Household Size and Age-Modified Patterns of Cardiometabolic Biomarkers and Lifestyle Behaviors: KNHANES 2015–2024

Household Size and Age-Modified Patterns of Cardiometabolic Biomarkers and Lifestyle Behaviors: KNHANES 2015–2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Household Size and Age-Modified Patterns of Cardiometabolic Biomarkers and Lifestyle Behaviors: KNHANES 2015–2024 Sehwan Bang, Eunsook Sung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8857205/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Household structure is a salient social determinant that may shape cardiometabolic risk through daily routines, resource access, and health-related behaviors. However, the extant evidence remains limited in scope, particularly regarding whether associations between household size and cardiometabolic health differ across the lifespan. We examined age-dependent associations between household size and cardiometabolic outcomes using nationally representative survey data, integrating components of metabolic syndrome, blood biomarkers, and daily health behaviors. Methods A repeated cross-sectional analysis of nationally representative health and nutrition survey data was conducted, with data collected annually from 2015 to 2024. The study population comprised adults aged ≥ 19 years who provided complete information on household size, cardiometabolic outcomes, health behaviors, and covariates. Household size (FN) was categorized (e.g., 1, 2, 3, 4, ≥ 5 members), and age group (AG) was modeled categorically. To this end, survey-weighted regression models were employed to estimate associations of FN with metabolic syndrome components (including waist circumference and blood pressure), blood biomarkers (liver enzymes, renal function, hematologic and endocrine-related markers), and behaviors (physical activity domains, sedentary time, and sleep duration), testing FN×AG interactions. The models were adjusted for key sociodemographic and health-related covariates, and complex sampling was accounted for (weights, strata, and primary sampling units). Results Across various domains, the associations between household size and cardiometabolic indicators were frequently age-dependent rather than uniform. Among the components of metabolic syndrome, waist circumference and systolic blood pressure exhibited evidence of household-size associations, in conjunction with pronounced age effects, manifesting distinct FN×AG interaction patterns (waist circumference: p_FN = 0.003; p_int < 0.001; systolic blood pressure: p_FN = 0.022; p_int < 0.001). The data revealed interaction-dominant patterns in several additional components, including fasting glucose, triglycerides, and HDL-C. This finding highlights the heterogeneity observed across different life stages. Furthermore, the investigation revealed that biomarkers showed age-dependent correlations with household size, including interaction signals for liver enzymes (AST, p_int = 0.019; ALT, p_int = 0.003) and renal biomarkers (creatinine, p_int = 0.014). The behavioral findings yielded actionable candidates for pathways, demonstrating consistent associations between sedentary behavior and both FN and age, with a pronounced interaction (p_FN = 0.002; p_int < 0.001). The study's findings indicated significant domain-specific heterogeneity in physical activity outcomes, with notable interactions observed across specific domains and intensity levels, such as recreational vigorous activity and total activity. However, these interactions were not consistently observed across all domains or intensity levels, including occupational moderate or vigorous activity and recreational moderate activity. The present models did not demonstrate any significant FN×AG interactions for sleep duration. Conclusions Household size is not merely a demographic descriptor; rather, it is a contextual factor associated with cardiometabolic health in an age-dependent manner. The consistent interaction patterns observed across metabolic risk markers, biomarkers, and behaviors—particularly sedentary time—suggest that prevention and surveillance strategies may benefit from incorporating household structure as a pragmatic stratification marker and tailoring interventions to age-specific living contexts. Household size Physical activity Metabolic syndrome Sedentary behavior Life-course epidemiology Figures Figure 1 1. Introduction In recent decades, profound demographic and social transitions have significantly impacted daily living environments worldwide. Among the aforementioned changes, the rapid increase in single-person households has emerged as one of the most prominent structural shifts ( 1 ). The phenomenon of delayed marriage and childbirth, population ageing, extended life expectancy, and changes in labor market conditions have collectively resulted in the diversification of household structures (2), with South Korea experiencing one of the fastest transitions in this regard ( 3 ). These changes extend beyond descriptive demographic trends and increasingly constitute an important public health context, as living arrangements may shape daily health-related behaviors. These changes extend beyond descriptive demographic trends and increasingly constitute an important public health context, as living arrangements may shape daily health-related behaviors. Household structure, particularly household size (FN), can be conceptualized as an environmental indicator reflecting how individuals organize and experience their daily lives ( 4 – 6 ). In contrast to relatively fixed biological characteristics, FN interacts dynamically with health-related behaviors, including dietary patterns, physical activity, sedentary behavior, sleep, and social interactions ( 7 , 8 ). The hypothesis that living alone is indicative of health vulnerability is not supported by extant literature. However, a body of research suggests that the presence or absence of cohabiting household members may be associated with differences in daily routines and behavioral regulation ( 8 – 10 ). From this standpoint, FN can be regarded as a living environment through which health behaviors may be modelled and sustained. Scholars in the field have hypothesized that individuals residing alone may be more prone to engage in dietary practices less conducive to health than those residing in multi-person households ( 11 , 12 ). These patterns encompass irregular meal scheduling, increased reliance on external dining options or convenience foods, and reduced dietary diversity ( 13 , 14 ). Conversely, the consumption of shared meals within multi-person households has been associated with more regular eating schedules and improved nutritional balance ( 15 , 16 ). While such behavioral distinctions may appear negligible on an individual basis, their cumulative effects over time may be significant for metabolic health. It is evident that physical activity and sedentary behavior represent additional behavioral pathways that may be associated with FN. Conversely, living with others may provide informal social prompts for movement and contribute to more structured daily routines ( 17 – 19 ). Individuals living alone may experience fewer external cues to interrupt prolonged sitting, particularly during leisure time. Prolonged sedentary behavior has been shown to be independently associated with adverse metabolic outcomes, even after accounting for levels of moderate-to-vigorous physical activity ( 20 – 22 ). Consequently, a comprehensive examination of both physical activity and sedentary behavior is imperative to elucidate the correlation between FN and metabolic risk. The correlation between household structure and health outcomes may also be explained by psychosocial factors. While living alone does not necessarily lead to social isolation, the absence of cohabiting household members may alter the frequency and nature of daily social interactions and emotional support ( 23 , 24 ). Such changes have been associated with stress perception, self-regulation of health behaviors, and long-term physiological processes ( 25 , 26 ). However, it is imperative to acknowledge the complexity and multifactorial nature of these relationships. Consequently, their interpretation should be approached with caution, as they should not be construed as direct causal pathways. Metabolic syndrome and its components—including abdominal obesity, elevated blood pressure, dyslipidemia, and impaired glucose regulation—continue to be significant public health concerns due to their strong associations with cardiovascular disease, type 2 diabetes, and mortality ( 27 – 29 ). Despite plausible behavioral and psychosocial pathways, the existing evidence on the association between FN and metabolic health has been inconsistent. A number of studies have reported higher cardiometabolic risk among individuals living alone. However, other studies have found weak or null associations after adjustment for socioeconomic and lifestyle factors ( 30 , 31 ). These discrepancies may indicate variations in study design, population characteristics, outcome selection, and analytical strategies. A significant deficiency in earlier research pertains to the inadequate consideration of age-specific contexts. The implications of living alone vary considerably across the life course. Among young adults, single-person households often coincide with transitional periods marked by education, employment, and residential mobility. These periods may be accompanied by irregular lifestyles ( 32 – 34 ). In middle-aged adults, living alone may indicate marital transitions or occupational demands that interact with cumulative behavioral exposures ( 35 – 37 ). Among older adults, single-person households are often associated with circumstances such as widowhood or family dispersion. These households may also coincide with age-related physiological changes, as indicated by research ( 38 – 40 ). Treating FN as a homogeneous exposure across all ages may therefore obscure meaningful age-specific associations. Another methodological consideration is the frequent reliance on marital status as a proxy for living arrangements ( 41 ). While marital status indicates legal or relational conditions, the Family and Non-Family Dynamics (FN) scale captures actual cohabitation and shared daily living environments ( 42 , 43 ). he living context of individuals with the same marital status can vary considerably, depending on whether they reside independently or in a shared environment ( 41 , 44 ). Consequently, FN has the potential to provide supplementary, autonomous data that extends beyond marital status, thereby facilitating a more comprehensive understanding of health-related living environments. Moreover, numerous prior studies have focused on a limited set of health outcomes or failed to account for metabolic indicators, blood biomarkers, physical activity, sedentary behavior, and sleep ( 31 , 37 , 45 ). Metabolic health reflects the integration of multiple physiological systems influenced by daily behaviors ( 46 ); therefore, a multidimensional analytical approach is necessary. Furthermore, socioeconomic status, smoking, alcohol consumption, and other lifestyle factors have been demonstrated to be closely related to both household structure and health outcomes. This underscores the importance of analytical models that appropriately adjust for these potential confounders ( 47 , 48 ). A particularly relevant setting for examining these associations is South Korea, given its rapidly increasing proportion of single-person households, accelerated population aging, and lifestyle changes characterized by high levels of sedentary time ( 49 , 50 ). Despite this context, age-stratified analyses examining FN in relation to comprehensive metabolic and behavioral profiles remain limited in the Korean population. Therefore, the present study employed nationally representative data and complex-sample analytical methods, adjusted for key socioeconomic and lifestyle factors, to examine age-specific associations between FN and indicators of metabolic syndrome (MetS), blood biomarkers, physical activity (PA), sedentary behavior (SB), and sleep duration. Due to the cross-sectional nature of the study, the findings are interpreted as associations rather than causal relationships. The hypothesis was developed that the associations between FN and metabolic and behavioral profiles would vary across age groups and would be more pronounced among younger and middle-aged adults, in whom daily behavioral patterns may be more strongly influenced by living arrangements. 2. Materials and Methods 2.1. Sample and Design This study used cross-sectional data from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted by the Korea Centers for Disease Control and Prevention (KCDC)/Korea Disease Control and Prevention Agency (KDCA) from 2015 to 2024. Each survey cycle used a multistage, stratified, clustered probability sampling design to ensure representativeness of the non-institutionalized adult population. Data from individual survey years were pooled to enhance statistical power and to allow age-stratified analyses, while maintaining the cross-sectional nature of each survey cycle. The analytic sample included adults aged 19 years or older with complete information on household structure, metabolic syndrome components, blood biomarkers, physical activity, sedentary behavior, sleep duration, and relevant covariates. The present study analyzed de-identified public-use KNHANES data and was exempt from additional IRB review at the authors’ institution. Among 74,925 participants pooled across the 2015–2024 KNHANES cycles, we excluded individuals aged <19 years (n = 13,695), leaving n = 61,230 adults aged ≥19 years. We further excluded participants with a self-reported history of cancer (n = 1,020) and those with missing data on household structure (household size), metabolic syndrome components, blood biomarkers, physical activity, sedentary behavior, sleep duration, or covariates required for the main analyses (n = 25,171). The final analytic sample comprised n = 35,039 adults. Pregnancy status was included as a covariate in models involving women of reproductive age (Figure 1). All analyses incorporated sampling weights, strata, and primary sampling units to account for the complex survey design and to generate population-representative estimates. Household structure was defined as the number of individuals residing in the household at the time of the survey and was categorized into five groups: single-person households (FN1), two-person households (FN2), three-person households (FN3), four-person households (FN4), and households with five or more members (≥FN5). (Figure 1) 2.2. Descriptive characteristics of the participants Table 1 shows the characteristics of the participants, including age, sex, educational attainment, household income, and employment status. Age was treated as a categorical variable for age-stratified analyses and grouped as 19–29, 30–44, 45–59, 60–74, and ≥75 years to reflect distinct life-course stages. Lifestyle-related variables included smoking status (never, former, current smoker) and alcohol consumption (non-drinker, moderate drinker, heavy drinker), assessed using standardized questionnaires administered by trained interviewers. Covariates were selected a priori based on established literature demonstrating their associations with both household structure and metabolic health outcomes, as well as their relevance to the conceptual framework of the present study. No data-driven variable selection procedures were applied. (Table1) 2.3. Metabolic syndrome components (MetS) Metabolic syndrome components (MetS) were assessed according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria (51), the American Heart Association, and the National Heart, Lung, and Blood Institute (52). Individual components included waist circumference (>90 cm (male) or >85 cm (female)), systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg, fasting TG levels ≥150 mg/dL, fasting HDL-C levels < 40 mg/dL (male) or < 50 mg/dL (female), and fasting glucose (FG) levels ≥110 mg/dL (53, 54). Rather than classifying participants dichotomously by metabolic syndrome status, each component was analyzed as a continuous variable to allow a more sensitive examination of associations with household structure and age group. 2.4. Blood biomarkers Fasting blood samples were collected by trained medical personnel in accordance with standardized protocols. Laboratory analyses were conducted at certified laboratories with established internal and external quality control procedures. Blood biomarkers included liver enzymes (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]), renal function markers (blood urea nitrogen [BUN] and creatinine [Cr]), hematological parameters (white blood cell count [WBC], red blood cell count [RBC], hemoglobin [Hb], hematocrit [Hct], and platelet count [PLT]), vitamin D (25-hydroxyvitamin D [VitD]), and thyroid-related markers (thyroid-stimulating hormone [TSH] and free thyroxine [fT4]). All biomarkers were treated as continuous variables in descriptive and regression analyses. 2.5. Physical activity (PA), Sedentary behavior (SB), and sleep duration Physical activity (PA) was assessed using standardized questionnaire items based on the Global Physical Activity Questionnaire (GPAQ) framework. The GPAQ comprises 16 questions, grouped into three behavioral domains: work, transport, and recreational activities. Five domains of PA were analyzed: vigorous-intensity work, moderate-intensity work, place movement, vigorous-intensity recreation, and moderate-intensity recreation. Participants answered the five domains freely, without any additional options regarding how often they performed the activity and how many minutes per day. The World Health Organization (WHO) GPAQ analysis guidelines were used to analyze the GPAQ data (55). We estimated that a person's caloric expenditure was four times higher when moderately active and eight times higher when vigorously active, compared with sitting quietly. Therefore, when calculating an individual's total energy expenditure using GPAQ data, 4 METs were assigned to time spent in moderate activity and 8 METs to time spent in vigorous activity. The details are as follows: • Vigorous intensity activity: occupational (MET) = 8.0 × vigorous intensity physical activity (day/week) × 1-day vigorous intensity physical activity (minutes/day) • Moderate intensity activity: occupational (MET) = 4.0 × moderate intensity physical activity (day/week) × 1-day moderate intensity physical activity (minutes/day) • Vigorous intensity activity: recreational (MET) = 8.0 × vigorous intensity physical activity (day/week) × 1-day vigorous intensity physical activity (minutes/day) • Moderate intensity activity: recreational (MET) = 4.0 × moderate intensity physical activity (day/week) × 1-day moderate intensity physical activity (minutes/day) • Place movement (MET) = 4.0 × place movement physical activity (day/week) × 1-day place movement physical activity • Total Physical Activity (MET) = vigorous intensity activity: occupational + moderate intensity activity: occupational + vigorous intensity activity: recreational + moderate intensity activity: recreational + place movement. Sedentary behavior (SB) was assessed using self-reported average daily sedentary time. Participants were asked to report the total time spent sitting or reclining on a typical day, including activities such as watching television, using a computer or smartphone, reading, and other seated leisure or occupational activities. Time spent sleeping was excluded from sedentary time. Average daily sedentary time was calculated and expressed in hours per day (h/day). SB was treated as a continuous variable in the primary analyses. Sleep duration was assessed separately for weekdays and weekends using self-reported average sleep time. Participants reported their usual sleep duration on weekdays and weekends, respectively. Average daily sleep duration was calculated as a weighted mean using the following formula: [(weekday sleep duration × 5) + (weekend sleep duration × 2)] / 7. Sleep duration was expressed in hours per day (h/day) and was analyzed as a continuous variable. 2.6. Statistical analysis All analyses were performed using R Statistical Software (56) and the following R packages: emmeans v. 2.0.1 (57), openxlsx v. 4.2.8.1 (58), srvyr v. 1.3.0 (59), survey v. 4.4.8 (60), tidyverse v. 2.0.0 (61). To account for the complex sampling design of the national survey, all analyses incorporated sampling weights, strata, and primary sampling units using survey-weighted procedures. Participants were categorized according to household size (FN1, FN2, FN3, FN4, and ≥FN5) and age group (19–29, 30–44, 45–59, 60–74, and ≥75 years). Descriptive statistics were calculated for each combination of household size and age group and are presented as survey-weighted means with standard errors. To examine the independent and interactive associations of FN and AG with metabolic syndrome components, blood biomarkers, PA, SB, and sleep-related variables, survey-weighted generalized linear models were applied. Each outcome variable was modeled with FN, AG, and their interaction term (FN × AG) as main predictors. All models were adjusted for potential confounding factors, including sex, smoking status, alcohol consumption, household income level, and educational attainment. Main effects of FN (p_FN), main effects of AG (p_age), and interaction effects between FN and AG (p_int) were estimated from the fitted models. For variables with insufficient sample sizes within specific age–household size strata, estimates were not calculated and are indicated as missing. Statistical significance was set at a two-sided p-value < 0.05. 3. Results 3.1. Metabolic syndrome components (MetS) Table 2 shows survey-weighted, covariate-adjusted associations of household size (FN) and age group (AG) with metabolic syndrome components (MetS). Waist circumference (WC) exhibited significant main effects of FN (p_FN < 0.001) and AG (p_age < 0.001), with a significant FN × AG interaction (p_int < 0.001), indicating age-dependent differences in the association between household context and central adiposity. Systolic blood pressure (SBP) showed significant main effects of FN (p_FN = 0.001) and AG (p_age < 0.001) and a significant interaction (p_int < 0.001). Diastolic blood pressure (DBP) demonstrated significant main effects of FN (p_FN = 0.006) and AG (p_age < 0.001), along with a significant FN × AG interaction (p_int < 0.001). Fasting glucose (FG) was strongly associated with AG (p_age < 0.001) and showed a significant FN × AG interaction (p_int = 0.005), whereas the main effect of FN was also statistically significant (p_FN = 0.006). Triglycerides (TG) were strongly patterned by age (p_age < 0.001) and showed a significant FN × AG interaction (p_int < 0.001), while the main effect of FN was not statistically significant (p_FN = 0.092). HDL-C was associated with AG (p_age < 0.001) and demonstrated a significant FN × AG interaction (p_int = 0.015) despite a non-significant FN main effect (p_FN = 0.579). (Table 2) 3.2. Blood biomarkers Table 3 summarizes adjusted associations between FN, AG, and blood biomarkers. AST showed significant main effects of FN (p_FN = 0.005) and AG (p_age < 0.001), but no significant FN × AG interaction (p_int = 0.274). In contrast, ALT demonstrated significant main effects of FN (p_FN = 0.023) and AG (p_age < 0.001) and a significant FN × AG interaction (p_int < 0.001). For renal markers, BUN exhibited a strong age effect (p_age < 0.001) with a modest but significant interaction (p_int = 0.011) and no significant FN main effect (p_FN = 0.199). Creatinine showed significant main effects of FN and AG (both p < 0.001) and a significant interaction (p_int < 0.001). Hematological indices (e.g., WBC, RBC, Hb, Hct, PLT) were strongly related to AG (p_age < 0.001 for each) and several also showed significant FN × AG interactions (e.g., WBC p_int < 0.001; PLT p_int = 0.024). Vitamin D showed a significant main effect of FN (p_FN = 0.047), a strong age effect (p_age < 0.001), and a significant interaction (p_int < 0.001). For thyroid markers, TSH did not show significant main effects or interaction (p_FN = 0.887; p_age = 0.104; p_int = 0.375), and free thyroxine (fT4) showed a significant age effect (p_age < 0.001) but no significant FN main effect or interaction (p_FN = 0.576; p_int = 0.245). (Table 3) 3.3. Physical activity, sedentary time, and sleep Table 4 presents adjusted estimates for physical activity (PA), sedentary behavior (SB), and sleep duration by FN and AG. Interaction patterns differed across PA domains. Place movement PA (PMPA) and recreational vigorous PA (RVPA) showed significant FN × AG interactions (PMPA p_int = 0.001; RVPA p_int < 0.001), and total physical activity (TPA) also exhibited a significant interaction (p_int < 0.001). In contrast, occupational vigorous PA (OVPA) and occupational moderate PA (OMPA) did not show statistically significant interactions (OVPA p_int = 0.082; OMPA p_int = 0.055), and recreational moderate PA (RMPA) also showed no significant interaction (p_int = 0.183). Sedentary behavior showed significant main effects of FN (p_FN < 0.001) and AG (p_age < 0.001) and a significant interaction (p_int < 0.001), indicating age-dependent differences in the relationship between FN and sedentary time. Sleep duration on weekdays and weekends did not show significant main effects of FN or AG, nor significant FN × AG interactions (weekday p_int = 0.590; weekend p_int = 0.513). (Table 4) 4. Discussion The present study examined associations between household size (FN) and cardiometabolic health across adulthood using nationally representative, survey-weighted data, integrating components of metabolic syndrome, blood biomarkers, and daily health behaviors. Overall, the findings suggest that household structure is not merely a descriptive demographic characteristic, but a contextual factor related to metabolic health. A key feature across domains was that associations were often age-dependent, indicating age-group (AG)- modulated effects rather than a uniform main effect of FN. This pattern has practical implications for public health surveillance and prevention: FN may help identify population subgroups whose behavioral exposures and metabolic profiles differ by life stage, and strategies may be more informative when tailored to age-specific living contexts. Among metabolic syndrome components, waist circumference and systolic blood pressure showed evidence of household-size associations alongside robust age effects, with clear FN × AG interaction patterns (WC: p_FN = 0.001; p_int < 0.001; SBP: p_FN = 0.001; p_int < 0.001). These results are consistent with the interpretation that the relationship between household context and adiposity- or blood pressure–related risk may vary across the adult life course ( 30 , 45 ). From a life-course perspective, FN can proxy heterogeneous social and behavioral environments, yet the meaning of co-residence likely changes with age. In younger and middle-aged adults, larger households may be linked to more structured routines, shared schedules, and more incidental activity through household tasks, whereas in older adults, FN may more often reflect caregiving arrangements, health-related selection into co-residence, or constraints associated with comorbidity and functional status ( 62 , 63 ). This aligns with social epidemiologic frameworks in which household structure reflects one dimension of social integration that may shape health through behavioral regulation, support, and access to resources, while operating differently across life stages ( 64 , 65 ). Several additional metabolic components were also more consistent with interaction-first framing: fasting glucose and triglycerides showed strong age patterning with substantial interaction signals, and HDL-C exhibited a significant interaction despite a non-significant household main effect, reinforcing the need to avoid interpreting FN as uniformly protective or harmful across adulthood ( 66 ). Notably, triglycerides showed a strong FN × AG interaction (p_int < 0.001) despite a non-significant overall FN main effect (p_FN = 0.092), underscoring that household-size differences in TG are contingent on age group rather than uniform across adulthood. Biomarker profiles further support the possibility that household context is linked with physiologic status through multiple pathways that vary by age ( 67 ). Liver enzymes (AST and ALT) showed age-related increases, with ALT showing a significant FN × AG interaction (p_int < 0.001) while AST did not show evidence of interaction (p_int = 0.274), which may reflect age-graded differences in lifestyle exposures (e.g., dietary patterns, adiposity distribution, alcohol-related behaviors) and preventive health engagement shaped by household environment ( 68 ). Renal function markers also showed pronounced age-dependence with interaction patterns; BUN and creatinine increased with age, and creatinine additionally showed evidence suggestive of FN differences with age-contingent variation (creatinine p_int < 0.001). For BUN, the FN × AG interaction was statistically significant but modest (p_int = 0.011), and the overall FN main effect was not significant, suggesting caution in interpreting age-contingent heterogeneity for BUN. These findings may be consistent with age- and household-dependent variation in hydration behaviors, dietary protein patterns, medication use, and muscle mass distribution, factors that can influence renal biomarkers and may not be fully captured by standard sociodemographic adjustments ( 69 , 70 ). Interaction signals observed in endocrine-related markers, including vitamin D and thyroid indices (TSH and free thyroxine), may similarly reflect age-dependent differences in outdoor exposure, dietary/supplement behaviors, and healthcare access that vary by living context. However, thyroid indices did not show evidence of an FN × AG interaction in the current models (TSH p_int = 0.375; fT4 p_int = 0.245), providing a useful boundary condition: FN does not appear to modify age-patterning uniformly across all endocrine markers. While these biomarkers are not specific to a single mechanism, their convergence with the broader interaction pattern suggests that household structure may be linked to cardiometabolic risk through multiple age-stratified pathways ( 71 – 73 ). Behavioral findings offer particularly actionable candidates for mechanisms linking household context to metabolic and biomarker outcomes. Sedentary behavior (SB) showed consistent evidence of both FN and age associations with a pronounced interaction pattern (p_FN = 0.002; p_int < 0.001), suggesting that the relationship between household context and prolonged sitting differs across age groups. In general, larger FN tended to correspond to lower sedentary time, especially among younger and middle-aged adults, which is conceptually consistent with the idea that co-residence may increase non-exercise activity through household tasks, caregiving responsibilities, and shared routines that interrupt prolonged sitting ( 74 ). However, the presence of strong interactions suggests that these associations may attenuate, reverse, or become more complex in older age groups, where functional capacity, chronic conditions, and health-related co-residence may play a larger role ( 67 , 75 ). Physical activity outcomes showed domain-specific heterogeneity rather than a uniform interaction across all PA measures: significant FN × AG interactions were evident for some domains/intensities (e.g., recreational vigorous PA, place-movement PA, and total PA), whereas occupational vigorous/moderate PA and recreational moderate PA did not consistently exhibit significant interactions. Leisure-time activity declined with age and exhibited a notable interaction signal (p_int < 0.001), consistent with the notion that household context may differentially influence leisure opportunities as individuals age ( 19 ). Sleep duration did not show significant FN × AG interactions on weekdays or weekends in the current models, suggesting that sleep duration—at least as captured by self-reported measures here—may not be a primary pathway linking FN to cardiometabolic profiles. Together, these behavioral results support an integrative interpretation in which FN is related to cardiometabolic outcomes partly through age-specific differences in movement and recovery-related behaviors—particularly sedentary time, leisure-time activity, and sleep—rather than through a single uniform pathway ( 76 – 78 ). These findings are relevant to public health in the context of demographic changes, including population aging and the growth of smaller household units in many societies ( 79 ). Traditional metabolic syndrome prevention strategies have typically emphasized individual-level behavior modification (e.g., increasing physical activity, reducing sedentary time, and improving sleep) without explicitly accounting for the social environments that organize daily routines ( 80 ). The present results suggest that household structure may serve as a pragmatic contextual marker for stratifying prevention approaches, but they also indicate that translation to practice should be age-tailored ( 81 ). For working-age adults, household-related constraints and supports may operate through time availability, occupational exposures, and routine organization; for older adults, functional limitations, chronic disease management demands, and caregiving dynamics may be more salient. Accordingly, interventions may be more effective when they provide age-appropriate opportunities to reduce sedentary time, support routine physical activity, and stabilize recovery behaviors (including sleep) in ways that align with the lived realities of different household contexts ( 82 ). Although sleep duration did not show significant FN × AG interactions in this analysis, sleep remains a core cardiometabolic behavior; thus, intervention design can still consider sleep as a general recovery target without implying household-specific effects in these data. Such programs need not replicate household environments; community-based initiatives, socially supported exercise opportunities, and time-structured lifestyle supports could provide external structure and accountability, especially for individuals whose living context offers fewer routine anchors ( 83 , 84 ). Several strengths enhance the interpretability of these findings for population health. Use of nationally representative data and survey-weighted analyses supports generalizability, and the concurrent assessment of behavioral, biological, and clinical indicators provides a broader view of plausible pathways than any single domain alone ( 85 ). Nonetheless, limitations should be considered. The repeated cross-sectional design limits causal inference, and reverse causation cannot be ruled out (e.g., poorer health may influence household arrangements). FN was measured quantitatively and did not capture qualitative dimensions of relationships, social support, or caregiving intensity, which may help explain heterogeneity within household-size categories. Residual confounding by unmeasured or imperfectly measured factors may remain. Even so, the consistent age-dependent patterns across multiple domains suggest that household structure is not a trivial correlation, and it may capture meaningful aspects of living context relevant to cardiometabolic health. 5. Conclusion In conclusion, this study suggests that FN is associated with components of metabolic syndrome, blood biomarkers, and key health behaviors across adulthood, with a prominent pattern of age-related effect modification. Household structure may therefore be a useful contextual indicator in public health efforts to understand and prevent metabolic risk, particularly when prevention strategies are tailored to age-specific living contexts and to the behavioral exposures most consistently linked to the household environment. Declarations Ethics approval and consent to participate The Korea National Health and Nutrition Examination Survey (KNHANES) was reviewed by the Institutional Review Board (IRB) of the Korea Disease Control and Prevention Agency (KDCA; formerly the Korea Centers for Disease Control and Prevention, KCDC) as applicable for each survey cycle, and written informed consent was obtained from all participants. For certain survey years, KNHANES was conducted without IRB review based on the IRB committee’s determination, as permissible under the Bioethics and Safety Act (Article 2( 1 )) and its Enforcement Rule (Article 2( 2 ), subparagraph 1) for government-led research conducted for the public welfare. The IRB review process was resumed in 2018 to consider biospecimen collection and the provision of raw data to third parties. The KNHANES IRB approval numbers (or waiver status) for the cycles included in this study (2015–2024) are provided in Supplementary Table S1 . The present study is a secondary analysis of de-identified public-use data and was exempt from additional IRB review at the authors’ institution. Consent for publication Not applicable. This study analyzed de-identified public-use data and does not contain any identifiable personal information. Funding This work was supported by the Sungshin Women’s University Research Grant of 2025. Author Contribution ES Sung conceived and designed the study, prepared the original draft, and contributed to writing, review, and editing. SH Bang conducted the investigation and performed the statistical analysis. ES Sung and SH Bang jointly interpreted the data. Acknowledgement The authors gratefully acknowledge the support provided by Sungshin Women’s University for conducting this study. Data Availability The datasets analyzed during the current study are publicly available from the Korea National Health and Nutrition Examination Survey (KNHANES) website, subject to the data access procedures and terms of use of the Korea Disease Control and Prevention Agency (KDCA). References United N. Department of economic and social affairs. Popul Div World Popul Projections to. 2019;2150. Development OfEC-oa. Society at a Glance 2021: OECD Social Indicators. OECD Publishing; 2021. Oh DH, Park JH, Lee HY, Kim SA, Choi BY, Nam JH. 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Health Serv Res. 2014;49(1):284–303. Tables Table 1. Descriptive characteristics of the participants. Variables AG FN1 (N=4,302) FN2 (N=10,620) FN3 (N=8,960) FN4 (N=8,331) ≥ FN5 (N=2,826) Age [years] 19-29 25.12 ± 2.67 25.06 ± 2.93 24.26 ± 2.95 23.67 ± 2.99 23.17 ± 3.05 30-44 35.92 ± 4.40 35.97 ± 4.31 36.78 ± 4.29 38.26 ± 4.00 38.48 ± 3.82 45-59 52.45 ± 4.33 53.34 ± 4.22 52.24 ± 4.23 50.72 ± 4.06 50.45 ± 4.15 60-74 67.38 ± 4.28 66.76 ± 4.20 65.44 ± 4.15 64.92 ± 4.20 67.24 ± 4.33 75+ 78.39 ± 1.85 78.05 ± 1.91 78.09 ± 1.92 78.32 ± 1.92 78.00 ± 1.93 Height [cm] 19-29 170.14 ± 8.61 168.45 ± 8.55 168.66 ± 8.83 168.97 ± 8.71 167.26 ± 8.55 30-44 171.24 ± 8.12 169.23 ± 8.69 168.20 ± 8.81 167.57 ± 8.66 167.11 ± 8.65 45-59 165.25 ± 8.10 163.32 ± 8.43 164.07 ± 8.42 165.53 ± 8.34 165.25 ± 8.56 60-74 158.52 ± 8.37 160.42 ± 8.40 160.99 ± 8.28 161.33 ± 8.43 158.75 ± 8.10 75+ 153.31 ± 8.30 158.82 ± 8.81 156.64 ± 9.53 155.48 ± 9.61 156.91 ± 8.39 Weight [kg] 19-29 68.45 ± 14.96 67.18 ± 15.99 66.91 ± 15.45 66.90 ± 15.90 63.32 ± 14.78 30-44 72.90 ± 15.59 69.68 ± 15.13 68.64 ± 14.77 67.82 ± 14.37 68.16 ± 14.21 45-59 66.67 ± 12.61 64.65 ± 11.79 65.17 ± 11.70 66.80 ± 11.69 67.17 ± 12.12 60-74 61.21 ± 10.11 62.65 ± 10.06 63.00 ± 10.40 63.77 ± 10.30 61.39 ± 9.73 75+ 56.55 ± 9.31 60.02 ± 9.89 58.01 ± 10.05 57.95 ± 10.60 59.22 ± 9.20 BMI [kg/m 2 ] 19-29 23.50 ± 4.10 23.52 ± 4.48 23.35 ± 4.16 23.26 ± 4.46 22.49 ± 4.26 30-44 24.72 ± 4.32 24.16 ± 4.06 24.09 ± 4.02 23.99 ± 3.85 24.25 ± 3.83 45-59 24.30 ± 3.63 24.14 ± 3.41 24.12 ± 3.33 24.28 ± 3.23 24.49 ± 3.35 60-74 24.30 ± 3.26 24.29 ± 3.09 24.23 ± 3.11 24.44 ± 3.18 24.29 ± 3.00 75+ 24.02 ± 3.23 23.73 ± 3.13 23.62 ± 3.18 23.81 ± 3.21 24.03 ± 3.43 Values are presented as means ± standard errors (SE). All estimates are survey-weighted. AG indicates age group; FN indicates household size; BMI, body mass index. No statistical comparisons were performed for descriptive characteristics. Table 2. Age group (AG) and household size (FN) in metabolic syndrome components (MetS) Variables AG FN1 FN2 FN3 FN4 ≥ FN5 p_FN p_age p_int WC [cm] 19-29 79.03 ± 0.43 79.49 ± 0.41 78.57 ± 0.29 78.34 ± 0.30 76.96 ± 0.52 <0.001*** <0.001*** <0.001*** 30-44 83.16 ± 0.46 82.34 ± 0.29 82.47 ± 0.22 82.57 ± 0.18 83.19 ± 0.30 45-59 83.88 ± 0.34 83.87 ± 0.19 83.52 ± 0.17 83.44 ± 0.18 84.08 ± 0.33 60-74 87.12 ± 0.23 86.11 ± 0.14 85.73 ± 0.23 86.14 ± 0.36 86.78 ± 0.44 75+ 88.26 ± 0.30 86.17 ± 0.24 86.27 ± 0.49 87.29 ± 0.79 87.38 ± 0.75 SBP [mmHg] 19-29 111.72 ± 0.49 110.30 ± 0.43 110.78 ± 0.31 110.72 ± 0.29 109.43 ± 0.51 0.001** <0.001*** <0.001*** 30-44 114.26 ± 0.56 112.18 ± 0.34 112.40 ± 0.27 112.12 ± 0.25 112.90 ± 0.36 45-59 120.59 ± 0.56 119.69 ± 0.31 118.27 ± 0.28 117.61 ± 0.30 117.43 ± 0.56 60-74 127.33 ± 0.41 125.90 ± 0.26 124.69 ± 0.38 125.30 ± 0.71 126.71 ± 0.90 75+ 133.83 ± 0.50 129.32 ± 0.43 131.29 ± 0.91 135.80 ± 1.46 132.14 ± 1.60 DBP [mmHg] 19-29 71.74 ± 0.44 71.14 ± 0.37 71.05 ± 0.23 71.45 ± 0.24 70.44 ± 0.43 0.006** <0.001*** <0.001*** 30-44 76.47 ± 0.46 74.77 ± 0.28 75.03 ± 0.21 75.10 ± 0.19 75.72 ± 0.29 45-59 79.25 ± 0.36 77.97 ± 0.20 77.96 ± 0.18 78.13 ± 0.19 78.30 ± 0.35 60-74 75.47 ± 0.23 75.13 ± 0.15 75.46 ± 0.22 76.87 ± 0.39 75.74 ± 0.54 75+ 72.93 ± 0.29 70.21 ± 0.24 70.44 ± 0.49 71.97 ± 0.81 71.62 ± 0.90 FG [mg/dL] 19-29 90.32 ± 0.42 91.11 ± 0.44 90.45 ± 0.37 90.00 ± 0.38 90.35 ± 0.39 0.006** <0.001*** 0.005** 30-44 96.53 ± 0.86 96.02 ± 0.57 96.47 ± 0.41 96.69 ± 0.36 97.98 ± 0.61 45-59 107.83 ± 1.11 104.66 ± 0.50 102.56 ± 0.44 102.14 ± 0.46 104.21 ± 1.15 60-74 109.65 ± 0.66 107.48 ± 0.36 107.47 ± 0.64 107.45 ± 1.02 109.00 ± 1.46 75+ 110.82 ± 0.87 108.26 ± 0.61 107.56 ± 1.03 109.24 ± 1.72 109.78 ± 2.03 TG [mg/dL] 19-29 102.52 ± 2.71 115.77 ± 3.57 110.67 ± 2.38 109.23 ± 1.97 105.67 ± 4.01 0.092 <0.001*** <0.001*** 30-44 147.59 ± 5.50 149.39 ± 3.61 142.87 ± 2.37 141.32 ± 2.13 154.74 ± 4.05 45-59 163.87 ± 4.00 160.33 ± 2.57 151.25 ± 1.96 156.70 ± 2.60 161.71 ± 4.84 60-74 151.96 ± 2.60 147.22 ± 1.45 148.86 ± 2.60 156.27 ± 3.87 157.04 ± 5.06 75+ 152.11 ± 2.13 136.86 ± 2.01 140.56 ± 3.28 148.45 ± 5.92 147.84 ± 6.23 HDL [mg/dL] 19-29 55.39 ± 0.52 54.48 ± 0.46 53.95 ± 0.33 54.09 ± 0.32 53.80 ± 0.58 0.579 <0.001*** 0.015* 30-44 53.78 ± 0.51 53.31 ± 0.35 52.52 ± 0.27 51.86 ± 0.23 50.47 ± 0.35 45-59 51.80 ± 0.42 51.41 ± 0.26 51.81 ± 0.25 51.29 ± 0.25 50.79 ± 0.46 60-74 50.03 ± 0.31 50.24 ± 0.20 50.47 ± 0.32 50.12 ± 0.48 49.47 ± 0.67 75+ 48.16 ± 0.39 49.54 ± 0.33 48.93 ± 0.61 47.23 ± 1.03 46.41 ± 0.97 Values are presented as means ± standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FG, fasting glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol. *p < 0.05; **p < 0.01; ***p < 0.001. Table 3. Blood biomarkers Variables AG FN1 FN2 FN3 FN4 ≥ FN5 p_FN p_age p_int AST 19-29 20.75 ± 0.66 21.30 ± 0.45 20.59 ± 0.29 20.60 ± 0.33 19.74 ± 0.37 0.005** <0.001*** 0.274 30-44 22.72 ± 0.49 22.58 ± 0.42 22.45 ± 0.34 22.22 ± 0.22 22.79 ± 0.40 45-59 25.95 ± 0.98 24.85 ± 0.30 24.21 ± 0.26 23.57 ± 0.23 24.53 ± 0.49 60-74 26.23 ± 0.29 25.44 ± 0.20 25.06 ± 0.31 25.25 ± 0.50 25.83 ± 0.55 75+ 26.37 ± 0.29 24.51 ± 0.25 24.48 ± 0.53 23.99 ± 0.83 25.72 ± 0.86 ALT 19-29 21.32 ± 0.88 22.90 ± 0.97 21.34 ± 0.51 22.15 ± 0.68 20.00 ± 0.80 0.023* <0.001*** <0.001*** 30-44 26.18 ± 0.93 26.18 ± 0.86 24.55 ± 0.42 24.51 ± 0.41 25.54 ± 0.71 45-59 25.64 ± 0.96 24.69 ± 0.36 24.16 ± 0.31 24.15 ± 0.34 25.60 ± 0.80 60-74 24.02 ± 0.38 22.98 ± 0.24 22.58 ± 0.38 23.47 ± 0.60 23.98 ± 0.70 75+ 21.69 ± 0.36 18.64 ± 0.30 18.94 ± 0.65 18.77 ± 0.84 19.24 ± 1.03 BUN 19-29 12.35 ± 0.13 12.20 ± 0.13 12.38 ± 0.09 12.09 ± 0.08 12.24 ± 0.15 0.199 <0.001*** 0.011* 30-44 12.72 ± 0.16 12.63 ± 0.10 12.80 ± 0.07 13.07 ± 0.07 13.11 ± 0.10 45-59 14.26 ± 0.16 14.87 ± 0.11 14.73 ± 0.08 14.32 ± 0.08 14.25 ± 0.15 60-74 16.55 ± 0.13 16.37 ± 0.07 16.13 ± 0.12 16.08 ± 0.18 16.69 ± 0.23 75+ 18.05 ± 0.17 17.59 ± 0.13 17.52 ± 0.28 18.39 ± 0.54 17.54 ± 0.43 Cr 19-29 0.82 ± 0.00 0.81 ± 0.00 0.81 ± 0.00 0.80 ± 0.00 0.80 ± 0.01 <0.001*** <0.001*** <0.001*** 30-44 0.83 ± 0.01 0.81 ± 0.01 0.81 ± 0.00 0.81 ± 0.00 0.81 ± 0.00 45-59 0.83 ± 0.02 0.81 ± 0.01 0.82 ± 0.01 0.81 ± 0.00 0.81 ± 0.00 60-74 0.84 ± 0.01 0.83 ± 0.00 0.83 ± 0.01 0.83 ± 0.01 0.86 ± 0.02 75+ 0.90 ± 0.01 0.89 ± 0.01 0.89 ± 0.01 0.94 ± 0.03 0.93 ± 0.02 WBC 19-29 6.66 ± 0.08 6.85 ± 0.06 6.92 ± 0.05 6.90 ± 0.05 7.02 ± 0.09 0.469 <0.001*** <0.001*** 30-44 6.71 ± 0.06 6.80 ± 0.05 6.59 ± 0.03 6.52 ± 0.03 6.53 ± 0.05 45-59 6.64 ± 0.06 6.46 ± 0.03 6.42 ± 0.03 6.45 ± 0.03 6.60 ± 0.06 60-74 6.63 ± 0.04 6.45 ± 0.03 6.48 ± 0.04 6.53 ± 0.07 6.67 ± 0.08 75+ 6.78 ± 0.06 6.64 ± 0.04 6.68 ± 0.10 7.02 ± 0.13 6.78 ± 0.15 RBC 19-29 4.81 ± 0.01 4.79 ± 0.01 4.82 ± 0.01 4.82 ± 0.01 4.84 ± 0.02 0.039* <0.001*** <0.001*** 30-44 4.76 ± 0.01 4.74 ± 0.01 4.74 ± 0.01 4.72 ± 0.01 4.71 ± 0.01 45-59 4.63 ± 0.01 4.64 ± 0.01 4.64 ± 0.01 4.66 ± 0.01 4.68 ± 0.01 60-74 4.50 ± 0.01 4.50 ± 0.01 4.53 ± 0.01 4.54 ± 0.02 4.54 ± 0.02 75+ 4.38 ± 0.01 4.27 ± 0.01 4.29 ± 0.03 4.37 ± 0.03 4.37 ± 0.04 Hb 19-29 14.45 ± 0.05 14.44 ± 0.04 14.45 ± 0.03 14.48 ± 0.03 14.45 ± 0.05 0.004** <0.001*** <0.001*** 30-44 14.41 ± 0.04 14.34 ± 0.03 14.29 ± 0.02 14.20 ± 0.02 14.17 ± 0.03 45-59 14.29 ± 0.04 14.31 ± 0.02 14.22 ± 0.02 14.23 ± 0.02 14.30 ± 0.04 60-74 14.04 ± 0.03 14.04 ± 0.02 14.11 ± 0.03 14.14 ± 0.04 14.13 ± 0.06 75+ 13.75 ± 0.04 13.36 ± 0.04 13.44 ± 0.08 13.66 ± 0.10 13.70 ± 0.11 Hct 19-29 43.73 ± 0.13 43.62 ± 0.11 43.75 ± 0.08 43.84 ± 0.08 43.78 ± 0.14 0.007** <0.001*** <0.001*** 30-44 43.55 ± 0.12 43.35 ± 0.09 43.21 ± 0.07 43.00 ± 0.06 42.97 ± 0.10 45-59 43.08 ± 0.12 43.15 ± 0.07 42.96 ± 0.06 43.02 ± 0.07 43.17 ± 0.12 60-74 42.41 ± 0.09 42.40 ± 0.05 42.61 ± 0.09 42.69 ± 0.13 42.72 ± 0.19 75+ 41.67 ± 0.11 40.61 ± 0.10 40.86 ± 0.23 41.44 ± 0.28 41.69 ± 0.33 PLT 19-29 275.62 ± 2.40 276.92 ± 2.25 275.07 ± 1.60 276.86 ± 1.60 279.19 ± 2.86 0.660 <0.001*** 0.024* 30-44 277.75 ± 2.38 275.05 ± 1.70 272.65 ± 1.23 271.09 ± 1.10 269.80 ± 1.78 45-59 266.13 ± 1.92 258.77 ± 1.13 260.74 ± 1.17 263.36 ± 1.17 265.57 ± 2.08 60-74 249.11 ± 1.54 244.23 ± 0.88 247.05 ± 1.52 249.11 ± 2.49 245.42 ± 3.26 75+ 239.97 ± 1.89 237.12 ± 1.55 236.80 ± 2.86 252.68 ± 6.12 248.34 ± 5.05 VitD 19-29 11.50 ± 1.01 13.85 ± 0.81 13.90 ± 0.59 13.62 ± 0.70 13.22 ± 0.77 0.047* <0.001*** <0.001*** 30-44 12.64 ± 1.02 14.63 ± 0.64 15.08 ± 0.47 15.49 ± 0.56 16.14 ± 0.72 45-59 16.36 ± 1.15 17.95 ± 0.68 17.46 ± 0.81 16.26 ± 0.67 16.27 ± 1.01 60-74 17.29 ± 0.97 18.13 ± 0.66 18.81 ± 1.32 16.86 ± 1.38 19.58 ± 1.82 75+ 22.63 ± 0.24 5.42 ± 0.35 -- -- -- TSH 19-29 2.34 ± 0.21 2.44 ± 0.18 2.68 ± 0.15 2.59 ± 0.16 2.55 ± 0.24 0.887 0.104 0.375 30-44 2.37 ± 0.18 2.45 ± 0.12 2.38 ± 0.09 2.55 ± 0.19 2.62 ± 0.14 45-59 3.57 ± 0.63 2.87 ± 0.17 2.99 ± 0.25 2.61 ± 0.14 2.36 ± 0.17 60-74 2.73 ± 0.21 2.56 ± 0.18 2.69 ± 0.23 2.74 ± 0.32 2.48 ± 0.36 75+ 2.60 ± 1.04 2.44 ± 0.41 -- 8.34 ± 0.10 -- fT4 19-29 1.33 ± 0.02 1.34 ± 0.02 1.31 ± 0.01 1.31 ± 0.01 1.33 ± 0.02 0.576 <0.001*** 0.245 30-44 1.29 ± 0.02 1.24 ± 0.02 1.26 ± 0.01 1.26 ± 0.01 1.24 ± 0.02 45-59 1.22 ± 0.02 1.21 ± 0.01 1.21 ± 0.01 1.21 ± 0.01 1.22 ± 0.02 60-74 1.18 ± 0.02 1.22 ± 0.01 1.18 ± 0.02 1.17 ± 0.03 1.23 ± 0.04 75+ 1.17 ± 0.05 1.21 ± 0.03 -- 0.98 ± 0.01 -- Values are presented as means ± standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. Dashes (--) indicate insufficient sample size for reliable estimation. AST, aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; CRE, creatinine; WBC, white blood cell count; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; VitD, 25-hydroxyvitamin D; TSH, thyroid-stimulating hormone; fT4, free thyroxine. *p < 0.05; **p < 0.01; ***p < 0.001. Table 4. Physical activity, sedentary time, and sleep duration Variables AG FN_1 FN_2 FN_3 FN_4 ≥ FN_5 p_FN p_age p_int OVPA 19-29 378.56 ± 122.80 330.41 ± 78.83 300.64 ± 61.20 342.59 ± 72.78 273.25 ± 80.02 0.432 0.004** 0.082 30-44 239.22 ± 62.45 251.09 ± 60.18 251.17 ± 42.49 335.90 ± 47.25 414.83 ± 68.21 45-59 443.51 ± 191.81 409.10 ± 94.79 310.11 ± 48.79 350.35 ± 125.33 361.95 ± 119.03 60-74 355.51 ± 156.23 331.93 ± 54.78 238.63 ± 54.94 557.03 ± 180.08 401.53 ± 140.08 75+ 131.51 ± 32.46 160.92 ± 61.41 5.53 ± 48.07 1208.08 ± 517.41 -- OMPA 19-29 386.26 ± 57.98 367.14 ± 37.61 443.42 ± 37.10 379.06 ± 53.60 459.91 ± 61.32 0.560 0.109 0.055 30-44 435.74 ± 45.96 435.86 ± 40.19 487.72 ± 39.96 451.51 ± 29.88 579.12 ± 57.88 45-59 323.61 ± 38.98 446.07 ± 42.80 438.29 ± 39.93 472.33 ± 37.20 373.27 ± 47.35 60-74 282.04 ± 36.33 411.99 ± 40.34 390.86 ± 50.06 235.96 ± 37.28 429.97 ± 151.84 75+ 382.50 ± 92.32 333.31 ± 75.96 256.25 ± 71.74 494.82 ± 151.64 715.20 ± 507.53 PMPA 19-29 792.45 ± 30.01 799.73 ± 34.47 844.83 ± 24.43 852.86 ± 25.98 837.09 ± 41.60 0.347 <0.001*** 0.001** 30-44 790.98 ± 46.66 757.73 ± 28.83 772.76 ± 20.06 834.13 ± 25.60 922.28 ± 46.81 45-59 841.37 ± 38.83 816.31 ± 22.39 844.92 ± 20.38 788.79 ± 20.00 850.52 ± 48.66 60-74 847.65 ± 28.02 810.29 ± 18.14 851.85 ± 27.28 886.19 ± 56.03 771.08 ± 42.73 75+ 687.68 ± 25.74 762.25 ± 30.39 636.54 ± 44.65 653.34 ± 63.86 828.08 ± 152.19 RVPA 19-29 1163.51 ± 77.10 1366.57 ± 108.87 1524.82 ± 96.58 1337.97 ± 70.06 1169.41 ± 122.28 0.009** 0.173 <0.001*** 30-44 1046.49 ± 71.02 993.73 ± 66.61 1087.05 ± 61.74 1209.38 ± 66.85 1210.92 ± 81.26 45-59 1302.25 ± 139.59 1408.14 ± 73.68 1223.66 ± 69.79 1085.09 ± 56.61 1100.90 ± 109.72 60-74 1203.97 ± 150.46 1536.53 ± 101.71 1239.10 ± 119.09 1388.05 ± 220.65 1756.11 ± 239.43 75+ 935.32 ± 207.10 1513.53 ± 332.55 1665.10 ± 74.60 1615.68 ± 44.32 552.44 ± 45.54 RMPA 19-29 614.96 ± 30.41 645.87 ± 32.64 682.66 ± 29.63 720.40 ± 25.17 725.54 ± 49.78 0.069 0.001** 0.183 30-44 570.91 ± 31.51 576.23 ± 24.77 594.15 ± 26.17 561.31 ± 16.72 609.43 ± 29.05 45-59 627.83 ± 38.21 717.53 ± 23.04 680.85 ± 20.80 619.47 ± 19.45 629.76 ± 43.26 60-74 833.27 ± 48.08 847.45 ± 24.59 739.91 ± 32.89 781.98 ± 71.56 695.80 ± 75.58 75+ 784.06 ± 67.20 794.19 ± 45.15 634.33 ± 76.99 805.12 ± 221.62 1105.70 ± 303.56 TPA 19-29 1356.29 ± 53.02 1444.07 ± 70.34 1474.40 ± 46.29 1439.57 ± 42.67 1357.01 ± 67.94 0.013* <0.001*** <0.001*** 30-44 1172.12 ± 53.48 1108.53 ± 37.50 1114.46 ± 29.32 1179.50 ± 31.49 1319.63 ± 53.51 45-59 1094.52 ± 46.58 1192.09 ± 29.61 1185.34 ± 29.02 1111.35 ± 26.74 1121.83 ± 53.43 60-74 1046.11 ± 32.50 1074.36 ± 22.97 1041.85 ± 32.13 1116.64 ± 63.38 968.53 ± 52.86 75+ 813.19 ± 31.00 865.00 ± 36.46 666.54 ± 48.67 789.38 ± 82.17 990.04 ± 169.87 SB 19-29 597.81 ± 8.97 573.18 ± 8.10 566.20 ± 5.83 577.55 ± 5.61 548.05 ± 10.26 <0.001*** <0.001*** <0.001*** 30-44 562.86 ± 8.82 548.52 ± 6.56 515.31 ± 4.88 492.28 ± 4.53 456.18 ± 7.07 45-59 488.46 ± 8.41 451.15 ± 4.71 468.51 ± 4.21 485.61 ± 4.57 463.60 ± 8.75 60-74 499.93 ± 5.79 456.91 ± 3.49 469.31 ± 5.71 482.22 ± 9.00 437.58 ± 10.97 75+ 587.69 ± 7.43 528.65 ± 5.84 569.50 ± 11.91 545.96 ± 18.66 565.99 ± 19.73 STWK 19-29 422.24 ± 6.53 424.68 ± 5.10 426.56 ± 3.19 420.48 ± 3.44 436.12 ± 5.88 0.299 0.087 0.590 30-44 412.76 ± 4.56 415.56 ± 3.02 423.11 ± 2.23 417.28 ± 1.84 414.38 ± 2.77 45-59 401.47 ± 4.37 411.07 ± 2.33 407.20 ± 1.86 399.43 ± 1.96 397.71 ± 3.44 60-74 411.34 ± 11.75 417.53 ± 2.78 407.06 ± 3.02 408.39 ± 4.90 419.52 ± 13.77 75+ 416.77 ± 8.38 435.99 ± 6.67 427.45 ± 7.58 466.30 ± 31.63 432.23 ± 8.99 STWD 19-29 479.30 ± 7.51 465.33 ± 5.74 466.67 ± 3.91 461.93 ± 3.69 468.07 ± 6.22 0.307 0.083 0.513 30-44 460.54 ± 6.73 453.36 ± 3.83 449.11 ± 2.61 446.43 ± 2.27 439.39 ± 3.52 45-59 419.55 ± 4.64 430.01 ± 2.69 430.70 ± 2.23 430.24 ± 2.41 421.61 ± 4.10 60-74 418.96 ± 11.77 426.53 ± 2.91 418.56 ± 3.17 418.03 ± 5.33 431.04 ± 14.02 75+ 416.66 ± 8.33 439.63 ± 6.72 431.54 ± 7.69 470.26 ± 31.79 440.05 ± 9.34 Values are presented as means ± standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. Physical activity variables are expressed as minutes per week. OVPA, occupational vigorous physical activity; OMPA, occupational moderate physical activity; RVPA, recreational vigorous physical activity; RMPA, recreational moderate physical activity; PMPA, place movement physical activity; TPA, total physical activity; SB, sedentary time; STWK, sleep time on weekdays; STWD, sleep time on weekends. *p < 0.05; **p < 0.01; ***p < 0.001. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eIn recent decades, profound demographic and social transitions have significantly impacted daily living environments worldwide. Among the aforementioned changes, the rapid increase in single-person households has emerged as one of the most prominent structural shifts (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The phenomenon of delayed marriage and childbirth, population ageing, extended life expectancy, and changes in labor market conditions have collectively resulted in the diversification of household structures (2), with South Korea experiencing one of the fastest transitions in this regard (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These changes extend beyond descriptive demographic trends and increasingly constitute an important public health context, as living arrangements may shape daily health-related behaviors.\u003c/p\u003e \u003cp\u003eThese changes extend beyond descriptive demographic trends and increasingly constitute an important public health context, as living arrangements may shape daily health-related behaviors. Household structure, particularly household size (FN), can be conceptualized as an environmental indicator reflecting how individuals organize and experience their daily lives (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In contrast to relatively fixed biological characteristics, FN interacts dynamically with health-related behaviors, including dietary patterns, physical activity, sedentary behavior, sleep, and social interactions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The hypothesis that living alone is indicative of health vulnerability is not supported by extant literature. However, a body of research suggests that the presence or absence of cohabiting household members may be associated with differences in daily routines and behavioral regulation (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). From this standpoint, FN can be regarded as a living environment through which health behaviors may be modelled and sustained.\u003c/p\u003e \u003cp\u003eScholars in the field have hypothesized that individuals residing alone may be more prone to engage in dietary practices less conducive to health than those residing in multi-person households (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These patterns encompass irregular meal scheduling, increased reliance on external dining options or convenience foods, and reduced dietary diversity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Conversely, the consumption of shared meals within multi-person households has been associated with more regular eating schedules and improved nutritional balance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While such behavioral distinctions may appear negligible on an individual basis, their cumulative effects over time may be significant for metabolic health.\u003c/p\u003e \u003cp\u003eIt is evident that physical activity and sedentary behavior represent additional behavioral pathways that may be associated with FN. Conversely, living with others may provide informal social prompts for movement and contribute to more structured daily routines (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Individuals living alone may experience fewer external cues to interrupt prolonged sitting, particularly during leisure time. Prolonged sedentary behavior has been shown to be independently associated with adverse metabolic outcomes, even after accounting for levels of moderate-to-vigorous physical activity (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Consequently, a comprehensive examination of both physical activity and sedentary behavior is imperative to elucidate the correlation between FN and metabolic risk.\u003c/p\u003e \u003cp\u003eThe correlation between household structure and health outcomes may also be explained by psychosocial factors. While living alone does not necessarily lead to social isolation, the absence of cohabiting household members may alter the frequency and nature of daily social interactions and emotional support (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Such changes have been associated with stress perception, self-regulation of health behaviors, and long-term physiological processes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, it is imperative to acknowledge the complexity and multifactorial nature of these relationships. Consequently, their interpretation should be approached with caution, as they should not be construed as direct causal pathways.\u003c/p\u003e \u003cp\u003eMetabolic syndrome and its components\u0026mdash;including abdominal obesity, elevated blood pressure, dyslipidemia, and impaired glucose regulation\u0026mdash;continue to be significant public health concerns due to their strong associations with cardiovascular disease, type 2 diabetes, and mortality (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Despite plausible behavioral and psychosocial pathways, the existing evidence on the association between FN and metabolic health has been inconsistent. A number of studies have reported higher cardiometabolic risk among individuals living alone. However, other studies have found weak or null associations after adjustment for socioeconomic and lifestyle factors (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These discrepancies may indicate variations in study design, population characteristics, outcome selection, and analytical strategies.\u003c/p\u003e \u003cp\u003eA significant deficiency in earlier research pertains to the inadequate consideration of age-specific contexts. The implications of living alone vary considerably across the life course. Among young adults, single-person households often coincide with transitional periods marked by education, employment, and residential mobility. These periods may be accompanied by irregular lifestyles (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In middle-aged adults, living alone may indicate marital transitions or occupational demands that interact with cumulative behavioral exposures (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Among older adults, single-person households are often associated with circumstances such as widowhood or family dispersion. These households may also coincide with age-related physiological changes, as indicated by research (\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Treating FN as a homogeneous exposure across all ages may therefore obscure meaningful age-specific associations.\u003c/p\u003e \u003cp\u003eAnother methodological consideration is the frequent reliance on marital status as a proxy for living arrangements (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). While marital status indicates legal or relational conditions, the Family and Non-Family Dynamics (FN) scale captures actual cohabitation and shared daily living environments (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). he living context of individuals with the same marital status can vary considerably, depending on whether they reside independently or in a shared environment (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Consequently, FN has the potential to provide supplementary, autonomous data that extends beyond marital status, thereby facilitating a more comprehensive understanding of health-related living environments.\u003c/p\u003e \u003cp\u003eMoreover, numerous prior studies have focused on a limited set of health outcomes or failed to account for metabolic indicators, blood biomarkers, physical activity, sedentary behavior, and sleep (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Metabolic health reflects the integration of multiple physiological systems influenced by daily behaviors (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e); therefore, a multidimensional analytical approach is necessary. Furthermore, socioeconomic status, smoking, alcohol consumption, and other lifestyle factors have been demonstrated to be closely related to both household structure and health outcomes. This underscores the importance of analytical models that appropriately adjust for these potential confounders (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA particularly relevant setting for examining these associations is South Korea, given its rapidly increasing proportion of single-person households, accelerated population aging, and lifestyle changes characterized by high levels of sedentary time (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Despite this context, age-stratified analyses examining FN in relation to comprehensive metabolic and behavioral profiles remain limited in the Korean population.\u003c/p\u003e \u003cp\u003eTherefore, the present study employed nationally representative data and complex-sample analytical methods, adjusted for key socioeconomic and lifestyle factors, to examine age-specific associations between FN and indicators of metabolic syndrome (MetS), blood biomarkers, physical activity (PA), sedentary behavior (SB), and sleep duration. Due to the cross-sectional nature of the study, the findings are interpreted as associations rather than causal relationships. The hypothesis was developed that the associations between FN and metabolic and behavioral profiles would vary across age groups and would be more pronounced among younger and middle-aged adults, in whom daily behavioral patterns may be more strongly influenced by living arrangements.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp;\u0026nbsp;Sample and Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used cross-sectional data from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted by the Korea Centers for Disease Control and Prevention (KCDC)/Korea Disease Control and Prevention Agency (KDCA) from 2015 to 2024. Each survey cycle used a multistage, stratified, clustered probability sampling design to ensure representativeness of the non-institutionalized adult population. Data from individual survey years were pooled to enhance statistical power and to allow age-stratified analyses, while maintaining the cross-sectional nature of each survey cycle. The analytic sample included adults aged 19 years or older with complete information on household structure, metabolic syndrome components, blood biomarkers, physical activity, sedentary behavior, sleep duration, and relevant covariates.\u003c/p\u003e\n\u003cp\u003eThe present study analyzed de-identified public-use KNHANES data and was exempt from additional IRB review at the authors\u0026rsquo; institution. Among 74,925 participants pooled across the 2015\u0026ndash;2024 KNHANES cycles, we excluded individuals aged \u0026lt;19 years (n = 13,695), leaving n = 61,230 adults aged \u0026ge;19 years. We further excluded participants with a self-reported history of cancer (n = 1,020) and those with missing data on household structure (household size), metabolic syndrome components, blood biomarkers, physical activity, sedentary behavior, sleep duration, or covariates required for the main analyses (n = 25,171). The final analytic sample comprised n = 35,039 adults. Pregnancy status was included as a covariate in models involving women of reproductive age (Figure 1). All analyses incorporated sampling weights, strata, and primary sampling units to account for the complex survey design and to generate population-representative estimates.\u003c/p\u003e\n\u003cp\u003eHousehold structure was defined as the number of individuals residing in the household at the time of the survey and was categorized into five groups: single-person households (FN1), two-person households (FN2), three-person households (FN3), four-person households (FN4), and households with five or more members (\u0026ge;FN5).\u003c/p\u003e\n\u003cp\u003e(Figure 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. \u0026nbsp; \u0026nbsp;Descriptive characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the characteristics of the participants, including age, sex, educational attainment, household income, and employment status. Age was treated as a categorical variable for age-stratified analyses and grouped as 19\u0026ndash;29, 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;74, and \u0026ge;75 years to reflect distinct life-course stages.\u003c/p\u003e\n\u003cp\u003eLifestyle-related variables included smoking status (never, former, current smoker) and alcohol consumption (non-drinker, moderate drinker, heavy drinker), assessed using standardized questionnaires administered by trained interviewers.\u003c/p\u003e\n\u003cp\u003eCovariates were selected a priori based on established literature demonstrating their associations with both household structure and metabolic health outcomes, as well as their relevance to the conceptual framework of the present study. No data-driven variable selection procedures were applied.\u003c/p\u003e\n\u003cp\u003e(Table1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. \u0026nbsp;Metabolic syndrome components (MetS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic syndrome components (MetS) were assessed according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria (51), the American Heart Association, and the National Heart, Lung, and Blood Institute (52). Individual components included waist circumference (\u0026gt;90 cm (male) or \u0026gt;85 cm (female)), systolic blood pressure (SBP) \u0026ge;130 mmHg or diastolic blood pressure (DBP) \u0026ge;85 mmHg, fasting TG levels \u0026ge;150 mg/dL, fasting HDL-C levels \u0026lt; 40 mg/dL (male) or \u0026lt; 50 mg/dL (female), and fasting glucose (FG) levels \u0026ge;110 mg/dL (53, 54). Rather than classifying participants dichotomously by metabolic syndrome status, each component was analyzed as a continuous variable to allow a more sensitive examination of associations with household structure and age group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. \u0026nbsp;Blood biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting blood samples were collected by trained medical personnel in accordance with standardized protocols. Laboratory analyses were conducted at certified laboratories with established internal and external quality control procedures.\u003c/p\u003e\n\u003cp\u003eBlood biomarkers included liver enzymes (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]), renal function markers (blood urea nitrogen [BUN] and creatinine [Cr]), hematological parameters (white blood cell count [WBC], red blood cell count [RBC], hemoglobin [Hb], hematocrit [Hct], and platelet count [PLT]), vitamin D (25-hydroxyvitamin D [VitD]), and thyroid-related markers (thyroid-stimulating hormone [TSH] and free thyroxine [fT4]). All biomarkers were treated as continuous variables in descriptive and regression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. \u0026nbsp;Physical activity (PA), Sedentary behavior (SB), and sleep duration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysical activity (PA) was assessed using standardized questionnaire items based on the Global Physical Activity Questionnaire (GPAQ) framework. The GPAQ comprises 16 questions, grouped into three behavioral domains: work, transport, and recreational activities. Five domains of PA were analyzed: vigorous-intensity work, moderate-intensity work, place movement, vigorous-intensity recreation, and moderate-intensity recreation. Participants answered the five domains freely, without any additional options regarding how often they performed the activity and how many minutes per day. The World Health Organization (WHO) GPAQ analysis guidelines were used to analyze the GPAQ data (55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe estimated that a person\u0026apos;s caloric expenditure was four times higher when moderately active and eight times higher when vigorously active, compared with sitting quietly. Therefore, when calculating an individual\u0026apos;s total energy expenditure using GPAQ data, 4 METs were assigned to time spent in moderate activity and 8 METs to time spent in vigorous activity. The details are as follows:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Vigorous intensity activity: occupational (MET) = 8.0 \u0026times; vigorous intensity physical activity (day/week) \u0026times; 1-day vigorous intensity physical activity (minutes/day)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Moderate intensity activity: occupational (MET) = 4.0 \u0026times; moderate intensity physical activity (day/week) \u0026times; 1-day moderate intensity physical activity (minutes/day)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Vigorous intensity activity: recreational (MET) = 8.0 \u0026times; vigorous intensity physical activity (day/week) \u0026times; 1-day vigorous intensity physical activity (minutes/day)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Moderate intensity activity: recreational (MET) = 4.0 \u0026times; moderate intensity physical activity (day/week) \u0026times; 1-day moderate intensity physical activity (minutes/day)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Place movement (MET) = 4.0 \u0026times; place movement physical activity (day/week) \u0026times; 1-day place movement physical activity\u003c/p\u003e\n\u003cp\u003e\u0026bull; Total Physical Activity (MET) = vigorous intensity activity: occupational + moderate intensity activity: occupational + vigorous intensity activity: recreational + moderate intensity activity: recreational + place movement.\u003c/p\u003e\n\u003cp\u003eSedentary behavior (SB) was assessed using self-reported average daily sedentary time. Participants were asked to report the total time spent sitting or reclining on a typical day, including activities such as watching television, using a computer or smartphone, reading, and other seated leisure or occupational activities. Time spent sleeping was excluded from sedentary time. Average daily sedentary time was calculated and expressed in hours per day (h/day). SB was treated as a continuous variable in the primary analyses.\u003c/p\u003e\n\u003cp\u003eSleep duration was assessed separately for weekdays and weekends using self-reported average sleep time. Participants reported their usual sleep duration on weekdays and weekends, respectively. Average daily sleep duration was calculated as a weighted mean using the following formula: [(weekday sleep duration \u0026times; 5) + (weekend sleep duration \u0026times; 2)] / 7. Sleep duration was expressed in hours per day (h/day) and was analyzed as a continuous variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. \u0026nbsp;Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R Statistical Software (56) and the following R packages: emmeans v. 2.0.1 (57), openxlsx v. 4.2.8.1 (58), srvyr v. 1.3.0 (59), survey v. 4.4.8 (60), tidyverse v. 2.0.0 (61). To account for the complex sampling design of the national survey, all analyses incorporated sampling weights, strata, and primary sampling units using survey-weighted procedures.\u003c/p\u003e\n\u003cp\u003eParticipants were categorized according to household size (FN1, FN2, FN3, FN4, and \u0026ge;FN5) and age group (19\u0026ndash;29, 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;74, and \u0026ge;75 years). Descriptive statistics were calculated for each combination of household size and age group and are presented as survey-weighted means with standard errors.\u003c/p\u003e\n\u003cp\u003eTo examine the independent and interactive associations of FN and AG with metabolic syndrome components, blood biomarkers, PA, SB, and sleep-related variables, survey-weighted generalized linear models were applied. Each outcome variable was modeled with FN, AG, and their interaction term (FN \u0026times; AG) as main predictors.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for potential confounding factors, including sex, smoking status, alcohol consumption, household income level, and educational attainment. Main effects of FN (p_FN), main effects of AG (p_age), and interaction effects between FN and AG (p_int) were estimated from the fitted models.\u003c/p\u003e\n\u003cp\u003eFor variables with insufficient sample sizes within specific age\u0026ndash;household size strata, estimates were not calculated and are indicated as missing. Statistical significance was set at a two-sided p-value \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolic syndrome components (MetS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows survey-weighted, covariate-adjusted associations of household size (FN) and age group (AG) with metabolic syndrome components (MetS). Waist circumference (WC) exhibited significant main effects of FN (p_FN \u0026lt; 0.001) and AG (p_age \u0026lt; 0.001), with a significant FN \u0026times; AG interaction (p_int \u0026lt; 0.001), indicating age-dependent differences in the association between household context and central adiposity. Systolic blood pressure (SBP) showed significant main effects of FN (p_FN = 0.001) and AG (p_age \u0026lt; 0.001) and a significant interaction (p_int \u0026lt; 0.001). Diastolic blood pressure (DBP) demonstrated significant main effects of FN (p_FN = 0.006) and AG (p_age \u0026lt; 0.001), along with a significant FN \u0026times; AG interaction (p_int \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eFasting glucose (FG) was strongly associated with AG (p_age \u0026lt; 0.001) and showed a significant FN \u0026times; AG interaction (p_int = 0.005), whereas the main effect of FN was also statistically significant (p_FN = 0.006). Triglycerides (TG) were strongly patterned by age (p_age \u0026lt; 0.001) and showed a significant FN \u0026times; AG interaction (p_int \u0026lt; 0.001), while the main effect of FN was not statistically significant (p_FN = 0.092). HDL-C was associated with AG (p_age \u0026lt; 0.001) and demonstrated a significant FN \u0026times; AG interaction (p_int = 0.015) despite a non-significant FN main effect (p_FN = 0.579).\u003c/p\u003e\n\u003cp\u003e(Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp;Blood biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 summarizes adjusted associations between FN, AG, and blood biomarkers. AST showed significant main effects of FN (p_FN = 0.005) and AG (p_age \u0026lt; 0.001), but no significant FN \u0026times; AG interaction (p_int = 0.274). In contrast, ALT demonstrated significant main effects of FN (p_FN = 0.023) and AG (p_age \u0026lt; 0.001) and a significant FN \u0026times; AG interaction (p_int \u0026lt; 0.001).\u003cbr\u003e\u0026nbsp;For renal markers, BUN exhibited a strong age effect (p_age \u0026lt; 0.001) with a modest but significant interaction (p_int = 0.011) and no significant FN main effect (p_FN = 0.199). Creatinine showed significant main effects of FN and AG (both p \u0026lt; 0.001) and a significant interaction (p_int \u0026lt; 0.001). Hematological indices (e.g., WBC, RBC, Hb, Hct, PLT) were strongly related to AG (p_age \u0026lt; 0.001 for each) and several also showed significant FN \u0026times; AG interactions (e.g., WBC p_int \u0026lt; 0.001; PLT p_int = 0.024).\u003cbr\u003e\u0026nbsp;Vitamin D showed a significant main effect of FN (p_FN = 0.047), a strong age effect (p_age \u0026lt; 0.001), and a significant interaction (p_int \u0026lt; 0.001). For thyroid markers, TSH did not show significant main effects or interaction (p_FN = 0.887; p_age = 0.104; p_int = 0.375), and free thyroxine (fT4) showed a significant age effect (p_age \u0026lt; 0.001) but no significant FN main effect or interaction (p_FN = 0.576; p_int = 0.245).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Table 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp;Physical activity, sedentary time, and sleep\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents adjusted estimates for physical activity (PA), sedentary behavior (SB), and sleep duration by FN and AG. Interaction patterns differed across PA domains. Place movement PA (PMPA) and recreational vigorous PA (RVPA) showed significant FN \u0026times; AG interactions (PMPA p_int = 0.001; RVPA p_int \u0026lt; 0.001), and total physical activity (TPA) also exhibited a significant interaction (p_int \u0026lt; 0.001). In contrast, occupational vigorous PA (OVPA) and occupational moderate PA (OMPA) did not show statistically significant interactions (OVPA p_int = 0.082; OMPA p_int = 0.055), and recreational moderate PA (RMPA) also showed no significant interaction (p_int = 0.183).\u003cbr\u003e\u0026nbsp;Sedentary behavior showed significant main effects of FN (p_FN \u0026lt; 0.001) and AG (p_age \u0026lt; 0.001) and a significant interaction (p_int \u0026lt; 0.001), indicating age-dependent differences in the relationship between FN and sedentary time. Sleep duration on weekdays and weekends did not show significant main effects of FN or AG, nor significant FN \u0026times; AG interactions (weekday p_int = 0.590; weekend p_int = 0.513).\u003c/p\u003e\n\u003cp\u003e(Table 4)\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study examined associations between household size (FN) and cardiometabolic health across adulthood using nationally representative, survey-weighted data, integrating components of metabolic syndrome, blood biomarkers, and daily health behaviors. Overall, the findings suggest that household structure is not merely a descriptive demographic characteristic, but a contextual factor related to metabolic health. A key feature across domains was that associations were often age-dependent, indicating age-group (AG)- modulated effects rather than a uniform main effect of FN. This pattern has practical implications for public health surveillance and prevention: FN may help identify population subgroups whose behavioral exposures and metabolic profiles differ by life stage, and strategies may be more informative when tailored to age-specific living contexts.\u003c/p\u003e \u003cp\u003eAmong metabolic syndrome components, waist circumference and systolic blood pressure showed evidence of household-size associations alongside robust age effects, with clear FN \u0026times; AG interaction patterns (WC: p_FN\u0026thinsp;=\u0026thinsp;0.001; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SBP: p_FN\u0026thinsp;=\u0026thinsp;0.001; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results are consistent with the interpretation that the relationship between household context and adiposity- or blood pressure\u0026ndash;related risk may vary across the adult life course (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). From a life-course perspective, FN can proxy heterogeneous social and behavioral environments, yet the meaning of co-residence likely changes with age. In younger and middle-aged adults, larger households may be linked to more structured routines, shared schedules, and more incidental activity through household tasks, whereas in older adults, FN may more often reflect caregiving arrangements, health-related selection into co-residence, or constraints associated with comorbidity and functional status (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). This aligns with social epidemiologic frameworks in which household structure reflects one dimension of social integration that may shape health through behavioral regulation, support, and access to resources, while operating differently across life stages (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Several additional metabolic components were also more consistent with interaction-first framing: fasting glucose and triglycerides showed strong age patterning with substantial interaction signals, and HDL-C exhibited a significant interaction despite a non-significant household main effect, reinforcing the need to avoid interpreting FN as uniformly protective or harmful across adulthood (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Notably, triglycerides showed a strong FN \u0026times; AG interaction (p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001) despite a non-significant overall FN main effect (p_FN\u0026thinsp;=\u0026thinsp;0.092), underscoring that household-size differences in TG are contingent on age group rather than uniform across adulthood.\u003c/p\u003e \u003cp\u003eBiomarker profiles further support the possibility that household context is linked with physiologic status through multiple pathways that vary by age (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Liver enzymes (AST and ALT) showed age-related increases, with ALT showing a significant FN \u0026times; AG interaction (p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001) while AST did not show evidence of interaction (p_int\u0026thinsp;=\u0026thinsp;0.274), which may reflect age-graded differences in lifestyle exposures (e.g., dietary patterns, adiposity distribution, alcohol-related behaviors) and preventive health engagement shaped by household environment (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Renal function markers also showed pronounced age-dependence with interaction patterns; BUN and creatinine increased with age, and creatinine additionally showed evidence suggestive of FN differences with age-contingent variation (creatinine p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For BUN, the FN \u0026times; AG interaction was statistically significant but modest (p_int\u0026thinsp;=\u0026thinsp;0.011), and the overall FN main effect was not significant, suggesting caution in interpreting age-contingent heterogeneity for BUN. These findings may be consistent with age- and household-dependent variation in hydration behaviors, dietary protein patterns, medication use, and muscle mass distribution, factors that can influence renal biomarkers and may not be fully captured by standard sociodemographic adjustments (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Interaction signals observed in endocrine-related markers, including vitamin D and thyroid indices (TSH and free thyroxine), may similarly reflect age-dependent differences in outdoor exposure, dietary/supplement behaviors, and healthcare access that vary by living context. However, thyroid indices did not show evidence of an FN \u0026times; AG interaction in the current models (TSH p_int\u0026thinsp;=\u0026thinsp;0.375; fT4 p_int\u0026thinsp;=\u0026thinsp;0.245), providing a useful boundary condition: FN does not appear to modify age-patterning uniformly across all endocrine markers. While these biomarkers are not specific to a single mechanism, their convergence with the broader interaction pattern suggests that household structure may be linked to cardiometabolic risk through multiple age-stratified pathways (\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBehavioral findings offer particularly actionable candidates for mechanisms linking household context to metabolic and biomarker outcomes. Sedentary behavior (SB) showed consistent evidence of both FN and age associations with a pronounced interaction pattern (p_FN\u0026thinsp;=\u0026thinsp;0.002; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the relationship between household context and prolonged sitting differs across age groups. In general, larger FN tended to correspond to lower sedentary time, especially among younger and middle-aged adults, which is conceptually consistent with the idea that co-residence may increase non-exercise activity through household tasks, caregiving responsibilities, and shared routines that interrupt prolonged sitting (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). However, the presence of strong interactions suggests that these associations may attenuate, reverse, or become more complex in older age groups, where functional capacity, chronic conditions, and health-related co-residence may play a larger role (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Physical activity outcomes showed domain-specific heterogeneity rather than a uniform interaction across all PA measures: significant FN \u0026times; AG interactions were evident for some domains/intensities (e.g., recreational vigorous PA, place-movement PA, and total PA), whereas occupational vigorous/moderate PA and recreational moderate PA did not consistently exhibit significant interactions. Leisure-time activity declined with age and exhibited a notable interaction signal (p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with the notion that household context may differentially influence leisure opportunities as individuals age (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Sleep duration did not show significant FN \u0026times; AG interactions on weekdays or weekends in the current models, suggesting that sleep duration\u0026mdash;at least as captured by self-reported measures here\u0026mdash;may not be a primary pathway linking FN to cardiometabolic profiles. Together, these behavioral results support an integrative interpretation in which FN is related to cardiometabolic outcomes partly through age-specific differences in movement and recovery-related behaviors\u0026mdash;particularly sedentary time, leisure-time activity, and sleep\u0026mdash;rather than through a single uniform pathway (\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings are relevant to public health in the context of demographic changes, including population aging and the growth of smaller household units in many societies (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Traditional metabolic syndrome prevention strategies have typically emphasized individual-level behavior modification (e.g., increasing physical activity, reducing sedentary time, and improving sleep) without explicitly accounting for the social environments that organize daily routines (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). The present results suggest that household structure may serve as a pragmatic contextual marker for stratifying prevention approaches, but they also indicate that translation to practice should be age-tailored (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). For working-age adults, household-related constraints and supports may operate through time availability, occupational exposures, and routine organization; for older adults, functional limitations, chronic disease management demands, and caregiving dynamics may be more salient. Accordingly, interventions may be more effective when they provide age-appropriate opportunities to reduce sedentary time, support routine physical activity, and stabilize recovery behaviors (including sleep) in ways that align with the lived realities of different household contexts (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Although sleep duration did not show significant FN \u0026times; AG interactions in this analysis, sleep remains a core cardiometabolic behavior; thus, intervention design can still consider sleep as a general recovery target without implying household-specific effects in these data. Such programs need not replicate household environments; community-based initiatives, socially supported exercise opportunities, and time-structured lifestyle supports could provide external structure and accountability, especially for individuals whose living context offers fewer routine anchors (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral strengths enhance the interpretability of these findings for population health. Use of nationally representative data and survey-weighted analyses supports generalizability, and the concurrent assessment of behavioral, biological, and clinical indicators provides a broader view of plausible pathways than any single domain alone (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Nonetheless, limitations should be considered. The repeated cross-sectional design limits causal inference, and reverse causation cannot be ruled out (e.g., poorer health may influence household arrangements). FN was measured quantitatively and did not capture qualitative dimensions of relationships, social support, or caregiving intensity, which may help explain heterogeneity within household-size categories. Residual confounding by unmeasured or imperfectly measured factors may remain. Even so, the consistent age-dependent patterns across multiple domains suggest that household structure is not a trivial correlation, and it may capture meaningful aspects of living context relevant to cardiometabolic health.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study suggests that FN is associated with components of metabolic syndrome, blood biomarkers, and key health behaviors across adulthood, with a prominent pattern of age-related effect modification. Household structure may therefore be a useful contextual indicator in public health efforts to understand and prevent metabolic risk, particularly when prevention strategies are tailored to age-specific living contexts and to the behavioral exposures most consistently linked to the household environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The Korea National Health and Nutrition Examination Survey (KNHANES) was reviewed by the Institutional Review Board (IRB) of the Korea Disease Control and Prevention Agency (KDCA; formerly the Korea Centers for Disease Control and Prevention, KCDC) as applicable for each survey cycle, and written informed consent was obtained from all participants. For certain survey years, KNHANES was conducted without IRB review based on the IRB committee\u0026rsquo;s determination, as permissible under the Bioethics and Safety Act (Article 2(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)) and its Enforcement Rule (Article 2(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), subparagraph 1) for government-led research conducted for the public welfare. The IRB review process was resumed in 2018 to consider biospecimen collection and the provision of raw data to third parties. The KNHANES IRB approval numbers (or waiver status) for the cycles included in this study (2015\u0026ndash;2024) are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The present study is a secondary analysis of de-identified public-use data and was exempt from additional IRB review at the authors\u0026rsquo; institution.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. This study analyzed de-identified public-use data and does not contain any identifiable personal information.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Sungshin Women\u0026rsquo;s University Research Grant of 2025.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eES Sung conceived and designed the study, prepared the original draft, and contributed to writing, review, and editing. SH Bang conducted the investigation and performed the statistical analysis. ES Sung and SH Bang jointly interpreted the data.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the support provided by Sungshin Women\u0026rsquo;s University for conducting this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are publicly available from the Korea National Health and Nutrition Examination Survey (KNHANES) website, subject to the data access procedures and terms of use of the Korea Disease Control and Prevention Agency (KDCA).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited N. Department of economic and social affairs. Popul Div World Popul Projections to. 2019;2150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevelopment OfEC-oa. Society at a Glance 2021: OECD Social Indicators. 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BMC Public Health. 2022;22(1):476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM, Cleeman JI, Bairey Merz CN, Brewer HB, Clark LT, Hunninghake DB, et al. Implications of recent clinical trials for the national cholesterol education program adult treatment panel III guidelines. J Am Coll Cardiol. 2004;44(3):720\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation. 2004;109(3):433\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo MH, Lee W-Y, Kim SS, Kang J-H, Kang J-H, Kim KK, et al. 2018 Korean society for the study of obesity guideline for the management of obesity in Korea. 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J open source Softw. 2019;4(43):1686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y, Yeung W-JJ. Cohort matters: The relationships between living arrangements and psychological health from the Korean Longitudinal Study of Aging (KLoSA). J Affect Disord. 2022;299:652\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao M, Song K, Zhao Z. The nexus between living arrangements and health in older adults: behavioral and psychological pathways. Crit Public Health. 2025;35(1):2581343.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerkman LF, Glass T, Social Integration S, Networks. In: Berkman LFPD, Kawachi IMDPD, editors. Social Support, and Health. Social Epidemiology: Oxford University Press; 2000. p. 0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrimmins EM, Preston SH, Cohen B. Countries NRCPoUDTiLiH-I. The role of social networks and social integration. Explaining divergent levels of longevity in high-income countries. National Academies Press (US); 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim H-J, Shin M-S, Kim K-H, Jung M-H, Cho D-H, Lee J-H, et al. Metabolic syndrome awareness in the general Korean population: results from a nationwide survey. Korean J Intern Med. 2024;39(2):272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacRae C, Mercer SW, Abubakar E, Lawson A, Lone N, Rawlings A, et al. Impact of household size and co-resident multimorbidity on unplanned hospitalisation and transition to care home. Nat Commun. 2025;16(1):1718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026auml;nnist\u0026ouml; V, Salomaa V, Jula A, Lundqvist A, M\u0026auml;nnist\u0026ouml; S, Perola M, et al. ALT levels, alcohol use, and metabolic risk factors have prognostic relevance for liver-related outcomes in the general population. JHEP Rep. 2024;6(10):101172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulus MC, Melchers M, van Es A, Kouw IWK, van Zanten ARH. The urea-to-creatinine ratio as an emerging biomarker in critical care: a scoping review and meta-analysis. Crit Care. 2025;29(1):175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvila M, Mora S\u0026aacute;nchez MG, Bernal Amador AS, Paniagua R. The metabolism of creatinine and its usefulness to evaluate kidney function and body composition in clinical practice. Biomolecules. 2025;15(1):41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamefors M, Tengblad A, \u0026Ouml;stgren CJ. Sunlight exposure and vitamin D levels in older people-an intervention study in Swedish nursing homes. J Nutr health aging. 2020;24(10):1047\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen DW, Ospina NS, Haymart MR. Social determinants of health and disparities in thyroid care. 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EClinicalMedicine. 2022;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Guidelines on physical activity, sedentary behaviour and sleep for children under 5 years of age. World Health Organization; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeiris CL, Taylor NF, Verswijveren SJ. Associations of 24-hr Movement Behaviors With Cardiometabolic Risk Factors and Metabolic Syndrome in Adults Receiving Outpatient Rehabilitation: A Compositional Time-Use Analysis. J Aging Phys Act. 2024;33(3):262\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiquelme R, Rezende LF, Marques A, Drenowatz C, Ferrari G. Association between 24-h movement guidelines and cardiometabolic health in Chilean adults. Sci Rep. 2022;12(1):5805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. World health statistics 2025: monitoring health for the SDGs. Sustainable Development Goals: World Health Organization; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobrowolski P, Prejbisz A, Kuryłowicz A, Baska A, Burchardt P, Chlebus K, et al. Metabolic syndrome\u0026mdash;A new definition and management guidelines. Arterial Hypertens. 2022;26(3):99\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Guidance on person-centred assessment and pathways in primary care. World Health Organization; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd-Jones DM, Allen NB, Anderson CA, Black T, Brewer LC, Foraker RE, et al. Life\u0026rsquo;s essential 8: updating and enhancing the American Heart Association\u0026rsquo;s construct of cardiovascular health: a presidential advisory from the American Heart Association. Circulation. 2022;146(5):e18\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinhoff P, Reiner A. Physical activity and functional social support in community-dwelling older adults: a scoping review. BMC Public Health. 2024;24(1):1355.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuGoff EH, Schuler M, Stuart EA. Generalizing observational study results: applying propensity score methods to complex surveys. Health Serv Res. 2014;49(1):284\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Descriptive characteristics of the participants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN1\u003cbr\u003e\u0026nbsp;(N=4,302)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=10,620)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=8,960)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN4\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=8,331)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; FN5 (N=2,826)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[years]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e25.12 \u0026plusmn; 2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e25.06 \u0026plusmn; 2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.26 \u0026plusmn; 2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.67 \u0026plusmn; 2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.17 \u0026plusmn; 3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e35.92 \u0026plusmn; 4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e35.97 \u0026plusmn; 4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e36.78 \u0026plusmn; 4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e38.26 \u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e38.48 \u0026plusmn; 3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e52.45 \u0026plusmn; 4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e53.34 \u0026plusmn; 4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e52.24 \u0026plusmn; 4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e50.72 \u0026plusmn; 4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e50.45 \u0026plusmn; 4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e67.38 \u0026plusmn; 4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.76 \u0026plusmn; 4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e65.44 \u0026plusmn; 4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e64.92 \u0026plusmn; 4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e67.24 \u0026plusmn; 4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.39 \u0026plusmn; 1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.05 \u0026plusmn; 1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.09 \u0026plusmn; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.32 \u0026plusmn; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.00 \u0026plusmn; 1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[cm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e170.14 \u0026plusmn; 8.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e168.45 \u0026plusmn; 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e168.66 \u0026plusmn; 8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e168.97 \u0026plusmn; 8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e167.26 \u0026plusmn; 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e171.24 \u0026plusmn; 8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e169.23 \u0026plusmn; 8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e168.20 \u0026plusmn; 8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e167.57 \u0026plusmn; 8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e167.11 \u0026plusmn; 8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e165.25 \u0026plusmn; 8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e163.32 \u0026plusmn; 8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e164.07 \u0026plusmn; 8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e165.53 \u0026plusmn; 8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e165.25 \u0026plusmn; 8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e158.52 \u0026plusmn; 8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e160.42 \u0026plusmn; 8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e160.99 \u0026plusmn; 8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e161.33 \u0026plusmn; 8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e158.75 \u0026plusmn; 8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e153.31 \u0026plusmn; 8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e158.82 \u0026plusmn; 8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e156.64 \u0026plusmn; 9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e155.48 \u0026plusmn; 9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e156.91 \u0026plusmn; 8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[kg]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e68.45 \u0026plusmn; 14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e67.18 \u0026plusmn; 15.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.91 \u0026plusmn; 15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.90 \u0026plusmn; 15.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e63.32 \u0026plusmn; 14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e72.90 \u0026plusmn; 15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e69.68 \u0026plusmn; 15.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e68.64 \u0026plusmn; 14.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e67.82 \u0026plusmn; 14.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e68.16 \u0026plusmn; 14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.67 \u0026plusmn; 12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e64.65 \u0026plusmn; 11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e65.17 \u0026plusmn; 11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.80 \u0026plusmn; 11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e67.17 \u0026plusmn; 12.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e61.21 \u0026plusmn; 10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e62.65 \u0026plusmn; 10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e63.00 \u0026plusmn; 10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e63.77 \u0026plusmn; 10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e61.39 \u0026plusmn; 9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e56.55 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e60.02 \u0026plusmn; 9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e58.01 \u0026plusmn; 10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e57.95 \u0026plusmn; 10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e59.22 \u0026plusmn; 9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003cbr\u003e [kg/m\u003csup\u003e2\u003c/sup\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.50 \u0026plusmn; 4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.52 \u0026plusmn; 4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.35 \u0026plusmn; 4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.26 \u0026plusmn; 4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e22.49 \u0026plusmn; 4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.72 \u0026plusmn; 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.16 \u0026plusmn; 4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.09 \u0026plusmn; 4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.99 \u0026plusmn; 3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.25 \u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.30 \u0026plusmn; 3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.14 \u0026plusmn; 3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.12 \u0026plusmn; 3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.28 \u0026plusmn; 3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.49 \u0026plusmn; 3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.30 \u0026plusmn; 3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.29 \u0026plusmn; 3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.23 \u0026plusmn; 3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.44 \u0026plusmn; 3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.29 \u0026plusmn; 3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.02 \u0026plusmn; 3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.73 \u0026plusmn; 3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.62 \u0026plusmn; 3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.81 \u0026plusmn; 3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24.03 \u0026plusmn; 3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 601px;\"\u003e\n \u003cp\u003eValues are presented as means \u0026plusmn; standard errors (SE). All estimates are survey-weighted. AG indicates age group; FN indicates household size; BMI, body mass index. No statistical comparisons were performed for descriptive characteristics.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Age group (AG) and household size (FN) in metabolic syndrome components (MetS)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFN5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_FN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep_int\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003cp\u003e[cm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e79.03 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e79.49 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e78.57 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e78.34 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e76.96 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.16 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e82.34 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e82.47 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e82.57 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.19 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.88 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.87 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.52 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e83.44 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e84.08 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e87.12 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86.11 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e85.73 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86.14 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86.78 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e88.26 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86.17 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e86.27 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e87.29 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e87.38 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003cp\u003e[mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e111.72 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e110.30 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e110.78 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e110.72 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.43 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e114.26 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e112.18 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e112.40 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e112.12 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e112.90 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e120.59 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e119.69 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e118.27 \u0026plusmn; 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e117.61 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e117.43 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e127.33 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e125.90 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e124.69 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e125.30 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e126.71 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e133.83 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e129.32 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e131.29 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e135.80 \u0026plusmn; 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e132.14 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003cp\u003e[mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.74 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.14 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.05 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.45 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e70.44 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e76.47 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e74.77 \u0026plusmn; 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.03 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.10 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.72 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e79.25 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e77.97 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e77.96 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e78.13 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e78.30 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.47 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.13 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.46 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e76.87 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.74 \u0026plusmn; 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e72.93 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e70.21 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e70.44 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.97 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e71.62 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eFG\u003c/p\u003e\n \u003cp\u003e[mg/dL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.32 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e91.11 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.45 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.00 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90.35 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e96.53 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e96.02 \u0026plusmn; 0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e96.47 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e96.69 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e97.98 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107.83 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e104.66 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e102.56 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e102.14 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e104.21 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.65 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107.48 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107.47 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107.45 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.00 \u0026plusmn; 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e110.82 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e108.26 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107.56 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.24 \u0026plusmn; 1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.78 \u0026plusmn; 2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003cp\u003e[mg/dL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e102.52 \u0026plusmn; 2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e115.77 \u0026plusmn; 3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e110.67 \u0026plusmn; 2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e109.23 \u0026plusmn; 1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e105.67 \u0026plusmn; 4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e147.59 \u0026plusmn; 5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e149.39 \u0026plusmn; 3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e142.87 \u0026plusmn; 2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e141.32 \u0026plusmn; 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e154.74 \u0026plusmn; 4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e163.87 \u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e160.33 \u0026plusmn; 2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e151.25 \u0026plusmn; 1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e156.70 \u0026plusmn; 2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e161.71 \u0026plusmn; 4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e151.96 \u0026plusmn; 2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e147.22 \u0026plusmn; 1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e148.86 \u0026plusmn; 2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e156.27 \u0026plusmn; 3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e157.04 \u0026plusmn; 5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e152.11 \u0026plusmn; 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e136.86 \u0026plusmn; 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e140.56 \u0026plusmn; 3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e148.45 \u0026plusmn; 5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e147.84 \u0026plusmn; 6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 52px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003cp\u003e[mg/dL]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e55.39 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e54.48 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e53.95 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e54.09 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e53.80 \u0026plusmn; 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e53.78 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e53.31 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e52.52 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.86 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.47 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.80 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.41 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.81 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.29 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.79 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.03 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.24 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.47 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50.12 \u0026plusmn; 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e49.47 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e48.16 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e49.54 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e48.93 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e47.23 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e46.41 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 616px;\"\u003e\n \u003cp\u003eValues are presented as means \u0026plusmn; standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FG, fasting glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Blood biomarkers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFN4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;FN5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003ep_FN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep_age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003ep_int\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e20.75 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e21.30 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e20.59 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e20.60 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19.74 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.72 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.58 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.45 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.22 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.79 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.95 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.85 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.21 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23.57 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.53 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.23 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.44 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.06 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.25 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.83 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.37 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.51 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.48 \u0026plusmn; 0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23.99 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.72 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e21.32 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.90 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e21.34 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.15 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e20.00 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.18 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.18 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.55 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.51 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.54 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.64 \u0026plusmn; 0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.69 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.16 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.15 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e25.60 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24.02 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.98 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.58 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23.47 \u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23.98 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e21.69 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.64 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.94 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.77 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19.24 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.35 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.20 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.38 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.09 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.24 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.72 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.63 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.80 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.07 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.11 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.26 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.87 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.73 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.32 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.25 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.55 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.37 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.13 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.08 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.69 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.05 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.59 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.52 \u0026plusmn; 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.39 \u0026plusmn; 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.54 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.82 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.80 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.80 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.82 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.84 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.86 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.90 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.89 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.89 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.93 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.66 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.85 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.92 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.90 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e7.02 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.71 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.80 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.59 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.52 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.53 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.64 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.46 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.42 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.45 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.60 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.63 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.45 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.48 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.53 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.67 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.78 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.64 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.68 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e7.02 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.78 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.81 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.79 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.82 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.82 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.84 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.76 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.74 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.74 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.72 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.71 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.63 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.64 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.64 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.66 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.68 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.50 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.50 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.53 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.54 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.54 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.27 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.29 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.37 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4.37 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.45 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.44 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.45 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.48 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.45 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.41 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.34 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.29 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.20 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.17 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.29 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.31 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.22 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.23 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.30 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.04 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.04 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.11 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.14 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.13 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.75 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.36 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.44 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.66 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.70 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.73 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.62 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.75 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.84 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.78 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.55 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.35 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.21 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.00 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.97 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.08 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.15 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.96 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.02 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.17 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.41 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.40 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.61 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.69 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e42.72 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e41.67 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e40.61 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e40.86 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e41.44 \u0026plusmn; 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e41.69 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e275.62 \u0026plusmn; 2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e276.92 \u0026plusmn; 2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e275.07 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e276.86 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e279.19 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.024*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e277.75 \u0026plusmn; 2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e275.05 \u0026plusmn; 1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e272.65 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e271.09 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e269.80 \u0026plusmn; 1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e266.13 \u0026plusmn; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e258.77 \u0026plusmn; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e260.74 \u0026plusmn; 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e263.36 \u0026plusmn; 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e265.57 \u0026plusmn; 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e249.11 \u0026plusmn; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e244.23 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e247.05 \u0026plusmn; 1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e249.11 \u0026plusmn; 2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e245.42 \u0026plusmn; 3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e239.97 \u0026plusmn; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e237.12 \u0026plusmn; 1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e236.80 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e252.68 \u0026plusmn; 6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e248.34 \u0026plusmn; 5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eVitD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e11.50 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.85 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.90 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.62 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13.22 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.047*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.64 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14.63 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e15.08 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e15.49 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.14 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.36 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.95 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.46 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.26 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.27 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17.29 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.13 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.81 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e16.86 \u0026plusmn; 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19.58 \u0026plusmn; 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.63 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5.42 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.34 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.44 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.68 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.59 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.55 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.37 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.45 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.38 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.55 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.62 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.57 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.87 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.61 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.36 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.73 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.56 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.69 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.74 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.48 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.60 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.44 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e8.34 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 60px;\"\u003e\n \u003cp\u003efT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.33 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.31 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.31 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.33 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.29 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.18 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.18 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.17 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.23 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.17 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.98 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 601px;\"\u003e\n \u003cp\u003eValues are presented as means \u0026plusmn; standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. Dashes (--) indicate insufficient sample size for reliable estimation. AST, aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; CRE, creatinine; WBC, white blood cell count; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; VitD, 25-hydroxyvitamin D; TSH, thyroid-stimulating hormone; fT4, free thyroxine. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Physical activity, sedentary time, and sleep duration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFN_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFN_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFN_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFN_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;FN_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003ep_FN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ep_age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003ep_int\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOVPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e378.56 \u0026plusmn; 122.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e330.41 \u0026plusmn; 78.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e300.64 \u0026plusmn; 61.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e342.59 \u0026plusmn; 72.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e273.25 \u0026plusmn; 80.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e239.22 \u0026plusmn; 62.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e251.09 \u0026plusmn; 60.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e251.17 \u0026plusmn; 42.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e335.90 \u0026plusmn; 47.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e414.83 \u0026plusmn; 68.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e443.51 \u0026plusmn; 191.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e409.10 \u0026plusmn; 94.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e310.11 \u0026plusmn; 48.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e350.35 \u0026plusmn; 125.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e361.95 \u0026plusmn; 119.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e355.51 \u0026plusmn; 156.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e331.93 \u0026plusmn; 54.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e238.63 \u0026plusmn; 54.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e557.03 \u0026plusmn; 180.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e401.53 \u0026plusmn; 140.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e131.51 \u0026plusmn; 32.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e160.92 \u0026plusmn; 61.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.53 \u0026plusmn; 48.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1208.08 \u0026plusmn; 517.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOMPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e386.26 \u0026plusmn; 57.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e367.14 \u0026plusmn; 37.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e443.42 \u0026plusmn; 37.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e379.06 \u0026plusmn; 53.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e459.91 \u0026plusmn; 61.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e435.74 \u0026plusmn; 45.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e435.86 \u0026plusmn; 40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e487.72 \u0026plusmn; 39.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e451.51 \u0026plusmn; 29.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e579.12 \u0026plusmn; 57.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e323.61 \u0026plusmn; 38.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e446.07 \u0026plusmn; 42.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e438.29 \u0026plusmn; 39.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e472.33 \u0026plusmn; 37.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e373.27 \u0026plusmn; 47.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e282.04 \u0026plusmn; 36.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e411.99 \u0026plusmn; 40.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e390.86 \u0026plusmn; 50.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e235.96 \u0026plusmn; 37.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e429.97 \u0026plusmn; 151.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e382.50 \u0026plusmn; 92.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e333.31 \u0026plusmn; 75.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e256.25 \u0026plusmn; 71.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e494.82 \u0026plusmn; 151.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e715.20 \u0026plusmn; 507.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePMPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e792.45 \u0026plusmn; 30.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e799.73 \u0026plusmn; 34.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e844.83 \u0026plusmn; 24.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e852.86 \u0026plusmn; 25.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e837.09 \u0026plusmn; 41.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e790.98 \u0026plusmn; 46.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e757.73 \u0026plusmn; 28.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e772.76 \u0026plusmn; 20.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e834.13 \u0026plusmn; 25.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e922.28 \u0026plusmn; 46.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e841.37 \u0026plusmn; 38.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e816.31 \u0026plusmn; 22.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e844.92 \u0026plusmn; 20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e788.79 \u0026plusmn; 20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e850.52 \u0026plusmn; 48.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e847.65 \u0026plusmn; 28.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e810.29 \u0026plusmn; 18.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e851.85 \u0026plusmn; 27.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e886.19 \u0026plusmn; 56.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e771.08 \u0026plusmn; 42.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e687.68 \u0026plusmn; 25.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e762.25 \u0026plusmn; 30.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e636.54 \u0026plusmn; 44.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e653.34 \u0026plusmn; 63.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e828.08 \u0026plusmn; 152.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRVPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1163.51 \u0026plusmn; 77.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1366.57 \u0026plusmn; 108.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1524.82 \u0026plusmn; 96.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1337.97 \u0026plusmn; 70.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1169.41 \u0026plusmn; 122.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1046.49 \u0026plusmn; 71.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e993.73 \u0026plusmn; 66.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1087.05 \u0026plusmn; 61.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1209.38 \u0026plusmn; 66.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1210.92 \u0026plusmn; 81.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1302.25 \u0026plusmn; 139.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1408.14 \u0026plusmn; 73.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1223.66 \u0026plusmn; 69.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1085.09 \u0026plusmn; 56.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1100.90 \u0026plusmn; 109.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1203.97 \u0026plusmn; 150.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1536.53 \u0026plusmn; 101.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1239.10 \u0026plusmn; 119.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1388.05 \u0026plusmn; 220.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1756.11 \u0026plusmn; 239.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e935.32 \u0026plusmn; 207.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1513.53 \u0026plusmn; 332.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1665.10 \u0026plusmn; 74.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1615.68 \u0026plusmn; 44.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e552.44 \u0026plusmn; 45.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRMPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e614.96 \u0026plusmn; 30.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e645.87 \u0026plusmn; 32.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e682.66 \u0026plusmn; 29.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e720.40 \u0026plusmn; 25.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e725.54 \u0026plusmn; 49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e570.91 \u0026plusmn; 31.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e576.23 \u0026plusmn; 24.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e594.15 \u0026plusmn; 26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e561.31 \u0026plusmn; 16.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e609.43 \u0026plusmn; 29.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e627.83 \u0026plusmn; 38.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e717.53 \u0026plusmn; 23.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e680.85 \u0026plusmn; 20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e619.47 \u0026plusmn; 19.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e629.76 \u0026plusmn; 43.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e833.27 \u0026plusmn; 48.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e847.45 \u0026plusmn; 24.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e739.91 \u0026plusmn; 32.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e781.98 \u0026plusmn; 71.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e695.80 \u0026plusmn; 75.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e784.06 \u0026plusmn; 67.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e794.19 \u0026plusmn; 45.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e634.33 \u0026plusmn; 76.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e805.12 \u0026plusmn; 221.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1105.70 \u0026plusmn; 303.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1356.29 \u0026plusmn; 53.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1444.07 \u0026plusmn; 70.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1474.40 \u0026plusmn; 46.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1439.57 \u0026plusmn; 42.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1357.01 \u0026plusmn; 67.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1172.12 \u0026plusmn; 53.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1108.53 \u0026plusmn; 37.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1114.46 \u0026plusmn; 29.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1179.50 \u0026plusmn; 31.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1319.63 \u0026plusmn; 53.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1094.52 \u0026plusmn; 46.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1192.09 \u0026plusmn; 29.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1185.34 \u0026plusmn; 29.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1111.35 \u0026plusmn; 26.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1121.83 \u0026plusmn; 53.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1046.11 \u0026plusmn; 32.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1074.36 \u0026plusmn; 22.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1041.85 \u0026plusmn; 32.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1116.64 \u0026plusmn; 63.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e968.53 \u0026plusmn; 52.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e813.19 \u0026plusmn; 31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e865.00 \u0026plusmn; 36.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e666.54 \u0026plusmn; 48.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e789.38 \u0026plusmn; 82.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e990.04 \u0026plusmn; 169.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e597.81 \u0026plusmn; 8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e573.18 \u0026plusmn; 8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e566.20 \u0026plusmn; 5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e577.55 \u0026plusmn; 5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e548.05 \u0026plusmn; 10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e562.86 \u0026plusmn; 8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e548.52 \u0026plusmn; 6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e515.31 \u0026plusmn; 4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e492.28 \u0026plusmn; 4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e456.18 \u0026plusmn; 7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e488.46 \u0026plusmn; 8.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e451.15 \u0026plusmn; 4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e468.51 \u0026plusmn; 4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e485.61 \u0026plusmn; 4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e463.60 \u0026plusmn; 8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e499.93 \u0026plusmn; 5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e456.91 \u0026plusmn; 3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e469.31 \u0026plusmn; 5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e482.22 \u0026plusmn; 9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e437.58 \u0026plusmn; 10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e587.69 \u0026plusmn; 7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e528.65 \u0026plusmn; 5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e569.50 \u0026plusmn; 11.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e545.96 \u0026plusmn; 18.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e565.99 \u0026plusmn; 19.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSTWK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e422.24 \u0026plusmn; 6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e424.68 \u0026plusmn; 5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e426.56 \u0026plusmn; 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e420.48 \u0026plusmn; 3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e436.12 \u0026plusmn; 5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e412.76 \u0026plusmn; 4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e415.56 \u0026plusmn; 3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e423.11 \u0026plusmn; 2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e417.28 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e414.38 \u0026plusmn; 2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e401.47 \u0026plusmn; 4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e411.07 \u0026plusmn; 2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e407.20 \u0026plusmn; 1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e399.43 \u0026plusmn; 1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e397.71 \u0026plusmn; 3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e411.34 \u0026plusmn; 11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e417.53 \u0026plusmn; 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e407.06 \u0026plusmn; 3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e408.39 \u0026plusmn; 4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e419.52 \u0026plusmn; 13.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e416.77 \u0026plusmn; 8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e435.99 \u0026plusmn; 6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e427.45 \u0026plusmn; 7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e466.30 \u0026plusmn; 31.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e432.23 \u0026plusmn; 8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSTWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e479.30 \u0026plusmn; 7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e465.33 \u0026plusmn; 5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e466.67 \u0026plusmn; 3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e461.93 \u0026plusmn; 3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e468.07 \u0026plusmn; 6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e460.54 \u0026plusmn; 6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e453.36 \u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e449.11 \u0026plusmn; 2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e446.43 \u0026plusmn; 2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e439.39 \u0026plusmn; 3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e419.55 \u0026plusmn; 4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e430.01 \u0026plusmn; 2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e430.70 \u0026plusmn; 2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e430.24 \u0026plusmn; 2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e421.61 \u0026plusmn; 4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e418.96 \u0026plusmn; 11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e426.53 \u0026plusmn; 2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e418.56 \u0026plusmn; 3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e418.03 \u0026plusmn; 5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e431.04 \u0026plusmn; 14.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e416.66 \u0026plusmn; 8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e439.63 \u0026plusmn; 6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e431.54 \u0026plusmn; 7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e470.26 \u0026plusmn; 31.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e440.05 \u0026plusmn; 9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 601px;\"\u003e\n \u003cp\u003eValues are presented as means \u0026plusmn; standard errors (SE). All estimates are survey-weighted and derived from multivariable models. p_FN indicates the main effect of household size; p_age indicates the main effect of age group; p_int indicates the interaction effect between household size and age group. Models were adjusted for sex, smoking status, alcohol consumption, income level, and education. Physical activity variables are expressed as minutes per week. OVPA, occupational vigorous physical activity; OMPA, occupational moderate physical activity; RVPA, recreational vigorous physical activity; RMPA, recreational moderate physical activity; PMPA, place movement physical activity; TPA, total physical activity; SB, sedentary time; STWK, sleep time on weekdays; STWD, sleep time on weekends. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Household size, Physical activity, Metabolic syndrome, Sedentary behavior, Life-course epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-8857205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8857205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHousehold structure is a salient social determinant that may shape cardiometabolic risk through daily routines, resource access, and health-related behaviors. However, the extant evidence remains limited in scope, particularly regarding whether associations between household size and cardiometabolic health differ across the lifespan. We examined age-dependent associations between household size and cardiometabolic outcomes using nationally representative survey data, integrating components of metabolic syndrome, blood biomarkers, and daily health behaviors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA repeated cross-sectional analysis of nationally representative health and nutrition survey data was conducted, with data collected annually from 2015 to 2024. The study population comprised adults aged\u0026thinsp;\u0026ge;\u0026thinsp;19 years who provided complete information on household size, cardiometabolic outcomes, health behaviors, and covariates. Household size (FN) was categorized (e.g., 1, 2, 3, 4, \u0026ge;\u0026thinsp;5 members), and age group (AG) was modeled categorically. To this end, survey-weighted regression models were employed to estimate associations of FN with metabolic syndrome components (including waist circumference and blood pressure), blood biomarkers (liver enzymes, renal function, hematologic and endocrine-related markers), and behaviors (physical activity domains, sedentary time, and sleep duration), testing FN\u0026times;AG interactions. The models were adjusted for key sociodemographic and health-related covariates, and complex sampling was accounted for (weights, strata, and primary sampling units).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAcross various domains, the associations between household size and cardiometabolic indicators were frequently age-dependent rather than uniform. Among the components of metabolic syndrome, waist circumference and systolic blood pressure exhibited evidence of household-size associations, in conjunction with pronounced age effects, manifesting distinct FN\u0026times;AG interaction patterns (waist circumference: p_FN\u0026thinsp;=\u0026thinsp;0.003; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001; systolic blood pressure: p_FN\u0026thinsp;=\u0026thinsp;0.022; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The data revealed interaction-dominant patterns in several additional components, including fasting glucose, triglycerides, and HDL-C. This finding highlights the heterogeneity observed across different life stages. Furthermore, the investigation revealed that biomarkers showed age-dependent correlations with household size, including interaction signals for liver enzymes (AST, p_int\u0026thinsp;=\u0026thinsp;0.019; ALT, p_int\u0026thinsp;=\u0026thinsp;0.003) and renal biomarkers (creatinine, p_int\u0026thinsp;=\u0026thinsp;0.014). The behavioral findings yielded actionable candidates for pathways, demonstrating consistent associations between sedentary behavior and both FN and age, with a pronounced interaction (p_FN\u0026thinsp;=\u0026thinsp;0.002; p_int\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The study's findings indicated significant domain-specific heterogeneity in physical activity outcomes, with notable interactions observed across specific domains and intensity levels, such as recreational vigorous activity and total activity. However, these interactions were not consistently observed across all domains or intensity levels, including occupational moderate or vigorous activity and recreational moderate activity. The present models did not demonstrate any significant FN\u0026times;AG interactions for sleep duration.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHousehold size is not merely a demographic descriptor; rather, it is a contextual factor associated with cardiometabolic health in an age-dependent manner. The consistent interaction patterns observed across metabolic risk markers, biomarkers, and behaviors\u0026mdash;particularly sedentary time\u0026mdash;suggest that prevention and surveillance strategies may benefit from incorporating household structure as a pragmatic stratification marker and tailoring interventions to age-specific living contexts.\u003c/p\u003e","manuscriptTitle":"Household Size and Age-Modified Patterns of Cardiometabolic Biomarkers and Lifestyle Behaviors: KNHANES 2015–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 05:43:45","doi":"10.21203/rs.3.rs-8857205/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1209d1e4-9fdc-4d12-9511-bc996655200f","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T08:26:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 05:43:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8857205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8857205","identity":"rs-8857205","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}