The impact of Fetal and Infant Earthquake Stress Exposure on Adult Hyperlipidemia Subtypes: A Cross-Sectional Study

The impact of Fetal and Infant Earthquake Stress Exposure on Adult Hyperlipidemia Subtypes: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impact of Fetal and Infant Earthquake Stress Exposure on Adult Hyperlipidemia Subtypes: A Cross-Sectional Study Shuang Wang, Na Li, Xiaochuan Zhao, Yuehong Cheng, Yuanyuan Gao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9570569/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective : To investigate the association between fetal and infant exposure to catastrophic earthquake stress and adult-onset hyperlipidemia. Methods : This cross-sectional study utilized data from the Kailuan cohort, including 999 adults born between July 1975 and April 1977, who experienced the 1976 Tangshan earthquake at different developmental stages: first trimester (n=91), second trimester (n=124), third trimester (n=130), infancy (n=159), and an unexposed control group (n=495). Blood lipid profiles (total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C]) were measured, and dyslipidemia subtypes were diagnosed according to the 2016 Chinese guidelines. Multivariate logistic regression was performed to assess associations between early-life earthquake exposure and adult dyslipidemia, adjusting for potential confounders (sex, age, BMI, hypertension, smoking, alcohol use, diabetes). Results : Compared with the unexposed group, infantile exposure was significantly associated with hypercholesterolemia (OR=3.654, P =0.001). Late-gestation exposure significantly increased the risks of hypercholesterolemia (OR=2.267, P =0.043), hypertriglyceridemia (OR=2.156, P =0.036), and combined dyslipidemia (OR=5.007, P =0.026). Elevated BMI was an independent risk factor for hypertriglyceridemia and combined dyslipidemia, while heavy alcohol consumption increased the risks of hypercholesterolemia and hypertriglyceridemia (both P <0.05). No significant associations were observed for first- or second-trimester exposure or for low HDL-C. Conclusion : Prenatal stress during late gestation and stress during infancy are critical sensitive windows that differentially program adult lipid profiles. Late-gestation exposure is linked to a broader atherogenic lipid phenotype, whereas infantile exposure primarily elevates hypercholesterolemia risk. These findings support the DOHaD framework and highlight the need for early-life targeted cardiovascular risk surveillance in disaster-exposed populations. Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Early-life stress Fetal exposure Infantile exposure Dyslipidemia subtypes DOHaD Tangshan earthquake Cross-sectional study Introduction The mother gestates life, and every new life commences with conception. Throughout the nine-month intrauterine period, fetal development constitutes a highly ordered, multi-stage coordinated biological process encompassing cell differentiation, organogenesis, systemic maturation, and maternal–fetal interplay. This extraordinary process delineates the inception of each individual and is fundamentally critical to human growth [ 1 ] . In 1986, Barker DJ and colleagues published three seminal studies in The Lancet [ 2 ] . Their findings revealed that fetal malnutrition and low birth weight were associated with the onset of coronary heart disease in the same individuals five decades later. This discovery gave rise to the Developmental Origins of Health and Disease (DOHaD) hypothesis—a paradigm positing that environmental exposures (e.g., nutrition, stress, pollutants) during sensitive developmental windows in early life (including preconception, gestation, and early postnatal periods) exert profound long-term effects on health trajectories, thereby elevating the risk of chronic non-communicable diseases in adulthood [ 3 ] . This hypothesis was subsequently corroborated, most notably by the Dutch Famine Birth Cohort Study, which demonstrated that children exposed to famine during late gestation exhibited a higher propensity to develop glucose intolerance and diabetes in later life, whereas nutritional deprivation during early gestation augmented the risk of atherosclerosis and coronary artery disease [ 4 ] . Climate-related disasters—encompassing extreme weather events (e.g., floods, droughts) and geophysical hazards (e.g., earthquakes, tsunamis)—constitute pressing challenges in global public health. Such disasters not only inflict catastrophic damage upon infrastructure and ecosystems but also engender profound psychological repercussions at both individual and societal levels. A paradigmatic case is the 1976 Tangshan Earthquake (latitude 39°38′ N, longitude 118°11′ E), a magnitude 7.8 event on the Richter scale that attained a maximum seismic intensity of XI at a focal depth of 11 km. Occurring at 03:42 on July 28, this catastrophic earthquake resulted in over 240,000 fatalities and 160,000 severe injuries, with direct economic losses exceeding 3 billion RMB (in 1976 terms), rendering it the deadliest seismic event of the 20th century. Our prior investigations have indicated that prenatal earthquake exposure significantly elevates the risk of diabetes and metabolic syndrome in adulthood, whereas infant exposure adversely affects visuospatial memory (as assessed by the Brief Visuospatial Memory Test–Revised, BVMT-R). Crucially, both exposure windows are associated with an increased risk of hypertension, and exposure during the second to third trimesters exacerbates neurocognitive deficits [ 5 – 9 ] . Nevertheless, the association between early-life earthquake stress and adult hyperlipidemia remains inadequately elucidated. Employing a population-based epidemiological cross-sectional design, the present study aims to examine the potential relationship between stress exposure during fetal and infant stages and the subsequent incidence of hyperlipidemia in adulthood. Materials and Methods 1. Study Population On July 28, 1976, a catastrophic earthquake measuring 7.8 on the Richter scale struck the city of Tangshan. The present study utilized the 1976 Tangshan Earthquake as a naturalistic stressor. Data were derived from the project "Brain Mechanisms Underlying the Impact of Severe Maternal Stress During Pregnancy on Offspring Neuropsychological Development in Adulthood," funded by the National Natural Science Foundation of China. Our data were sourced from the Kailuan Cohort Study. Initiated in 2006, the Kailuan Cohort Study encompasses approximately 150,000 employees and their family members affiliated with the Kailuan Group, with biennial health examinations and longitudinal follow-up data collection. All participants provided informed consent and signed written informed consent forms prior to inclusion. For this investigation, we selected individuals born between July 1975 and April 1977 (aged 36–39 years at the time of the 2014 survey). This specific age bracket was chosen because it represents early adulthood, a developmental window during which cardiometabolic disorders-including hyperlipidemia-first manifest clinically detectable features, preceding the peak incidence observed in middle age [ 10 ] . Examining this age cohort facilitates the examination of potential early-life programming effects on adult hyperlipidemia risk prior to the accumulation of substantial confounding factors attributable to prolonged exposure to adult lifestyle-related risk factors. As demonstrated by Srinivasan SR et al. in the Bogalusa Heart Study, childhood non-HDL-C levels exhibit the strongest correlation with lipid profiles observed at ages 30–40 years [ 11 ] . This study constitutes a cross-sectional survey conducted between January and December 2014, covering nine mining districts, three communities, and five affiliated units under the jurisdiction of Tangshan Kailuan Mining Group. A total of 1,187 eligible participants were enrolled and successfully completed the comprehensive examination battery. Trained and qualified investigators, adhering to rigorous standardized protocols, systematically collected comprehensive demographic and health-related information from the participants. Based on developmental stage at the time of the Tangshan Earthquake (magnitude = 7.8) on July 28, 1976, participants were stratified into five mutually exclusive cohorts: (1) First Trimester Exposure: Birth dates between January 29, 1977, and April 28, 1977 (gestational weeks 1–12). (2) Second Trimester Exposure: Birth dates between October 29, 1976, and January 28, 1977 (gestational weeks 13–28). (3) Third Trimester Exposure: Birth dates between July 29, 1976, and October 28, 1976 (gestational weeks 29–40). (4) Infant Exposure: Birth dates between July 29, 1975, and July 28, 1976 (infancy period). (5) Unexposed Control Group: Birth dates after April 28, 1977 (conception occurred subsequent to the disaster). Exclusion Criteria Participants meeting any of the following criteria were excluded from the analysis: (1) active pregnancy or lactation; (2) presence of acute systemic infection (e.g., pneumonia, sepsis) or perioperative complications (major trauma surgery within 3 × upper limit of normal, or estimated glomerular filtration rate < 30 mL/min/1.73 m²); (4) diagnosed psychiatric disorder (conforming to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria) or exposure to an acute life-threatening stressor within the preceding 6 months. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Hebei Medical University (Ethical Approval No. 2014005) and was conducted in strict adherence to the Declaration of Helsinki and the International Council for Harmonisation–Good Clinical Practice (ICH-GCP) guidelines. Written informed consent was obtained from all participants prior to enrollment. 2. Sample Size Estimation Utilizing the Kailuan Cohort Study population, participants were dichotomized into an exposed group and an unexposed control group based on prenatal earthquake exposure status. This study employed a cross-sectional design to compare the prevalence of hyperlipidemia between distinct exposure cohorts and the unexposed group. Sample size estimation was predicated on the following assumptions: Based on prior evidence, the prevalence of dyslipidemia among healthy individuals is approximately 28.5% [ 12 ] , the incidence risk ratio among adults experiencing post-traumatic stress disorder is of 1.432 (95% confidence interval: 1.287–1.592) [ 13 ] , yielding an estimated prevalence of approximately 40.8% in the exposed group. Sample size calculation was performed using the formula for comparing two independent proportions in a cross-sectional study, with parameters set as follows: two-tailed significance level α = 0.05 and statistical power of 80% (β = 0.2). Formula (comparison of two independent proportions with equal sample size allocation): $$\:n=\frac{({Z}_{\alpha\:/2}+{Z}_{\beta\:}{)}^{2}\times\:[{p}_{1}(1-{p}_{1})+{p}_{2}(1-{p}_{2})]}{({p}_{1}-{p}_{2}{)}^{2}}$$ Where p1 = 0.285 (prevalence in the control group),p2 = 0.408 (prevalence in the exposed group), Zα/2 = 1.96 (α = 0.05, two-tailed), and Zβ = 0.84 (statistical power = 80%). Result: A minimum of 234 participants per group was required. Assuming equal allocation across five groups (four exposure subgroups plus one control group), the minimum total sample size was calculated to be 1,170 participants. In the present study, a total of 1,187 participants met the inclusion criteria and submitted questionnaires. However, due to incomplete data or refusal to undergo further clinical examinations among a subset of individuals, the final analytic sample comprised 999 participants (504 in the exposed group and 495 in the control group). Although this figure is marginally below the estimated requirement, the sample was deemed sufficiently powered based on the following considerations: (1) the observed difference in prevalence between the actual exposed and control groups was substantial (12.3%), reflecting a pronounced effect size; (2) in multivariable logistic regression analyses, the odds ratios (ORs) for key exposure windows (infancy and third trimester) demonstrated statistical significance (OR range: 2.156–5.007); (3) the relatively large sample size of the control group (n = 495) enhanced the precision of the comparison. Consequently, the available sample size adequately supports the reliability and validity of the study conclusions. 3. Questionnaire 3.1 Data Collection Instruments A structured questionnaire was developed in accordance with the World Health Organization STEPwise Approach to Surveillance (STEPS) guidelines and validated through pilot testing (Cronbach's α = 0.82). The instrument encompassed three principal domains: Sociodemographic Characteristics:Core variables included: chronological age (calculated based on birth records), biological sex (male/female), educational attainment (categorized according to the International Standard Classification of Education 2011), marital status (legally registered), and household income (RMB/month, verified via payroll records), family bereavement. 3.2 Substance Use Definitions Tobacco Exposure:Current Smoker: Consumption of ≥ 1 cigarette per day for a continuous period of ≥ 6 months. Former Smoker: History of sustained smoking cessation exceeding 6 months. Alcohol Consumption Criteria (National Institute on Alcohol Abuse and Alcoholism [NIAAA] Classification): Moderate Drinking: ≤14 standard drinks per week for men (approx. 140 g ethanol) and ≤ 7 standard drinks per week for women. Heavy Drinking: >14 standard drinks per week or > 4 drinks per day for men; >7 drinks per week or > 3 drinks per day for women. Note A standard drink was defined as containing 14 grams of pure ethanol. 4. Study Design and Implementation Anthropometric measurements were obtained using a calibrated RGZ-120 weight scale. Participants were weighed while wearing lightweight clothing and without shoes or hats. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Waist circumference was measured at the narrowest point of the torso and recorded to the nearest 0.1 cm. Blood pressure was measured after participants had abstained from smoking, tea, and coffee for 30 minutes. Brachial artery blood pressure of the right arm was assessed using a calibrated mercury sphygmomanometer. Measurements were taken three times at 1–2-minute intervals, and the average of the three readings was recorded for analysis. Diagnostic Definitions:Hypertension: Defined as Systolic Blood Pressure (SBP) ≥ 140 mmHg and/or Diastolic Blood Pressure (DBP) ≥ 90 mmHg, or current use of antihypertensive medication. Diabetes Mellitus: Defined as Fasting Plasma Glucose (FPG) > 126 mg/dL (7.0 mmol/L), or current use of glucose-lowering medication or insulin therapy. 5. Data Collection Framework 5.1 Demographic and Behavioral Data A validated 15-item questionnaire was administered to collect the following parameters: Biometrics: Height (SECA 213 stadiometer), Weight (Tanita BC-418MA), BMI (kg/m²). Cardiovascular Indices: SBP/DBP (Omron HEM-7322, average of three readings). Lifestyle Factors: Smoking status (WHO definition: ≥1 cigarette/day for ≥ 6 months); Alcohol consumption (NIH criteria: >14 drinks/week for men; >7 drinks/week for women). 5.2 Laboratory Analysis Fasting venous blood samples were collected between 7:00 AM and 9:00 AM into 5 mL EDTA vacuum tubes (Insepack, ST750EK, Sekisui Medical Co., Ltd., Osaka, Japan). Samples were centrifuged at 3000 × g for 10 minutes at room temperature, and the supernatant was collected for the measurement of Fasting Plasma Glucose (FPG), Triglycerides (TG), Low-Density Lipoprotein Cholesterol (LDL-C), Total Cholesterol (TC), and High-Density Lipoprotein Cholesterol (HDL-C). FPG levels were determined using the hexokinase/glucose-6-phosphate dehydrogenase enzymatic method (Mind Bioengineering Co. Ltd., Shanghai, China). Concurrently, serum lipid profiles (TG, LDL-C, TC, and HDL-C) were measured using an automated biochemical analyzer (Hitachi 747, Tokyo, Japan) at the Central Laboratory of Kailuan Hospital. 6. Diagnostic Criteria Hyperlipidemia: Diagnosed in accordance with the 2016 Chinese Guidelines for the Prevention and Treatment of Dyslipidemia in Adults. Diagnosis required the fulfillment of any one of the following criteria: Total Cholesterol (TC) ≥ 6.2 mmol/L, Triglycerides (TG) ≥ 2.3 mmol/L, Low-Density Lipoprotein Cholesterol (LDL-C) ≥ 4.1 mmol/L, or High-Density Lipoprotein Cholesterol (HDL-C) < 1.0 mmol/L. Specific subtypes were further classified as: hypercholesterolemia, hypertriglyceridemia, low HDL-C cholesterolemia, and mixed dyslipidemia (concurrent elevation of both TC and TG). 7. Statistical Analysis All statistical analyses were performed using SPSS version 22.0 software (SPSS Inc., Chicago, IL, USA). Quantitative data are presented as mean ± standard deviation (SD). Comparisons of continuous variables between groups were conducted using One-Way Analysis of Variance (ANOVA). Where appropriate, the Kruskal–Wallis test or independent samples t-test was employed to compare continuous variables between exposed and unexposed participants. Chi-square (χ 2 ) tests were utilized to compare the prevalence rates of hypertension and hyperlipidemia among the groups. Multivariable logistic regression analysis was performed with the incidence of hyperlipidemia designated as the dependent variable. Independent covariates included hypertension, sex, age, degree of earthquake stress exposure across different gestational stages and infancy, body mass index, smoking history, alcohol consumption history, and experience of bereavement. A two-tailed P-value of < 0.05 was considered the threshold for statistical significance. Results 1. General Demographic Characteristics and Baseline Data Comparison of Participants A total of 1,187 participants met the inclusion criteria and submitted completed questionnaires. Of these, 999 questionnaires (84.2%) were deemed valid for subsequent analysis. The exposed group (n = 504) was further stratified into the following subgroups: first trimester exposure (n = 91), second trimester exposure (n = 124), third trimester exposure (n = 130), and infant exposure (n = 159). The unexposed group served as the control cohort (n = 495). Baseline sociodemographic characteristics of the participants are presented in detail in Table 1 . With the exception of chronological age (P < 0.001), no statistically significant differences were observed among the exposure subgroups (including the unexposed group) with respect to sex distribution (P = 0.26), educational attainment (P = 0.29), smoking history (P = 0.90), alcohol consumption patterns (P = 1.00), reported familial bereavement during the earthquake (P = 0.36), cumulative incidence of diabetes mellitus (P = 0.69), or cumulative incidence of hypertension (P = 0.17). Table 1 Demographic distribution and clinical characteristics of the longitudinal cohort (N = 999) Exposure to earthquake Non-exposure to earthquake χ 2 p Early gestation Mid gestation Late gestation Infant N 91 124 130 159 495 Sex(male), n(%) 81(89.0) 112(90.3) 118(90.8) 134(84.3) 421(85.1) 5.27 0.26 Education attainment, n(%) 5.35 0.29 Elementary ( 12 years) 27(29.7) 36(29.1) 31(23.9) 40(25.2) 166(33.5) Age, years(mean ± SD) 37.9 ± 0.25 38.7 ± 0.51 38.9 ± 0.97 39.0 ± 0.22 37.5 ± 0.93 937.66 <0.001 Earthquake-related bereavement, n(%) 18(19.8) 18(14.5) 28(21.5) 38(23.8) 109(22.0) 4.34 0.36 Diabetes mellitus, n(%) 2(2.2) 2(1.6) 2(1.5) 4(2.4) 6(1.2) 5.64 0.69 Hypertension 2(2.2) 4(3.2) 2(1.5) 4(2.4) 14(2.8) 6.36 0.17 Tobacco use, n(%) 3.49 0.90 Current smoker 47(51.6) 62(50.0) 61(46.9) 78(49.1) 239(48.3) Former smoker 6(6.6) 9(7.3) 14(10.8) 10(6.3) 50(10.1) Never smoked 38(41.8) 53(42.7) 55(42.3) 71(44.6) 206(41.6) Alcohol consumption, n(%) 4.63 1.00 Never drinker 25(27.5) 41(33.1) 39(30.0) 48(30.2) 141(28.5) Moderate drinker 50(54.9) 60(48.4) 66(50.8) 82(51.6) 252(50.9) Heavy drinker 16(17.6) 23(18.5) 25(19.2) 29(18.2) 102(20.6) 2. Anthropometric and Metabolic Characteristics The anthropometric and metabolic profiles of the study population are summarized in Table 2 . Compared with the unexposed group (5.10 ± 1.48 mmol/L), elevated mean total cholesterol (TC) levels were observed in the second trimester exposure group (5.17 ± 0.93 mmol/L), third trimester exposure group (5.27 ± 1.17 mmol/L), and infant exposure group (5.27 ± 1.18 mmol/L). Similarly, relative to the unexposed group (2.81 ± 1.14 mmol/L), higher mean low-density lipoprotein cholesterol (LDL-C) concentrations were documented in the second trimester exposure group (2.86 ± 0.78 mmol/L) and the infant exposure group (2.94 ± 0.82 mmol/L). Table 2 Anthropometric and metabolic characteristics of the study population (mean ± SD or median) Exposure to earthquake No exposure Early gestation Mid gestation Late gestation Infant N 91 124 130 159 495 BMI (kg/m 2 ) 24.99 ± 3.54 24.67 ± 3.10 25.71 ± 3.30 24.90 ± 3.30 25.45 ± 5.00 TC (mmol/L) 5.05 ± 1.02 5.17 ± 0.93 5.27 ± 1.17 5.27 ± 1.18 5.10 ± 1.48 TG (mmol/L) 1.4(0.8, 2.0) 1.2 (0.8, 2.2) 1.2 (0.8, 2.5) 1.2(0.8, 2.1) 1.3 (0.8, 2.0) HDL (mmol/L) 1.60 ± 0.35 1.67 ± 0.40 1.62 ± 0.33 1.69 ± 0.41 1.62 ± 0.38 LDL (mmol/L) 2.81 ± 0.73 2.86 ± 0.78 2.81 ± 0.83 2.94 ± 0.82 2.81 ± 1.14 FPG (mmol/L) 5.10 ± 1.54 5.12 ± 1.12 5.21 ± 1.15 5.16 ± 1.05 5.07 ± 1.32 SBP (mmHg) 123.5 ± 9.6 123.3 ± 10.5 121.5 ± 10.2 122.4 ± 9.7 120.2 ± 11.7 DBP (mmHg) 81.2 ± 8.1 81.3 ± 8.2 80.2 ± 7.1 80.5 ± 7.9 82.8 ± 9.1 3. Association Between Early-Life Earthquake Exposure and Adult Hyperlipidemia Multivariable logistic regression analyses were performed to examine the associations between early-life earthquake exposure and distinct dyslipidemia subtypes. The incidence of hypercholesterolemia, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C) cholesterolemia, and mixed dyslipidemia (concurrent elevation of total cholesterol [TC] and triglycerides [TG]) were designated as dependent variables. Independent covariates included hypertension, sex, age, earthquake stress exposure across specific gestational windows and infancy, body mass index (BMI), and family history of dyslipidemia. The detailed results are presented in Tables 3 – 6 . Infant exposure exhibited a statistically significant association with hypercholesterolemia (Odds Ratio [OR] = 3.654, 95% Confidence Interval [CI]: 1.759–7.589, P = 0.001). Third trimester exposure was significantly associated with an elevated risk of three distinct dyslipidemia subtypes: hypercholesterolemia (OR = 2.267, 95% CI: 1.025–5.012, P = 0.043), hypertriglyceridemia (OR = 2.156, 95% CI: 1.050–4.427, P = 0.036), and mixed dyslipidemia (OR = 5.007, 95% CI: 1.215–20.631, P = 0.026). Furthermore, elevated body mass index (BMI) was identified as an independent risk factor for both hypertriglyceridemia (OR = 1.154, 95% CI: 1.076–1.237, P = 0.031) and mixed dyslipidemia (OR = 1.149, 95% CI: 1.030–1.284, P = 0.013). In addition, heavy alcohol consumption significantly increased the risk of hypercholesterolemia (OR = 2.065, 95% CI: 0.965–4.420, P = 0.042) and hypertriglyceridemia (OR = 1.996, 95% CI: 1.030–3.868, P = 0.041). Table 3 Multivariate logistic regression analysis of risk factors of ypercholesterolemia Sex(male), n(%) B S.E. Walds p OR 95%CI 0.857 0.535 2.569 0.109 2.356 (0.826, 6.716) Age (per year) -0.038 0.128 0.087 0.768 0.963 (0.750, 1.236) BMI(per kg/m²) 0.033 0.042 0.614 0.433 1.033 (0.952, 1.122) Hypertension -0.751 0.581 1.674 0.196 0.472 (0.151, 1.472) Exposure timing(Ref: no exposed) 12.822 0.012 Early gestation 0.660 0.423 2.433 0.119 1.935 (0.844, 4.434) Mid gestation 0.448 0.444 1.017 0.313 1.565 (0.655, 3.740) Late gestation 0.818 0.405 4.087 0.043* 2.267 (1.025, 5.012) Infant 1.296 0.373 12.075 0.001* 3.654 (1.759, 7.589) Smoking status(Ref: never) 0.275 0.872 Light smoker 0.234 0.447 0.272 0.602 1.263 (0.525, 3.036) Heavy smoker 0.071 0.288 0.061 0.804 1.074 (0.610, 1.890) Alcohol intake(Ref: never) 3.856 0.277 Moderate drinker 0.237 0.326 0.529 0.467 1.268 (0.669, 2.403) Heavy drinker 0.725 0.388 3.491 0.042* 2.065 (0.965, 4.420) Diabetes -1.504 0.782 3.702 0.054 0.222 (0.048, 1.030) Bereavement 0.038 0.301 0.016 0.900 1.039 (0.575, 1.875) Constant -1.996 4.812 0.167 0.683 0.140 Note: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P > 0.05. Table 4 Multivariate logistic regression analysis of risk factors ohypertriglyceridemia Sex(male), n(%) B S.E. Walds p OR 95%CI 1.041 0.773 1.815 0.178 2.832 (0.623, 12.875) Age -0.024 0.103 0.055 0.815 0.976 (0.799, 1.194) BMI(per kg/m²) 0.143 0.036 16.255 0.031* 1.154 (1.076, 1.237) Hypertension 0.240 0.807 0.089 0.766 1.271 (0.262, 6.182) Exposure timing(Ref: no exposed) 5.387 0.250 Early gestation 0.189 0.404 0.219 0.640 1.208 (0.547, 2.666) Mid gestation 0.142 0.416 0.117 0.732 1.153 (0.511, 2.604) Late gestation 0.768 0.367 4.377 0.036* 2.156 (1.050, 4.427) Infant -0.037 0.399 0.009 0.926 0.963 (0.441, 2.106) Smoking status(Ref: never) 0.414 0.813 Light smoker -0.353 0.493 0.513 0.474 0.703 (0.268, 1.845) Heavy smoker 0.059 0.278 0.045 0.831 1.061 (0.615, 1.831) Alcohol intake(Ref: never) 3.502 0.174 Moderate drinker 0.612 0.401 2.322 0.128 1.844 (0.839, 4.050) Heavy drinker 0.691 0.338 4.194 0.041* 1.996 (1.030, 3.868) Diabetes -1.761 0.952 3.421 0.064 0.172 (0.027, 1.111) Bereavement 0.347 0.317 1.198 0.274 1.415 (0.760, 2.633) Constant -4.597 4.171 1.215 0.270 0.010 Note: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P > 0.05. Table 5 Multivariate logistic regression analysis of risk factors of low HDL-C cholesterol Age B S.E. Walds p OR 95%CI 0.035 0.300 0.013 0.908 1.035 (0.574, 1.866) BMI(per kg/m²) 0.065 0.062 1.101 0.294 1.067 (0.945, 1.205) Exposure timing(Ref: no exposed) 1.247 0.870 Mid gestation -0.673 1.155 0.339 0.560 0.510 (0.053, 4.913) Late gestation 0.121 0.952 0.016 0.899 1.129 (0.174, 7.299) Infant 0.541 0.806 0.452 0.502 1.718 (0.354, 8.334) Smoking status(Ref: never) 1.141 0.565 Light smoker 0.742 0.694 1.141 0.285 2.099 (0.538, 8.184) Alcohol intake(Ref: never) 0.433 0.805 Moderate drinker 0.408 0.933 0.192 0.662 1.504 (0.242, 9.358) Heavy drinker 0.538 0.821 0.430 0.512 1.713 (0.343, 8.561) Bereavement 1.312 1.066 1.515 0.218 3.714 (0.460, 30.008) Constant -1.892 1.205 3.526 0.520 0.146 Due to complete separation issues, stable estimates could not be obtained for variables such as gender. Table 6 Multivariate logistic regression analysis of risk factors combined dyslipidemia Sex(male), n(%) B S.E. Walds p OR 95%CI 0.752 0.421 3.192 0.074 2.121 (0.929, 4.847) Age 0.110 0.450 0.059 0.807 1.116 (0.462, 2.692) BMI(per kg/m²) 0.139 0.056 6.157 0.013* 1.149 (1.030, 1.284) Hypertension 0.498 1.677 0.088 0.767 1.645 (0.062, 43.990) Exposure timing(Ref: no exposed) 5.462 0.243 Early gestation 0.905 0.918 0.973 0.324 2.472 (0.409, 14.929) Mid gestation 1.272 0.825 2.378 0.123 3.569 (0.708, 17.782) Late gestation 1.611 0.722 4.971 0.026* 5.007 (1.215, 20.631) Infant 1.275 0.757 2.840 0.092 3.579 (0.812, 15.771) Smoking status(Ref: never) 1.309 0.520 Light smoker -0.549 0.548 1.005 0.316 0.577 (0.197, 1.690) Heavy smoker -0.865 1.109 0.609 0.435 0.421 (0.048, 3.700) Alcohol intake(Ref: never) 0.907 0.635 Moderate drinker 0.554 0.858 0.418 0.518 1.741 (0.324, 9.352) Heavy drinker 0.656 0.689 0.906 0.341 1.927 (0.499, 7.441) Diabetes -2.074 1.719 1.455 0.228 0.126 (0.004, 3.654) Bereavement 0.902 0.809 1.243 0.265 2.464 (0.505, 12.204) Constant -3.215 1.892 2.567 0.867 0.426 Note: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P > 0.05. Discussion The present study, leveraging the Kailuan Cohort Study and utilizing the 1976 Tangshan Earthquake as a natural experimental model, constitutes the first systematic investigation of the association between fetal and infant exposure to catastrophic stress and the subsequent risk of adult hyperlipidemia and its constituent subtypes. Our principal findings are as follows: the third trimester of gestation and infancy represent critical windows of vulnerability to earthquake stress exposure. Specifically, infant exposure significantly elevates the risk of adult hypercholesterolemia, whereas third trimester exposure is independently associated with an increased risk of hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia. Furthermore, elevated body mass index (BMI) and heavy alcohol consumption in adulthood were identified as modifiable risk factors for adult hyperlipidemia. Collectively, these findings provide novel evidence from an extreme stress paradigm that supports the Developmental Origins of Health and Disease (DOHaD) hypothesis and furnish an epidemiological foundation for understanding the early-life origins and targeted prevention of hyperlipidemia. The exposure stage-specific effects observed in this study carry profound biological significance. The third trimester of gestation represents a critical developmental window for fetal hepatic maturation, adipose tissue development, and the establishment of lipid metabolism-related enzymatic systems [ 14 ] . A substantial body of research indicates that prenatal stress can activate the maternal hypothalamic-pituitary-adrenal (HPA) axis, resulting in elevated circulating cortisol levels that subsequently traverse the placenta and impact the developing fetus. Excessive glucocorticoid exposure has the potential to permanently "reprogram" the fetal metabolic regulatory apparatus, including the upregulation of hepatic rate-limiting enzymes responsible for cholesterol synthesis—such as 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase—or the alteration of lipoprotein receptor density and binding affinity [ 15 , 16 ] . Prior investigations have substantiated that maternal psychological distress experienced during critical phases of fetal development exerts deleterious effects on the neuroendocrine system of the offspring [ 17 ] . Converging lines of evidence derived from both human populations and non-human animal models indicate that psychological trauma may precipitate elevated circulating glucose concentrations, a phenomenon largely attributable to dysregulation of the stress-responsive hormonal pathways. In the aftermath of major trauma, both human subjects and animal models manifest pathological hyperactivity of the HPA axis, resulting in the secretion of cortisol and related glucocorticoids at concentrations that exceed physiological norms. This endocrine dysregulation can contribute to a clinical spectrum encompassing obesity, visceral adiposity, dyslipidemia, and exacerbated insulin resistance [ 18 ] . Excessive fetal exposure to maternal glucocorticoids during gestation may induce intrauterine growth retardation (IUGR), and the enzyme 11β-hydroxysteroid dehydrogenase (11β-HSD) plays a pivotal regulatory role in modulating fetal glucocorticoid bioavailability [ 19 ] . The intrauterine milieu is intimately linked to fetal growth trajectories and may exert sustained influences extending into adulthood. The preponderance of current evidence suggests that maternal stress during pregnancy can adversely impact gestational outcomes and fetal developmental programming, thereby conferring long-term susceptibility to disease in childhood and adulthood. Glucocorticoids are essential for the regulation of fetal development, growth, and metabolism. The two distinct isoforms of 11β-HSD mediate and govern the actions and bioactivity of glucocorticoids. Maternal stress during gestation may influence the pathophysiological mechanisms governing placental 11β-HSD isozyme activity, thereby precipitating detrimental effects on gestational physiology, fetal development, and metabolic homeostasis. Both chronic and acute maternal stress during pregnancy have been shown to modulate the activity and expression of placental 11β-HSD isozymes and may contribute to adverse outcomes including preeclampsia, preterm birth, and the delivery of infants with IUGR or small for gestational age [ 20 ] . This mechanistic framework may partially elucidate why individuals exposed during the third trimester manifest a more extensive spectrum of lipid profile abnormalities in adulthood, including mixed dyslipidemia—a more severe form of metabolic derangement (OR = 5.007). The pronounced effects observed in the infant exposure group merit considerable attention. During the early postnatal period, homeostatic regulatory mechanisms governing lipid metabolism are still undergoing developmental maturation and refinement. Exposure to extreme stress during this phase—whether stemming from nutritional deprivation, heightened infectious risk, or disruptions in maternal–infant bonding consequent to the earthquake—may induce enduring alterations in the expression of genes implicated in lipid metabolism via epigenetic modifications, such as DNA methylation [ 21 , 22 ] . In the present study, infant exposure was predominantly associated with hypercholesterolemia, implying that this developmental stage may be particularly susceptible to the "programming" of cholesterol metabolic pathways. This observation resonates with our prior findings demonstrating an adverse effect of infant earthquake exposure on visuospatial memory [ 7 ] , underscoring the fact that early infancy is characterized by pronounced neural plasticity [ 23 ] . Moreover, infancy—especially the lactational period—constitutes a central phase for metabolic phenotypic programming and represents a critical window for the programming of both metabolic disorders and neuronal alterations [ 24 ] . Notably, first trimester exposure did not exhibit a statistically significant association with hyperlipidemia risk in this study. This null finding may be attributable to the predominance of embryonic cellular differentiation processes during early gestation [ 25 ] , a period during which the lipid metabolic system has not yet fully formed. Additionally, this observation may reflect the influence of survivor bias, given that approximately 50%–67.8% of early spontaneous abortions are associated with embryonic chromosomal abnormalities, of which 86% constitute aneuploidies, largely stemming from maternal meiotic errors. The presence of mosaicism, particularly trisomic mosaicism, substantially elevates the risk of pregnancy loss [ 26 ] . Consequently, surviving individuals may possess inherently greater metabolic resilience. Conceptually, our findings align with the seminal observations derived from the Dutch Famine Birth Cohort Study. That classic study demonstrated that prenatal exposure to undernutrition during late gestation was associated with an elevated risk of abnormal glucose tolerance in adulthood, whereas exposure during early gestation correlated with an increased risk of coronary heart disease [ 4 ] . Although the nature of the stressor differs (nutritional deficiency versus psychophysiological stress), both lines of evidence converge upon the overarching paradigm of early-life environmental "programming" of long-term health outcomes. The present investigation extends this paradigm into the domain of lipid metabolism and further refines the risk patterns associated with specific dyslipidemia subtypes. Furthermore, the current results corroborate and extend our prior findings within this same cohort. We have previously reported that prenatal earthquake exposure is associated with an increased risk of adult-onset diabetes mellitus and metabolic syndrome [ 5 , 6 ] . Given that hyperlipidemia represents a core constituent component of metabolic syndrome, the findings presented herein complete a critical pathophysiological link in this chain of events. Notably, the substantial elevation in risk observed for mixed dyslipidemia—characterized by concurrent elevation of both TC and TG (OR = 5.007)—suggests that third trimester stress exposure may precipitate a more severe metabolic phenotype. As such, this subgroup of individuals warrants particular attention and prioritization in future primary prevention strategies targeting cardiovascular disease. This study reaffirms the central role of adult lifestyle factors in the pathogenesis of dyslipidemia. Elevated BMI emerged as an independent risk factor for both hypertriglyceridemia and mixed dyslipidemia, whereas heavy alcohol consumption significantly augmented the risk of hypercholesterolemia and hypertriglyceridemia. These observations are consistent with findings from numerous large-scale epidemiological investigations conducted both domestically and internationally [ 27 , 28 ] . From a public health vantage point, this finding holds considerable translational significance. Although the "programming" effects established during early development may prove challenging to reverse entirely, it remains plausible that the excess risk conferred by early-life stress may be partially mitigated or offset through the modulation of modifiable adult factors, including weight management and moderation of alcohol intake. This provides an evidence-based rationale for the formulation of targeted, stratified prevention strategies, whereby enhanced lipid screening and intensive lifestyle interventions are implemented for earthquake-exposed populations, particularly those with exposure during the third trimester or infancy. To the best of our knowledge, this is the first study to utilize a catastrophic earthquake as a natural experiment to delineate stage-specific associations between early-life stress exposure and adult hyperlipidemia subtypes. Furthermore, it evaluates the interplay between early-life "programming" effects and adult lifestyle factors within a real-world population context. The potential underlying biological mechanisms are likely to involve multisystem interactions: beyond glucocorticoid programming mediated by the HPA axis, these may encompass activation of the sympathetic nervous system, dysregulation of inflammatory homeostasis, and perturbations of the gut microbiome [ 29 – 31 ] . Recent advances in epigenetics suggest that DNA methylation alterations induced by early-life adversity may constitute the molecular substrate underpinning the long-term persistence of these effects [ 32 ] . Future investigations integrating multi-omics analyses of biospecimens from this cohort hold promise for unraveling the deeper mechanistic underpinnings of these associations. Several limitations inherent to the present study warrant consideration. First, the cross-sectional design precludes definitive inference regarding causal relationships. Although the cohort was stratified based on the timing of exposure, lipid measurements and exposure ascertainment were conducted contemporaneously; hence, the possibility of reverse causality cannot be entirely excluded. Second, the potential for survivor bias cannot be discounted. The Tangshan Earthquake resulted in catastrophic mortality, and survivor bias among the population born in the years surrounding 1976 may have skewed the sample toward healthier individuals, potentially leading to an underestimation of the true effect magnitude. Third, the study is susceptible to recall bias. Certain perinatal parameters—such as maternal health status during gestation—were retrospectively reported by participants in adulthood and are therefore subject to memory inaccuracies. Fourth, residual confounding due to unmeasured variables remains a concern. Despite adjustment for multiple potential confounders, the influence of genetic background, early postnatal nutritional status, and adult dietary composition cannot be fully ruled out. Fifth, the study encountered sample size constraints. Although the overall sample size approached the estimated requirement, the number of events in certain subgroups—particularly low HDL-C cholesterolemia—was limited, resulting in diminished statistical power and instability of the parameter estimates. Future research directions stemming from this study should encompass the following endeavors: First, the implementation of longitudinal follow-up studies is warranted to delineate the long-term trajectories of lipid profiles and to ascertain the incidence of hard cardiovascular endpoints within this population. Second, the incorporation of epigenetic biomarkers, such as the methylation status of specific candidate genes, is essential to elucidate the molecular mechanisms underpinning early-life stress programming. Third, further analyses should consider BMI and alcohol consumption as potential effect modifiers, exploring their statistical and biological interactions with early-life exposure. Fourth, replication of these findings in larger cohorts or alternative disaster-exposed populations is necessary to enhance the generalizability and robustness of the conclusions. In summary, our findings provide robust evidence substantiating an association between exposure to a catastrophic earthquake during early developmental stages and an elevated risk of hyperlipidemia in early adulthood (ages 36–39 years), thereby reinforcing the theoretical framework of the Developmental Origins of Health and Disease (DOHaD) hypothesis. These observations illuminate the enduring metabolic consequences of early-life adversity and underscore a pressing public health imperative: the implementation of proactive, long-term surveillance programs for cardiometabolic risk factors, including periodic lipid screening, among individuals with a documented history of significant fetal or infant exposure to stressors such as natural disasters. Such targeted intervention strategies are of paramount importance in disaster-prone regions, offering the dual benefits of facilitating early detection and intervention while effectively mitigating the future burden of dyslipidemia and associated cardiovascular disease in vulnerable populations. Conclusion The present study provides the first evidence delineating stage-specific "programming" effects of early-life stress on lipid metabolism. Specifically, third trimester exposure to catastrophic stress was found to significantly elevate the subsequent risk of adult hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia, whereas infant exposure was primarily associated with an increased risk of hypercholesterolemia. Furthermore, elevated body mass index (BMI) and heavy alcohol consumption in adulthood were identified as independent risk factors for hyperlipidemia, which, in conjunction with early-life stress exposure, synergistically shape the metabolic risk phenotype of the individual. Collectively, these findings underscore the critical importance of shifting the preventive focus for hyperlipidemia forward into early developmental windows and highlight the imperative for enhanced long-term metabolic health surveillance among disaster-exposed populations. Future investigations should integrate longitudinal follow-up designs with multi-omics technologies to further elucidate the epigenetic mechanisms underlying stress-induced metabolic programming alterations during early life. Such endeavors will not only provide deeper scientific insight into the developmental origins of cardiometabolic disease but also furnish a robust theoretical foundation for the implementation of targeted interventions in high-risk populations. Declarations Conflict of Interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Shuang Wang, Xueyi Wang, Lulu Yu, Na Li, Xiaochuan Zhao contributed to the conception and design of the study; Na Li, Xiaochuan Zhao, Yuehong Cheng, Yuanyuan Gao, Mei Song, Lan Wang, Lulu Yu performed the experiments, collected and analyzed data; Shuang Wang, Xueyi Wang wrote the manuscript; All authors reviewed and approved the final version of the manuscript. Funding This work was supported by research grants from National Science Foundation of China (No. 81271489), Supported by the Provincial Science and Technology Program of Hebei Province (21377711D) ,Supported by the Provincial Science and Technology Program of Hebei Province (199776245D) ,Hebei Provincial Health Commission Government-funded Project for Outstanding Clinical Medical Talents (LS201903), Hebei Province's Project for Introducing Foreign Intelligence (YZ202306),supported by the Hebei Provincial Medical Research Project Plan(20260228) Availability of data and materials: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References Vafai, Y. et al. The association between first-trimester omega-3 fatty acid supplementation and fetal growth trajectories. Am J Obstet Gynecol. 228(2): 224.e1-224.e16. (2023). 10.1016/j.ajog.2022.08.007 Barker, D. J. & Osmond, C. 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Long-term effect of early-life stress from earthquake exposure on working memory in adulthood. Neuropsychiatr Dis. Treat. 11 , 2959–2965. 10.2147/NDT.S88770 (2015). Li, N. et al. Effects of multiple stress events at different stages of life on the incidence of metabolic syndrome. Front. Endocrinol. (Lausanne) . 15 , 1419443DOI. 10.3389/fendo.2024.1419443 (2024). Li, N. et al. Perinatal exposure to earthquake stress increases the risks of hypertension and diabetes in subsequent adult life: A cross-sectional study. J. Clin. Hypertens. (Greenwich) . 22 (12), 2354–2360. 10.1111/jch.14083 (2020). Wilkins, J. T. et al. Prediction of Cumulative Exposure to Atherogenic Lipids During Early Adulthood. J. Am. Coll. Cardiol. 84 (11), 961–973. 10.1016/j.jacc.2024.05.070 (2024). Hong, N. et al. The relationship between dyslipidemia and inflammation among adults in east coast China: A cross-sectional study. Front. Immunol. 13 , 937201. 10.3389/fimmu.2022.937201 (2022). Khalil, M. et al. 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M. et al. Intrauterine growth restriction alters the activity of drug metabolising enzymes in the maternal-placental-fetal unit. Life Sci. 285 , 120016DOI. 10.1016/j.lfs.2021.120016 (2021). Lautarescu, A., Craig, M. C. & Glover, V. Prenatal stress: Effects on fetal and child brain development. Int. Rev. Neurobiol. 150 , 17–40. 10.1016/bs.irn.2019.11.002 (2020). Deer, L. K., Su, C., Thwaites, N. A., Davis, E. P. & Doom, J. R. A framework for testing pathways from prenatal stress-responsive hormones to cardiovascular disease risk. Front. Endocrinol. (Lausanne) . 14 , 1111474DOI. 10.3389/fendo.2023.1111474 (2023). Ge, C. et al. Inhibition of placental 11β-HSD2 expression through cAMP/PKA signaling pathway induces intrauterine growth retardation. Toxicol. Lett. 410 , 83–95. 10.1016/j.toxlet.2025.06.007 (2025). Pavli, P., Mastorakos, G., Eleftheriades, M. & Valsamakis, G. The Effect of Maternal Stress on 11beta-Hydroxysteroid Dehydrogenase Activity During Pregnancy: Evidence for Potential Pregnancy Complications and Consequences on Fetal Development and Metabolism. Int. J. Mol. Sci. 26 (22). 10.3390/ijms262211071 (2025). Burugupalli, S. et al. Ontogeny of circulating lipid metabolism in pregnancy and early childhood - a longitudinal population study. Elife 11 10.7554/eLife.72779 (2022). Mulder, R. H. et al. Interactive effects of genotype with prenatal stress on DNA methylation at birth. Mol. Psychiatry . 30 (12), 5749–5759. 10.1038/s41380-025-03312-6 (2025). Blumberg, M. S., Dooley, J. C. & Tiriac, A. Sleep, plasticity, and sensory neurodevelopment.Neuron.2022; 110 (20):3230–3242 .DOI: 1016/j.neuron.2022.08.005. Amaro, A. et al. Sex-specificities in offspring neurodevelopment and behaviour upon maternal glycation: Putative underlying neurometabolic and synaptic changes. Life Sci. 321 , 121597. 10.1016/j.lfs.2023.121597 (2023). Rodgers, S. K. et al. A Lexicon for First-Trimester US: Society of Radiologists in Ultrasound Consensus Conference Recommendations. Am J Obstet Gynecol. ;232(1):1–16. (2025). 10.1016/j.ajog.2024.07.038 Essers, R. et al. Prevalence of chromosomal alterations in first-trimester spontaneous pregnancy loss. Nat. Med. 29 (12), 3233–3242 (2023). Lee, M. J., Khang, A. R., Kang, Y. H., Yun, M. S. & Yi, D. Synergistic Interaction between Hyperuricemia and Abdominal Obesity as a Risk Factor for Metabolic Syndrome Components in Korean Population. Diabetes Metab. J. 2022 46 (5): 756–766 . 10.1038/s41591-023-02645-5 Choi, S., Park, T. & Je, Y. Long-term alcohol consumption and incident health risk conditions related to cardiometabolic risk markers: A 20-year prospective cohort study. Addiction 120 (9), 1840–1852. 10.1111/add.70092 (2025). Wu, S., Liu, J., Huang, S., Guo, Y. & Bi, Y. Chronic Stress Induces Hepatic Steatosis via Brain-Hepatic Sympathetic Axis Mediated Catecholamine Resistance. Int. J. Biol. Sci. 22 (3), 1407–1424. 10.7150/ijbs.126058 (2026). Tian, B. et al. Anthocyanins from the fruits of Lycium ruthenicum Murray improve high-fat diet-induced insulin resistance by ameliorating inflammation and oxidative stress in mice. Food Funct. 12 (9), 3855–3871. 10.1039/d0fo02936j (2021). Ge, Z. et al. Lipid metabolic dysregulation-induced neuroinflammation in the pathophysiology of major depressive disorder. Front. Immunol. 16 , 1625087DOI. 10.3389/fimmu.2025.1625087 (2025). Daredia, S. et al. Cumulative Epigenetic Aging From Birth to Young Adulthood and Prospective Associations With Cardiometabolic Health in the CHAMACOS Study. J. Am. Heart Assoc. 14 (19), e043818. 10.1161/JAHA.125.043818 (2025). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9570569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638207264,"identity":"42afbfe8-a5b9-4ccf-a5bd-86a1094d58f6","order_by":0,"name":"Shuang Wang","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Wang","suffix":""},{"id":638207265,"identity":"e7c78e59-c30c-4788-9141-947adabe6855","order_by":1,"name":"Na Li","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""},{"id":638207266,"identity":"cc91494d-6364-41e5-ae7e-82dd25edfb2e","order_by":2,"name":"Xiaochuan Zhao","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaochuan","middleName":"","lastName":"Zhao","suffix":""},{"id":638207267,"identity":"78e31a8f-e48f-4001-a000-d396cbb51944","order_by":3,"name":"Yuehong Cheng","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuehong","middleName":"","lastName":"Cheng","suffix":""},{"id":638207268,"identity":"08d1d9b7-1005-4eb3-8a26-a75615374cb8","order_by":4,"name":"Yuanyuan Gao","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Gao","suffix":""},{"id":638207269,"identity":"88da670a-503c-484a-b996-58edd4f1f6b8","order_by":5,"name":"Mei Song","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Song","suffix":""},{"id":638207270,"identity":"c038832c-aa21-48ae-947e-320b55fbab05","order_by":6,"name":"Lan Wang","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Wang","suffix":""},{"id":638207271,"identity":"943dcadc-8c55-4498-82e5-ce2c3647ba68","order_by":7,"name":"Lulu Yu","email":"","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Yu","suffix":""},{"id":638207272,"identity":"c79f20ff-525a-46b3-b10b-ac666b285c2e","order_by":8,"name":"Xueyi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACNvmHjY8ZGA6AORJEaeFnSD5sTJoWyYa0NGnStBgcOGNWXVBxJ9rgAPPB2zwMdnmEtRzsMbs948yz3A0H2JKteRiSiwlrOcxjdpu37TBQC4+ZNA/DgcQGglqO8ZgV8/4DaeH/RpwWyR62NGbeBrAtbMRp4ZdgPizNc+xZ7szDbMaWcwySCWthk2Bs/MxTcye373jzwxtvKuwIa0EAZhBhQLz6UTAKRsEoGAV4AAAWED4qFh249QAAAABJRU5ErkJggg==","orcid":"","institution":"the First Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xueyi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-30 01:09:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9570569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9570569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109205219,"identity":"03b58e2f-db52-4a06-8946-3bcbc441f8a3","added_by":"auto","created_at":"2026-05-13 15:03:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":573426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9570569/v1/d275891a-edca-4c04-9d47-f58cf1b47be7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of Fetal and Infant Earthquake Stress Exposure on Adult Hyperlipidemia Subtypes: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe mother gestates life, and every new life commences with conception. Throughout the nine-month intrauterine period, fetal development constitutes a highly ordered, multi-stage coordinated biological process encompassing cell differentiation, organogenesis, systemic maturation, and maternal\u0026ndash;fetal interplay. This extraordinary process delineates the inception of each individual and is fundamentally critical to human growth \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In 1986, Barker DJ and colleagues published three seminal studies in The Lancet \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Their findings revealed that fetal malnutrition and low birth weight were associated with the onset of coronary heart disease in the same individuals five decades later. This discovery gave rise to the Developmental Origins of Health and Disease (DOHaD) hypothesis\u0026mdash;a paradigm positing that environmental exposures (e.g., nutrition, stress, pollutants) during sensitive developmental windows in early life (including preconception, gestation, and early postnatal periods) exert profound long-term effects on health trajectories, thereby elevating the risk of chronic non-communicable diseases in adulthood \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis hypothesis was subsequently corroborated, most notably by the Dutch Famine Birth Cohort Study, which demonstrated that children exposed to famine during late gestation exhibited a higher propensity to develop glucose intolerance and diabetes in later life, whereas nutritional deprivation during early gestation augmented the risk of atherosclerosis and coronary artery disease \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClimate-related disasters\u0026mdash;encompassing extreme weather events (e.g., floods, droughts) and geophysical hazards (e.g., earthquakes, tsunamis)\u0026mdash;constitute pressing challenges in global public health. Such disasters not only inflict catastrophic damage upon infrastructure and ecosystems but also engender profound psychological repercussions at both individual and societal levels. A paradigmatic case is the 1976 Tangshan Earthquake (latitude 39\u0026deg;38\u0026prime; N, longitude 118\u0026deg;11\u0026prime; E), a magnitude 7.8 event on the Richter scale that attained a maximum seismic intensity of XI at a focal depth of 11 km. Occurring at 03:42 on July 28, this catastrophic earthquake resulted in over 240,000 fatalities and 160,000 severe injuries, with direct economic losses exceeding 3\u0026nbsp;billion RMB (in 1976 terms), rendering it the deadliest seismic event of the 20th century.\u003c/p\u003e \u003cp\u003eOur prior investigations have indicated that prenatal earthquake exposure significantly elevates the risk of diabetes and metabolic syndrome in adulthood, whereas infant exposure adversely affects visuospatial memory (as assessed by the Brief Visuospatial Memory Test\u0026ndash;Revised, BVMT-R). Crucially, both exposure windows are associated with an increased risk of hypertension, and exposure during the second to third trimesters exacerbates neurocognitive deficits \u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the association between early-life earthquake stress and adult hyperlipidemia remains inadequately elucidated. Employing a population-based epidemiological cross-sectional design, the present study aims to examine the potential relationship between stress exposure during fetal and infant stages and the subsequent incidence of hyperlipidemia in adulthood.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\n\u003ch3\u003e1. Study Population\u003c/h3\u003e\n\u003cp\u003eOn July 28, 1976, a catastrophic earthquake measuring 7.8 on the Richter scale struck the city of Tangshan. The present study utilized the 1976 Tangshan Earthquake as a naturalistic stressor. Data were derived from the project \"Brain Mechanisms Underlying the Impact of Severe Maternal Stress During Pregnancy on Offspring Neuropsychological Development in Adulthood,\" funded by the National Natural Science Foundation of China. Our data were sourced from the Kailuan Cohort Study. Initiated in 2006, the Kailuan Cohort Study encompasses approximately 150,000 employees and their family members affiliated with the Kailuan Group, with biennial health examinations and longitudinal follow-up data collection. All participants provided informed consent and signed written informed consent forms prior to inclusion.\u003c/p\u003e \u003cp\u003eFor this investigation, we selected individuals born between July 1975 and April 1977 (aged 36\u0026ndash;39 years at the time of the 2014 survey). This specific age bracket was chosen because it represents early adulthood, a developmental window during which cardiometabolic disorders-including hyperlipidemia-first manifest clinically detectable features, preceding the peak incidence observed in middle age\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Examining this age cohort facilitates the examination of potential early-life programming effects on adult hyperlipidemia risk prior to the accumulation of substantial confounding factors attributable to prolonged exposure to adult lifestyle-related risk factors. As demonstrated by Srinivasan SR et al. in the Bogalusa Heart Study, childhood non-HDL-C levels exhibit the strongest correlation with lipid profiles observed at ages 30\u0026ndash;40 years \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study constitutes a cross-sectional survey conducted between January and December 2014, covering nine mining districts, three communities, and five affiliated units under the jurisdiction of Tangshan Kailuan Mining Group. A total of 1,187 eligible participants were enrolled and successfully completed the comprehensive examination battery. Trained and qualified investigators, adhering to rigorous standardized protocols, systematically collected comprehensive demographic and health-related information from the participants.\u003c/p\u003e \u003cp\u003eBased on developmental stage at the time of the Tangshan Earthquake (magnitude\u0026thinsp;=\u0026thinsp;7.8) on July 28, 1976, participants were stratified into five mutually exclusive cohorts:\u003c/p\u003e \u003cp\u003e(1) First Trimester Exposure: Birth dates between January 29, 1977, and April 28, 1977 (gestational weeks 1\u0026ndash;12).\u003c/p\u003e \u003cp\u003e(2) Second Trimester Exposure: Birth dates between October 29, 1976, and January 28, 1977 (gestational weeks 13\u0026ndash;28).\u003c/p\u003e \u003cp\u003e(3) Third Trimester Exposure: Birth dates between July 29, 1976, and October 28, 1976 (gestational weeks 29\u0026ndash;40).\u003c/p\u003e \u003cp\u003e(4) Infant Exposure: Birth dates between July 29, 1975, and July 28, 1976 (infancy period).\u003c/p\u003e \u003cp\u003e(5) Unexposed Control Group: Birth dates after April 28, 1977 (conception occurred subsequent to the disaster).\u003c/p\u003e \u003cp\u003eExclusion Criteria\u003c/p\u003e \u003cp\u003eParticipants meeting any of the following criteria were excluded from the analysis: (1) active pregnancy or lactation; (2) presence of acute systemic infection (e.g., pneumonia, sepsis) or perioperative complications (major trauma surgery within \u0026lt;\u0026thinsp;3 months); (3) clinically significant hepatic or renal dysfunction (defined as alanine aminotransferase\u0026thinsp;\u0026gt;\u0026thinsp;3 \u0026times; upper limit of normal, or estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u0026sup2;); (4) diagnosed psychiatric disorder (conforming to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria) or exposure to an acute life-threatening stressor within the preceding 6 months.\u003c/p\u003e \u003cp\u003e The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Hebei Medical University (Ethical Approval No. 2014005) and was conducted in strict adherence to the Declaration of Helsinki and the International Council for Harmonisation\u0026ndash;Good Clinical Practice (ICH-GCP) guidelines. Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\n\u003ch3\u003e2. Sample Size Estimation\u003c/h3\u003e\n\u003cp\u003eUtilizing the Kailuan Cohort Study population, participants were dichotomized into an exposed group and an unexposed control group based on prenatal earthquake exposure status. This study employed a cross-sectional design to compare the prevalence of hyperlipidemia between distinct exposure cohorts and the unexposed group. Sample size estimation was predicated on the following assumptions: Based on prior evidence, the prevalence of dyslipidemia among healthy individuals is approximately 28.5% \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, the incidence risk ratio among adults experiencing post-traumatic stress disorder is of 1.432 (95% confidence interval: 1.287\u0026ndash;1.592) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, yielding an estimated prevalence of approximately 40.8% in the exposed group. Sample size calculation was performed using the formula for comparing two independent proportions in a cross-sectional study, with parameters set as follows: two-tailed significance level α\u0026thinsp;=\u0026thinsp;0.05 and statistical power of 80% (β\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e \u003cp\u003eFormula (comparison of two independent proportions with equal sample size allocation):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{({Z}_{\\alpha\\:/2}+{Z}_{\\beta\\:}{)}^{2}\\times\\:[{p}_{1}(1-{p}_{1})+{p}_{2}(1-{p}_{2})]}{({p}_{1}-{p}_{2}{)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere p1\u0026thinsp;=\u0026thinsp;0.285 (prevalence in the control group),p2\u0026thinsp;=\u0026thinsp;0.408 (prevalence in the exposed group), Zα/2\u0026thinsp;=\u0026thinsp;1.96 (α\u0026thinsp;=\u0026thinsp;0.05, two-tailed), and Zβ\u0026thinsp;=\u0026thinsp;0.84 (statistical power\u0026thinsp;=\u0026thinsp;80%).\u003c/p\u003e \u003cp\u003eResult: A minimum of 234 participants per group was required. Assuming equal allocation across five groups (four exposure subgroups plus one control group), the minimum total sample size was calculated to be 1,170 participants.\u003c/p\u003e \u003cp\u003eIn the present study, a total of 1,187 participants met the inclusion criteria and submitted questionnaires. However, due to incomplete data or refusal to undergo further clinical examinations among a subset of individuals, the final analytic sample comprised 999 participants (504 in the exposed group and 495 in the control group). Although this figure is marginally below the estimated requirement, the sample was deemed sufficiently powered based on the following considerations: (1) the observed difference in prevalence between the actual exposed and control groups was substantial (12.3%), reflecting a pronounced effect size; (2) in multivariable logistic regression analyses, the odds ratios (ORs) for key exposure windows (infancy and third trimester) demonstrated statistical significance (OR range: 2.156\u0026ndash;5.007); (3) the relatively large sample size of the control group (n\u0026thinsp;=\u0026thinsp;495) enhanced the precision of the comparison. Consequently, the available sample size adequately supports the reliability and validity of the study conclusions.\u003c/p\u003e\n\u003ch3\u003e3. Questionnaire\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Collection Instruments\u003c/h2\u003e \u003cp\u003e A structured questionnaire was developed in accordance with the World Health Organization STEPwise Approach to Surveillance (STEPS) guidelines and validated through pilot testing (Cronbach's α\u0026thinsp;=\u0026thinsp;0.82). The instrument encompassed three principal domains:\u003c/p\u003e \u003cp\u003eSociodemographic Characteristics:Core variables included: chronological age (calculated based on birth records), biological sex (male/female), educational attainment (categorized according to the International Standard Classification of Education 2011), marital status (legally registered), and household income (RMB/month, verified via payroll records), family bereavement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Substance Use Definitions\u003c/h2\u003e \u003cp\u003eTobacco Exposure:Current Smoker: Consumption of \u0026ge;\u0026thinsp;1 cigarette per day for a continuous period of \u0026ge;\u0026thinsp;6 months.\u003c/p\u003e \u003cp\u003eFormer Smoker: History of sustained smoking cessation exceeding 6 months.\u003c/p\u003e \u003cp\u003eAlcohol Consumption Criteria (National Institute on Alcohol Abuse and Alcoholism [NIAAA] Classification):\u003c/p\u003e \u003cp\u003eModerate Drinking: \u0026le;14 standard drinks per week for men (approx. 140 g ethanol) and \u0026le;\u0026thinsp;7 standard drinks per week for women.\u003c/p\u003e \u003cp\u003eHeavy Drinking: \u0026gt;14 standard drinks per week or \u0026gt;\u0026thinsp;4 drinks per day for men; \u0026gt;7 drinks per week or \u0026gt;\u0026thinsp;3 drinks per day for women.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eA standard drink was defined as containing 14 grams of pure ethanol.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Study Design and Implementation\u003c/h3\u003e\n\u003cp\u003eAnthropometric measurements were obtained using a calibrated RGZ-120 weight scale. Participants were weighed while wearing lightweight clothing and without shoes or hats. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). Waist circumference was measured at the narrowest point of the torso and recorded to the nearest 0.1 cm.\u003c/p\u003e \u003cp\u003eBlood pressure was measured after participants had abstained from smoking, tea, and coffee for 30 minutes. Brachial artery blood pressure of the right arm was assessed using a calibrated mercury sphygmomanometer. Measurements were taken three times at 1\u0026ndash;2-minute intervals, and the average of the three readings was recorded for analysis.\u003c/p\u003e \u003cp\u003eDiagnostic Definitions:Hypertension: Defined as Systolic Blood Pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or Diastolic Blood Pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medication.\u003c/p\u003e \u003cp\u003eDiabetes Mellitus: Defined as Fasting Plasma Glucose (FPG)\u0026thinsp;\u0026gt;\u0026thinsp;126 mg/dL (7.0 mmol/L), or current use of glucose-lowering medication or insulin therapy.\u003c/p\u003e\n\u003ch3\u003e5. Data Collection Framework\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Demographic and Behavioral Data\u003c/h2\u003e \u003cp\u003eA validated 15-item questionnaire was administered to collect the following parameters:\u003c/p\u003e \u003cp\u003eBiometrics: Height (SECA 213 stadiometer), Weight (Tanita BC-418MA), BMI (kg/m\u0026sup2;).\u003c/p\u003e \u003cp\u003eCardiovascular Indices: SBP/DBP (Omron HEM-7322, average of three readings).\u003c/p\u003e \u003cp\u003eLifestyle Factors: Smoking status (WHO definition: \u0026ge;1 cigarette/day for \u0026ge;\u0026thinsp;6 months); Alcohol consumption (NIH criteria: \u0026gt;14 drinks/week for men; \u0026gt;7 drinks/week for women).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Laboratory Analysis\u003c/h2\u003e \u003cp\u003eFasting venous blood samples were collected between 7:00 AM and 9:00 AM into 5 mL EDTA vacuum tubes (Insepack, ST750EK, Sekisui Medical Co., Ltd., Osaka, Japan). Samples were centrifuged at 3000 \u0026times; g for 10 minutes at room temperature, and the supernatant was collected for the measurement of Fasting Plasma Glucose (FPG), Triglycerides (TG), Low-Density Lipoprotein Cholesterol (LDL-C), Total Cholesterol (TC), and High-Density Lipoprotein Cholesterol (HDL-C). FPG levels were determined using the hexokinase/glucose-6-phosphate dehydrogenase enzymatic method (Mind Bioengineering Co. Ltd., Shanghai, China). Concurrently, serum lipid profiles (TG, LDL-C, TC, and HDL-C) were measured using an automated biochemical analyzer (Hitachi 747, Tokyo, Japan) at the Central Laboratory of Kailuan Hospital.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e6. Diagnostic Criteria\u003c/h3\u003e\n\u003cp\u003e Hyperlipidemia: Diagnosed in accordance with the 2016 Chinese Guidelines for the Prevention and Treatment of Dyslipidemia in Adults. Diagnosis required the fulfillment of any one of the following criteria: Total Cholesterol (TC)\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, Triglycerides (TG)\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L, Low-Density Lipoprotein Cholesterol (LDL-C)\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L, or High-Density Lipoprotein Cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L. Specific subtypes were further classified as: hypercholesterolemia, hypertriglyceridemia, low HDL-C cholesterolemia, and mixed dyslipidemia (concurrent elevation of both TC and TG).\u003c/p\u003e\n\u003ch3\u003e7. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 22.0 software (SPSS Inc., Chicago, IL, USA). Quantitative data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Comparisons of continuous variables between groups were conducted using One-Way Analysis of Variance (ANOVA). Where appropriate, the Kruskal\u0026ndash;Wallis test or independent samples t-test was employed to compare continuous variables between exposed and unexposed participants. Chi-square (χ\u003csup\u003e2\u003c/sup\u003e) tests were utilized to compare the prevalence rates of hypertension and hyperlipidemia among the groups.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis was performed with the incidence of hyperlipidemia designated as the dependent variable. Independent covariates included hypertension, sex, age, degree of earthquake stress exposure across different gestational stages and infancy, body mass index, smoking history, alcohol consumption history, and experience of bereavement. A two-tailed P-value of \u0026lt;\u0026thinsp;0.05 was considered the threshold for statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. General Demographic Characteristics and Baseline Data Comparison of Participants\u003c/h3\u003e\n\u003cp\u003eA total of 1,187 participants met the inclusion criteria and submitted completed questionnaires. Of these, 999 questionnaires (84.2%) were deemed valid for subsequent analysis. The exposed group (n\u0026thinsp;=\u0026thinsp;504) was further stratified into the following subgroups: first trimester exposure (n\u0026thinsp;=\u0026thinsp;91), second trimester exposure (n\u0026thinsp;=\u0026thinsp;124), third trimester exposure (n\u0026thinsp;=\u0026thinsp;130), and infant exposure (n\u0026thinsp;=\u0026thinsp;159). The unexposed group served as the control cohort (n\u0026thinsp;=\u0026thinsp;495). Baseline sociodemographic characteristics of the participants are presented in detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWith the exception of chronological age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), no statistically significant differences were observed among the exposure subgroups (including the unexposed group) with respect to sex distribution (P\u0026thinsp;=\u0026thinsp;0.26), educational attainment (P\u0026thinsp;=\u0026thinsp;0.29), smoking history (P\u0026thinsp;=\u0026thinsp;0.90), alcohol consumption patterns (P\u0026thinsp;=\u0026thinsp;1.00), reported familial bereavement during the earthquake (P\u0026thinsp;=\u0026thinsp;0.36), cumulative incidence of diabetes mellitus (P\u0026thinsp;=\u0026thinsp;0.69), or cumulative incidence of hypertension (P\u0026thinsp;=\u0026thinsp;0.17).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic distribution and clinical characteristics of the longitudinal cohort (N\u0026thinsp;=\u0026thinsp;999)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eExposure to earthquake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-exposure to earthquake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMid gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLate gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInfant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex(male), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112(90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118(90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134(84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e421(85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation attainment, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElementary\u0026nbsp;(\u0026lt;\u0026thinsp;6 years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary\u0026nbsp;(6\u0026ndash;12 years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97(74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324(65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTertiary\u0026nbsp;(\u0026gt;\u0026thinsp;12 years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166(33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e937.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarthquake-related bereavement, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco use, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFormer smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever smoked\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e206(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e252(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e2. Anthropometric and Metabolic Characteristics\u003c/h3\u003e\n\u003cp\u003eThe anthropometric and metabolic profiles of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with the unexposed group (5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48 mmol/L), elevated mean total cholesterol (TC) levels were observed in the second trimester exposure group (5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 mmol/L), third trimester exposure group (5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17 mmol/L), and infant exposure group (5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 mmol/L). Similarly, relative to the unexposed group (2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14 mmol/L), higher mean low-density lipoprotein cholesterol (LDL-C) concentrations were documented in the second trimester exposure group (2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 mmol/L) and the infant exposure group (2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 mmol/L).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric and metabolic characteristics of the study population (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eExposure to earthquake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo exposure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMid gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLate gestation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInfant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(0.8, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.8, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.8, 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2(0.8, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3 (0.8, 2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFPG (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3. Association Between Early-Life Earthquake Exposure and Adult Hyperlipidemia\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression analyses were performed to examine the associations between early-life earthquake exposure and distinct dyslipidemia subtypes. The incidence of hypercholesterolemia, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C) cholesterolemia, and mixed dyslipidemia (concurrent elevation of total cholesterol [TC] and triglycerides [TG]) were designated as dependent variables. Independent covariates included hypertension, sex, age, earthquake stress exposure across specific gestational windows and infancy, body mass index (BMI), and family history of dyslipidemia. The detailed results are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eInfant exposure exhibited a statistically significant association with hypercholesterolemia (Odds Ratio [OR]\u0026thinsp;=\u0026thinsp;3.654, 95% Confidence Interval [CI]: 1.759\u0026ndash;7.589, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Third trimester exposure was significantly associated with an elevated risk of three distinct dyslipidemia subtypes: hypercholesterolemia (OR\u0026thinsp;=\u0026thinsp;2.267, 95% CI: 1.025\u0026ndash;5.012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), hypertriglyceridemia (OR\u0026thinsp;=\u0026thinsp;2.156, 95% CI: 1.050\u0026ndash;4.427, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), and mixed dyslipidemia (OR\u0026thinsp;=\u0026thinsp;5.007, 95% CI: 1.215\u0026ndash;20.631, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026).\u003c/p\u003e \u003cp\u003eFurthermore, elevated body mass index (BMI) was identified as an independent risk factor for both hypertriglyceridemia (OR\u0026thinsp;=\u0026thinsp;1.154, 95% CI: 1.076\u0026ndash;1.237, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) and mixed dyslipidemia (OR\u0026thinsp;=\u0026thinsp;1.149, 95% CI: 1.030\u0026ndash;1.284, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). In addition, heavy alcohol consumption significantly increased the risk of hypercholesterolemia (OR\u0026thinsp;=\u0026thinsp;2.065, 95% CI: 0.965\u0026ndash;4.420, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and hypertriglyceridemia (OR\u0026thinsp;=\u0026thinsp;1.996, 95% CI: 1.030\u0026ndash;3.868, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of risk factors of ypercholesterolemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex(male), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.569\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.356\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.826, 6.716)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.750, 1.236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(per kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.952, 1.122)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.151, 1.472)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure timing(Ref: no exposed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.844, 4.434)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMid gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.655, 3.740)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLate gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.025, 5.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.759, 7.589)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLight smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.525, 3.036)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.610, 1.890)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.669, 2.403)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.965, 4.420)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.048, 1.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBereavement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.575, 1.875)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of risk factors ohypertriglyceridemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex(male), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.815\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.832\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.623, 12.875)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.799, 1.194)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(per kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.076, 1.237)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.262, 6.182)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure timing(Ref: no exposed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.547, 2.666)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMid gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.511, 2.604)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLate gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.050, 4.427)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.441, 2.106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLight smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.268, 1.845)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.615, 1.831)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.839, 4.050)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.030, 3.868)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.027, 1.111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBereavement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.760, 2.633)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of risk factors of low HDL-C cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.574, 1.866)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(per kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.945, 1.205)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure timing(Ref: no exposed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMid gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.053, 4.913)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLate gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.174, 7.299)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.354, 8.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLight smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.538, 8.184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.242, 9.358)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.343, 8.561)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBereavement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.460, 30.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDue to complete separation issues, stable estimates could not be obtained for variables such as gender.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of risk factors combined dyslipidemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex(male), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.192\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.121\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.929, 4.847)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.462, 2.692)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(per kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.030, 1.284)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.062, 43.990)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure timing(Ref: no exposed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.409, 14.929)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMid gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.708, 17.782)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLate gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(1.215, 20.631)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.812, 15.771)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLight smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.197, 1.690)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.048, 3.700)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake(Ref: never)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.324, 9.352)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeavy drinker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.499, 7.441)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.004, 3.654)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBereavement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.505, 12.204)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Variable selection was performed using the backward stepwise (Likelihood Ratio) method, with a removal criterion of P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study, leveraging the Kailuan Cohort Study and utilizing the 1976 Tangshan Earthquake as a natural experimental model, constitutes the first systematic investigation of the association between fetal and infant exposure to catastrophic stress and the subsequent risk of adult hyperlipidemia and its constituent subtypes. Our principal findings are as follows: the third trimester of gestation and infancy represent critical windows of vulnerability to earthquake stress exposure. Specifically, infant exposure significantly elevates the risk of adult hypercholesterolemia, whereas third trimester exposure is independently associated with an increased risk of hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia. Furthermore, elevated body mass index (BMI) and heavy alcohol consumption in adulthood were identified as modifiable risk factors for adult hyperlipidemia. Collectively, these findings provide novel evidence from an extreme stress paradigm that supports the Developmental Origins of Health and Disease (DOHaD) hypothesis and furnish an epidemiological foundation for understanding the early-life origins and targeted prevention of hyperlipidemia.\u003c/p\u003e \u003cp\u003eThe exposure stage-specific effects observed in this study carry profound biological significance. The third trimester of gestation represents a critical developmental window for fetal hepatic maturation, adipose tissue development, and the establishment of lipid metabolism-related enzymatic systems \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. A substantial body of research indicates that prenatal stress can activate the maternal hypothalamic-pituitary-adrenal (HPA) axis, resulting in elevated circulating cortisol levels that subsequently traverse the placenta and impact the developing fetus. Excessive glucocorticoid exposure has the potential to permanently \"reprogram\" the fetal metabolic regulatory apparatus, including the upregulation of hepatic rate-limiting enzymes responsible for cholesterol synthesis\u0026mdash;such as 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase\u0026mdash;or the alteration of lipoprotein receptor density and binding affinity \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrior investigations have substantiated that maternal psychological distress experienced during critical phases of fetal development exerts deleterious effects on the neuroendocrine system of the offspring \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Converging lines of evidence derived from both human populations and non-human animal models indicate that psychological trauma may precipitate elevated circulating glucose concentrations, a phenomenon largely attributable to dysregulation of the stress-responsive hormonal pathways. In the aftermath of major trauma, both human subjects and animal models manifest pathological hyperactivity of the HPA axis, resulting in the secretion of cortisol and related glucocorticoids at concentrations that exceed physiological norms. This endocrine dysregulation can contribute to a clinical spectrum encompassing obesity, visceral adiposity, dyslipidemia, and exacerbated insulin resistance \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExcessive fetal exposure to maternal glucocorticoids during gestation may induce intrauterine growth retardation (IUGR), and the enzyme 11β-hydroxysteroid dehydrogenase (11β-HSD) plays a pivotal regulatory role in modulating fetal glucocorticoid bioavailability \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The intrauterine milieu is intimately linked to fetal growth trajectories and may exert sustained influences extending into adulthood. The preponderance of current evidence suggests that maternal stress during pregnancy can adversely impact gestational outcomes and fetal developmental programming, thereby conferring long-term susceptibility to disease in childhood and adulthood. Glucocorticoids are essential for the regulation of fetal development, growth, and metabolism. The two distinct isoforms of 11β-HSD mediate and govern the actions and bioactivity of glucocorticoids. Maternal stress during gestation may influence the pathophysiological mechanisms governing placental 11β-HSD isozyme activity, thereby precipitating detrimental effects on gestational physiology, fetal development, and metabolic homeostasis. Both chronic and acute maternal stress during pregnancy have been shown to modulate the activity and expression of placental 11β-HSD isozymes and may contribute to adverse outcomes including preeclampsia, preterm birth, and the delivery of infants with IUGR or small for gestational age \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This mechanistic framework may partially elucidate why individuals exposed during the third trimester manifest a more extensive spectrum of lipid profile abnormalities in adulthood, including mixed dyslipidemia\u0026mdash;a more severe form of metabolic derangement (OR\u0026thinsp;=\u0026thinsp;5.007).\u003c/p\u003e \u003cp\u003eThe pronounced effects observed in the infant exposure group merit considerable attention. During the early postnatal period, homeostatic regulatory mechanisms governing lipid metabolism are still undergoing developmental maturation and refinement. Exposure to extreme stress during this phase\u0026mdash;whether stemming from nutritional deprivation, heightened infectious risk, or disruptions in maternal\u0026ndash;infant bonding consequent to the earthquake\u0026mdash;may induce enduring alterations in the expression of genes implicated in lipid metabolism via epigenetic modifications, such as DNA methylation \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In the present study, infant exposure was predominantly associated with hypercholesterolemia, implying that this developmental stage may be particularly susceptible to the \"programming\" of cholesterol metabolic pathways. This observation resonates with our prior findings demonstrating an adverse effect of infant earthquake exposure on visuospatial memory \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, underscoring the fact that early infancy is characterized by pronounced neural plasticity \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Moreover, infancy\u0026mdash;especially the lactational period\u0026mdash;constitutes a central phase for metabolic phenotypic programming and represents a critical window for the programming of both metabolic disorders and neuronal alterations \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, first trimester exposure did not exhibit a statistically significant association with hyperlipidemia risk in this study. This null finding may be attributable to the predominance of embryonic cellular differentiation processes during early gestation \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, a period during which the lipid metabolic system has not yet fully formed. Additionally, this observation may reflect the influence of survivor bias, given that approximately 50%\u0026ndash;67.8% of early spontaneous abortions are associated with embryonic chromosomal abnormalities, of which 86% constitute aneuploidies, largely stemming from maternal meiotic errors. The presence of mosaicism, particularly trisomic mosaicism, substantially elevates the risk of pregnancy loss \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Consequently, surviving individuals may possess inherently greater metabolic resilience.\u003c/p\u003e \u003cp\u003eConceptually, our findings align with the seminal observations derived from the Dutch Famine Birth Cohort Study. That classic study demonstrated that prenatal exposure to undernutrition during late gestation was associated with an elevated risk of abnormal glucose tolerance in adulthood, whereas exposure during early gestation correlated with an increased risk of coronary heart disease \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Although the nature of the stressor differs (nutritional deficiency versus psychophysiological stress), both lines of evidence converge upon the overarching paradigm of early-life environmental \"programming\" of long-term health outcomes. The present investigation extends this paradigm into the domain of lipid metabolism and further refines the risk patterns associated with specific dyslipidemia subtypes.\u003c/p\u003e \u003cp\u003eFurthermore, the current results corroborate and extend our prior findings within this same cohort. We have previously reported that prenatal earthquake exposure is associated with an increased risk of adult-onset diabetes mellitus and metabolic syndrome \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Given that hyperlipidemia represents a core constituent component of metabolic syndrome, the findings presented herein complete a critical pathophysiological link in this chain of events. Notably, the substantial elevation in risk observed for mixed dyslipidemia\u0026mdash;characterized by concurrent elevation of both TC and TG (OR\u0026thinsp;=\u0026thinsp;5.007)\u0026mdash;suggests that third trimester stress exposure may precipitate a more severe metabolic phenotype. As such, this subgroup of individuals warrants particular attention and prioritization in future primary prevention strategies targeting cardiovascular disease.\u003c/p\u003e \u003cp\u003eThis study reaffirms the central role of adult lifestyle factors in the pathogenesis of dyslipidemia. Elevated BMI emerged as an independent risk factor for both hypertriglyceridemia and mixed dyslipidemia, whereas heavy alcohol consumption significantly augmented the risk of hypercholesterolemia and hypertriglyceridemia. These observations are consistent with findings from numerous large-scale epidemiological investigations conducted both domestically and internationally \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. From a public health vantage point, this finding holds considerable translational significance. Although the \"programming\" effects established during early development may prove challenging to reverse entirely, it remains plausible that the excess risk conferred by early-life stress may be partially mitigated or offset through the modulation of modifiable adult factors, including weight management and moderation of alcohol intake. This provides an evidence-based rationale for the formulation of targeted, stratified prevention strategies, whereby enhanced lipid screening and intensive lifestyle interventions are implemented for earthquake-exposed populations, particularly those with exposure during the third trimester or infancy.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to utilize a catastrophic earthquake as a natural experiment to delineate stage-specific associations between early-life stress exposure and adult hyperlipidemia subtypes. Furthermore, it evaluates the interplay between early-life \"programming\" effects and adult lifestyle factors within a real-world population context. The potential underlying biological mechanisms are likely to involve multisystem interactions: beyond glucocorticoid programming mediated by the HPA axis, these may encompass activation of the sympathetic nervous system, dysregulation of inflammatory homeostasis, and perturbations of the gut microbiome \u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Recent advances in epigenetics suggest that DNA methylation alterations induced by early-life adversity may constitute the molecular substrate underpinning the long-term persistence of these effects \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Future investigations integrating multi-omics analyses of biospecimens from this cohort hold promise for unraveling the deeper mechanistic underpinnings of these associations.\u003c/p\u003e \u003cp\u003eSeveral limitations inherent to the present study warrant consideration. First, the cross-sectional design precludes definitive inference regarding causal relationships. Although the cohort was stratified based on the timing of exposure, lipid measurements and exposure ascertainment were conducted contemporaneously; hence, the possibility of reverse causality cannot be entirely excluded. Second, the potential for survivor bias cannot be discounted. The Tangshan Earthquake resulted in catastrophic mortality, and survivor bias among the population born in the years surrounding 1976 may have skewed the sample toward healthier individuals, potentially leading to an underestimation of the true effect magnitude. Third, the study is susceptible to recall bias. Certain perinatal parameters\u0026mdash;such as maternal health status during gestation\u0026mdash;were retrospectively reported by participants in adulthood and are therefore subject to memory inaccuracies. Fourth, residual confounding due to unmeasured variables remains a concern. Despite adjustment for multiple potential confounders, the influence of genetic background, early postnatal nutritional status, and adult dietary composition cannot be fully ruled out. Fifth, the study encountered sample size constraints. Although the overall sample size approached the estimated requirement, the number of events in certain subgroups\u0026mdash;particularly low HDL-C cholesterolemia\u0026mdash;was limited, resulting in diminished statistical power and instability of the parameter estimates.\u003c/p\u003e \u003cp\u003eFuture research directions stemming from this study should encompass the following endeavors: First, the implementation of longitudinal follow-up studies is warranted to delineate the long-term trajectories of lipid profiles and to ascertain the incidence of hard cardiovascular endpoints within this population. Second, the incorporation of epigenetic biomarkers, such as the methylation status of specific candidate genes, is essential to elucidate the molecular mechanisms underpinning early-life stress programming. Third, further analyses should consider BMI and alcohol consumption as potential effect modifiers, exploring their statistical and biological interactions with early-life exposure. Fourth, replication of these findings in larger cohorts or alternative disaster-exposed populations is necessary to enhance the generalizability and robustness of the conclusions.\u003c/p\u003e \u003cp\u003eIn summary, our findings provide robust evidence substantiating an association between exposure to a catastrophic earthquake during early developmental stages and an elevated risk of hyperlipidemia in early adulthood (ages 36\u0026ndash;39 years), thereby reinforcing the theoretical framework of the Developmental Origins of Health and Disease (DOHaD) hypothesis. These observations illuminate the enduring metabolic consequences of early-life adversity and underscore a pressing public health imperative: the implementation of proactive, long-term surveillance programs for cardiometabolic risk factors, including periodic lipid screening, among individuals with a documented history of significant fetal or infant exposure to stressors such as natural disasters. Such targeted intervention strategies are of paramount importance in disaster-prone regions, offering the dual benefits of facilitating early detection and intervention while effectively mitigating the future burden of dyslipidemia and associated cardiovascular disease in vulnerable populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study provides the first evidence delineating stage-specific \"programming\" effects of early-life stress on lipid metabolism. Specifically, third trimester exposure to catastrophic stress was found to significantly elevate the subsequent risk of adult hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia, whereas infant exposure was primarily associated with an increased risk of hypercholesterolemia. Furthermore, elevated body mass index (BMI) and heavy alcohol consumption in adulthood were identified as independent risk factors for hyperlipidemia, which, in conjunction with early-life stress exposure, synergistically shape the metabolic risk phenotype of the individual.\u003c/p\u003e \u003cp\u003eCollectively, these findings underscore the critical importance of shifting the preventive focus for hyperlipidemia forward into early developmental windows and highlight the imperative for enhanced long-term metabolic health surveillance among disaster-exposed populations. Future investigations should integrate longitudinal follow-up designs with multi-omics technologies to further elucidate the epigenetic mechanisms underlying stress-induced metabolic programming alterations during early life. Such endeavors will not only provide deeper scientific insight into the developmental origins of cardiometabolic disease but also furnish a robust theoretical foundation for the implementation of targeted interventions in high-risk populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuang Wang, Xueyi Wang, Lulu Yu, Na Li, Xiaochuan Zhao contributed to the conception and design of the study; Na Li, Xiaochuan Zhao, Yuehong Cheng, Yuanyuan Gao, Mei Song, Lan Wang, Lulu Yu performed the experiments, collected and analyzed data; Shuang Wang, Xueyi Wang wrote the manuscript; All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by research grants from National Science Foundation of China (No. 81271489), Supported by the Provincial Science and Technology Program of Hebei Province (21377711D) ,Supported by the Provincial Science and Technology Program of Hebei Province (199776245D) ,Hebei Provincial Health Commission Government-funded Project for Outstanding Clinical Medical Talents (LS201903), Hebei Province\u0026apos;s Project for Introducing Foreign Intelligence (YZ202306),supported by the Hebei Provincial Medical Research Project Plan(20260228)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVafai, Y. et al. 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Heart Assoc.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (19), e043818. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.125.043818\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.125.043818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early-life stress, Fetal exposure, Infantile exposure, Dyslipidemia subtypes, DOHaD, Tangshan earthquake, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-9570569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9570569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To investigate the association between fetal and infant exposure to catastrophic earthquake stress and adult-onset hyperlipidemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This cross-sectional study utilized data from the Kailuan cohort, including 999 adults born between July 1975 and April 1977, who experienced the 1976 Tangshan earthquake at different developmental stages: first trimester (n=91), second trimester (n=124), third trimester (n=130), infancy (n=159), and an unexposed control group (n=495). Blood lipid profiles (total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C]) were measured, and dyslipidemia subtypes were diagnosed according to the 2016 Chinese guidelines. Multivariate logistic regression was performed to assess associations between early-life earthquake exposure and adult dyslipidemia, adjusting for potential confounders (sex, age, BMI, hypertension, smoking, alcohol use, diabetes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Compared with the unexposed group, infantile exposure was significantly associated with hypercholesterolemia (OR=3.654, \u003cem\u003eP\u003c/em\u003e=0.001). Late-gestation exposure significantly increased the risks of hypercholesterolemia (OR=2.267, \u003cem\u003eP\u003c/em\u003e=0.043), hypertriglyceridemia (OR=2.156, \u003cem\u003eP\u003c/em\u003e=0.036), and combined dyslipidemia (OR=5.007, \u003cem\u003eP\u003c/em\u003e=0.026). Elevated BMI was an independent risk factor for hypertriglyceridemia and combined dyslipidemia, while heavy alcohol consumption increased the risks of hypercholesterolemia and hypertriglyceridemia (both \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). No significant associations were observed for first- or second-trimester exposure or for low HDL-C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Prenatal stress during late gestation and stress during infancy are critical sensitive windows that differentially program adult lipid profiles. Late-gestation exposure is linked to a broader atherogenic lipid phenotype, whereas infantile exposure primarily elevates hypercholesterolemia risk. These findings support the DOHaD framework and highlight the need for early-life targeted cardiovascular risk surveillance in disaster-exposed populations.\u003c/p\u003e","manuscriptTitle":"The impact of Fetal and Infant Earthquake Stress Exposure on Adult Hyperlipidemia Subtypes: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 05:12:51","doi":"10.21203/rs.3.rs-9570569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-04T21:32:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T18:14:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T12:19:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T12:19:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-30T01:01:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e24fa2d4-2f3e-437f-8878-7d3acf6a725d","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"9","date":"2026-05-04T21:32:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T18:14:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T12:19:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T12:19:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-30T01:01:20+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67925806,"name":"Health sciences/Cardiology"},{"id":67925807,"name":"Health sciences/Diseases"},{"id":67925808,"name":"Health sciences/Endocrinology"},{"id":67925809,"name":"Health sciences/Health care"},{"id":67925810,"name":"Health sciences/Medical research"},{"id":67925811,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-13T05:12:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 05:12:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9570569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9570569","identity":"rs-9570569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}