Effects of fetal famine exposure on the cardiovascular disease risk in the metabolic syndrome individuals

Effects of fetal famine exposure on the cardiovascular disease risk in the metabolic syndrome individuals | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of fetal famine exposure on the cardiovascular disease risk in the metabolic syndrome individuals zhe shu, Xiong Ding, Yue Qing, XiaoXu Ma, MinHong Liu, YunTao Wu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2020898/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Patients with metabolic syndrome (MS) have a higher incidence of cardiovascular disease (CVD), but the possible mechanisms are not fully understood and further exploration of the possible factors influencing the high incidence of CVD in patients with MS is still needed. Objectives This study aims to examine the association between fetal famine exposure and the risk of CVD in adulthood in people with MS. Methods The 13,744 MS patients free of CVD selected from the Kailuan cohort in 2006 (referred as the baseline survey) were included in the study. All patients were born between January 1, 1949, and December 31, 1974. Based on the date of birth, all patients were divided into the no-exposed group (born between January 1, 1963, and December 31, 1974), uterine famine exposed group (born between January 1, 1959 and December 31, 1962), and childhood famine exposed group (born between January 1, 1949 and December 31, 1958). After following up to December 31, 2019, the weighted Cox regression analysis model was used to calculate the effect of early life famine exposure in MS individuals on the risk of CVD in adulthood. Results During the 12.12 years of follow-up, the incidence of CVD was 5.87%, 10.13%, and 10.90% in the no-exposed group, uterine famine exposed group, and childhood famine exposed group, respectively. Compared with participants in the no-exposed group, the CVD risk and stroke risk increased in participants in the uterine famine exposed group (for CVD, HR: 1.32,95% CI:1.04–1.67; for stroke, HR:1.37,95% CI: 1.05–1.79), but not in childhood famine exposed group. However, the increased CVD risks were only observed in females or smokers. No increased MI risks were observed for participants in the uterine famine exposed group or childhood famine exposed group. Conclusions Our findings suggested that exposure to famine during fetal life significantly increased the risk of developing CVD in adulthood in individuals with MS, and this association was enhanced in females or smokers. cardiovascular disease China famine metabolic syndrome cohort study fetal Figures Figure 1 1. Introduction In recent years, the prevalence of metabolic syndrome (MS) has gradually risen 1 . In Chinese adults, the prevalence of MS has increased from 9.5% in 2002 to 18.7% in 2010–2012 2 , with 450 million patients. According to an estimate by the International Diabetes Federation(IDF), the worldwide prevalence of MS in the adults is on the rise with an estimated prevalence of 20–25% 3 . As is well known, MS can increase the risk of chronic disease, including cardiovascular disease (CVD). A meta-analysis showed that the risk of developing CVD in patients with MS will increase by about 2 times 4 , 5 . Although previous studies 6 –9 have suggested that hypertension, hyperglycemia, smoking and physical inactivity might attribute to the high incidence of CVD in MS patients, the etiology is still unclear, and possible influencing factors still need to be explored, which should be helpful in reducing the disease burden of CVD in patients with MS. "Developmental origins of health and disease" hypothesis suggested that exposure to malnutrition during early life will affect the health in adulthood 10 , 11 . Some studies have confirmed that exposure to famine in the fetus might increase the risk of MS 12 , cerebral hemorrhage 13 , diabetes 14 , hypertension 15 , cerebral infarction 16 and other diseases in adulthood, but few studies have examined whether famine exposure affects CVD risk in MS patients. The Great Famine in China (1959–1962) was one of the largest famines in human history, resulting in insufficient nutritional supply for a large number of people exposed to the famine environment. Our study is based on the Kailuan study, a large-scale, individual-based longitudinal cohort study with a decade-long follow-up. The expected results of this study will help to examine the association between exposure to the Great Chinese Famine in early life and the risk of CVD in adults in individuals with MS. 2. Methods 2.1 Study participants The Kailuan Study (accession number: ChiCTR-TNC-11001489) was a functional community individual-based cohort study in Tangshan, China, and the specific study design and procedures can be found in the team's previous studies 17 , 18 . The Kailuan study began in 2006 and included 101,510 adults (81,110 males and 20,400 females) aged 18 years or older, all of them completed standard questionnaires (medical history and lifestyle) between 2006 and 2007, underwent health assessments every two years, including physical examinations (waist circumference (WC), weight, height, and blood pressure measurements) and laboratory tests (lipid assessments, fasting blood glucose (FBG), and serum creatinine (SCr)). All participants were followed up until their death or December 31, 2019. According to the IDF global working definition of MS 19 , 14,241 MS patients who born between January 1, 1949, and December 31, 1974, were included. Individuals with missing data (n = 69) on WC, high density cholesterol (HDL), triglycerides, diastolic blood pressure (DBP), systolic blood pressure (SBP), fasting blood glucose (FBG), or those with CVD (n = 428) at the baseline survey in 2006 were excluded 16 . Finally, 13,744 participants were served as the baseline cohort. 2.2 Famine exposure Since famine in China occurred concentratedly from 1959 to 1962, we judged the period of famine exposed by birth time. Based on the previous Chinese famine research 20 , birth year was taken as the basis for classification of famine exposure, all participants were divided into three groups: no-exposed group (born between January 1, 1963 and December 31, 1974), uterine famine exposed group (born between January 1, 1959 and December 31, 1962), childhood famine exposed group (born between January 1, 1949 and December 31, 1958). 2.3 MS definition MS was defined following the IDF Global Working Definition (IDF criteria) with the following criteria 19 : the presence of central obesity (waist circumference ≥ 90 cm for males or ≥ 80 cm for females), plus any two of following factors: (i) raised triglyceride level:≥ 1.7 mmol/L (150 mg/dl) or taking triglyceride-lowering medications; (ii) reduced HDL cholesterol:<1.03 mmol/L for men or = 130 mmHg, or DBP > = 85 mmHg, or taking antihypertensive medications; (iv) raised FBG: >=100 mg/dL (5.6 mmol/L), or individuals who have been diagnosed with type 2 diabetes. 2.4 Follow-up and CVD The starting point was defined as the date of completion of the 2006 annual baseline questionnaire and individuals were followed up until December 31, 2019. During follow-up, CVD incidence was assessed annually, and biochemical markers were collected every two years. The outcome event for the study was the first occurrence of a major CVD, which was defined as the composite of stroke and myocardial infarction (MI) 21 , 22 . The Hospital Discharge Register and Municipal Social Insurance Institution database were linked to identify the incidence of CVD based on The International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) (I61 for intracerebral hemorrhagic stroke, I63 for ischemic stroke, and I21 for MI) 21 , 23 . These two databases are updated annually based on follow-up and cover information on all participants in the Kailuan study. An expert panel collected and reviewed annual discharges records from 11 local hospitals to identify patients who were suspected of CVD. Incident MI was diagnosed based on the World Health Organization's Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) criteria on basis of clinical symptoms and dynamic changes in clinical presentation, cardiac enzymes and electrocardiogram. Incident stroke diagnosed was according to neurological signs, clinical symptoms and neuroimaging (from CT or MRI) on the basis of World Health Organization's criteria. Death data were collected from provincial vital statistics offices, as described in previous studies 24 . 2.5 Covariates evaluation and measurement The questionnaire design, anthropometry and laboratory data testing were the same as the literature published by our research group 18 . The data collected by the research include birth, gender, smoking, drinking, physical exercise, education level, history of medications and other basic information. During the survey, professionally trained medical staff completed the physical examination, including the measurement of their height, weight, waist circumference and blood pressure. Height and waist circumference were measured to the nearest 0.1 cm using the disposable tape measure. Weight was determined by using calibrated portable digital weighing scale with 0.1 kg precision. To ensure the reliability of the biochemical measurement results, a venous blood sample was obtained from all subjects who fasted for at least 8 hours before the measurement, and 5 ml of fasting elbow venous blood was collected between 7–9 am on the day of the physical examination, and the blood sample was centrifuged to obtain the upper serum for FBG values and lipid levels. All operations were performed strictly in accordance with the manufacturer's instructions, and blood samples were tested on a Hitachi (7600) automated biochemistry analyzer. Age was calculated by subtracting the birthday from the beginning date of medical examination. Height and weight were measured to calculate body mass index (BMI) as weight (kg) / height 2 (m 2 ). Drinking was defined as consumption on average of 100 ml/day of spirits (alcohol content above 50%) in the past year. Smoking was defined as smoking an average of at least one cigarette per day for more than in the past year. Educational attainment was stratified into two levels: junior high school or below, senior high school or above. Physical exercise was defined as exercise time for > = 30 minutes at a time at least three times per week. Hypertension was defined as SBP over 140 mmHg or a DBP over 90 mmHg, or the fact that the patient was taking antihypertensive medications. Diabetes was defined as FBG > = 6.1 mmol/L, or the fact that the patient was taking hypoglycemic medications. 2.6 Statistical analysis of data All data processing and analyses were performed using SAS version 9.4(SAS Institute, Cary, North Carolina), and R software version 3.6.0 (R Core Team, Vienna, Austria). The database was established through epidata3.1, entered by uniformly trained medical staff, and uploaded to the Oracle database of Kailuan General Hospital. All statistical tests were 2-sided, and p < 0.05 was considered statistically significant. The normal or approximate normal distribution of the continuous variables was represented by x̄ ± s, the comparison between groups using analysis of variance, the skewed distribution was represented by M (P25, P75), and the intergroup comparison was used the Kruskal-Wallis test. The percentages described were used categorical variables and compared by χ 2 tests. Person-years of follow-up were calculated from the return date of the baseline questionnaire to the date of CVD diagnosis, death, loss to follow-up (n = 800, 5.82%), or end of follow-up (December 31, 2019) whichever occurred first. The incidence density of CVD in different groups in the MS individuals was calculated by dividing the number of events by the total number of follow-up person-years (1000/person-year), using the Log-rank test for comparison among groups. We used the weighted Cox regression model to analyze the effect of early life famine exposed in the MS individuals on the risk of CVD in adulthood, and the HR (Hazard Ratio) and 95%CI (confidence interval) was calculated. The model adjusted for age, gender, education level, smoking, drinking, physical exercise, BMI, history of diabetes, history of hypertension, low-density lipoprotein cholesterol, using antihypertensive medications, using antidiabetic medications, and using lipid-lowering medications. Taking CVD as the dependent variable, and famine exposure as the independent variable, a stratified analysis was carried out by gender, smoking, and drinking. To verify the robustness of the results, a sensitivity analysis was performed after removing the individuals who had CVD incidents within two years or lost-to-review individuals. 3. Results A total of 13,744 participants (10,254 males and 3,490 females) were enrolled in the current study, 1,777 participants had been exposed to the Chinese famine during utero stage, while 8,848 participants had been exposed to the famine during childhood stage, respectively. There were significant differences among the three groups in terms of age, gender, BMI, FBG, SBP, DBP, WC, smoking, physical exercise, hypertension, diabetes, antihypertensive medication-using, and antihyperglycemic medications-using (P < 0.001). Compared to the reference group, participants in the uterine famine exposed group were more likely to be female, have diabetes, hypertension, with greater WC, and higher prevalence of using antihypertensive medications and using antihyperglycemic medications (Table 1 ). Table 1 Basic Characteristics of 13,744 MS participants according to the famine exposure Components No-exposed (n = 3,119) Utero famine exposed (n = 1,777) Non-prenatal exposed (n = 8,848) P-value Age (year) 40.3±3.2 46.1±1.3 53.1±2.8 < 0.001 Male (%) 80.6 73.4 72.7 < 0.001 BMI (kg/m 2 ) 28.2±3.3 27.8±3.4 27.5±3.1 < 0.001 FBG (mmol/L) 6.0±2.0 6.3±2.2 6.3±2.2 < 0.001 SBP (mmHg) 135.3±17.9 138.0±19.1 141.7±19.3 < 0.001 DBP (mmHg) 89.9±12.2 90.1±12.2 90.2±11.5 0.1881 WC(cm) 95.0±7.5 95.1±7.9 95.1±7.5 0.9641 LDL (mmol/L) 2.37±0.83 2.37±0.84 2.34±0.94 0.1035 HDL (mmol/L) 1.44±0.40 1.45±0.38 1.48±0.42 0.0001 Education(%) < 0.001 Low 2,320(74.4) 1,461(82.2) 7,675(86.7) High 799(25.6) 316(17.8) 1,173(13.3) Smoker (n, %) 1,269(40.7) 705(39.7) 2,901(32.8) < 0.001 Drinker (n, %) 1,511(48.4) 735(41.4) 3,082(34.8) < 0.001 Physical exercise (%) 211(6.8) 125(7.0) 1,359(15.4) < 0.001 Hypertension (%) 1,890(60.6) 1,138(64.0) 6,325(71.5) < 0.001 Diabetes (%) 431(13.8) 359(20.2) 1,899(21.5) < 0.001 Use of antihypertensive medications (%) 365(11.7) 284(16.0) 1,879(21.2) < 0.001 Use of hypoglycemic medications (%) 63(2.0) 57(3.2) 440(5.0) < 0.001 Use of hypolipidemic medications (%) 44(1.4) 17(1.0) 159(1.8) 0.0228 Data were present as n (%), mean ± SD, or median (P25, P75) according to variable category. Pearson’s chi-square test, ANOVA analysis, or Kruskal-Wallis test was used to compare differences between groups properly. Abbreviations: BMI, body mass index; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglyceride. During a mean follow-up of 12.12 years, the cumulative incidences of CVD in uterine famine exposed group (5.87%) and the childhood famine exposed group (10.13%) were greater than that in no-exposed group (10.90%) (P < 0.05, Fig. 1). The incidence density of CVD was 4.70/1000, 8.32/1000, and 9.09/1000 person-years in the no-exposed group, the uterine famine exposed group, and the childhood famine exposed group, respectively (Table 2 ). Compared with no-exposed individuals, the CVD risk increased in participants with uterine famine exposed group (HR 1.32,95% CI 1.04–1.67), but not increased in childhood famine exposed individuals. Further results showed that the association only observed for stroke, not for MI. In the sensitive analysis, similar results were observed after removing the individuals with CVD occurring within two years or removing the lost-to-review individual (Table 3 ). Table 2 Cox proportional hazard model analysis of different famine groups and the incidence of end-point events components case/total IR (per 1000 person-years) Model 1 HR(95%CI) Model 2 HR(95%CI) Model 3 HR(95%CI) CVD No-exposed 183/3119 4.70 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 180/1777 8.32 1.41(1.11–1.78) 1.35(1.06–1.72) 1.32(1.04–1.67) Non-prenatal exposed 964/8848 9.09 1.10(0.80–1.51) 1.05(0.76–1.45) 1.03(0.75–1.42) Stroke No-exposed 145/3119 3.70 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 151/1777 6.94 1.47(1.13–1.92) 1.41(1.08–1.84) 1.37(1.05–1.79) Non-prenatal exposed 778/8848 7.27 1.10(0.77–1.57) 1.05(0.73–1.51) 1.04(0.72–1.48) Mi No-exposed 39/3119 0.99 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 33/1777 1.47 1.18(0.69–2.02) 1.16(0.68–1.98) 1.15(0.68–1.97) Non-prenatal exposed 213/8848 1.94 1.07(0.54–2.12) 1.05(0.53–2.06) 1.05(0.53–2.06) Abbreviations: HR, hazard ratio; CI, confidence interval; IR, incidence rate; Str, stroke; MI, myocardial infarction; Note: Model 1: Adjusted for age, and gender. Model 2: Included covariates in model 1 and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former). Model 3: Included covariates in model 2 and further adjusted for low-density lipoprotein, hypertension, diabetes, use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications. Table 3 Sensitivity analysis components case/total IR (per 1000 person-years) Model 1 HR(95%CI) Model 2 HR(95%CI) Model 3 HR(95%CI) Delete events that occurred within 2 years No-exposed 180/3107 4.62 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 173/1762 8.01 1.40(1.10,1.78) 1.35(1.06,1.71) 1.31(1.03,1.67) Non-prenatal exposed 941/8792 8.88 1.13(0.82,1.56) 1.09(0.79,1.50) 1.06(0.77,1.47) Delete lost-to-review individuals No-exposed 176/2981 4.71 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 166/1700 7.95 1.39(1.09,1.78) 1.34(1.05,1.72) 1.31(1.03,1.68) Non-prenatal exposed 878/8300 8.73 1.14(0.82,1.59) 1.10(0.79,1.53) 1.08(0.78,1.50) Model 1: Adjusted for age, and gender. Model 2: Included covariates in model 1 and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former). Model 3: Included covariates in model 2 and further adjusted for low-density lipoprotein, hypertension, diabetes, use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications To evaluate the effect of covariates on the CVD risk, stratified analysis by gender, smoking (yes/no), or drinking (yes/no) were performed. The results showed that the association between uterine famine exposure and increased CVD risk only observed in female (HR: 2.31, 95%CI: 1.13–4.73), but not in male. The similar results were observed in smokers (HR:1.53,95%CI: 1.08–2.17). However, no association between famine exposure and CVD risks were observed for patients with childhood famine exposure or drinking. Also, no interaction between famine exposure and gender, smoking or drinking were observed (Table 4 ). Table 4 Adjusted HR(95%CI) for incidence of CVD in the MS individual by exposure to famine by gender, smoking, drinking components case/total IR (per 1000 person-years) Model 1 HR(95%CI) Model 2 HR(95%CI) Model 3 HR(95%CI) P for interaction Male a No-exposed 168/2514 5.37 1 (Reference) 1 (Reference) 1 (Reference) 0.72 Utero exposed 148/1304 9.35 1.30(1.01–1.68) 1.25(0.97–1.62) 1.22(0.94–1.58) Non-prenatal exposed 807/6436 9.09 1.02(0.73–1.44) 0.98(0.70–1.37) 0.96(0.68–1.35) Female a No-exposed 15/605 1.96 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 32/473 5.52 2.36(1.19–4.67) 2.37(1.20–4.68) 2.31(1.13–4.73) Non-prenatal exposed 157/2,412 5.24 1.77(0.69–4.55) 1.84(0.72–4.71) 1.79(0.69–4.65) Smoking b 0.66 No-exposed 91/1269 5.79 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 92/705 10.84 1.59(1.12–2.25) 1.56(1.10–2.22) 1.53(1.08–2.17) Non-prenatal exposed 388/2901 11.37 1.37(0.85–2.21) 1.34(0.83–2.17) 1.33(0.83–2.15) No-Smoking b No-expose 92/1850 3.96 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 88/1072 6.70 1.26(0.91–1.75) 1.22(0.88–1.70) 1.20(0.86–1.67) Non-prenatal exposed 576/5947 8.02 0.94(0.61–1.45) 0.90(0.59–1.39) 0.89(0.58–1.37) Drinking c 0.94 No-exposed 93/1511 4.94 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 79/735 8.80 1.46(1.02,2.08) 1.38(0.97,1.97) 1.38(0.97,1.97) Non-prenatal exposed 363/3082 9.86 1.30(0.81,2.09) 1.23(0.77,1.98) 1.24(0.77,1.99) No-Drinking c No-exposed 90/1608 4.47 1 (Reference) 1 (Reference) 1 (Reference) Utero exposed 101/1042 7.98 1.39(1.01,1.93) 1.35(0.98,1.87) 1.32(0.95,1.83) Non-prenatal exposed 601/5766 8.69 1.00(0.65,1.55) 0.97(0.63,1.51) 0.96(0.62,1.49) Model 1 a : Adjusted for age. Model 1 b : Adjusted for age, and gender. Model 1 c : Adjusted for age, and gender. Model 2 a : Included covariates in model 1a and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former). Model 2 b : Included covariates in model 1b and further adjusted for education (junior high school or below, senior high school or above), drinking (current, never/former), physical activity (current, never/former). Model 2 c : Included covariates in model 1c and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), physical activity (current, never/former). Model 3 a : Included covariates in model 2a and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications. Model 3 b : Included covariates in model 2b and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications. Model 3 c : Included covariates in model 2c and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications. 4. Discussion Based on the prospective cohort, we found that exposure to Chinese famine during fetal life was associated with a higher risk of CVD in patients with MS. The results should help to elucidate the pathogenesis of CVD in MS individuals and emphasize the importance of adequate nutrition during the fetal period. In addition, our results showed the association was significant in smokers, but not in non-smokers, which should be helpful in providing recommendations of the lifestyle for individuals with MS. 4.1 Compared with other studies To our knowledge, the effect of famine exposure on CVD risk in MS patients has not been evaluated, but studies confirmed that fetal exposure to famine increases CVD risk in those with the component of MS. A cross-sectional study found that the association between early life famine exposure and adult CVD risk appears to be stronger in overweight than in normal individuals 25 . Other studies showed that fetal exposure to famine exacerbates the adverse effect of hypertension on CVD, especially in individuals with central obesity 6 , 26 . Zhang et al. found that exposure to famine, especially during fetal life, exacerbates the association between hyperglycemia and CVD. All these results support our research results to a certain extent. 4.2 Mechanism Although the mechanisms underlying this association between fetal famine exposure and adult CVD in MS individuals have not been elucidated, several mechanisms might explain the relationship. First, malnutrition early in life may affect structural changes in the cardiovascular system, and famine exposure during fetal life might lead to epigenetic changes, even with lifelong effects 27 . Second, findings from the Dutch Famine Study suggest that prenatal exposure to famine increases the preference for high-fat foods and a high prevalence of dyslipidemia 28 , which in turn increases the risk of CVD 29 . Third, several studies 6 , 7 have shown that confirmed the interaction between hypertension, hyperglycemia and famine on increase the risk of CVD. And hypertension and diabetes are components of MS, the risk of CVD might be significantly increased by experiencing fetal famine exposure in the MS individuals. 4.3 Stratification Analysis After stratification for sex, the results remained statistically significant in female, but not in male. Previous studies proved that female hormonal complex and CVD risk was deeply intertwined. For example, sex hormones have vasodilating properties that protective effect of blood vessel wall and estrogen appears to prevent coronary artery spasms. The major CVD risk factors were changed by loss of estrogen(the lipid profile changes with menopause, becoming more atherogenic with increase of LDL cholesterol levels) 30 .In addition, traditional Chinese values that favor boys and discriminate against girls may also be partly to blame. Most studies were conducted in times of food shortages when families tend to allocate food and other resources to their sons than to their daughters, helping more male infants who suffer from famine in the womb to survive and grow, so the female population is more affected by famine and at greater risk of CVD in adulthood 31 . After stratification for smoking, the results remained statistically significant in smoker, but not in non-smoker. One possible explanation for these relationships is that smoking might mediate CVD risk through shared pathophysiology, including dyslipidemia, hyperlipidemia, and abdominal obesity. Prevention strategies to reduce the burden of CVD therefore require the maintenance of a healthy lifestyle. 4.4 Advantages and limitations The main advantages of our study are its prospective nature, the long follow-up time, and the large sample size. In addition, CVD event information was collected through the health insurance system rather than self-reporting, so the data is more reliable and realistic. However, the study has some limitations. All participants in Kailuan cohort were employees from Kailuan Group, an industry dominated by coal mines with mostly male employees (75.53%), so it might be difficult in extrapolating to females or general individuals. In addition, due to the lack of exact famine exposure information in the current study, the grouping was based on year of birth, which might have classified those who did not suffer from famine into the uterine exposed group, resulting in a weaker famine effect. A proportion of participants in the uterine famine exposure group had also been exposed to famine in early childhood, which might have a synergistic effect on CVD. The lack of data in this study related to poor nutrition in the maternal diet, exposure to harmful agents, or risky lifestyle factors, might have confounded the findings, all of which should be explored in future studies. Thus, our findings are only suggestive of this association and need to be supported by further investigations data from famine individuals in other countries. 4.5 Conclusion In summary, we found that exposure to famine during fetal life in patients with MS is associated with a high risk of CVD in life, especially in female and smokers. Maintaining a healthy lifestyle might diminish this effect. 5. List Of Abbreviations BMI Body Mass Index CI Confidence Interval CVD Cardiovascular Disease DBP Diastolic Blood Pressure FBG Fasting Blood Glucose HDL High Density Cholesterol HR Hazard Ratio IDF International Diabetes Federation MI Myocardial Infarction MS Metabolic Syndrome SCr Serum Creatinine SBP Systolic Blood Pressure WC Waist Circumference 6. Declarations 6.1 Ethics approval and consent to participate The project protocol was approved by the ethics committee of Ethics Committee of the Kailuan Medical Group and was by the guidelines of the Helsinki Declaration, and all study individuals in this project signed an informed consent form at enrollment. 6.2 Consent for publication If the manuscript is accepted, we approve it for publication in Diabetology &Metabolic Syndrome. 6.3 Availability of data and materials The data that support the findings of this study are available from [third party name] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of the corresponding author. 6.4 Competing interests The authors declare no conflict of interest. 6.5 Funding This study was supported by Natural Science Foundation of Hebei Province. (H2021209018); This study was supported by Tangshan Science and Technology Innovation Team Program (20130206D). 6.6 Authors' contributions Writing – original draft (Zhe Shu, Xiong Ding, Qing Yue, Xiaoxu Ma, Hongmin Liu, Yuntao Wu, Peng Yang, Ying Wu, Yun Li, and Shouling Wu); Investigation (Zhe Shu, Xiong Ding, Qing Yue, Xiaoxu Ma, Hongmin Liu, Yuntao Wu, Peng Yang); Writing – review & editing (Zhe Shu, Ying Wu, Yun Li and Shouling Wu); Methodology (Zhe Shu, Xiong Ding, Yun Li, and Shouling Wu); Project administration and Funding (Hongmin Liu, and Ying Wu, Yun Li, and Shouling Wu); 6.7 Acknowledgements The authors thank the investigators who made this cohort study possible. 7. References Marcotte-Chenard A, Deshayes TA, Ghachem A, Brochu M. Prevalence of the metabolic syndrome between 1999 and 2014 in the United States adult population and the impact of the 2007–2008 recession: an NHANES study. Appl Physiol Nutr Metab 2019; 44 (8): 861–8. He Y, Li Y, Bai G, et al. Prevalence of metabolic syndrome and individual metabolic abnormalities in China, 2002–2012. Asia Pac J Clin Nutr 2019; 28 (3): 621–33. Klongthalay S, Suriyaprom K. Increased Uric Acid and Life Style Factors Associated with Metabolic Syndrome in Thais. Ethiop J Health Sci 2020; 30 (2): 199–208. Ballantyne CM, Hoogeveen RC, McNeill AM, et al. Metabolic syndrome risk for cardiovascular disease and diabetes in the ARIC study. Int J Obes (Lond) 2008; 32 Suppl 2 : S21-4. Liu B, Chen G, Zhao R, Huang D, Tao L. Temporal trends in the prevalence of metabolic syndrome among middle-aged and elderly adults from 2011 to 2015 in China: the China health and retirement longitudinal study (CHARLS). BMC Public Health 2021; 21 (1): 1045. Shi Z, Nicholls SJ, Taylor AW, Magliano DJ, Appleton S, Zimmet P. Early life exposure to Chinese famine modifies the association between hypertension and cardiovascular disease. J Hypertens 2018; 36 (1): 54–60. Zhang Y, Ying Y, Zhou L, Fu J, Shen Y, Ke C. Exposure to Chinese famine in early life modifies the association between hyperglycaemia and cardiovascular disease. Nutr Metab Cardiovasc Dis 2019; 29 (11): 1230–6. Ekblom-Bak E, Halldin M, Vikstrom M, et al. Physical activity attenuates cardiovascular risk and mortality in men and women with and without the metabolic syndrome - a 20-year follow-up of a population-based cohort of 60-year-olds. Eur J Prev Cardiol 2021; 28 (12): 1376–85. Zhang L, Guo Z, Wu M, Hu X, Xu Y, Zhou Z. Interaction of smoking and metabolic syndrome on cardiovascular risk in a Chinese cohort. Int J Cardiol 2013; 167 (1): 250–3. Gowland RL. Entangled lives: Implications of the developmental origins of health and disease hypothesis for bioarchaeology and the life course. Am J Phys Anthropol 2015; 158 (4): 530–40. Burlina S, Dalfra MG, Lapolla A. Short- and long-term consequences for offspring exposed to maternal diabetes: a review. J Matern Fetal Neonatal Med 2019; 32 (4): 687–94. Qin LL, Luo BA, Gao F, Feng XL, Liu JH. Effect of Exposure to Famine during Early Life on Risk of Metabolic Syndrome in Adulthood: A Meta-Analysis. J Diabetes Res 2020; 2020 : 3251275. Li Y, Li Y, Gurol ME, et al. In utero exposure to the Great Chinese Famine and risk of intracerebral hemorrhage in midlife. Neurology 2020; 94 (19): e1996-e2004. Wang B, Cheng J, Wan H, et al. Early-life exposure to the Chinese famine, genetic susceptibility and the risk of type 2 diabetes in adulthood. Diabetologia 2021; 64 (8): 1766–74. Xin X, Yao J, Yang F, Zhang D. Famine exposure during early life and risk of hypertension in adulthood: A meta-analysis. Crit Rev Food Sci Nutr 2018; 58 (14): 2306–13. Tao B, Yang P, Wang C, et al. Fetal exposure to the Great Chinese Famine and risk of ischemic stroke in midlife. Eur J Neurol 2021; 28 (4): 1244–52. Wu Z, Jin C, Vaidya A, et al. Longitudinal Patterns of Blood Pressure, Incident Cardiovascular Events, and All-Cause Mortality in Normotensive Diabetic People. Hypertension 2016; 68 (1): 71–7. Wu S, Huang Z, Yang X, et al. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes 2012; 5 (4): 487–93. Wang J, Perona JS, Schmidt-RioValle J, Chen Y, Jing J, Gonzalez-Jimenez E. Metabolic Syndrome and Its Associated Early-Life Factors among Chinese and Spanish Adolescents: A Pilot Study. Nutrients 2019; 11 (7). Ding X, Li J, Wu Y, et al. Ideal Cardiovascular Health Metrics Modify the Association Between Exposure to Chinese Famine in Fetal and Cardiovascular Disease: A Prospective Cohort Study. Front Cardiovasc Med 2021; 8 : 751910. Jin C, Chen S, Vaidya A, et al. Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes. Diabetes Care 2017; 40 (11): 1565–72. Ma C, Pavlova M, Liu Y, et al. Probable REM sleep behavior disorder and risk of stroke: A prospective study. Neurology 2017; 88 (19): 1849–55. Li W, Jin C, Vaidya A, et al. Blood Pressure Trajectories and the Risk of Intracerebral Hemorrhage and Cerebral Infarction: A Prospective Study. Hypertension 2017; 70 (3): 508–14. Wu S, An S, Li W, et al. Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA Netw Open 2019; 2 (5): e194758. Li Y, Jaddoe VW, Qi L, et al. Exposure to the Chinese famine in early life and the risk of hypertension in adulthood. J Hypertens 2011; 29 (6): 1085–92. Wang Y, Jin J, Peng Y, Chen Y. Exposure to Chinese Famine in the Early Life, Adulthood Obesity Patterns, and the Incidence of Hypertension: A 22-Year Cohort Study. Ann Nutr Metab 2021; 77 (2): 109–15. Heijmans BT, Tobi EW, Stein AD, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci U S A 2008; 105 (44): 17046–9. Lussana F, Painter RC, Ocke MC, Buller HR, Bossuyt PM, Roseboom TJ. Prenatal exposure to the Dutch famine is associated with a preference for fatty foods and a more atherogenic lipid profile. Am J Clin Nutr 2008; 88 (6): 1648–52. Hidayat K, Du X, Shi BM, Qin LQ. Foetal and childhood exposure to famine and the risks of cardiometabolic conditions in adulthood: A systematic review and meta-analysis of observational studies. Obes Rev 2020; 21 (5): e12981. Santilli F, D'Ardes D, Guagnano MT, Davi G. Metabolic Syndrome: Sex-Related Cardiovascular Risk and Therapeutic Approach. Curr Med Chem 2017; 24 (24): 2602–27. Lv S, Shen Z, Zhang H, et al. Association between exposure to the Chinese famine during early life and the risk of chronic kidney disease in adulthood. Environ Res 2020; 184 : 109312. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 08 Oct, 2022 Reviews received at journal 01 Oct, 2022 Reviewers agreed at journal 01 Oct, 2022 Reviewers agreed at journal 19 Sep, 2022 Reviewers invited by journal 03 Sep, 2022 Editor assigned by journal 03 Sep, 2022 Submission checks completed at journal 03 Sep, 2022 First submitted to journal 01 Sep, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-2020898","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":133834764,"identity":"df67259c-976b-4c58-b486-2927091650cd","order_by":0,"name":"zhe shu","email":"","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zhe","middleName":"","lastName":"shu","suffix":""},{"id":133834765,"identity":"f02a1d45-31f8-4225-8b69-47332193102a","order_by":1,"name":"Xiong Ding","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiong","middleName":"","lastName":"Ding","suffix":""},{"id":133834766,"identity":"c5cdb0c5-80c6-4b57-b908-5a1d8bde27d3","order_by":2,"name":"Yue Qing","email":"","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Qing","suffix":""},{"id":133834767,"identity":"736c5e8c-f469-4e2f-aac9-ef1dcaea04bb","order_by":3,"name":"XiaoXu Ma","email":"","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"XiaoXu","middleName":"","lastName":"Ma","suffix":""},{"id":133834768,"identity":"98f640bf-9c93-422e-9250-0bff05ad6351","order_by":4,"name":"MinHong Liu","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"MinHong","middleName":"","lastName":"Liu","suffix":""},{"id":133834769,"identity":"889ff1dc-83e9-49bd-8904-5719c4493a2e","order_by":5,"name":"YunTao Wu","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"YunTao","middleName":"","lastName":"Wu","suffix":""},{"id":133834770,"identity":"0540d289-35d4-42f3-8328-b9347b596a3b","order_by":6,"name":"Peng Yang","email":"","orcid":"","institution":"North China University of Science and Technology Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yang","suffix":""},{"id":133834771,"identity":"4e33bcc5-f7fe-4fc9-8ad1-b8aefd50d508","order_by":7,"name":"Ying Wu","email":"","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wu","suffix":""},{"id":133834772,"identity":"69348c1e-b154-40eb-94e0-2a3521e6025e","order_by":8,"name":"Yun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACPmYQWSEhx8/e2PjwAzFa2MBaztgYS/YcbjaWIEoLiGBsS0vccCO9TYCHKC3sPGbSBWcOMzbcfNjGIMFgJ6fbQNBhQC0zKg4zM85ObHtQwJBsbHaAGC08Zw6zMUsnthtIMBxI3EaUFt62wzxskgfbJHhI0JImwSPBSLQWtmJrnjM2BhI8icBANiDCL/z8hzfe5qmQqN9//PjDhx8q7OQIamFg4DBA4hjgVIYM2B8QpWwUjIJRMApGMAAATiY5UBZmFdoAAAAASUVORK5CYII=","orcid":"","institution":"North China University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Li","suffix":""},{"id":133834773,"identity":"e5ff8e05-c400-430b-a342-5df4fdf94a69","order_by":9,"name":"Shouling Wu","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shouling","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2022-09-01 08:44:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2020898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2020898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":26196948,"identity":"6afe7fa3-5e67-4197-957b-d5cf0fbb3211","added_by":"auto","created_at":"2022-09-07 22:56:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1270077,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence curve of CVD in three groups\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2020898/v1/62a95b8b1578951dc72afdc5.jpg"},{"id":26196949,"identity":"f7b17864-9d2d-40c7-a5c6-c087b2440d24","added_by":"auto","created_at":"2022-09-07 22:56:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":590708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2020898/v1/27fb56c8-f059-458f-9277-bc62ac245b17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of fetal famine exposure on the cardiovascular disease risk in the metabolic syndrome individuals","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, the prevalence of metabolic syndrome (MS) has gradually risen\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In Chinese adults, the prevalence of MS has increased from 9.5% in 2002 to 18.7% in 2010\u0026ndash;2012\u003csup\u003e2\u003c/sup\u003e, with 450\u0026nbsp;million patients. According to an estimate by the International Diabetes Federation(IDF), the worldwide prevalence of MS in the adults is on the rise with an estimated prevalence of 20\u0026ndash;25%\u003csup\u003e3\u003c/sup\u003e. As is well known, MS can increase the risk of chronic disease, including cardiovascular disease (CVD). A meta-analysis showed that the risk of developing CVD in patients with MS will increase by about 2 times \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although previous studies \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;9\u003c/sup\u003ehave suggested that hypertension, hyperglycemia, smoking and physical inactivity might attribute to the high incidence of CVD in MS patients, the etiology is still unclear, and possible influencing factors still need to be explored, which should be helpful in reducing the disease burden of CVD in patients with MS.\u003c/p\u003e \u003cp\u003e\"Developmental origins of health and disease\" hypothesis suggested that exposure to malnutrition during early life will affect the health in adulthood \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Some studies have confirmed that exposure to famine in the fetus might increase the risk of MS\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, cerebral hemorrhage\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, diabetes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, hypertension\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, cerebral infarction \u003csup\u003e16\u003c/sup\u003eand other diseases in adulthood, but few studies have examined whether famine exposure affects CVD risk in MS patients.\u003c/p\u003e \u003cp\u003eThe Great Famine in China (1959\u0026ndash;1962) was one of the largest famines in human history, resulting in insufficient nutritional supply for a large number of people exposed to the famine environment. Our study is based on the Kailuan study, a large-scale, individual-based longitudinal cohort study with a decade-long follow-up. The expected results of this study will help to examine the association between exposure to the Great Chinese Famine in early life and the risk of CVD in adults in individuals with MS.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study participants\u003c/h2\u003e\n\u003cp\u003eThe Kailuan Study (accession number: ChiCTR-TNC-11001489) was a functional community individual-based cohort study in Tangshan, China, and the specific study design and procedures can be found in the team's previous studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The Kailuan study began in 2006 and included 101,510 adults (81,110 males and 20,400 females) aged 18 years or older, all of them completed standard questionnaires (medical history and lifestyle) between 2006 and 2007, underwent health assessments every two years, including physical examinations (waist circumference (WC), weight, height, and blood pressure measurements) and laboratory tests (lipid assessments, fasting blood glucose (FBG), and serum creatinine (SCr)). All participants were followed up until their death or December 31, 2019.\u003c/p\u003e\n\u003cp\u003eAccording to the IDF global working definition of MS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, 14,241 MS patients who born between January 1, 1949, and December 31, 1974, were included. Individuals with missing data (n\u0026thinsp;=\u0026thinsp;69) on WC, high density cholesterol (HDL), triglycerides, diastolic blood pressure (DBP), systolic blood pressure (SBP), fasting blood glucose (FBG), or those with CVD (n\u0026thinsp;=\u0026thinsp;428) at the baseline survey in 2006 were excluded\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, 13,744 participants were served as the baseline cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Famine exposure\u003c/h2\u003e\n\u003cp\u003eSince famine in China occurred concentratedly from 1959 to 1962, we judged the period of famine exposed by birth time. Based on the previous Chinese famine research\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, birth year was taken as the basis for classification of famine exposure, all participants were divided into three groups: no-exposed group (born between January 1, 1963 and December 31, 1974), uterine famine exposed group (born between January 1, 1959 and December 31, 1962), childhood famine exposed group (born between January 1, 1949 and December 31, 1958).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 MS definition\u003c/h2\u003e\n\u003cp\u003eMS was defined following the IDF Global Working Definition (IDF criteria) with the following criteria\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e: the presence of central obesity (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for males or \u0026ge;\u0026thinsp;80 cm for females), plus any two of following factors: (i) raised triglyceride level:\u0026ge; 1.7 mmol/L (150 mg/dl) or taking triglyceride-lowering medications; (ii) reduced HDL cholesterol:\u0026lt;1.03 mmol/L for men or \u0026lt;\u0026thinsp;1.29 mmol/L for females, or on lipid-lowering medications; (iii) hypertension: SBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;130 mmHg, or DBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;85 mmHg, or taking antihypertensive medications; (iv) raised FBG: \u0026gt;=100 mg/dL (5.6 mmol/L), or individuals who have been diagnosed with type 2 diabetes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Follow-up and CVD\u003c/h2\u003e\n\u003cp\u003eThe starting point was defined as the date of completion of the 2006 annual baseline questionnaire and individuals were followed up until December 31, 2019. During follow-up, CVD incidence was assessed annually, and biochemical markers were collected every two years. The outcome event for the study was the first occurrence of a major CVD, which was defined as the composite of stroke and myocardial infarction (MI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The Hospital Discharge Register and Municipal Social Insurance Institution database were linked to identify the incidence of CVD based on The International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) (I61 for intracerebral hemorrhagic stroke, I63 for ischemic stroke, and I21 for MI) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These two databases are updated annually based on follow-up and cover information on all participants in the Kailuan study. An expert panel collected and reviewed annual discharges records from 11 local hospitals to identify patients who were suspected of CVD.\u003c/p\u003e\n\u003cp\u003eIncident MI was diagnosed based on the World Health Organization's Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) criteria on basis of clinical symptoms and dynamic changes in clinical presentation, cardiac enzymes and electrocardiogram. Incident stroke diagnosed was according to neurological signs, clinical symptoms and neuroimaging (from CT or MRI) on the basis of World Health Organization's criteria. Death data were collected from provincial vital statistics offices, as described in previous studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Covariates evaluation and measurement\u003c/h2\u003e\n\u003cp\u003eThe questionnaire design, anthropometry and laboratory data testing were the same as the literature published by our research group\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The data collected by the research include birth, gender, smoking, drinking, physical exercise, education level, history of medications and other basic information. During the survey, professionally trained medical staff completed the physical examination, including the measurement of their height, weight, waist circumference and blood pressure. Height and waist circumference were measured to the nearest 0.1 cm using the disposable tape measure.\u003c/p\u003e\n\u003cp\u003eWeight was determined by using calibrated portable digital weighing scale with 0.1 kg precision.\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of the biochemical measurement results, a venous blood sample was obtained from all subjects who fasted for at least 8 hours before the measurement, and 5 ml of fasting elbow venous blood was collected between 7\u0026ndash;9 am on the day of the physical examination, and the blood sample was centrifuged to obtain the upper serum for FBG values and lipid levels. All operations were performed strictly in accordance with the manufacturer's instructions, and blood samples were tested on a Hitachi (7600) automated biochemistry analyzer.\u003c/p\u003e\n\u003cp\u003eAge was calculated by subtracting the birthday from the beginning date of medical examination. Height and weight were measured to calculate body mass index (BMI) as weight (kg) / height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). Drinking was defined as consumption on average of 100 ml/day of spirits (alcohol content above 50%) in the past year. Smoking was defined as smoking an average of at least one cigarette per day for more than in the past year. Educational attainment was stratified into two levels: junior high school or below, senior high school or above. Physical exercise was defined as exercise time for \u0026gt;\u0026thinsp;=\u0026thinsp;30 minutes at a time at least three times per week. Hypertension was defined as SBP over 140 mmHg or a DBP over 90 mmHg, or the fact that the patient was taking antihypertensive medications. Diabetes was defined as FBG\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;6.1 mmol/L, or the fact that the patient was taking hypoglycemic medications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 Statistical analysis of data\u003c/h2\u003e\n\u003cp\u003eAll data processing and analyses were performed using SAS version 9.4(SAS Institute, Cary, North Carolina), and R software version 3.6.0 (R Core Team, Vienna, Austria). The database was established through epidata3.1, entered by uniformly trained medical staff, and uploaded to the Oracle database of Kailuan General Hospital. All statistical tests were 2-sided, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eThe normal or approximate normal distribution of the continuous variables was represented by x̄ \u0026plusmn; s, the comparison between groups using analysis of variance, the skewed distribution was represented by M (P25, P75), and the intergroup comparison was used the Kruskal-Wallis test. The percentages described were used categorical variables and compared by \u0026chi;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e tests.\u003c/p\u003e\n\u003cp\u003ePerson-years of follow-up were calculated from the return date of the baseline questionnaire to the date of CVD diagnosis, death, loss to follow-up (n\u0026thinsp;=\u0026thinsp;800, 5.82%), or end of follow-up (December 31, 2019) whichever occurred first. The incidence density of CVD in different groups in the MS individuals was calculated by dividing the number of events by the total number of follow-up person-years (1000/person-year), using the Log-rank test for comparison among groups. We used the weighted Cox regression model to analyze the effect of early life famine exposed in the MS individuals on the risk of CVD in adulthood, and the HR (Hazard Ratio) and 95%CI (confidence interval) was calculated. The model adjusted for age, gender, education level, smoking, drinking, physical exercise, BMI, history of diabetes, history of hypertension, low-density lipoprotein cholesterol, using antihypertensive medications, using antidiabetic medications, and using lipid-lowering medications.\u003c/p\u003e\n\u003cp\u003eTaking CVD as the dependent variable, and famine exposure as the independent variable, a stratified analysis was carried out by gender, smoking, and drinking. To verify the robustness of the results, a sensitivity analysis was performed after removing the individuals who had CVD incidents within two years or lost-to-review individuals.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 13,744 participants (10,254 males and 3,490 females) were enrolled in the current study, 1,777 participants had been exposed to the Chinese famine during utero stage, while 8,848 participants had been exposed to the famine during childhood stage, respectively. There were significant differences among the three groups in terms of age, gender, BMI, FBG, SBP, DBP, WC, smoking, physical exercise, hypertension, diabetes, antihypertensive medication-using, and antihyperglycemic medications-using (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to the reference group, participants in the uterine famine exposed group were more likely to be female, have diabetes, hypertension, with greater WC, and higher prevalence of using antihypertensive medications and using antihyperglycemic medications (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBasic Characteristics of 13,744 MS participants according to the famine exposure\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComponents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,119)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUtero famine exposed\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,777)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,848)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (year)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.3\u0026plusmn;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.1\u0026plusmn;1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.1\u0026plusmn;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.2\u0026plusmn;3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.8\u0026plusmn;3.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.5\u0026plusmn;3.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0\u0026plusmn;2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.3\u0026plusmn;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.3\u0026plusmn;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135.3\u0026plusmn;17.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.0\u0026plusmn;19.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141.7\u0026plusmn;19.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.9\u0026plusmn;12.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.1\u0026plusmn;12.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.2\u0026plusmn;11.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.1881\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWC(cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.0\u0026plusmn;7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.1\u0026plusmn;7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.1\u0026plusmn;7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.9641\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37\u0026plusmn;0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37\u0026plusmn;0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.34\u0026plusmn;0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.1035\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44\u0026plusmn;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.45\u0026plusmn;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.48\u0026plusmn;0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,320(74.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,461(82.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,675(86.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e799(25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316(17.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,173(13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoker (n, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,269(40.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e705(39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,901(32.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinker (n, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,511(48.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e735(41.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,082(34.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical exercise (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211(6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125(7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,359(15.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,890(60.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,138(64.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,325(71.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e431(13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e359(20.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,899(21.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of antihypertensive medications (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365(11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284(16.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,879(21.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of hypoglycemic medications (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63(2.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57(3.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e440(5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of hypolipidemic medications (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44(1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159(1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.0228\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eData were present as n (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (P25, P75) according to variable category. Pearson\u0026rsquo;s chi-square test, ANOVA analysis, or Kruskal-Wallis test was used to compare differences between groups properly.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eAbbreviations: BMI, body mass index; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglyceride.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring a mean follow-up of 12.12 years, the cumulative incidences of CVD in uterine famine exposed group (5.87%) and the childhood famine exposed group (10.13%) were greater than that in no-exposed group (10.90%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. 1). The incidence density of CVD was 4.70/1000, 8.32/1000, and 9.09/1000 person-years in the no-exposed group, the uterine famine exposed group, and the childhood famine exposed group, respectively (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with no-exposed individuals, the CVD risk increased in participants with uterine famine exposed group (HR 1.32,95% CI 1.04\u0026ndash;1.67), but not increased in childhood famine exposed individuals. Further results showed that the association only observed for stroke, not for MI. In the sensitive analysis, similar results were observed after removing the individuals with CVD occurring within two years or removing the lost-to-review individual (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCox proportional hazard model analysis of different famine groups and the incidence of end-point events\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecomponents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecase/total\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIR (per 1000 person-years)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183/3119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180/1777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41(1.11\u0026ndash;1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35(1.06\u0026ndash;1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.32(1.04\u0026ndash;1.67)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e964/8848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10(0.80\u0026ndash;1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05(0.76\u0026ndash;1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03(0.75\u0026ndash;1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145/3119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151/1777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e6.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47(1.13\u0026ndash;1.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41(1.08\u0026ndash;1.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.37(1.05\u0026ndash;1.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e778/8848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10(0.77\u0026ndash;1.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05(0.73\u0026ndash;1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04(0.72\u0026ndash;1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMi\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39/3119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33/1777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18(0.69\u0026ndash;2.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16(0.68\u0026ndash;1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15(0.68\u0026ndash;1.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213/8848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07(0.54\u0026ndash;2.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05(0.53\u0026ndash;2.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05(0.53\u0026ndash;2.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; IR, incidence rate; Str, stroke; MI, myocardial infarction;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003eModel 1: Adjusted for age, and gender.\u003c/p\u003e\n\u003cp\u003eModel 2: Included covariates in model 1 and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former).\u003c/p\u003e\n\u003cp\u003eModel 3: Included covariates in model 2 and further adjusted for low-density lipoprotein, hypertension, diabetes, use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSensitivity analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecomponents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecase/total\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIR (per 1000 person-years)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3 HR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDelete events that occurred within 2 years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180/3107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173/1762\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.40(1.10,1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35(1.06,1.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.31(1.03,1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e941/8792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13(0.82,1.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09(0.79,1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06(0.77,1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDelete lost-to-review individuals\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176/2981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166/1700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39(1.09,1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34(1.05,1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.31(1.03,1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e878/8300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14(0.82,1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10(0.79,1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08(0.78,1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eModel 1: Adjusted for age, and gender.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eModel 2: Included covariates in model 1 and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eModel 3: Included covariates in model 2 and further adjusted for low-density lipoprotein, hypertension, diabetes, use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the effect of covariates on the CVD risk, stratified analysis by gender, smoking (yes/no), or drinking (yes/no) were performed. The results showed that the association between uterine famine exposure and increased CVD risk only observed in female (HR: 2.31, 95%CI: 1.13\u0026ndash;4.73), but not in male. The similar results were observed in smokers (HR:1.53,95%CI: 1.08\u0026ndash;2.17). However, no association between famine exposure and CVD risks were observed for patients with childhood famine exposure or drinking. Also, no interaction between famine exposure and gender, smoking or drinking were observed (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAdjusted HR(95%CI) for incidence of CVD in the MS individual by exposure to famine by gender, smoking, drinking\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecomponents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecase/total\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIR (per 1000 person-years)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168/2514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148/1304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30(1.01\u0026ndash;1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25(0.97\u0026ndash;1.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22(0.94\u0026ndash;1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e807/6436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02(0.73\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.70\u0026ndash;1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.68\u0026ndash;1.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15/605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32/473\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36(1.19\u0026ndash;4.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37(1.20\u0026ndash;4.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.31(1.13\u0026ndash;4.73)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157/2,412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77(0.69\u0026ndash;4.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.84(0.72\u0026ndash;4.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.79(0.69\u0026ndash;4.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91/1269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92/705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e10.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59(1.12\u0026ndash;2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.56(1.10\u0026ndash;2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.53(1.08\u0026ndash;2.17)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e388/2901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e11.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.37(0.85\u0026ndash;2.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34(0.83\u0026ndash;2.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33(0.83\u0026ndash;2.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo-Smoking\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-expose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92/1850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e3.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88/1072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e6.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26(0.91\u0026ndash;1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22(0.88\u0026ndash;1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20(0.86\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e576/5947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94(0.61\u0026ndash;1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.59\u0026ndash;1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89(0.58\u0026ndash;1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93/1511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79/735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.46(1.02,2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38(0.97,1.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38(0.97,1.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e363/3082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e9.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30(0.81,2.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23(0.77,1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.24(0.77,1.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo-Drinking\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90/1608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e4.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtero exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101/1042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e7.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39(1.01,1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35(0.98,1.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32(0.95,1.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-prenatal exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e601/5766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e8.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.65,1.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97(0.63,1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.62,1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 1\u003csup\u003ea\u003c/sup\u003e: Adjusted for age.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 1\u003csup\u003eb\u003c/sup\u003e: Adjusted for age, and gender.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 1\u003csup\u003ec\u003c/sup\u003e: Adjusted for age, and gender.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 2\u003csup\u003ea\u003c/sup\u003e: Included covariates in model 1a and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), drinking (current, never/former), physical activity (current, never/former).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 2\u003csup\u003eb\u003c/sup\u003e: Included covariates in model 1b and further adjusted for education (junior high school or below, senior high school or above), drinking (current, never/former), physical activity (current, never/former).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 2\u003csup\u003ec\u003c/sup\u003e: Included covariates in model 1c and further adjusted for education (junior high school or below, senior high school or above), smoking (current, never/former), physical activity (current, never/former).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 3\u003csup\u003ea\u003c/sup\u003e: Included covariates in model 2a and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 3\u003csup\u003eb\u003c/sup\u003e: Included covariates in model 2b and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 3\u003csup\u003ec\u003c/sup\u003e: Included covariates in model 2c and further adjusted for use of antihypertensive medications, use of hypoglycemic medications, and use of hypolipidemic medications.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBased on the prospective cohort, we found that exposure to Chinese famine during fetal life was associated with a higher risk of CVD in patients with MS. The results should help to elucidate the pathogenesis of CVD in MS individuals and emphasize the importance of adequate nutrition during the fetal period. In addition, our results showed the association was significant in smokers, but not in non-smokers, which should be helpful in providing recommendations of the lifestyle for individuals with MS.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Compared with other studies\u003c/h2\u003e \u003cp\u003eTo our knowledge, the effect of famine exposure on CVD risk in MS patients has not been evaluated, but studies confirmed that fetal exposure to famine increases CVD risk in those with the component of MS. A cross-sectional study found that the association between early life famine exposure and adult CVD risk appears to be stronger in overweight than in normal individuals\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Other studies showed that fetal exposure to famine exacerbates the adverse effect of hypertension on CVD, especially in individuals with central obesity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Zhang et al. found that exposure to famine, especially during fetal life, exacerbates the association between hyperglycemia and CVD. All these results support our research results to a certain extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanism\u003c/h2\u003e \u003cp\u003eAlthough the mechanisms underlying this association between fetal famine exposure and adult CVD in MS individuals have not been elucidated, several mechanisms might explain the relationship. First, malnutrition early in life may affect structural changes in the cardiovascular system, and famine exposure during fetal life might lead to epigenetic changes, even with lifelong effects\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Second, findings from the Dutch Famine Study suggest that prenatal exposure to famine increases the preference for high-fat foods and a high prevalence of dyslipidemia\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which in turn increases the risk of CVD\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Third, several studies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e have shown that confirmed the interaction between hypertension, hyperglycemia and famine on increase the risk of CVD. And hypertension and diabetes are components of MS, the risk of CVD might be significantly increased by experiencing fetal famine exposure in the MS individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Stratification Analysis\u003c/h2\u003e \u003cp\u003eAfter stratification for sex, the results remained statistically significant in female, but not in male. Previous studies proved that female hormonal complex and CVD risk was deeply intertwined. For example, sex hormones have vasodilating properties that protective effect of blood vessel wall and estrogen appears to prevent coronary artery spasms. The major CVD risk factors were changed by loss of estrogen(the lipid profile changes with menopause, becoming more atherogenic with increase of LDL cholesterol levels) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.In addition, traditional Chinese values that favor boys and discriminate against girls may also be partly to blame. Most studies were conducted in times of food shortages when families tend to allocate food and other resources to their sons than to their daughters, helping more male infants who suffer from famine in the womb to survive and grow, so the female population is more affected by famine and at greater risk of CVD in adulthood\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAfter stratification for smoking, the results remained statistically significant in smoker, but not in non-smoker. One possible explanation for these relationships is that smoking might mediate CVD risk through shared pathophysiology, including dyslipidemia, hyperlipidemia, and abdominal obesity. Prevention strategies to reduce the burden of CVD therefore require the maintenance of a healthy lifestyle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Advantages and limitations\u003c/h2\u003e \u003cp\u003eThe main advantages of our study are its prospective nature, the long follow-up time, and the large sample size. In addition, CVD event information was collected through the health insurance system rather than self-reporting, so the data is more reliable and realistic. However, the study has some limitations. All participants in Kailuan cohort were employees from Kailuan Group, an industry dominated by coal mines with mostly male employees (75.53%), so it might be difficult in extrapolating to females or general individuals. In addition, due to the lack of exact famine exposure information in the current study, the grouping was based on year of birth, which might have classified those who did not suffer from famine into the uterine exposed group, resulting in a weaker famine effect. A proportion of participants in the uterine famine exposure group had also been exposed to famine in early childhood, which might have a synergistic effect on CVD. The lack of data in this study related to poor nutrition in the maternal diet, exposure to harmful agents, or risky lifestyle factors, might have confounded the findings, all of which should be explored in future studies. Thus, our findings are only suggestive of this association and need to be supported by further investigations data from famine individuals in other countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Conclusion\u003c/h2\u003e \u003cp\u003eIn summary, we found that exposure to famine during fetal life in patients with MS is associated with a high risk of CVD in life, especially in female and smokers. Maintaining a healthy lifestyle might diminish this effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. List Of Abbreviations","content":"\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI Confidence Interval\u003c/p\u003e\n\u003cp\u003eCVD Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eDBP Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eFBG Fasting Blood Glucose\u003c/p\u003e\n\u003cp\u003eHDL High Density Cholesterol\u003c/p\u003e\n\u003cp\u003eHR Hazard Ratio\u003c/p\u003e\n\u003cp\u003eIDF International Diabetes Federation\u003c/p\u003e\n\u003cp\u003eMI Myocardial Infarction\u003c/p\u003e\n\u003cp\u003eMS Metabolic Syndrome\u003c/p\u003e\n\u003cp\u003eSCr Serum Creatinine\u003c/p\u003e\n\u003cp\u003eSBP Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eWC Waist Circumference\u003c/p\u003e"},{"header":"6. Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project protocol was approved by the ethics committee of Ethics Committee of the Kailuan Medical Group and was by the guidelines of the Helsinki Declaration, and all study individuals in this project signed an informed consent form at enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf the manuscript is accepted, we approve it for publication in Diabetology \u0026amp;Metabolic Syndrome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from [third party name] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science Foundation of Hebei Province. (H2021209018); This study was supported by Tangshan Science and Technology Innovation Team Program (20130206D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft (Zhe Shu, Xiong Ding, Qing Yue, Xiaoxu Ma, Hongmin Liu, Yuntao Wu, Peng Yang, Ying Wu, Yun Li, and Shouling Wu); Investigation (Zhe Shu, Xiong Ding, Qing Yue, Xiaoxu Ma, Hongmin Liu, Yuntao Wu, Peng Yang); Writing \u0026ndash; review \u0026amp; editing (Zhe Shu, Ying Wu, Yun Li and Shouling Wu); Methodology (Zhe Shu, Xiong Ding, Yun Li, and Shouling Wu); Project administration and Funding (Hongmin Liu, and Ying Wu, Yun Li, and Shouling Wu);\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7 Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the investigators who made this cohort study possible.\u003c/p\u003e"},{"header":"7. References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarcotte-Chenard A, Deshayes TA, Ghachem A, Brochu M. Prevalence of the metabolic syndrome between 1999 and 2014 in the United States adult population and the impact of the 2007\u0026ndash;2008 recession: an NHANES study. Appl Physiol Nutr Metab 2019; \u003cb\u003e44\u003c/b\u003e(8): 861\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Li Y, Bai G, et al. Prevalence of metabolic syndrome and individual metabolic abnormalities in China, 2002\u0026ndash;2012. Asia Pac J Clin Nutr 2019; \u003cb\u003e28\u003c/b\u003e(3): 621\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlongthalay S, Suriyaprom K. Increased Uric Acid and Life Style Factors Associated with Metabolic Syndrome in Thais. Ethiop J Health Sci 2020; \u003cb\u003e30\u003c/b\u003e(2): 199\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallantyne CM, Hoogeveen RC, McNeill AM, et al. Metabolic syndrome risk for cardiovascular disease and diabetes in the ARIC study. Int J Obes (Lond) 2008; \u003cb\u003e32 Suppl 2\u003c/b\u003e: S21-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Chen G, Zhao R, Huang D, Tao L. Temporal trends in the prevalence of metabolic syndrome among middle-aged and elderly adults from 2011 to 2015 in China: the China health and retirement longitudinal study (CHARLS). BMC Public Health 2021; \u003cb\u003e21\u003c/b\u003e(1): 1045.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Z, Nicholls SJ, Taylor AW, Magliano DJ, Appleton S, Zimmet P. Early life exposure to Chinese famine modifies the association between hypertension and cardiovascular disease. 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Environ Res 2020; \u003cb\u003e184\u003c/b\u003e: 109312.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cardiovascular disease, China famine, metabolic syndrome, cohort study, fetal","lastPublishedDoi":"10.21203/rs.3.rs-2020898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2020898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with metabolic syndrome (MS) have a higher incidence of cardiovascular disease (CVD), but the possible mechanisms are not fully understood and further exploration of the possible factors influencing the high incidence of CVD in patients with MS is still needed.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to examine the association between fetal famine exposure and the risk of CVD in adulthood in people with MS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe 13,744 MS patients free of CVD selected from the Kailuan cohort in 2006 (referred as the baseline survey) were included in the study. All patients were born between January 1, 1949, and December 31, 1974. Based on the date of birth, all patients were divided into the no-exposed group (born between January 1, 1963, and December 31, 1974), uterine famine exposed group (born between January 1, 1959 and December 31, 1962), and childhood famine exposed group (born between January 1, 1949 and December 31, 1958). After following up to December 31, 2019, the weighted Cox regression analysis model was used to calculate the effect of early life famine exposure in MS individuals on the risk of CVD in adulthood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the 12.12 years of follow-up, the incidence of CVD was 5.87%, 10.13%, and 10.90% in the no-exposed group, uterine famine exposed group, and childhood famine exposed group, respectively. Compared with participants in the no-exposed group, the CVD risk and stroke risk increased in participants in the uterine famine exposed group (for CVD, HR: 1.32,95% CI:1.04\u0026ndash;1.67; for stroke, HR:1.37,95% CI: 1.05\u0026ndash;1.79), but not in childhood famine exposed group. However, the increased CVD risks were only observed in females or smokers. No increased MI risks were observed for participants in the uterine famine exposed group or childhood famine exposed group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggested that exposure to famine during fetal life significantly increased the risk of developing CVD in adulthood in individuals with MS, and this association was enhanced in females or smokers.\u003c/p\u003e","manuscriptTitle":"Effects of fetal famine exposure on the cardiovascular disease risk in the metabolic syndrome individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-07 22:56:43","doi":"10.21203/rs.3.rs-2020898/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-08T10:42:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-10-01T22:39:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ae66d6b4-086f-4a27-bf7a-136e7ebaa8f7","date":"2022-10-01T05:16:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9f3c52e8-2525-4e4b-bf2c-613822df3b0c","date":"2022-09-19T17:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-03T17:53:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-03T17:53:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-09-03T05:06:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2022-09-01T08:39:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8322254-16fc-4711-a9b6-49f37aa7243d","owner":[],"postedDate":"September 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-11-09T10:59:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-07 22:56:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2020898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2020898","identity":"rs-2020898","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}