 8-hour fasting period. They were immediately stored in a -20 0 C fridge until further analysis. The concentrations of Troponin, BNP, and NT-proBNP were quantified using the Human Cardiovascular Disease (CVD) Magnetic Bead Panel I immunoassay kits (Merck Millipore, USA). The cardiac biomarkers were treated as continuous measures [28, 45]. The extreme values (exceeding three standard deviations from the mean) are adjusted by winsorizing. They are log-transformed to minimize the skewness. Participants of KSHAP were asked to report the real names and residences of people with whom they discussed important matters during the last 12 months[46]. The question was read as follows: “From time to time, most individuals discuss things that are important to them with others. For example, these may include good or bad things that happen to you, problems you are having, or important concerns you may have. Looking back at the last 12 months, who are the individuals with whom you most often discussed things that were important to you?” Based on this information, we construct the sociocentric (global) social network of the township illustrated in Figure 1. Social network educational composition was measured as the number of social network members whose education is equal to or greater than a college degree and who resided in the same village and participated in the KSHAP. We measure the educational composition of friends of distance up to 4 and examine if they show a statistically significant relationship with cardiac biomarkers in our prospective data. For more details about the method to construct the socio-centric network of the entire village and social network educational composition, please read Supplementary Information and Baek et al (2024) [30]. The colour of the nodes represents the level of one of the cardiac biomarkers, BNP, for example. The darker, the healthier (lower level). The size of nodes represents the number of college-educated network members. The smallest black nodes represent discussion confidants living in Township K but did not participate in the survey. [Figure 1 about here] We control for the sociodemographic and health-related variables as possible confounding factors. Sociodemographic characteristics include sex (1 for females and 0 for males), respondent’s own education level, annual household income, and marital status (living with a spouse (coded as 1) or not (coded as 0)). Respondents’ educational levels are coded as a binary: those who had graduated high school or higher are coded as 1, and all others are coded as 0. College-educated individuals constitute only 2.7% of the baseline sample, so we could not use college education as a cutoff for respondent’s education level. The yearly income level is coded as a binary: 1 for about $10,000 or higher and 0 for otherwise. The participant's health status measures include comorbidity, cognitive functioning, physical health status, smoking (those who reported ever smoking coded as 1, otherwise 0), and hypertension diagnosis (1 for having ever diagnosed, 0 for otherwise). The comorbidity measure is constructed by counting the number of diagnoses out of the following illnesses for each participant: diabetes, cancer, angina, cataracts, and osteoporosis. Cognitive functioning is assessed using the Korean version of the Mini-Mental State Examination for Dementia Screening (K-MMSE) [47], with scores ranging from 0 to 30—higher values reflecting a better cognitive health status. Physical health statuses are assessed using the six-item physical component summary (PCS) from the SF-12 using standard methods. We excluded participants who had received a diagnosis of stroke, acute myocardial infarction (AMI), coronary heart disease (CHD), or angina over the study years to minimize the possibility of reverse causation. We also excluded participants whose social network size equalled zero (n=6), as it is not possible to measure their educational component of social ties. We also conducted analyses including these cases, and the major results were the same (available upon request). Statistical analysis We use fixed-effect regression models to analyze the data with person-years as the unit of analysis. To minimize the endogeneity of unobservable heterogeneity, we estimate a set of person fixed-effect panel models, which produce estimates of within-subject change in the variables over time and hold unobserved time-constant endogenous characteristics under control. The Hausman test is conducted for all models to assess whether random-effects models are more acceptable. In each case, the test indicates that unobserved heterogeneity is not randomly distributed, and therefore, the fixed-effect model is recommended. We also perform several sensitivity analyses to check the robustness of our findings, which are available in supplemental material. A detailed explanation will be given in the discussion section. We use Stata 17.0 (StataCorp LP., College Station, TX, USA) for the statistical analyses. Declarations Availability of materials and data All data files are available from the figshare database (https://doi.org/10.6084/m9.figshare.28696655.v1) Acknowledgement This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A3A2A02089737) and the Yonsei Signature Research Cluster Program of 2021 (2023-22-0016). Author Contributions E.B. contributed to conceptualization, formal analysis, writing—original draft, and writing—review & editing. S.L. contributed to writing—original draft and writing—review & editing. Y.P. contributed to funding acquisition and writing—review & editing. H.C.K. contributed to funding acquisition and writing—review & editing. Y.Y. contributed to conceptualization, funding acquisition, writing—original draft, and writing—review & editing. Additional Information The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Assari, S., Ethnicity, educational attainment, and physical health of older adults in the United States. Aging Medicine, 2019. 2 (2): p. 104-111. Hosseinpoor, A.R., et al., Social determinants of self-reported health in women and men: understanding the role of gender in population health. PloS one, 2012. 7 (4): p. e34799. Zhu, H., et al., Effects of educational attainment on comorbidity of pain and depression in Chinese older adults. Heliyon, 2024. 10 (17). Schiøtz, M.L., et al., Social disparities in the prevalence of multimorbidity–A register-based population study. 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Tables Table 1 Variables Mean SD Min Max R’s educational level Middle school and lower 0.85 High school and higher 0.15 Number of college-educated social ties of distance one (friends) 0.08 0.30 0.00 3.00 Number of college-educated social ties of distance two (friends of friends) 0.13 0.41 0.00 3.00 Network size 3.23 1.30 1.00 6.00 BNP (pg/mL) a 32.95 18.87 0.94 196.48 Troponin (pg/mL) a 275.77 457.81 0.01 1721.74 NT-proBNP (pg/mL) a 36.84 63.42 0.07 258.55 Sex Male 0.42 Female 0.58 Age 71.70 7.28 60.00 96.00 Marital status Not married 0.25 Married 0.75 Income <$10,000 0.6 2 ≥$10,000 0.3 8 Smoking Never smoked 0.69 Past and current smoker 0.31 Physical Health 46.29 9.01 14.61 63.97 Cognitive Health 23.91 4.92 0.00 30.00 Comorbidity 2.61 0.79 0.00 5.00 Hypertension diagnosis Yes 0.47 No 0.53 Table 2 Social network variables Log(BNP) Model 1 Model 2 Model 3 β 95% CI p-value β 95% CI p-value β 95% CI p-value R’s education: High school and higher (ref. middle school and lower) 0.03 -0.28,0.35 0.8 35 0.07 -0.25,0.39 0.6 59 0.06 -0.26,0.37 0.7 18 Number of college-educated friends -0.18 -0.37,0.01 0.067 -0.20 -0.39,-0.01 0.038 Number of college-educated friends of distance two (friends of friends) -0.23 -0.35,-0.11 0.000 Log(Troponin) Model 1 Model 2 Model 3 β 95% CI p-value β 95% CI p-value β 95% CI p-value R’s education: High school and higher (ref. middle school and lower) -0.0 6 -1.01,0.8 8 0.8 93 0.0 5 -0.90, 1.00 0.9 16 0.0 2 -0.93,0.9 6 0.975 Number of college-educated friends -0.54 -1.12,0.04 0.06 7 -0.6 1 -1.18,-0.03 0.03 8 Number of college-educated friends of distance two (friends of friends) -0.6 4 -1.01,-0.2 6 0.001 Log(NT-proBNP) Model 1 Model 2 Model 3 β 95% CI p-value β 95% CI p-value β 95% CI p-value R’s education: High school and higher (ref. middle school and lower) -0.15 -1.12,0.8 3 0.7 68 0.0 0 -0.97,0.9 8 0.99 7 -0.0 3 -1.00,0.9 4 0.9 51 Number of college-educated friends -0.69 -1.28,-0.11 0.021 -0.7 5 -1.33,-0.16 0.013 Number of college-educated friends of distance two (friends of friends) -0.5 4 -0.92,-0.1 5 0.00 6 Additional Declarations No competing interests reported. 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The size of nodes represents the amount of BNP (smaller size à lower levels). The smallest black nodes represent discussion confidants living in Township K but did not participate in the survey.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6729552/v1/2b0bde511eda1b518d4047a3.png"},{"id":89847014,"identity":"c165fe0e-f967-46f6-99f6-d1326974725f","added_by":"auto","created_at":"2025-08-25 16:37:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":826433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6729552/v1/a48ddd70-d2a1-4614-b966-8567c6b482bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between educational attainment of a social network and cardiac biomarkers: analysis of sociocentric data from Korean older adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndividuals with more education and higher-level degrees tend to have better physical functioning\u0026nbsp;[1], higher self-reported health\u0026nbsp;[2], fewer medical conditions\u0026nbsp;[3, 4], a lower risk of mortality\u0026nbsp;[5-7], and less probability of subclinical cardiovascular disease than those with lower levels of education\u0026nbsp;[8-11].\u0026nbsp;Also,\u0026nbsp;higher levels of education are associated with reduced levels of coronary heart disease risk factors like smoking, blood pressure, weight, and cholesterol levels,\u0026nbsp;and a lower risk of cardiovascular mortality [8, 12]. Older persons in the lowest educational and income groups had 41% and 50% \u0026nbsp; higher risk, respectively, for subclinical cardiovascular disease\u0026nbsp;[13]. Education may help us to prevent CVDs by increasing the tendency for healthier behaviours and decisions related to health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIf one’s educational benefit is so evident, having highly educated friends also must be beneficial. Proper and timely information, motivation, or attitudes for preventing CVDs can be constantly provided in daily life, even without awareness by friends or members of social networks. Also, if people need various advice regarding CVDs, they usually reach out to their friends first, not doctors.\u0026nbsp;For example,\u0026nbsp;people with larger and more supportive social networks, on average, experience better recovery after cardiovascular disease [14], less risk of stroke and cardiovascular disease incidence [15],\u0026nbsp;and less risk of death from coronary heart disease\u0026nbsp;[16]. Lack of support, social isolation, being unmarried, or not having a confidant is associated with poor prognosis and higher mortality after the incidence of coronary heart disease or other cardiac events\u0026nbsp;[17]. However, little attention has been paid to the educational attainment level of friends or social ties above and beyond the size of the supportive social networks. A few exceptions include\u0026nbsp;three recent studies about the educational attainment of network members and health outcomes\u0026nbsp;[18-20].\u0026nbsp;Hernandez, Pullen [19]\u0026nbsp;demonstrate that higher levels of social capital, measured as the proportion of college-educated network members, were associated with a higher probability of being vaccinated. Halpern-Manners and colleagues (2022) find that spouse education was beneficially related to one’s health net of the individual’s education. Brooks (2024) focuses on pathways between social networks and health, showing that a higher average education level of network members was associated with self-rated good physical health. Furthermore,\u0026nbsp;many\u0026nbsp;studies have confirmed the relationships between social networks and the recovery from CVDs\u0026nbsp;[17, 21-25]. In contrast, the evidence on the association of social networks with the incidence and development of cardiovascular disease is inconclusive at best\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eOur study explores the educational composition of social networks measured by the number of college-educated ties and\u0026nbsp;examines\u0026nbsp;its relationship with cardiovascular disease incidence-related biomarkers. We use three blood biomarkers to examine and define cardiac health conditions:\u0026nbsp;Brain Natriuretic Peptide (BNP), Troponin, and N-Terminal -proBrain Natriuretic Peptide (NT-proBNP)\u0026nbsp;[26]. Troponin is a protein produced in cardiac muscle. Recent studies suggest the importance of Troponin as a marker of chronic subclinical myocardial damage in asymptomatic populations with no history of cardiovascular disease [27]. BNP and NT-proBNP, markers of cardiac overload, are secreted when the myocardial wall experiences stress and are usually used to diagnose heart failure [28]. A handful of studies explored the relationships between levels of education and cardiac biomarkers. One study reported that individuals with low educational attainment had a 36% higher chance of elevated Troponin levels [29]. We provide further evidence of the relationships between cardiac biomarkers and the educational attainment of social networks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo pursue this, we use 8-year follow-up cohort data, including a sociocentric (global) social network of one entire village from the Korean Social Life, Health, and Aging Project (KSHAP) [30]. Since our study comprises relatively healthy older adults, we have too few cases to analyze the incidence of CVD. Therefore, we focus on the incidence-related cardiac biomarkers instead. We hypothesize that securing highly educated friends helps older adults avoid or delay cardiac incidence.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age at the baseline is 71.70 years (SD 7.28 years). About 58.6% of participants are women, 75.0% live with a spouse, and 15% have an education equal to and higher than high school (Table 1). 62% of respondents reported having an annual income of less than $10,000. Around 6% of respondents reported having at least one college-educated social network member. The average number of college-educated network members was 0.08 (SD=0.30) for friends and 0.13 (SD = 0.41) for friends of their friends (friends of social distance two).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the baseline, participants have average levels of BNP equalled 32.95 pg/ml (SD = 18.87), Troponin equalled 275.77 pg/ml (SD=457.81), and NT-proBNP equalled 36.84 pg/ml (SD=63.42). We use widely accepted thresholds for each biomarker to assess the proportion of high-risk participants in our study. We find that only twelve respondents have a BNP level higher than the normal range (\u0026gt;=100 pg/ml), and 21.2% of respondents have a Troponin level higher than the normal range (\u0026gt;=400 pg/ml). However, nobody has an NT-proBNP level higher than the normal range (\u0026gt;=900 pg/ml among 50~75 years respondents and \u0026gt;=1800 pg/ml among 75 years and higher respondents). The observational nature of the data from healthy older adults who could comply with the face-to-face surveys and clinical assessment protocols may explain the relatively low prevalence of high-risk CVD sample participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;[Table 1]\u003c/p\u003e\n\u003cp\u003eFirst, we examine the relationship between\u0026nbsp;the\u0026nbsp;respondents’ own education and cardiac biomarkers (Table 2 Model 1). We measure individual education levels to indicate whether a respondent reported high school or higher education. The analysis does not identify any statistically significant association between the respondent’s education level and levels of biomarkers (BNP: β=0.03, p=0.835, Troponin: β=-0.06, p=0.893, NT-proBNP: β=-0.15, p=0.768). We repeat analyses to check if significant associations are observed for up to college-educated respondents (2.96% of the sample), but these are also insignificant (results are not shown).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;[Table 2]\u003c/p\u003e\n\u003cp\u003eNext, we examine the impact of social network educational composition. For directly connected friends (Table 2 Model 2), coefficients were marginally significant for log(BNP) and for log(Troponin) (BNP: \u0026nbsp;β=-0.18, p = 0.067; Troponin: β=-0.54, p = 0.067) and statistically significant for log(NT-proBNP) (β=-0.69, p =0.021). All coefficients were negative, meaning that an increase in the number of college-educated friends was associated with a decrease in the levels of cardiac biomarkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, we examine the relationships between educational composition\u0026nbsp;friends of social distance up to two\u0026nbsp;and biomarker levels (Model 3). We found similar negative relationships and slightly higher magnitude in coefficients for BNP and Troponin, suggesting that having not only direct but also indirect college-educated network members is negatively associated with the levels of cardiac biomarkers. \u0026nbsp;For each additional college-educated network member among friends of friends, respondents had lower levels of every biomarker (BNP: β=-0.23, p = 0.000, Troponin: β=-0.64, p= 0.001, NT-proBNP: β=-0.54, p = 0.006). To put numbers in perspective, we calculated average predicted cardiac biomarkers values for respondents with zero and three college-educated friends of friends. For reference, the BNP values greater than 100 pg/ml are considered critical and used to diagnose heart failure. On average, having zero college-educated network members results in a level of BNP equal to 29.91, while having three college-educated friends of friends yields a number of 14.95 pg/ml.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further examine whether an increase in social distance remains associated with cardiac biomarkers. Previous studies have confirmed that interpersonal similarities and diffusion effects operate only up to a social distance of four. This has been observed in loneliness [31], obesity [32], smoking [33], and depression.[34] To assess this, we calculate the social network’s educational composition at distances of three and four and conduct analyses to determine the extent to which social distance is associated with cardiac biomarkers (Table S1). Our findings indicate that the social network’s educational composition at distances of three and four loses statistical significance in relation to cardiac biomarkers. Even if individuals have more college-educated network members at a social distance of three or four, it may not be practical to receive meaningful support from people at this level of social distance (i.e., friends of friends’ friends).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary objective of this study is to examine the association between the educational attainment of social ties among older adults and their cardiac biomarkers. Our findings reveal that (1) participants’ own educational\u0026nbsp;level is not associated with cardiac biomarkers; (2) an increase in the number of college-educated individuals within one’s social network is consistently linked\u0026nbsp;to a statistically significant\u0026nbsp;decrease in cardiac biomarkers; and (3) the educational attainment of social networks is associated with cardiac biomarkers only up to a social distance of two—that is, the educational level of friends (social distance of one) or friends of friends (social distance of two).\u0026nbsp;The educational composition of social networks demonstrates a robust association with cardiac biomarkers. As the number of college-educated individuals in one’s social discussion\u0026nbsp;network increases, levels of cardiac biomarkers decrease. Access to more individuals with tertiary education may enhance health information\u0026nbsp;access, improve health literacy and numeracy, and provide an advantage in obtaining health-related information through social\u0026nbsp;networks\u0026nbsp;over time [35, 36]\u003c/p\u003e\n\u003cp\u003eOur findings underscore the role of education within social networks as a critical mechanism for disseminating health-related knowledge, consistent with prior research on health information transmission through network resources\u0026nbsp;[18, 37]. For instance, studies have shown that greater educational attainment among social network members correlates with increased medical help-seeking, resource diversity, and financial support during the COVID-19 pandemic [38, 39]. Similarly, we find that the health of our sample respondents is associated with the number of college-educated individuals in their social networks. Beyond information, these educated network members may exert health-related influences and pressures, encouraging adjustments in beliefs, attitudes, or behaviors.\u003c/p\u003e\n\u003cp\u003eAdditional analyses revealed no association between the number of college-educated network members residing outside the village and cardiac biomarkers, suggesting that resource proximity significantly shapes health-related benefits. This may stem from distant network members providing less timely or contextually relevant information and influence. For instance, respondents’ children in urban areas may lack insight into rural health challenges, such as farming’s physical demands, health-related lifestyles, or limited healthcare access. Moreover, physical distance could reduce communication frequency and timely resource exchange, despite the perceived importance of these ties. This finding refines existing theories by highlighting the spatial and contextual factors influencing educational benefits, indicating that mechanisms from prior research vary with the geographic and social proximity of network ties.\u003c/p\u003e\n\u003cp\u003ePrior literature has established that an individual’s educational attainment contributes to cardiac health\u0026nbsp;[29, 40-44]. However, our study reveals no such association within this specific population, potentially due to the relatively homogeneous low educational background of the study population. Given that the residents of traditional Korean rural townships have, on average, very low educational attainment with minimal variation, the effect of an individual’s education may be difficult to detect. In fact, only 2.9% of participants in the KSHAP sample have a college degree.\u0026nbsp;This study utilizes sociocentric data from KSHAP, allowing us to examine the educational attainment of participants’ social ties and their association with cardiac biomarkers. However, this very strength also limits the generalizability of our findings beyond the context of remote rural villages.\u003c/p\u003e\n\u003cp\u003eDespite the limitations regarding generalizability, our findings hold significant practical implications for the early prevention of cardiac diseases among older adults in remote rural areas. Our results suggest that a low educational composition within one’s social network can be considered a significant risk factor for the development of cardiac diseases. The study provides a foundation for early screening of individuals with disadvantaged social network compositions and highlights the importance of targeted interventions to prevent the progression of cardiovascular conditions. Notably, we find that the estimated effect of social network education composition (β = -0.54) on NT-proBNP is comparable to, or even greater than, the effects found in previous studies. For example, Wassberg et al. reported that financial stress and feelings of depression contribute to an increase in NT-proBNP levels of 0.22 and 0.17 points, respectively[45]. These findings emphasize the potential role of social network education composition as a key factor in cardiovascular risk assessment and intervention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use\u0026nbsp;a part of the\u0026nbsp;prospective cohort data\u0026nbsp;obtained\u0026nbsp;from the Korean Social Life, Health and Aging Project (KSHAP), which\u0026nbsp;was collected across three waves:\u0026nbsp;wave 1 (2011), wave 4 (2015-2016), and wave 5 (2018-2019) [30, 46].\u0026nbsp;Older adults (60 years or older in 2011) and their spouses\u0026nbsp;were eligible to participate in the face-to-face survey and clinical assessment. Sociocentric (global) network data of one entire village were collected during the survey, and biomarkers were collected during the clinical\u0026nbsp;examination\u0026nbsp;[30]. Sociocentric network data aim to include the social ties of everybody in a given target population, not just the sample of the population of interest. Given that the 83.6% response rate is the lowest during the waves, we are confident our data captures the global social networks of the entire village well. This socicentric data enables us to examine the educational level of the social ties up to many social distances: in other words, we can probe the role of the educational level of friends, friends of friends, friends of friends of friends, and further. Township K is a typical rural Korean village where farming is the primary industry. With the aid of the public officers of township K and a pilot study, 860 people aged 60 or older and their spouses were identified as the KSHAP population. The face-to-face survey was completed with 814 out of the 860 target residents during wave 1, with a response rate of 94.7%. The final sample consists of 709 individuals after excluding participants with missing covariates, respondents younger than 60 years old, with diagnosed cardiovascular diseases, and those whose social network size equalled zero.\u0026nbsp;The respondents were educated on the nature of the survey and \u0026nbsp;informed written consent was obtained from all subjects and/or their legal guardian(s) before survey completion. The institutional review board approved this study of Yonsei University and all research was performed in accordance with relevant guidelines/regulations\u0026nbsp;(YUIRB-2011-012-01 in 2011; 7001988-201806-HRBR-244-04 in 2016; 7001988-201812-HR-505-02 in 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use Troponin, BNP, and NT-proBNP biomarkers collected during health assessments in 2011, 2015-2016, and 2018-2019. Blood samples were collected from the antecubital vein after an 8-hour fasting period. They were immediately stored in a -20 \u003csup\u003e0\u003c/sup\u003eC fridge until further analysis. The concentrations of Troponin, BNP, and NT-proBNP were quantified using the Human Cardiovascular Disease (CVD) Magnetic Bead Panel I immunoassay kits (Merck Millipore, USA). The cardiac biomarkers were treated as continuous measures\u0026nbsp;[28, 45]. The extreme values (exceeding three standard deviations from the mean) are adjusted by winsorizing. They are log-transformed to\u0026nbsp;minimize\u0026nbsp;the skewness.\u003c/p\u003e\n\u003cp\u003eParticipants of KSHAP were asked to report the real names and residences of people with whom they discussed important matters during the last 12 months[46]. The question was read as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e“From time to time, most individuals discuss things that are important to them with others. For example, these may include good or bad things that happen to you, problems you are having, or important concerns you may have. Looking back at the last 12 months, who are the individuals with whom you most often discussed things that were important to you?”\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on this information, we construct the sociocentric (global) social network of the township illustrated in Figure 1.\u0026nbsp;Social network educational composition was measured as the number of social network members whose education is equal to or greater than a college degree and who resided in the same village and participated in the KSHAP.\u0026nbsp;We measure the educational composition of friends of distance up to 4 and examine if they show a statistically significant relationship with cardiac biomarkers in our prospective data. For more details about the method to construct the socio-centric network of the entire village and social network educational composition, please read Supplementary Information and Baek et al (2024) [30]. The colour of the nodes represents the level of one of the cardiac biomarkers, BNP, for example. The darker, the healthier (lower level). The size of nodes represents the number of college-educated network members. The smallest black nodes represent discussion confidants living in Township K but did not participate in the survey.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Figure 1 about here]\u003c/p\u003e\n\u003cp\u003eWe control for the sociodemographic and health-related variables as possible confounding factors. Sociodemographic characteristics include sex (1 for females and 0 for males), respondent’s own education level, annual household income, and marital status (living with a spouse (coded as 1) or not (coded as 0)). Respondents’ educational levels are coded as a binary: those who had graduated high school or higher are coded as 1, and all others are coded as 0. College-educated individuals constitute only 2.7% of the baseline sample, so we could not use college education as a cutoff for respondent’s education level. The yearly income level is coded as a binary: 1 for about $10,000 or higher and 0 for otherwise. The participant's health status measures include comorbidity, cognitive functioning, physical health status, smoking (those who reported ever smoking coded as 1, otherwise 0), and hypertension diagnosis (1 for having ever diagnosed, 0 for otherwise). The comorbidity measure is constructed by counting the number of diagnoses out of the following illnesses for each participant: diabetes, cancer, angina, cataracts, and osteoporosis. Cognitive functioning is assessed using the Korean version of the Mini-Mental State Examination for Dementia Screening (K-MMSE) [47], with scores ranging from 0 to 30—higher values reflecting a better cognitive health status. Physical health statuses are assessed using the six-item physical component summary (PCS) from the SF-12 using standard methods. We excluded participants who had received a diagnosis of stroke, acute myocardial infarction (AMI), coronary heart disease (CHD), or angina over the study years to minimize the possibility of reverse causation. We also excluded participants whose social network size equalled zero (n=6), as it is not possible to measure their educational component of social ties. We also conducted analyses including these cases, and the major results were the same (available upon request).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use fixed-effect regression models to analyze the data with person-years as the unit of analysis.\u0026nbsp;To minimize the endogeneity of unobservable heterogeneity, we estimate a set of person fixed-effect panel models, which produce estimates of within-subject change in the variables over time and hold unobserved time-constant endogenous characteristics under control.\u0026nbsp;The Hausman test is conducted for all models to assess whether random-effects models are more acceptable. In each case, the test indicates that unobserved heterogeneity is not randomly distributed, and therefore, the fixed-effect model is recommended.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also perform several sensitivity analyses to check the robustness of our findings, which are available in supplemental material. A detailed explanation will be given in the discussion section. We use Stata 17.0 (StataCorp LP., College Station, TX, USA) for the statistical analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of materials and data\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eAll data files are available from the figshare database (https://doi.org/10.6084/m9.figshare.28696655.v1)\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Acknowledgement\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A3A2A02089737) and the Yonsei Signature Research Cluster Program of 2021 (2023-22-0016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eE.B.\u003c/strong\u003e contributed to conceptualization, formal analysis, writing—original draft, and writing—review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eS.L.\u003c/strong\u003e contributed to writing—original draft and writing—review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eY.P.\u003c/strong\u003e contributed to funding acquisition and writing—review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eH.C.K.\u003c/strong\u003e contributed to funding acquisition and writing—review \u0026amp; editing.\u003cbr\u003e\u003cstrong\u003eY.Y.\u003c/strong\u003e contributed to conceptualization, funding acquisition, writing—original draft, and writing—review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssari, S., \u003cem\u003eEthnicity, educational attainment, and physical health of older adults in the United States.\u003c/em\u003e Aging Medicine, 2019. \u003cstrong\u003e2\u003c/strong\u003e(2): p. 104-111.\u003c/li\u003e\n\u003cli\u003eHosseinpoor, A.R., et al., \u003cem\u003eSocial determinants of self-reported health in women and men: understanding the role of gender in population health.\u003c/em\u003e PloS one, 2012. \u003cstrong\u003e7\u003c/strong\u003e(4): p. e34799.\u003c/li\u003e\n\u003cli\u003eZhu, H., et al., \u003cem\u003eEffects of educational attainment on comorbidity of pain and depression in Chinese older adults.\u003c/em\u003e Heliyon, 2024. \u003cstrong\u003e10\u003c/strong\u003e(17).\u003c/li\u003e\n\u003cli\u003eSchi\u0026oslash;tz, M.L., et al., \u003cem\u003eSocial disparities in the prevalence of multimorbidity\u0026ndash;A register-based population study.\u003c/em\u003e BMC public health, 2017. \u003cstrong\u003e17\u003c/strong\u003e: p. 1-11.\u003c/li\u003e\n\u003cli\u003eMasters, R.K., R.A. 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Journal of the Korean neurological association, 300-308.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eR\u0026rsquo;s educational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMiddle school and lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHigh school and higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNumber of college-educated social ties of distance one (friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNumber of college-educated social ties of distance two (friends of friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNetwork size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eBNP (pg/mL)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e32.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e18.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e196.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTroponin (pg/mL)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e275.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e457.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1721.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNT-proBNP (pg/mL)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e36.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e63.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e258.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e71.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e96.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;$10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.6\u003cspan lang=\"RU\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026ge;$10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.3\u003cspan lang=\"RU\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNever smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePast and current smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003ePhysical Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e46.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e63.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCognitive Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHypertension diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eTable 2\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cem\u003eSocial network variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" height=\"24\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eLog(BNP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" height=\"21\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"21\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"21\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eR\u0026rsquo;s education: High school and higher (ref. middle school and lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.28,0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.8\u003cspan lang=\"RU\"\u003e35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.25,0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.6\u003cspan lang=\"RU\"\u003e59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.26,0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7\u003cspan lang=\"RU\"\u003e18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.37,0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.39,-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends of distance two (friends of friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.35,-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" height=\"18\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eLog(Troponin)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eR\u0026rsquo;s education: High school and higher (ref. middle school and lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.0\u003cspan lang=\"RU\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.01,0.8\u003cspan lang=\"RU\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.8\u003cspan lang=\"RU\"\u003e93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.0\u003cspan lang=\"RU\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.90,\u003cspan lang=\"RU\"\u003e1.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.9\u003cspan lang=\"RU\"\u003e16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0\u003cspan lang=\"RU\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.93,0.9\u003cspan lang=\"RU\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.975\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-1.12,0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.06\u003cspan lang=\"RU\"\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.6\u003cspan lang=\"RU\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-1.18,-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.03\u003cspan lang=\"RU\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends of distance two (friends of friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.6\u003cspan lang=\"RU\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-1.01,-0.2\u003cspan lang=\"RU\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" height=\"18\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eLog(NT-proBNP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" height=\"18\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eR\u0026rsquo;s education: High school and higher (ref. middle school and lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.12,0.8\u003cspan lang=\"RU\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.7\u003cspan lang=\"RU\"\u003e68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.0\u003cspan lang=\"RU\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.97,0.9\u003cspan lang=\"RU\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.99\u003cspan lang=\"RU\"\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.0\u003cspan lang=\"RU\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-1.00,0.9\u003cspan lang=\"RU\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.9\u003cspan lang=\"RU\"\u003e51\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-1.28,-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.7\u003cspan lang=\"RU\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-1.33,-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"18\" style=\"width: 37px;\"\u003e\n \u003cp\u003eNumber of college-educated friends of distance two (friends of friends)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.5\u003cspan lang=\"RU\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.92,-0.1\u003cspan lang=\"RU\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"18\" valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.00\u003cspan lang=\"RU\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cardiac biomarkers, social networks, education, educational composition, older adults, CVD","lastPublishedDoi":"10.21203/rs.3.rs-6729552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6729552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The link between education and recovery from cardiovascular diseases is well-established, yet the relationship between the educational attainment of social network members and the development of these conditions remains underexplored. This study examines how the educational composition of social ties correlates with cardiac biomarkers. This study utilizes longitudinal observational data from the Korean Social Life, Health, and Aging Project (KSHAP), tracking a prospective cohort of 709 older adults aged 60–95 years over eight years, with surveys conducted in 2011, 2016, and 2019. We analyzed a sociocentric network encompassing an entire village and measured three cardiac biomarkers: BNP, Troponin, and NT-proBNP. Fixed-effects models were employed to reduce unobserved heterogeneity by assessing within-individual changes. The educational attainment of rural older adults, who typically possess only basic education, shows no association with levels of cardiac biomarkers. In contrast, a higher number of college-educated individuals within one’s social network is negatively associated with all measured biomarkers (BNP: β = -0.23, 95% CI = -0.35 to -0.11; Troponin: β = -0.64, 95% CI = -1.01 to -0.26; NT-proBNP: β = -0.54, 95% CI = -0.92 to -0.15). The educational attainment of social networks, rather than one’s own, is consistently linked to cardiac biomarkers among less-educated older adults. Enriching social environments with college-educated ties may offer valuable information and stimulation for individuals with basic education, informing effective community-level strategies to prevent cardiac diseases.","manuscriptTitle":"The association between educational attainment of a social network and cardiac biomarkers: analysis of sociocentric data from Korean older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 05:34:50","doi":"10.21203/rs.3.rs-6729552/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-25T08:22:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T01:03:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T19:00:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210137100495485276729246073140126718838","date":"2025-06-11T13:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328116269924792563213318581413745761520","date":"2025-06-05T14:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T14:49:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T14:29:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-29T11:09:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T08:12:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-23T05:22:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a021d890-adc5-4de3-bfaa-d73f5c1503b6","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49687481,"name":"Health sciences/Biomarkers"},{"id":49687482,"name":"Health sciences/Cardiology"},{"id":49687483,"name":"Health sciences/Medical research"},{"id":49687484,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-08-25T16:30:03+00:00","versionOfRecord":{"articleIdentity":"rs-6729552","link":"https://doi.org/10.1038/s41598-025-16349-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-19 16:12:51","publishedOnDateReadable":"August 19th, 2025"},"versionCreatedAt":"2025-06-09 05:34:50","video":"","vorDoi":"10.1038/s41598-025-16349-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-16349-y","workflowStages":[]},"version":"v1","identity":"rs-6729552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6729552","identity":"rs-6729552","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}