Development of chronic hypertension among women with a history of de novo hypertension disorders during pregnancy: the role of metabolites

Development of chronic hypertension among women with a history of de novo hypertension disorders during pregnancy: the role of metabolites | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of chronic hypertension among women with a history of de novo hypertension disorders during pregnancy: the role of metabolites Yingjie Gu, Mengxin Yao, Qida He, Jingjing Ma, Qin Huang, Xiangxiang Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9229260/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Hypertensive disorders of pregnancy, including gestational hypertension (GH) and preeclampsia (PE), are associated with an elevated long-term risk of chronic hypertension (CH). However, evidence regarding the underlying mechanisms and effective risk prediction models remains limited. Methods This study utilized baseline metabolomics data from the UK Biobank, focusing on women with a history of GH or PE. Their CH status was assessed both at enrollment and during follow-up. In Phase I, a cross-sectional study employing logistic regression and Mendelian randomization analyses identified metabolites significantly associated with CH. The discriminatory performance of these metabolites for CH at enrollment was evaluated using the area under the receiver operating characteristic curve. In Phase II, a prospective study assessed the predictive performance of the identified metabolites for incident CH at three and five years. Results The study recruited 281 women with a history of GH or PE. Among them, 75 had prevalent CH at enrollment. Of the remaining 206 women without CH at baseline, 27 developed incident CH during follow-up. Nine metabolites were significantly associated with CH, including glycine and lipid components in lipoprotein subclasses. Notably, adding the metabolic profile to a conventional prediction model significantly improved the predictive performance for CH. Conclusion The findings suggest that metabolic dysregulation, particularly in lipid metabolism, is involved in the progression to CH following GH or PE. The identified metabolic profile may enhance CH risk prediction in this high-risk population. hypertensive disorders of pregnancy gestational hypertension preeclampsia chronic hypertension metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Hypertensive disorders of pregnancy (HDP), including chronic hypertension (CH, present before pregnancy or diagnosed before 20 weeks of gestation) and de novo disorders [gestational hypertension (GH) or preeclampsia (PE)], are leading complications of pregnancy[ 1 ]. GH is characterized by the presence of new-onset hypertension after 20 weeks of gestation, while PE is defined as the occurrence of GH along with new-onset proteinuria or other signs of organ damage[ 2 , 3 ]. HDP is considered as a state of failed maternal adaptation to pregnancy[ 4 ], a period marked by profound cardiovascular and hemodynamic changes. Although HDP typically resolves after delivery, with blood pressure gradually normalizing in the postpartum period[ 5 ], they indicate an increased risk for future cardiovascular health. A recent meta-analysis suggests that HDP is associated with an increased likelihood of CH[ 6 ]. However, affected women often do not sufficiently recognize HDP as a significant risk factor for future CH[ 7 , 8 ]. To improve long-term health outcomes, regular blood pressure monitoring and management, adherence to a healthy lifestyle, and maintenance of an ideal weight during the postpartum period are commonly recommended for women with a history of HDP[ 1 ]. Unfortunately, these recommendations are often not effectively implemented, and efforts to coordinate timely medical follow-up and preventive strategies remain inadequate[ 9 , 10 ]. Therefore, risk stratification to identify high-risk women for CH among those with a history of HDP, particularly de novo HDP, could help raise awareness and facilitate targeted preventive measures. Current research on risk factors for CH following HDP has largely focused on traditional factors such as age[ 11 , 12 ], obesity[ 11 – 13 ], parity[ 11 ], family history[ 12 ], educational level[ 12 ], and race[ 14 ]. However, these factors frequently overlap with those observed in the general population[ 15 ]. Hence, it is crucial to investigate specific biomarkers to predict CH in high-risk women with a history of HDP. HDP has been implicated in extensive metabolic alterations[ 16 , 17 ]. Metabolomics is an emerging high-throughput technique that provides simultaneous, quantitative analysis of all small-molecule metabolites in a biological system[ 18 ]. Metabolites associated with physiological conditions or aberrant processes can provide valuable insights into the progression of disease[ 19 ]. Metabolomics has been increasingly applied to elucidate risk factors for CH, with identified metabolites showing promise for early disease prediction in the general population[ 20 , 21 ]. Thus, combining novel metabolite markers with clinical factors may improve early prediction of future CH in women with a history of de novo HDP. Mendelian randomization (MR) employs genetic variants associated with an exposure as instrumental variables (IVs) to investigate causal relationships with an outcome. Since genetic variants are assigned at conception and generally unaffected by later-life confounders or disease processes, MR analysis helps mitigate issues of reverse causation and unmeasured confounding[ 22 ]. Thus, MR offers a robust framework for investigating potential causal links between metabolites and CH. Using Nuclear Magnetic Resonance (NMR) metabolomics data from the UK Biobank, this study aimed to: 1) evaluate the associations between metabolites and CH in women with a history of GH or PE; 2) identify metabolites with a potential causal relationship to CH using a two-sample MR analysis; and 3) evaluate the predictive performance of significant metabolites combined with the established risk factors for CH. Methods Study design The UK Biobank study is a prospective cohort study that recruited approximately 500,000 participants aged 40–69 years between 2006 and 2010. Baseline questionnaires and anthropometric measurements were completed at 22 assessment centers across England, Wales, and Scotland. The UK Biobank received ethical approval from the North West Multi-center Research Ethics Committee (16/NW/0274). All participants gave written informed consent in accordance with the principles of the Declaration of Helsinki. These analyses were conducted under UK Biobank application number 68136. To identify metabolomic biomarkers associated with the development of CH in high-risk women, the present study focused on women with a history of de novo HDP at baseline. GH and PE were identified from hospital inpatient records based on International Classification of Diseases, 10th Revision (ICD-10) code O13-O15 (O13: gestational hypertension without significant proteinuria; O14: gestational hypertension with significant proteinuria; O15: eclampsia). Women with a history of chronic HDP (n = 39; ICD-10 codes O10-O11) or pre-existing CH before pregnancy (n = 158; ICD-10 codes I10-I13, I15), and those without available metabolomic data (n = 894) were excluded. Finally, 281 women with a history of GH or PE were included in the analysis (Fig. 1 ). Among them, 75 participants (26.7%) had already been diagnosed with CH at baseline. The analysis proceeded in two stages. In the first stage of the cross-sectional study, metabolite profiles between the 75 CH cases at baseline and the 206 normotensive women were analyzed to explore the association between metabolites and the presence of CH. In the second stage, we focused on the 206 women without CH at enrollment to evaluate whether metabolites predicted incident CH during follow-up. The analytical workflow is presented in Fig. 2 . Metabolite quantification EDTA plasma samples were collected at baseline. Samples were shipped on dry ice to Nightingale Health's laboratories in Finland for high-throughput NMR metabolomics analysis. Between June 2019 and April 2020, approximately 118,000 randomly selected participants' samples were analyzed. This analysis quantified 249 metabolic biomarkers, including 168 directly measured compounds and 81 ratios. These biomarkers covered 14 lipoprotein subclasses, fatty acids, and various low-molecular-weight metabolites such as amino acids, ketone bodies, and glycolysis-related metabolites[ 23 ]. Assessment of covariates Demographic characteristics (age, race, and education level), lifestyle factors (smoking status, drinking status, and physical activity), parity, and family history of CH were collected via baseline questionnaires and nurse-led interviews. Physical activity was expressed as metabolic equivalent task (MET)-minutes per week[ 24 ]. Physical measurements, including blood pressure, height, and weight, were obtained using calibrated instruments following standard protocols. Body mass index (BMI) was calculated as weight in kilogram divided by height in meter squared. Biochemical biomarkers, including blood glucose, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides (TGs), were measured from baseline blood samples at a central UK Biobank laboratory. The Townsend deprivation index (TDI) score measures area deprivation based on four census variables (unemployment, car ownership, household overcrowding, and owner occupation) linked to a resident's postcode[ 25 ]. The details of the UK Biobank field IDs and the corresponding variables involved in this study are shown in Table S1 . Outcome ascertainment and follow-up Incident CH was defined using ICD-10 codes I10–I13 and I15 (I10: essential hypertension; I11: hypertensive heart disease; I12: hypertensive renal disease; I13: hypertensive heart and renal disease; I15: secondary hypertension). The follow-up period was defined as the interval from recruitment to the first occurrence of CH diagnosis, death, loss to follow-up, or the study end date (September 16, 2022). Statistical analyses Baseline characteristics of women with and without CH were compared using t-tests, χ² tests, Fisher's exact tests, or Wilcoxon rank-sum tests, as appropriate. The metabolite data were normalized by natural logarithm (log[x + 1]) and then Z-transformed. In the first stage of the cross-sectional study, logistic regression models were used to estimate the associations between metabolites and CH, adjusting for age, race, education, smoking status, drinking status, the TDI, physical activity, BMI, parity, and family history of CH. False discovery rate (FDR)-adjusted P values were utilized to address multiple hypothesis testing[ 26 ], with a significance threshold of 0.1[ 27 ]. Two-sample MR analyses were performed to determine whether metabolites had a causal effect on CH using summary statistics of Genome-wide association studies (GWASs). GWAS data for metabolites were obtained from the UK Biobank, comprising over 12.3 million single nucleotide polymorphisms (SNPs)[ 28 , 29 ]. Genetic association estimates for CH were derived from the FinnGen consortium R8 release, which includes 342,439 Finnish individuals[ 30 ]. The selection of IVs associated with metabolites was performed based on the conventional genome-wide significance threshold of P < 5×10 − 8 and a linkage disequilibrium threshold of r 2 < 0.001 within a clumping window of 10000kb. Significant outliers SNPs identified by the MR-PRESSO test were removed. The remaining SNPs as IVs were used in subsequent MR analyses. Horizontal pleiotropy was assessed using the MR-Egger intercept, and heterogeneity was evaluated via Cochran's Q statistic. The inverse-variance weighted (IVW) method was the main method to estimate the potential causal associations. A random-effects IVW model was applied when significant heterogeneity was detected (Q-test P < 0.05), otherwise a fixed-effects model was used. Evidence of a causal relationship between the metabolites and CH was considered when P FDR <0.1[ 31 ]. In addition, MR-Egger, weighted median, MR-Robust Adjusted Profile Score (MR-RAPS), and maximum likelihood served as alternative analyses. Consistency in the direction of effect estimates across MR methods provides reliable evidence for a causal association. [ 32 ]. Subsequently, receiver operating characteristic (ROC) curve was conducted to evaluate the discriminative ability of different models for distinguishing CH among women with a history of GH and PE. The conventional risk factor model included age, race, education, BMI, parity, and family history of CH. The combined model added selected metabolites to these conventional risk factors. In the second stage of the prospective study, we focused on 206 women who were not diagnosed with CH at enrollment. Time-dependent ROC curves and the corresponding area under the curve (AUC) were used to assess predictive performance for incident CH. The conventional risk factor model in this stage included age, race, education, BMI, parity, family history of CH, and baseline systolic blood pressure (SBP) and diastolic blood pressure (DBP). The combined model added metabolites identified in the first stage. AUCs were compared using the DeLong test[ 33 ]. A two-sided P value < 0.05 was considered statistically significant. All statistical analyses were performed using Statistical Analysis System software (SAS, version 9.4) and R platform (version 4.2.2). Results Cross-sectional study Baseline characteristics A total of 281 women with a history of de novo HDP and available NMR metabolomics data were included in this study. Of these, 75 participants (26.7%) had already developed chronic hypertension (CH) at baseline. Table 1 summarizes the baseline characteristics of the participants stratified by CH status at enrollment. Compared to women without CH at baseline, those with CH were older, had a lower TDI and exhibited a higher prevalence of obesity. Significant differences were also observed in blood pressure and blood glucose levels between the two groups. Table 1 Baseline characteristics of women with a history of GH and PE at enrollment Characteristics Presence of CH P value No (n = 206) Yes (n = 75) Age, mean (SD), years 47.18 (7.09) 51.93 (9.09) < 0.001 Race, No. (%) 0.116 White 193 (93.7) 66 (88.0) Others 13 (6.3) 9 (12.0) Education level, No. (%) 0.877 College or university degree 79 (38.3) 28 (37.3) Others 127 (61.7) 47 (62.7) Smoking status, No. (%) 0.217 Never 135 (65.5) 55 (73.3) Former/current 71 (34.5) 20 (26.7) Drinking status, No. (%) 0.369 Never 9 (4.4) 6 (8.0) Former/current 197 (95.6) 69 (92.0) TDI, mean (SD) -1.85 (2.87) -0.86 (3.24) 0.014 Physical activity, median (IQR), min/week 1653.00 (655.00, 3202.00) 1807.50 (665.00, 2490.00) 0.838 BMI, No. (%) 2 births 54 (26.2) 23 (30.7) Family history of hypertension, No. (%) 0.272 No 92 (44.7) 28 (37.3) Yes 114 (55.3) 47 (62.7) Blood-based measurements SBP, mean (SD), mmHg 130.63 (15.97) 143.12 (18.40) < 0.001 DBP, mean (SD), mmHg 82.17 (9.95) 86.05 (10.20) 0.004 Blood glucose, mean (SD), mmol/L 4.94 (0.78) 5.34 (1.22) 0.001 Total cholesterol, mean (SD), mmol/L 5.51 (1.02) 5.42 (1.21) 0.544 LDL cholesterol, mean (SD), mmol/L 3.44 (0.77) 3.31 (0.91) 0.222 HDL cholesterol, mean (SD), mmol/L 1.53 (0.34) 1.44 (0.37) 0.066 Triglycerides, mean (SD), mmol/L 1.41 (0.96) 1.68 (1.29) 0.057 Abbreviations: CH, chronic hypertension; SD, standard deviation; IQR, interquartile range; TDI, the Townsend deprivation index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein. Association of metabolites with Prevalent CH After adjusting for potential confounders and accounting for multiple testing, 58 of the 249 metabolic biomarkers showed statistically significant associations with the presence of CH in women with a history of GH or PE ( P FDR <0.1; Table 2 , Table S2 ). Except glycine [odds ratio (OR) = 0.631; 95% confident interval (CI): 0.446–0.892; P FDR =0.060], all other identified metabolic biomarkers belong to lipid and lipoprotein categories. Table 2 Adjusted ORs of metabolites for the presence of CH after multiple testing at enrollment Metabolites OR (95%CI) P value P FDR Cholesterol Total Cholesterol 0.652 (0.474, 0.898) 0.009 0.060 Total Cholesterol Minus HDL-C 0.644 (0.467, 0.888) 0.007 0.060 Remnant Cholesterol (Non-HDL, Non-LDL -Cholesterol) 0.662 (0.479, 0.916) 0.013 0.067 Clinical LDL Cholesterol 0.629 (0.459, 0.862) 0.004 0.060 LDL Cholesterol 0.647 (0.471, 0.889) 0.007 0.060 Phospholipids Phospholipids in LDL 0.651 (0.474, 0.894) 0.008 0.060 Cholesteryl esters Total Esterified Cholesterol 0.657 (0.478, 0.902) 0.009 0.060 Cholesteryl Esters in LDL 0.662 (0.481, 0.911) 0.011 0.065 Free cholesterol Total Free Cholesterol 0.648 (0.468, 0.897) 0.009 0.060 Free Cholesterol in LDL 0.625 (0.456, 0.857) 0.004 0.060 Total lipids Total Lipids in LDL 0.656 (0.478, 0.900) 0.009 0.060 Lipoprotein particle Concentration of LDL Particles 0.661 (0.477, 0.917) 0.013 0.067 Apolipoproteins Apolipoprotein B 0.655 (0.473, 0.907) 0.011 0.064 Amino acids Glycine 0.631 (0.446, 0.892) 0.009 0.060 Lipoprotein subclasses Cholesterol in Medium VLDL 0.638 (0.459, 0.887) 0.008 0.060 Cholesteryl Esters in Medium VLDL 0.625 (0.453, 0.863) 0.004 0.060 Concentration of IDL Particles 0.651 (0.473, 0.898) 0.009 0.060 Total Lipids in IDL 0.640 (0.465, 0.881) 0.006 0.060 Phospholipids in IDL 0.648 (0.469, 0.894) 0.008 0.060 Cholesterol in IDL 0.625 (0.455, 0.858) 0.004 0.060 Cholesteryl Esters in IDL 0.621 (0.452, 0.853) 0.003 0.060 Free Cholesterol in IDL 0.650 (0.474, 0.891) 0.007 0.060 Concentration of Large LDL Particles 0.667 (0.484, 0.920) 0.013 0.067 Total Lipids in Large LDL 0.651 (0.475, 0.893) 0.008 0.060 Phospholipids in Large LDL 0.647 (0.471, 0.888) 0.007 0.060 Cholesterol in Large LDL 0.643 (0.468, 0.883) 0.006 0.060 Cholesteryl Esters in Large LDL 0.658 (0.479, 0.904) 0.010 0.060 Free Cholesterol in Large LDL 0.618 (0.450, 0.850) 0.003 0.060 Total Lipids in Medium LDL 0.685 (0.497, 0.944) 0.021 0.090 Phospholipids in Medium LDL 0.682 (0.496, 0.938) 0.019 0.082 Cholesterol in Medium LDL 0.676 (0.490, 0.933) 0.017 0.078 Free Cholesterol in Medium LDL 0.652 (0.477, 0.892) 0.008 0.060 Concentration of Small LDL Particles 0.658 (0.471, 0.917) 0.013 0.067 Total Lipids in Small LDL 0.660 (0.478, 0.912) 0.012 0.065 Phospholipids in Small LDL 0.655 (0.476, 0.900) 0.009 0.060 Cholesterol in Small LDL 0.654 (0.474, 0.903) 0.010 0.060 Cholesteryl Esters in Small LDL 0.676 (0.487, 0.937) 0.019 0.082 Free Cholesterol in Small LDL 0.639 (0.468, 0.871) 0.005 0.060 Ratios Phospholipids to total lipids ratio in medium VLDL 0.660 (0.490, 0.889) 0.006 0.060 Cholesterol to total lipids ratio in medium VLDL 0.692 (0.511, 0.937) 0.017 0.078 Free cholesterol to total lipids ratio in medium VLDL 0.674 (0.500, 0.909) 0.010 0.060 Triglycerides to total lipids ratio in medium VLDL 1.509 (1.111, 2.048) 0.008 0.060 Phospholipids to total lipids ratio in small VLDL 0.613 (0.451, 0.834) 0.002 0.060 Cholesterol to total lipids ratio in small VLDL 0.685 (0.509, 0.922) 0.012 0.067 Free cholesterol to total lipids ratio in small VLDL 0.622 (0.455, 0.851) 0.003 0.060 Triglycerides to total lipids ratio in small VLDL 1.543 (1.143, 2.082) 0.005 0.060 Cholesterol to total lipids ratio in very small VLDL 0.642 (0.463, 0.890) 0.008 0.060 Cholesteryl esters to total lipids ratio in very small VLDL 0.644 (0.459, 0.905) 0.011 0.065 Free cholesterol to total lipids ratio in very small VLDL 0.668 (0.499, 0.896) 0.007 0.060 Triglycerides to total lipids ratio in very small VLDL 1.579 (1.158, 2.152) 0.004 0.060 Cholesterol to total lipids ratio in IDL 0.664 (0.495, 0.892) 0.007 0.060 Cholesteryl esters to total lipids ratio in IDL 0.662 (0.491, 0.892) 0.007 0.060 Triglycerides to total lipids ratio in IDL 1.596 (1.168, 2.180) 0.003 0.060 Cholesterol to total lipids ratio in large LDL 0.481 (0.266, 0.870) 0.015 0.073 Free cholesterol to total lipids ratio in large LDL 0.466 (0.251, 0.865) 0.016 0.073 Triglycerides to total lipids ratio in large LDL 1.586 (1.134, 2.218) 0.007 0.060 Triglycerides to total lipids ratio in medium LDL 1.500 (1.105, 2.037) 0.009 0.060 Phospholipids to total lipids ratio in large HDL 1.496 (1.085, 2.064) 0.014 0.068 ORs are per 1 SD higher of Z-transformed metabolic profiling and are adjusted for age, race, education, smoking status, drinking status, TDI, physical activity, BMI, parity, and family history of CH. Abbreviations: OR, odds ratio; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein. Detailed information on the IVs of these 58 metabolites used in MR analyses is shown in Table S3 . We found that 11 metabolites were significantly associated with CH ( P FDR of IVW < 0.1). However, two of these metabolic biomarkers (remnant cholesterol and small LDL particles) exhibited an opposite direction from the previous logistic analyses ( Table S4 ). Therefore, we focused on the remaining 9 metabolic biomarkers, which are listed in Table 3 . Among them, TGs to total lipids ratio in medium very-low-density lipoprotein (VLDL, OR = 1.108, 95% CI:1.035–1.186), in very small VLDL (OR = 1.114, 95% CI:1.050–1.182), and in intermediate-density lipoprotein (IDL, OR = 1.084, 95% CI:1.024–1.148) were positively associated with CH. Glycine remained inversely associated with CH in the MR analysis (OR = 0.950, 95% CI:0.921–0.980). Table 3 MR estimates of the causal relationship between metabolites and incident CH after multiple testing Metabolites SNPs Method OR (95%CI) P value P FDR Amino acids Glycine 33 IVW 0.950 (0.921, 0.980) 1.23E-03 1.19E-02 MR Egger 0.961 (0.927, 0.996) 3.88E-02 6.97E-01 Weighted median 0.956 (0.934, 0.979) 2.13E-04 3.10E-03 MR-RAPS 0.950 (0.930, 0.971) 4.19E-06 9.07E-01 Maximum likelihood 0.950 (0.929, 0.971) 4.46E-06 2.88E-05 Ratios Cholesterol to total lipids ratio in medium VLDL 58 IVW 0.880 (0.824, 0.941) 1.76E-04 3.40E-03 MR Egger 0.953 (0.854, 1.064) 3.98E-01 6.97E-01 Weighted median 0.901 (0.838, 0.968) 4.65E-03 4.49E-02 MR-RAPS 0.884 (0.852, 0.918) 6.16E-11 9.07E-01 Maximum likelihood 0.881 (0.847, 0.916) 2.31E-10 4.46E-09 Triglycerides to total lipids ratio in medium VLDL 51 IVW 1.108 (1.035, 1.186) 3.06E-03 2.31E-02 MR Egger 1.025 (0.912, 1.152) 6.80E-01 7.45E-01 Weighted median 1.096 (1.014, 1.186) 2.16E-02 1.21E-01 MR-RAPS 1.107 (1.065, 1.149) 1.67E-07 9.07E-01 Maximum likelihood 1.111 (1.067, 1.156) 3.43E-07 3.31E-06 Free cholesterol to total lipids ratio in small VLDL 50 IVW 0.912 (0.859, 0.970) 3.19E-03 2.31E-02 MR Egger 0.962 (0.864, 1.070) 4.77E-01 6.97E-01 Weighted median 0.914 (0.851, 0.982) 1.41E-02 1.17E-01 MR-RAPS 0.914 (0.881, 0.947) 8.92E-07 9.07E-01 Maximum likelihood 0.913 (0.878, 0.949) 3.63E-06 2.63E-05 Cholesterol to total lipids ratio in very small VLDL 50 IVW 0.910 (0.860, 0.961) 8.20E-04 9.52E-03 MR Egger 0.917 (0.834, 1.009) 8.05E-02 6.97E-01 Weighted median 0.892 (0.835, 0.952) 6.04E-04 7.01E-03 MR-RAPS 0.912 (0.883, 0.943) 4.06E-08 9.66E-01 Maximum likelihood 0.909 (0.878, 0.942) 9.38E-08 1.09E-06 Cholesteryl esters to total lipids ratio in very small VLDL 52 IVW 0.881 (0.832, 0.932) 1.14E-05 3.30E-04 MR Egger 0.894 (0.812, 0.985) 2.73E-02 6.97E-01 Weighted median 0.886 (0.831, 0.944) 1.91E-04 3.10E-03 MR-RAPS 0.887 (0.857, 0.918) 7.36E-12 9.66E-01 Maximum likelihood 0.881 (0.849, 0.914) 1.10E-11 3.19E-10 Triglycerides to total lipids ratio in very small VLDL 52 IVW 1.114 (1.050, 1.182) 3.58E-04 5.19E-03 MR Egger 1.077 (0.969, 1.197) 1.75E-01 6.97E-01 Weighted median 1.140 (1.070, 1.214) 4.90E-05 1.42E-03 MR-RAPS 1.111 (1.073, 1.150) 4.07E-09 9.66E-01 Maximum likelihood 1.117 (1.076, 1.159) 5.05E-09 7.32E-08 Triglycerides to total lipids ratio in IDL 57 IVW 1.084 (1.024, 1.148) 5.64E-03 3.27E-02 MR Egger 1.068 (0.971, 1.175) 1.78E-01 6.97E-01 Weighted median 1.048 (0.993, 1.106) 8.86E-02 1.22E-01 MR-RAPS 1.083 (1.051, 1.117) 2.27E-07 9.66E-01 Maximum likelihood 1.085 (1.051, 1.120) 5.63E-07 4.67E-06 Free cholesterol to total lipids ratio in large LDL 59 IVW 0.854 (0.806, 0.905) 9.24E-08 5.36E-06 MR Egger 0.889 (0.815, 0.971) 1.16E-02 6.71E-01 Weighted median 0.851 (0.801, 0.904) 1.71E-07 9.93E-06 MR-RAPS 0.870 (0.841, 0.899) 1.13E-16 9.07E-01 Maximum likelihood 0.854 (0.823, 0.886) 2.66E-17 1.55E-15 Abbreviations: SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization; RAPS, Robust Adjusted Profile Score; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein. Discriminatory performance of metabolites for CH The AUC for the conventional risk factor model (including age, race, education, BMI, parity, and family history of CH) was 0.744 (95% CI: 0.676–0.812). Adding the 9 identified metabolic biomarkers to this model, the AUC significantly improved to 0.797 (95% CI = 0.737–0.856, DeLong’s test P = 0.014, Fig. 3 ). Conventional risk factors included age, race, education, BMI, parity, and family history of hypertension. The combined model integrated selected metabolites with risk factors. There was a significant difference ( P = 0.014) between the AUC of the conventional risk factor model and the combined model. Prospective study Baseline characteristics A total of 201 women who did not develop CH at enrollment were included in this phase of the study. After a median follow-up of 13.3 years (interquartile range: 12.4–14.0 years), 27 (13.43%) participants developed CH. Baseline characteristics of all women stratified by the incidence of CH are exhibited in Table S5 . Participants who later developed CH had significantly higher baseline SBP, DBP, total cholesterol, LDL cholesterol, and HDL cholesterol compared with those who remained normotensive. Predictive performance of metabolites for CH To evaluate the predictive performance of the 9 metabolites for future CH, time-dependent ROC analyses were performed. The AUC of the conventional risk factor model was 0.570 (95% CI: 0.291–0.848) at 3 years and 0.720 (95% CI: 0.489–0.951) at 5 years. Adding the 9 metabolites significantly improved the AUC to 0.619 (95% CI: 0.366–0.872) at 3 years and 0.759 (95% CI: 0.549–0.968) at 5 years (Fig. 4 , P for 3 years = 0.013; P for 5 years = 0.011). The conventional risk factor model included age, race, education, BMI, parity, family history of CH, SBP, and DBP at enrollment. The combined model integrated selected metabolites with risk factors. The combined model showed significant improvements over the conventional risk factor model in predicting CH at both 3 years ( P = 0.013) and 5 years ( P = 0.011). Discussion Using targeted NMR-based metabolomic profiling from the UK Biobank, 9 metabolites potentially linked to the progression from de novo HDP to CH were identified by both logistic regression and MR analyses. These included glycine and lipid components within VLDL, IDL, and LDL subclasses. Notably, adding these metabolites to conventional risk factor model significantly improved CH discrimination and prediction in women with prior GH or PE. Glycine was inversely associated with CH, consistent with previous studies[ 34 – 36 ]. As a major amino acid[ 37 , 38 ], glycine promotes the production of nitric oxide and supports vascular function and blood pressure regulation[ 39 ]. Additionally, glycine serves as a glutathione precursor, thereby enhancing antioxidant capacity and reducing oxidative stress-induced endothelial damage [ 40 ]. These mechanisms support a protective role for glycine against CH following HDP. All other identified metabolites belong to lipids and lipoproteins. Lipoprotein particles comprise a hydrophobic core mainly composed of cholesteryl esters and TGs, surrounded by a hydrophilic monolayer of phospholipids, free cholesterol, and various apolipoproteins[ 41 ]. Lipoprotein particles are classified into five major classes: chylomicrons, VLDL, LDL, IDL, and HDL[ 42 ]. In addition, different lipoprotein particles can be categorized based on their size. Our results suggested that the proportion of TGs in subclasses of lipoprotein particles increases significantly in CH patients, including the TGs to total lipids ratio in medium VLDL, in very small VLDL, and in IDL. Both VLDL and IDL belong to TG-rich lipoproteins[ 43 ]. Insufficient lipolysis of TG-rich lipoproteins can result in the accumulation of remnant lipoproteins, leading to increased TG content and extended residence time[ 44 ], which can cause impaired endothelial vasomotor function[ 45 ]. Moreover, serum TG levels were reported to be significantly associated with new-onset hypertension[ 46 ]. Our study revealed subclass-specific alterations in TGs during the occurrence and development of CH. Cholesterol-to-total-lipids ratios decreased in several subclasses (medium/small/very small VLDL, large LDL) in CH patients. The absolute levels of cholesterol or cholesteryl esters in subclasses of lipoprotein particles did not exhibit a significant increase or decrease. It was shown that the lipid composition of lipoprotein particles can also be associated with disease risk, independent of total lipid concentrations[ 47 ]. The composition, content, and distribution of lipoprotein subclasses play an important role in lipid metabolism and blood pressure regulation[ 48 ]. Adding identified metabolic profiling to the conventional risk factor model significantly improved CH discrimination. Among them, the AUCs for combined models of cross-sectional diagnosis and 5-year prediction were both above 0.75. These findings support the potential clinical utility of metabolite markers for early risk stratification in women with GH or PE. However, given the dynamic nature of metabolites, their long-term predictive stability warrants caution[ 49 ]. Strengths and limitations The study examined the metabolism related to the development of CH in the susceptible population with a history of GH and PE. Moreover, the metabolites identified in the cross-sectional study were further supported by MR analyses, providing evidence for causal relationships. However, limitations should also be considered. Firstly, only 249 metabolites (primarily lipid-related) were measured, potentially overlooking other relevant metabolic pathways. Secondly, the sample size was relatively small, which may limit statistical power and generalizability. Future studies in larger, more diverse populations are needed to validate our findings. Lastly, the GWAS summary statistics of metabolites in the MR analyses were derived from the general population, rather than being restricted to female participants or high-risk women with a history of GH or PE. Thus, further investigation in the target population is required to confirm the causal roles of these metabolites. Conclusions In summary, the progression from GH or PE to CH is accompanied by metabolic changes, with amino acids, lipids, and lipoproteins emerging as the most significant factors. These findings highlight the importance of monitoring metabolic dysregulation for predicting CH in women with a history of GH or PE. Abbreviations HDP hypertensive disorders of pregnancy PE preeclampsia GH gestational hypertension CH chronic hypertension MR Mendelian randomization IV instrumental variable NMR Nuclear Magnetic Resonance ICD International Classification of Disease Tenth Version MET metabolic equivalent task BMI body mass index LDL low-density lipoprotein HDL high-density lipoprotein TG triglyceride TDL the Townsend deprivation index FDR false discovery rate GWAS Genome-wide association studies SNP Single Nucleotide Polymorphism IVW inverse-variance weighted RAPS Robust Adjusted Profile Score ROC the receiver operating characteristic AUC the area under the curve SBP systolic blood pressure DBP diastolic blood pressure OR odds ratio CI confident interval VLDL very-low-density lipoprotein IDL intermediate-density lipoprotein. Declarations Author’s contributions YG, MY, and QH were responsible for study conception, data analysis, and manuscript drafting. JM and QH contributed to data collection and methodology. XX and YD performed the statistical analysis. JY, YS, and YC designed the study and revised the manuscript. All authors reviewed the manuscript. Availability of data and materials Data from the UK Biobank are available upon request at https://www.ukbiobank.ac.uk/. Competing interests The authors declare no competing interests. Ethics approval Ethical approval for the UK Biobank study was obtained from the North West Multi-centre Research Ethics Committee (16/NW/0274). All participants provided written informed consent in accordance with the principles of the Declaration of Helsinki. Clinical trial number Not applicable. Consent for publication Not applicable. Funding None. Acknowledgments We are grateful to the investigators and participants of the UK Biobank. References Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertens (Dallas Tex: 1979). 2018;72(1):24–43. Phipps EA, Thadhani R, Benzing T, Karumanchi SA. Pre-eclampsia: pathogenesis, novel diagnostics and therapies. Nat Rev Nephrol. 2019;15(5):275–89. Gestational Hypertension and Preeclampsia. ACOG Practice Bulletin, Number 222. Obstet Gynecol. 2020;135(6):e237–60. Sanghavi M, Rutherford JD. Cardiovascular physiology of pregnancy. Circulation. 2014;130(12):1003–8. Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy. Obstet Gynecol. 2013;122(5):1122–31. Xu J, Li T, Wang Y, Xue L, Miao Z, Long W, et al. The Association Between Hypertensive Disorders in Pregnancy and the Risk of Developing Chronic Hypertension. Front Cardiovasc Med. 2022;9:897771. Sutherland L, Neale D, Henderson J, Clark J, Levine D, Bennett WL. Provider Counseling About and Risk Perception for Future Chronic Disease Among Women with Gestational Diabetes and Preeclampsia. J Womens Health (Larchmt). 2020;29(9):1168–75. Andraweera PH, Lassi ZS, Pathirana MM, Plummer MD, Dekker GA, Roberts CT, et al. Pregnancy complications and cardiovascular disease risk perception: A qualitative study. PLoS ONE. 2022;17(7):e0271722. Jones EJ, Hernandez TL, Edmonds JK, Ferranti EP. Continued Disparities in Postpartum Follow-Up and Screening Among Women With Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Systematic Review. J Perinat Neonatal Nurs. 2019;33(2):136–48. Blumer V, Pagidipati NJ. Hypertensive Disorders of Pregnancy and Long-Term Heart Failure Risk: A Missed Opportunity for Prevention. Hypertens (Dallas Tex: 1979). 2020;76(5):1420–2. Shopen N, Schiff E, Koren-Morag N, Grossman E. Factors That Predict the Development of Hypertension in Women With Pregnancy-Induced Hypertension. Am J Hypertens. 2016;29(1):141–6. Visser VS, Hermes W, Twisk J, Franx A, van Pampus MG, Koopmans C, et al. Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term. Pregnancy Hypertens. 2017;10:118–23. Timpka S, Stuart JJ, Tanz LJ, Rimm EB, Franks PW, Rich-Edwards JW. Lifestyle in progression from hypertensive disorders of pregnancy to chronic hypertension in Nurses' Health Study II: observational cohort study. BMJ (Clinical Res ed). 2017;358:j3024. Chornock R, Iqbal SN, Kawakita T. Racial Disparity in Postpartum Readmission due to Hypertension among Women with Pregnancy-Associated Hypertension. Am J Perinatol. 2021;38(12):1297–302. Meng XJ, Dong GH, Wang D, Liu MM, Lin Q, Tian S, et al. Prevalence, awareness, treatment, control, and risk factors associated with hypertension in urban adults from 33 communities of China: the CHPSNE study. J Hypertens. 2011;29(7):1303–10. Yao M, Xiao Y, Yang Z, Ge W, Liang F, Teng H, et al. Identification of Biomarkers for Preeclampsia Based on Metabolomics. Clin Epidemiol. 2022;14:337–60. Dasgupta S, Subramani E, Mitra I, Bhattacharya A, Sherpa DD, Joshi M, et al. Discovery of novel metabolic signatures for early identification of women at risk of developing gestational hypertension. Metabolomics: Official J Metabolomic Soc. 2023;19(5):50. Yang Q, Zhang AH, Miao JH, Sun H, Han Y, Yan GL, et al. Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Adv. 2019;9(64):37245–57. Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol. 2018;36(4):316–20. Louca P, Menni C, Padmanabhan S. Genomic Determinants of Hypertension With a Focus on Metabolomics and the Gut Microbiome. Am J Hypertens. 2020;33(6):473–81. Onuh JO, Aliani M. Metabolomics profiling in hypertension and blood pressure regulation: a review. Clin Hypertens. 2020;26(1):23. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33(1):30–42. Julkunen H, Cichońska A, Tiainen M, Koskela H, Nybo K, Mäkelä V, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14(1):604. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95. Klee M, Leist AK, Veldsman M, Ranson JM, Llewellyn DJ. Socioeconomic Deprivation, Genetic Risk, and Incident Dementia. Am J Prev Med. 2023;64(5):621–30. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67(8):850–7. Nemoda Z, Massart R, Suderman M, Hallett M, Li T, Coote M, et al. Maternal depression is associated with DNA methylation changes in cord blood T lymphocytes and adult hippocampi. Translational psychiatry. 2015;5(4):e545. Nightingale Health. and UK Biobank announces major initiative to analyse half a million blood samples to facilitate global medical research. https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/nightingale-health-and-uk-biobank-announces-major-initiative-to-analyse-half-a-million-blood-samples-to-facilitate-global-medical-research Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599(7886):628–34. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18. Li P, Wang H, Guo L, Gou X, Chen G, Lin D, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20(1):443. Cai J, Wei Z, Chen M, He L, Wang H, Li M, et al. Socioeconomic status, individual behaviors and risk for mental disorders: A Mendelian randomization study. Eur psychiatry: J Association Eur Psychiatrists. 2022;65(1):e28. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. Dietrich S, Floegel A, Weikert C, Prehn C, Adamski J, Pischon T, et al. Identification of Serum Metabolites Associated With Incident Hypertension in the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. Hypertens (Dallas Tex: 1979). 2016;68(2):471–7. Lin C, Sun Z, Mei Z, Zeng H, Zhao M, Hu J, et al. The causal associations of circulating amino acids with blood pressure: a Mendelian randomization study. BMC Med. 2022;20(1):414. Louca P, Nogal A, Moskal A, Goulding NJ, Shipley MJ, Alkis T et al. Cross-Sectional Blood Metabolite Markers of Hypertension: A Multicohort Analysis of 44,306 Individuals from the COnsortium of METabolomics Studies. Metabolites 2022;12(7). Wu G, Wu Z, Dai Z, Yang Y, Wang W, Liu C, et al. Dietary requirements of nutritionally non-essential amino acids by animals and humans. Amino Acids. 2013;44(4):1107–13. Imenshahidi M, Hossenzadeh H. Effects of glycine on metabolic syndrome components: a review. J Endocrinol Investig. 2022;45(5):927–39. El Hafidi M, Pérez I, Baños G. Is glycine effective against elevated blood pressure? Curr Opin Clin Nutr Metab Care. 2006;9(1):26–31. Labarrere CA, Kassab GS, Glutathione. A Samsonian life-sustaining small molecule that protects against oxidative stress, ageing and damaging inflammation. Front Nutr. 2022;9:1007816. Feingold KR et al. Introduction to Lipids and Lipoproteins. In: Feingold KR, Anawalt B, Blackman MR, Boyce A, Chrousos G, Corpas E, editors. Endotext. ) MDText.com, Inc. Copyright © 2000–2023, MDText.com, Inc.: South Dartmouth (MA), 2000. Venugopal SK, Anoruo M, Jialal I, Biochemistry. Low Density Lipoprotein. StatPearls. ) StatPearls Publishing Copyright © 2023, StatPearls Publishing LLC.: Treasure Island (FL) ineligible companies. Disclosure: McDamian Anoruo declares no relevant financial relationships with ineligible companies. Disclosure: Ishwarlal Jialal declares no relevant financial relationships with ineligible companies; 2023. Hodis HN, Mack WJ. Triglyceride-rich lipoproteins and progression of atherosclerosis. Eur Heart J. 1998;19(Suppl A):A40–4. Winkler K, Wetzka B, Hoffmann MM, Friedrich I, Kinner M, Baumstark MW, et al. Triglyceride-rich lipoproteins are associated with hypertension in preeclampsia. J Clin Endocrinol Metab. 2003;88(3):1162–6. Kugiyama K, Doi H, Motoyama T, Soejima H, Misumi K, Kawano H, et al. Association of remnant lipoprotein levels with impairment of endothelium-dependent vasomotor function in human coronary arteries. Circulation. 1998;97(25):2519–26. Tomita Y, Sakata S, Arima H, Yamato I, Ibaraki A, Ohtsubo T, et al. Relationship between casual serum triglyceride levels and the development of hypertension in Japanese. J Hypertens. 2021;39(4):677–82. Gebhard C, Rhainds D, Tardif JC. HDL and cardiovascular risk: is cholesterol in particle subclasses relevant? Eur Heart J. 2015;36(1):10–2. Tian L, Fu M. The relationship between high density lipoprotein subclass profile and plasma lipids concentrations. Lipids Health Dis. 2010;9:118. Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. Sci Adv. 2022;8(42):eadd6155. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 26 Mar, 2026 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 Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9229260","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616682351,"identity":"9721f3eb-0d99-4cc5-aed3-95983d812d57","order_by":0,"name":"Yingjie Gu","email":"","orcid":"","institution":"the First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yingjie","middleName":"","lastName":"Gu","suffix":""},{"id":616682352,"identity":"748d29da-c1a3-4eed-821c-f371e0ae4726","order_by":1,"name":"Mengxin Yao","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Mengxin","middleName":"","lastName":"Yao","suffix":""},{"id":616682353,"identity":"01951412-9519-4d65-b1d0-c13e13ec6a12","order_by":2,"name":"Qida He","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Qida","middleName":"","lastName":"He","suffix":""},{"id":616682354,"identity":"4b3441aa-2e22-4096-8b55-8d46d5540fee","order_by":3,"name":"Jingjing Ma","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Ma","suffix":""},{"id":616682355,"identity":"7e4be0bc-4920-4765-a3b2-a0aabbb30077","order_by":4,"name":"Qin Huang","email":"","orcid":"","institution":"the First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Huang","suffix":""},{"id":616682358,"identity":"a8c6dfa1-06b2-42c8-8bb8-96377a927265","order_by":5,"name":"Xiangxiang Xu","email":"","orcid":"","institution":"the Affiliated Suzhou Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Xu","suffix":""},{"id":616682360,"identity":"57976748-eca5-4a6e-954e-392d44046424","order_by":6,"name":"Yaling Ding","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yaling","middleName":"","lastName":"Ding","suffix":""},{"id":616682362,"identity":"7fb20f74-a5f8-453e-91b6-34e218afbd05","order_by":7,"name":"Jieyun Yin","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jieyun","middleName":"","lastName":"Yin","suffix":""},{"id":616682366,"identity":"280d0174-308d-4dc1-9e69-f6f957a59a1e","order_by":8,"name":"Yueping Shen","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yueping","middleName":"","lastName":"Shen","suffix":""},{"id":616682367,"identity":"952f5a5e-cae3-4b36-a30d-4679e68c1abd","order_by":9,"name":"Youguo Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCTiL+QADAxuIkUC0FrYEkrXwGBCnhX9287HHPBWH7TYc7/n8mafMjoGfPceA4ecOPJbcOZZuzHPmcPKGM2e3SfOcS2aQ7HljwNh7BrcWA4kcM2netsPJBjdytzHztjEzGNzIMWBmbMOnJf8bVEvO48+8bfUM9oS15LCBtNgBtTCAGCAR/FokbqSZSc45k54geeYYkHHuOI/EmWcFB3vxaOGfkfxM4k2FtT3f8ebHH96UVcvxtydvfPATjxYQYOJhYEhsgHJ4QMQB/BoYGBh/MDDYE1I0CkbBKBgFIxgAAD1ZTrKAmjUmAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Youguo","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-26 04:54:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9229260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9229260/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106450657,"identity":"5c557d5d-7d38-47df-b62f-0f38bdf56663","added_by":"auto","created_at":"2026-04-08 16:30:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264753,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of participant selection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/cbff602f6b89ca699f76e6af.png"},{"id":106450654,"identity":"0e505854-7ff8-4dcb-86af-c7cafa560df2","added_by":"auto","created_at":"2026-04-08 16:30:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":257501,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagram of the study design and analysis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/a0eb5a5a2b47408e36e87f5a.png"},{"id":106450656,"identity":"c6aec9a8-e8ca-42ec-ae90-52625ecc5159","added_by":"auto","created_at":"2026-04-08 16:30:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260999,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the model for distinguishing the presence of CH\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/bcb57ff247f5de13d2e1d479.png"},{"id":106450655,"identity":"3477dcc8-19ee-4ff1-9782-bdbd9cded133","added_by":"auto","created_at":"2026-04-08 16:30:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164152,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the model for predicting incident CH during the follow-up periods\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/2dd86192148275df06c611a5.png"},{"id":106725025,"identity":"895f1c0a-127d-472c-bd57-91c4b5cdb2fb","added_by":"auto","created_at":"2026-04-12 18:31:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2386728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/547da855-813e-4714-9fd0-987bfe532d7e.pdf"},{"id":106450653,"identity":"9f5d7df6-a2f1-43a8-b6e3-d459a4a5a3f7","added_by":"auto","created_at":"2026-04-08 16:30:27","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":113185,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.doc","url":"https://assets-eu.researchsquare.com/files/rs-9229260/v1/389c9cd8a10129397cb6e4df.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of chronic hypertension among women with a history of de novo hypertension disorders during pregnancy: the role of metabolites","fulltext":[{"header":"Background","content":"\u003cp\u003eHypertensive disorders of pregnancy (HDP), including chronic hypertension (CH, present before pregnancy or diagnosed before 20 weeks of gestation) and de novo disorders [gestational hypertension (GH) or preeclampsia (PE)], are leading complications of pregnancy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. GH is characterized by the presence of new-onset hypertension after 20 weeks of gestation, while PE is defined as the occurrence of GH along with new-onset proteinuria or other signs of organ damage[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HDP is considered as a state of failed maternal adaptation to pregnancy[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], a period marked by profound cardiovascular and hemodynamic changes.\u003c/p\u003e \u003cp\u003eAlthough HDP typically resolves after delivery, with blood pressure gradually normalizing in the postpartum period[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], they indicate an increased risk for future cardiovascular health. A recent meta-analysis suggests that HDP is associated with an increased likelihood of CH[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, affected women often do not sufficiently recognize HDP as a significant risk factor for future CH[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To improve long-term health outcomes, regular blood pressure monitoring and management, adherence to a healthy lifestyle, and maintenance of an ideal weight during the postpartum period are commonly recommended for women with a history of HDP[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unfortunately, these recommendations are often not effectively implemented, and efforts to coordinate timely medical follow-up and preventive strategies remain inadequate[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, risk stratification to identify high-risk women for CH among those with a history of HDP, particularly de novo HDP, could help raise awareness and facilitate targeted preventive measures. Current research on risk factors for CH following HDP has largely focused on traditional factors such as age[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], obesity[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], parity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], family history[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], educational level[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and race[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, these factors frequently overlap with those observed in the general population[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hence, it is crucial to investigate specific biomarkers to predict CH in high-risk women with a history of HDP.\u003c/p\u003e \u003cp\u003eHDP has been implicated in extensive metabolic alterations[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Metabolomics is an emerging high-throughput technique that provides simultaneous, quantitative analysis of all small-molecule metabolites in a biological system[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Metabolites associated with physiological conditions or aberrant processes can provide valuable insights into the progression of disease[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Metabolomics has been increasingly applied to elucidate risk factors for CH, with identified metabolites showing promise for early disease prediction in the general population[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, combining novel metabolite markers with clinical factors may improve early prediction of future CH in women with a history of de novo HDP.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) employs genetic variants associated with an exposure as instrumental variables (IVs) to investigate causal relationships with an outcome. Since genetic variants are assigned at conception and generally unaffected by later-life confounders or disease processes, MR analysis helps mitigate issues of reverse causation and unmeasured confounding[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, MR offers a robust framework for investigating potential causal links between metabolites and CH.\u003c/p\u003e \u003cp\u003eUsing Nuclear Magnetic Resonance (NMR) metabolomics data from the UK Biobank, this study aimed to: 1) evaluate the associations between metabolites and CH in women with a history of GH or PE; 2) identify metabolites with a potential causal relationship to CH using a two-sample MR analysis; and 3) evaluate the predictive performance of significant metabolites combined with the established risk factors for CH.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe UK Biobank study is a prospective cohort study that recruited approximately 500,000 participants aged 40\u0026ndash;69 years between 2006 and 2010. Baseline questionnaires and anthropometric measurements were completed at 22 assessment centers across England, Wales, and Scotland. The UK Biobank received ethical approval from the North West Multi-center Research Ethics Committee (16/NW/0274). All participants gave written informed consent in accordance with the principles of the Declaration of Helsinki. These analyses were conducted under UK Biobank application number 68136.\u003c/p\u003e \u003cp\u003eTo identify metabolomic biomarkers associated with the development of CH in high-risk women, the present study focused on women with a history of de novo HDP at baseline. GH and PE were identified from hospital inpatient records based on International Classification of Diseases, 10th Revision (ICD-10) code O13-O15 (O13: gestational hypertension without significant proteinuria; O14: gestational hypertension with significant proteinuria; O15: eclampsia). Women with a history of chronic HDP (n\u0026thinsp;=\u0026thinsp;39; ICD-10 codes O10-O11) or pre-existing CH before pregnancy (n\u0026thinsp;=\u0026thinsp;158; ICD-10 codes I10-I13, I15), and those without available metabolomic data (n\u0026thinsp;=\u0026thinsp;894) were excluded. Finally, 281 women with a history of GH or PE were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, 75 participants (26.7%) had already been diagnosed with CH at baseline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis proceeded in two stages. In the first stage of the cross-sectional study, metabolite profiles between the 75 CH cases at baseline and the 206 normotensive women were analyzed to explore the association between metabolites and the presence of CH. In the second stage, we focused on the 206 women without CH at enrollment to evaluate whether metabolites predicted incident CH during follow-up. The analytical workflow is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetabolite quantification\u003c/h3\u003e\n\u003cp\u003eEDTA plasma samples were collected at baseline. Samples were shipped on dry ice to Nightingale Health's laboratories in Finland for high-throughput NMR metabolomics analysis. Between June 2019 and April 2020, approximately 118,000 randomly selected participants' samples were analyzed. This analysis quantified 249 metabolic biomarkers, including 168 directly measured compounds and 81 ratios. These biomarkers covered 14 lipoprotein subclasses, fatty acids, and various low-molecular-weight metabolites such as amino acids, ketone bodies, and glycolysis-related metabolites[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics (age, race, and education level), lifestyle factors (smoking status, drinking status, and physical activity), parity, and family history of CH were collected via baseline questionnaires and nurse-led interviews. Physical activity was expressed as metabolic equivalent task (MET)-minutes per week[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Physical measurements, including blood pressure, height, and weight, were obtained using calibrated instruments following standard protocols. Body mass index (BMI) was calculated as weight in kilogram divided by height in meter squared. Biochemical biomarkers, including blood glucose, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides (TGs), were measured from baseline blood samples at a central UK Biobank laboratory. The Townsend deprivation index (TDI) score measures area deprivation based on four census variables (unemployment, car ownership, household overcrowding, and owner occupation) linked to a resident's postcode[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The details of the UK Biobank field IDs and the corresponding variables involved in this study are shown in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eOutcome ascertainment and follow-up\u003c/h3\u003e\n\u003cp\u003eIncident CH was defined using ICD-10 codes I10\u0026ndash;I13 and I15 (I10: essential hypertension; I11: hypertensive heart disease; I12: hypertensive renal disease; I13: hypertensive heart and renal disease; I15: secondary hypertension). The follow-up period was defined as the interval from recruitment to the first occurrence of CH diagnosis, death, loss to follow-up, or the study end date (September 16, 2022).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics of women with and without CH were compared using t-tests, χ\u0026sup2; tests, Fisher's exact tests, or Wilcoxon rank-sum tests, as appropriate. The metabolite data were normalized by natural logarithm (log[x\u0026thinsp;+\u0026thinsp;1]) and then Z-transformed.\u003c/p\u003e \u003cp\u003eIn the first stage of the cross-sectional study, logistic regression models were used to estimate the associations between metabolites and CH, adjusting for age, race, education, smoking status, drinking status, the TDI, physical activity, BMI, parity, and family history of CH. False discovery rate (FDR)-adjusted \u003cem\u003eP\u003c/em\u003e values were utilized to address multiple hypothesis testing[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], with a significance threshold of 0.1[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo-sample MR analyses were performed to determine whether metabolites had a causal effect on CH using summary statistics of Genome-wide association studies (GWASs). GWAS data for metabolites were obtained from the UK Biobank, comprising over 12.3\u0026nbsp;million single nucleotide polymorphisms (SNPs)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Genetic association estimates for CH were derived from the FinnGen consortium R8 release, which includes 342,439 Finnish individuals[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The selection of IVs associated with metabolites was performed based on the conventional genome-wide significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and a linkage disequilibrium threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a clumping window of 10000kb. Significant outliers SNPs identified by the MR-PRESSO test were removed. The remaining SNPs as IVs were used in subsequent MR analyses. Horizontal pleiotropy was assessed using the MR-Egger intercept, and heterogeneity was evaluated via Cochran's Q statistic. The inverse-variance weighted (IVW) method was the main method to estimate the potential causal associations. A random-effects IVW model was applied when significant heterogeneity was detected (Q-test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), otherwise a fixed-effects model was used. Evidence of a causal relationship between the metabolites and CH was considered when \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.1[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, MR-Egger, weighted median, MR-Robust Adjusted Profile Score (MR-RAPS), and maximum likelihood served as alternative analyses. Consistency in the direction of effect estimates across MR methods provides reliable evidence for a causal association. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubsequently, receiver operating characteristic (ROC) curve was conducted to evaluate the discriminative ability of different models for distinguishing CH among women with a history of GH and PE. The conventional risk factor model included age, race, education, BMI, parity, and family history of CH. The combined model added selected metabolites to these conventional risk factors.\u003c/p\u003e \u003cp\u003eIn the second stage of the prospective study, we focused on 206 women who were not diagnosed with CH at enrollment. Time-dependent ROC curves and the corresponding area under the curve (AUC) were used to assess predictive performance for incident CH. The conventional risk factor model in this stage included age, race, education, BMI, parity, family history of CH, and baseline systolic blood pressure (SBP) and diastolic blood pressure (DBP). The combined model added metabolites identified in the first stage. AUCs were compared using the DeLong test[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using Statistical Analysis System software (SAS, version 9.4) and R platform (version 4.2.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCross-sectional study\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 281 women with a history of de novo HDP and available NMR metabolomics data were included in this study. Of these, 75 participants (26.7%) had already developed chronic hypertension (CH) at baseline. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of the participants stratified by CH status at enrollment. Compared to women without CH at baseline, those with CH were older, had a lower TDI and exhibited a higher prevalence of obesity. Significant differences were also observed in blood pressure and blood glucose levels between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of women with a history of GH and PE at enrollment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePresence of CH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, mean (SD), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.18 (7.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.93 (9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation level, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or university degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking status, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer/current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDrinking status, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer/current\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTDI, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.85 (2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.86 (3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity, median (IQR), min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1653.00 (655.00, 3202.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1807.50 (665.00, 2490.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight or normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParity, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 births\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 births\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamily history of hypertension, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood-based measurements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSBP, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.63 (15.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143.12 (18.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDBP, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.17 (9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.05 (10.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBlood glucose, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.94 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34 (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.51 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.42 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLDL cholesterol, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHDL cholesterol, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTriglycerides, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CH, chronic hypertension; SD, standard deviation; IQR, interquartile range; TDI, the Townsend deprivation index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of metabolites with Prevalent CH\u003c/h2\u003e \u003cp\u003eAfter adjusting for potential confounders and accounting for multiple testing, 58 of the 249 metabolic biomarkers showed statistically significant associations with the presence of CH in women with a history of GH or PE (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.1; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable S2\u003c/b\u003e). Except glycine [odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.631; 95% confident interval (CI): 0.446\u0026ndash;0.892; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.060], all other identified metabolic biomarkers belong to lipid and lipoprotein categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted ORs of metabolites for the presence of CH after multiple testing at enrollment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003eFDR\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.652 (0.474, 0.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol Minus HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.644 (0.467, 0.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant Cholesterol (Non-HDL, Non-LDL -Cholesterol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662 (0.479, 0.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical LDL Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.629 (0.459, 0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647 (0.471, 0.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhospholipids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651 (0.474, 0.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesteryl esters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Esterified Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.657 (0.478, 0.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl Esters in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662 (0.481, 0.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFree cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Free Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.648 (0.468, 0.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Cholesterol in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625 (0.456, 0.857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal lipids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Lipids in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656 (0.478, 0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipoprotein particle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of LDL Particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.661 (0.477, 0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApolipoproteins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.655 (0.473, 0.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmino acids\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.631 (0.446, 0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipoprotein subclasses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol in Medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.638 (0.459, 0.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl Esters in Medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625 (0.453, 0.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of IDL Particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651 (0.473, 0.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Lipids in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.640 (0.465, 0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.648 (0.469, 0.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625 (0.455, 0.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl Esters in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.621 (0.452, 0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Cholesterol in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.650 (0.474, 0.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of Large LDL Particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667 (0.484, 0.920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Lipids in Large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651 (0.475, 0.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in Large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647 (0.471, 0.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol in Large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.643 (0.468, 0.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl Esters in Large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.658 (0.479, 0.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Cholesterol in Large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.618 (0.450, 0.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Lipids in Medium LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685 (0.497, 0.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in Medium LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.682 (0.496, 0.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol in Medium LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.676 (0.490, 0.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Cholesterol in Medium LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.652 (0.477, 0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of Small LDL Particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.658 (0.471, 0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Lipids in Small LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.660 (0.478, 0.912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in Small LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.655 (0.476, 0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol in Small LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.654 (0.474, 0.903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl Esters in Small LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.676 (0.487, 0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Cholesterol in Small LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.639 (0.468, 0.871)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatios\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.660 (0.490, 0.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692 (0.511, 0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.674 (0.500, 0.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.509 (1.111, 2.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids to total lipids ratio in small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613 (0.451, 0.834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685 (0.509, 0.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622 (0.455, 0.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.543 (1.143, 2.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.642 (0.463, 0.890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.644 (0.459, 0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.668 (0.499, 0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.579 (1.158, 2.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.664 (0.495, 0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters to total lipids ratio in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662 (0.491, 0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.596 (1.168, 2.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.481 (0.266, 0.870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.466 (0.251, 0.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.586 (1.134, 2.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in medium LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.500 (1.105, 2.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids to total lipids ratio in large HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.496 (1.085, 2.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eORs are per 1 SD higher of Z-transformed metabolic profiling and are adjusted for age, race, education, smoking status, drinking status, TDI, physical activity, BMI, parity, and family history of CH.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: OR, odds ratio; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDetailed information on the IVs of these 58 metabolites used in MR analyses is shown in \u003cb\u003eTable S3\u003c/b\u003e. We found that 11 metabolites were significantly associated with CH (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e of IVW\u0026thinsp;\u0026lt;\u0026thinsp;0.1). However, two of these metabolic biomarkers (remnant cholesterol and small LDL particles) exhibited an opposite direction from the previous logistic analyses (\u003cb\u003eTable S4\u003c/b\u003e). Therefore, we focused on the remaining 9 metabolic biomarkers, which are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, TGs to total lipids ratio in medium very-low-density lipoprotein (VLDL, OR\u0026thinsp;=\u0026thinsp;1.108, 95% CI:1.035\u0026ndash;1.186), in very small VLDL (OR\u0026thinsp;=\u0026thinsp;1.114, 95% CI:1.050\u0026ndash;1.182), and in intermediate-density lipoprotein (IDL, OR\u0026thinsp;=\u0026thinsp;1.084, 95% CI:1.024\u0026ndash;1.148) were positively associated with CH. Glycine remained inversely associated with CH in the MR analysis (OR\u0026thinsp;=\u0026thinsp;0.950, 95% CI:0.921\u0026ndash;0.980).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMR estimates of the causal relationship between metabolites and incident CH after multiple testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003eFDR\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950 (0.921, 0.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.961 (0.927, 0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.88E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956 (0.934, 0.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.13E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.10E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950 (0.930, 0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950 (0.929, 0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.88E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatios\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880 (0.824, 0.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.40E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.953 (0.854, 1.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.98E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.901 (0.838, 0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.65E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.49E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884 (0.852, 0.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.16E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.881 (0.847, 0.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.46E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in medium VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.108 (1.035, 1.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025 (0.912, 1.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.80E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.45E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.096 (1.014, 1.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.107 (1.065, 1.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.111 (1.067, 1.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.43E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.31E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912 (0.859, 0.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.19E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962 (0.864, 1.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.77E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914 (0.851, 0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914 (0.881, 0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.92E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.913 (0.878, 0.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.63E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.910 (0.860, 0.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.20E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.52E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917 (0.834, 1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.05E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.892 (0.835, 0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.04E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.01E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912 (0.883, 0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.06E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.66E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.909 (0.878, 0.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.38E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.881 (0.832, 0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.30E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.894 (0.812, 0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886 (0.831, 0.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.10E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.887 (0.857, 0.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.36E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.66E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.881 (0.849, 0.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.19E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.114 (1.050, 1.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.58E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.19E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.077 (0.969, 1.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.140 (1.070, 1.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.111 (1.073, 1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.07E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.66E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.117 (1.076, 1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.32E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides to total lipids ratio in IDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.084 (1.024, 1.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.64E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.27E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.068 (0.971, 1.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.048 (0.993, 1.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.86E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.083 (1.051, 1.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.66E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.085 (1.051, 1.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.63E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.67E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol to total lipids ratio in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854 (0.806, 0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.24E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.889 (0.815, 0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.71E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.851 (0.801, 0.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.93E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-RAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870 (0.841, 0.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854 (0.823, 0.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.66E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization; RAPS, Robust Adjusted Profile Score; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminatory performance of metabolites for CH\u003c/h2\u003e \u003cp\u003eThe AUC for the conventional risk factor model (including age, race, education, BMI, parity, and family history of CH) was 0.744 (95% CI: 0.676\u0026ndash;0.812). Adding the 9 identified metabolic biomarkers to this model, the AUC significantly improved to 0.797 (95% CI\u0026thinsp;=\u0026thinsp;0.737\u0026ndash;0.856, DeLong\u0026rsquo;s test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConventional risk factors included age, race, education, BMI, parity, and family history of hypertension. The combined model integrated selected metabolites with risk factors. There was a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) between the AUC of the conventional risk factor model and the combined model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProspective study\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 201 women who did not develop CH at enrollment were included in this phase of the study. After a median follow-up of 13.3 years (interquartile range: 12.4\u0026ndash;14.0 years), 27 (13.43%) participants developed CH. Baseline characteristics of all women stratified by the incidence of CH are exhibited in \u003cb\u003eTable S5\u003c/b\u003e. Participants who later developed CH had significantly higher baseline SBP, DBP, total cholesterol, LDL cholesterol, and HDL cholesterol compared with those who remained normotensive.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictive performance of metabolites for CH\u003c/h2\u003e \u003cp\u003eTo evaluate the predictive performance of the 9 metabolites for future CH, time-dependent ROC analyses were performed. The AUC of the conventional risk factor model was 0.570 (95% CI: 0.291\u0026ndash;0.848) at 3 years and 0.720 (95% CI: 0.489\u0026ndash;0.951) at 5 years. Adding the 9 metabolites significantly improved the AUC to 0.619 (95% CI: 0.366\u0026ndash;0.872) at 3 years and 0.759 (95% CI: 0.549\u0026ndash;0.968) at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, P for 3 years\u0026thinsp;=\u0026thinsp;0.013; \u003cem\u003eP\u003c/em\u003e for 5 years\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe conventional risk factor model included age, race, education, BMI, parity, family history of CH, SBP, and DBP at enrollment. The combined model integrated selected metabolites with risk factors. The combined model showed significant improvements over the conventional risk factor model in predicting CH at both 3 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and 5 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing targeted NMR-based metabolomic profiling from the UK Biobank, 9 metabolites potentially linked to the progression from de novo HDP to CH were identified by both logistic regression and MR analyses. These included glycine and lipid components within VLDL, IDL, and LDL subclasses. Notably, adding these metabolites to conventional risk factor model significantly improved CH discrimination and prediction in women with prior GH or PE.\u003c/p\u003e \u003cp\u003eGlycine was inversely associated with CH, consistent with previous studies[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. As a major amino acid[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], glycine promotes the production of nitric oxide and supports vascular function and blood pressure regulation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, glycine serves as a glutathione precursor, thereby enhancing antioxidant capacity and reducing oxidative stress-induced endothelial damage [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These mechanisms support a protective role for glycine against CH following HDP.\u003c/p\u003e \u003cp\u003eAll other identified metabolites belong to lipids and lipoproteins. Lipoprotein particles comprise a hydrophobic core mainly composed of cholesteryl esters and TGs, surrounded by a hydrophilic monolayer of phospholipids, free cholesterol, and various apolipoproteins[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Lipoprotein particles are classified into five major classes: chylomicrons, VLDL, LDL, IDL, and HDL[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, different lipoprotein particles can be categorized based on their size. Our results suggested that the proportion of TGs in subclasses of lipoprotein particles increases significantly in CH patients, including the TGs to total lipids ratio in medium VLDL, in very small VLDL, and in IDL. Both VLDL and IDL belong to TG-rich lipoproteins[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Insufficient lipolysis of TG-rich lipoproteins can result in the accumulation of remnant lipoproteins, leading to increased TG content and extended residence time[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which can cause impaired endothelial vasomotor function[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Moreover, serum TG levels were reported to be significantly associated with new-onset hypertension[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our study revealed subclass-specific alterations in TGs during the occurrence and development of CH. Cholesterol-to-total-lipids ratios decreased in several subclasses (medium/small/very small VLDL, large LDL) in CH patients. The absolute levels of cholesterol or cholesteryl esters in subclasses of lipoprotein particles did not exhibit a significant increase or decrease. It was shown that the lipid composition of lipoprotein particles can also be associated with disease risk, independent of total lipid concentrations[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The composition, content, and distribution of lipoprotein subclasses play an important role in lipid metabolism and blood pressure regulation[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdding identified metabolic profiling to the conventional risk factor model significantly improved CH discrimination. Among them, the AUCs for combined models of cross-sectional diagnosis and 5-year prediction were both above 0.75. These findings support the potential clinical utility of metabolite markers for early risk stratification in women with GH or PE. However, given the dynamic nature of metabolites, their long-term predictive stability warrants caution[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe study examined the metabolism related to the development of CH in the susceptible population with a history of GH and PE. Moreover, the metabolites identified in the cross-sectional study were further supported by MR analyses, providing evidence for causal relationships. However, limitations should also be considered. Firstly, only 249 metabolites (primarily lipid-related) were measured, potentially overlooking other relevant metabolic pathways. Secondly, the sample size was relatively small, which may limit statistical power and generalizability. Future studies in larger, more diverse populations are needed to validate our findings. Lastly, the GWAS summary statistics of metabolites in the MR analyses were derived from the general population, rather than being restricted to female participants or high-risk women with a history of GH or PE. Thus, further investigation in the target population is required to confirm the causal roles of these metabolites.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the progression from GH or PE to CH is accompanied by metabolic changes, with amino acids, lipids, and lipoproteins emerging as the most significant factors. These findings highlight the importance of monitoring metabolic dysregulation for predicting CH in women with a history of GH or PE.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehypertensive disorders of pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epreeclampsia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egestational hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNuclear Magnetic Resonance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Disease Tenth Version\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic equivalent task\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Townsend deprivation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRobust Adjusted Profile Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe receiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ethe area under the curve\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfident interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003every-low-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintermediate-density lipoprotein.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYG, MY, and QH were responsible for study conception, data analysis, and manuscript drafting. JM and QH contributed to data collection and methodology. XX and YD performed the statistical analysis. JY, YS, and YC designed the study and revised the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the UK Biobank are available upon request at https://www.ukbiobank.ac.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the UK Biobank study was obtained from the North West Multi-centre Research Ethics Committee (16/NW/0274). All participants provided written informed consent in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the investigators and participants of the UK Biobank.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertens (Dallas Tex: 1979). 2018;72(1):24\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhipps EA, Thadhani R, Benzing T, Karumanchi SA. Pre-eclampsia: pathogenesis, novel diagnostics and therapies. Nat Rev Nephrol. 2019;15(5):275\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGestational Hypertension and Preeclampsia. ACOG Practice Bulletin, Number 222. Obstet Gynecol. 2020;135(6):e237\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanghavi M, Rutherford JD. Cardiovascular physiology of pregnancy. Circulation. 2014;130(12):1003\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists\u0026rsquo; Task Force on Hypertension in Pregnancy. Obstet Gynecol. 2013;122(5):1122\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Li T, Wang Y, Xue L, Miao Z, Long W, et al. The Association Between Hypertensive Disorders in Pregnancy and the Risk of Developing Chronic Hypertension. Front Cardiovasc Med. 2022;9:897771.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutherland L, Neale D, Henderson J, Clark J, Levine D, Bennett WL. Provider Counseling About and Risk Perception for Future Chronic Disease Among Women with Gestational Diabetes and Preeclampsia. J Womens Health (Larchmt). 2020;29(9):1168\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndraweera PH, Lassi ZS, Pathirana MM, Plummer MD, Dekker GA, Roberts CT, et al. Pregnancy complications and cardiovascular disease risk perception: A qualitative study. PLoS ONE. 2022;17(7):e0271722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones EJ, Hernandez TL, Edmonds JK, Ferranti EP. Continued Disparities in Postpartum Follow-Up and Screening Among Women With Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Systematic Review. J Perinat Neonatal Nurs. 2019;33(2):136\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlumer V, Pagidipati NJ. Hypertensive Disorders of Pregnancy and Long-Term Heart Failure Risk: A Missed Opportunity for Prevention. Hypertens (Dallas Tex: 1979). 2020;76(5):1420\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShopen N, Schiff E, Koren-Morag N, Grossman E. Factors That Predict the Development of Hypertension in Women With Pregnancy-Induced Hypertension. Am J Hypertens. 2016;29(1):141\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisser VS, Hermes W, Twisk J, Franx A, van Pampus MG, Koopmans C, et al. Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term. Pregnancy Hypertens. 2017;10:118\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimpka S, Stuart JJ, Tanz LJ, Rimm EB, Franks PW, Rich-Edwards JW. Lifestyle in progression from hypertensive disorders of pregnancy to chronic hypertension in Nurses' Health Study II: observational cohort study. BMJ (Clinical Res ed). 2017;358:j3024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChornock R, Iqbal SN, Kawakita T. Racial Disparity in Postpartum Readmission due to Hypertension among Women with Pregnancy-Associated Hypertension. Am J Perinatol. 2021;38(12):1297\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng XJ, Dong GH, Wang D, Liu MM, Lin Q, Tian S, et al. Prevalence, awareness, treatment, control, and risk factors associated with hypertension in urban adults from 33 communities of China: the CHPSNE study. J Hypertens. 2011;29(7):1303\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao M, Xiao Y, Yang Z, Ge W, Liang F, Teng H, et al. Identification of Biomarkers for Preeclampsia Based on Metabolomics. Clin Epidemiol. 2022;14:337\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDasgupta S, Subramani E, Mitra I, Bhattacharya A, Sherpa DD, Joshi M, et al. Discovery of novel metabolic signatures for early identification of women at risk of developing gestational hypertension. Metabolomics: Official J Metabolomic Soc. 2023;19(5):50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q, Zhang AH, Miao JH, Sun H, Han Y, Yan GL, et al. Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Adv. 2019;9(64):37245\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol. 2018;36(4):316\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouca P, Menni C, Padmanabhan S. Genomic Determinants of Hypertension With a Focus on Metabolomics and the Gut Microbiome. Am J Hypertens. 2020;33(6):473\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnuh JO, Aliani M. Metabolomics profiling in hypertension and blood pressure regulation: a review. Clin Hypertens. 2020;26(1):23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33(1):30\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJulkunen H, Cichońska A, Tiainen M, Koskela H, Nybo K, M\u0026auml;kel\u0026auml; V, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14(1):604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraig CL, Marshall AL, Sj\u0026ouml;str\u0026ouml;m M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlee M, Leist AK, Veldsman M, Ranson JM, Llewellyn DJ. Socioeconomic Deprivation, Genetic Risk, and Incident Dementia. Am J Prev Med. 2023;64(5):621\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67(8):850\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemoda Z, Massart R, Suderman M, Hallett M, Li T, Coote M, et al. Maternal depression is associated with DNA methylation changes in cord blood T lymphocytes and adult hippocampi. Translational psychiatry. 2015;5(4):e545.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNightingale Health. and UK Biobank announces major initiative to analyse half a million blood samples to facilitate global medical research. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/nightingale-health-and-uk-biobank-announces-major-initiative-to-analyse-half-a-million-blood-samples-to-facilitate-global-medical-research\u003c/span\u003e\u003cspan address=\"https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/nightingale-health-and-uk-biobank-announces-major-initiative-to-analyse-half-a-million-blood-samples-to-facilitate-global-medical-research\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBackman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599(7886):628\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurki MI, Karjalainen J, Palta P, Sipil\u0026auml; TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi P, Wang H, Guo L, Gou X, Chen G, Lin D, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20(1):443.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai J, Wei Z, Chen M, He L, Wang H, Li M, et al. Socioeconomic status, individual behaviors and risk for mental disorders: A Mendelian randomization study. Eur psychiatry: J Association Eur Psychiatrists. 2022;65(1):e28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDietrich S, Floegel A, Weikert C, Prehn C, Adamski J, Pischon T, et al. Identification of Serum Metabolites Associated With Incident Hypertension in the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. Hypertens (Dallas Tex: 1979). 2016;68(2):471\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin C, Sun Z, Mei Z, Zeng H, Zhao M, Hu J, et al. The causal associations of circulating amino acids with blood pressure: a Mendelian randomization study. BMC Med. 2022;20(1):414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouca P, Nogal A, Moskal A, Goulding NJ, Shipley MJ, Alkis T et al. Cross-Sectional Blood Metabolite Markers of Hypertension: A Multicohort Analysis of 44,306 Individuals from the COnsortium of METabolomics Studies. Metabolites 2022;12(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu G, Wu Z, Dai Z, Yang Y, Wang W, Liu C, et al. Dietary requirements of nutritionally non-essential amino acids by animals and humans. Amino Acids. 2013;44(4):1107\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImenshahidi M, Hossenzadeh H. Effects of glycine on metabolic syndrome components: a review. J Endocrinol Investig. 2022;45(5):927\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Hafidi M, P\u0026eacute;rez I, Ba\u0026ntilde;os G. Is glycine effective against elevated blood pressure? Curr Opin Clin Nutr Metab Care. 2006;9(1):26\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabarrere CA, Kassab GS, Glutathione. A Samsonian life-sustaining small molecule that protects against oxidative stress, ageing and damaging inflammation. Front Nutr. 2022;9:1007816.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeingold KR et al. Introduction to Lipids and Lipoproteins. In: Feingold KR, Anawalt B, Blackman MR, Boyce A, Chrousos G, Corpas E, editors. \u003cem\u003eEndotext.\u003c/em\u003e) MDText.com, Inc. Copyright \u0026copy; 2000\u0026ndash;2023, MDText.com, Inc.: South Dartmouth (MA), 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenugopal SK, Anoruo M, Jialal I, Biochemistry. Low Density Lipoprotein. \u003cem\u003eStatPearls.\u003c/em\u003e) StatPearls Publishing Copyright \u0026copy; 2023, StatPearls Publishing LLC.: Treasure Island (FL) ineligible companies. Disclosure: McDamian Anoruo declares no relevant financial relationships with ineligible companies. Disclosure: Ishwarlal Jialal declares no relevant financial relationships with ineligible companies; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodis HN, Mack WJ. Triglyceride-rich lipoproteins and progression of atherosclerosis. Eur Heart J. 1998;19(Suppl A):A40\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler K, Wetzka B, Hoffmann MM, Friedrich I, Kinner M, Baumstark MW, et al. Triglyceride-rich lipoproteins are associated with hypertension in preeclampsia. J Clin Endocrinol Metab. 2003;88(3):1162\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKugiyama K, Doi H, Motoyama T, Soejima H, Misumi K, Kawano H, et al. Association of remnant lipoprotein levels with impairment of endothelium-dependent vasomotor function in human coronary arteries. Circulation. 1998;97(25):2519\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomita Y, Sakata S, Arima H, Yamato I, Ibaraki A, Ohtsubo T, et al. Relationship between casual serum triglyceride levels and the development of hypertension in Japanese. J Hypertens. 2021;39(4):677\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebhard C, Rhainds D, Tardif JC. HDL and cardiovascular risk: is cholesterol in particle subclasses relevant? Eur Heart J. 2015;36(1):10\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian L, Fu M. The relationship between high density lipoprotein subclass profile and plasma lipids concentrations. Lipids Health Dis. 2010;9:118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. Sci Adv. 2022;8(42):eadd6155.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, chronic hypertension, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-9229260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9229260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHypertensive disorders of pregnancy, including gestational hypertension (GH) and preeclampsia (PE), are associated with an elevated long-term risk of chronic hypertension (CH). However, evidence regarding the underlying mechanisms and effective risk prediction models remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized baseline metabolomics data from the UK Biobank, focusing on women with a history of GH or PE. Their CH status was assessed both at enrollment and during follow-up. In Phase I, a cross-sectional study employing logistic regression and Mendelian randomization analyses identified metabolites significantly associated with CH. The discriminatory performance of these metabolites for CH at enrollment was evaluated using the area under the receiver operating characteristic curve. In Phase II, a prospective study assessed the predictive performance of the identified metabolites for incident CH at three and five years.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study recruited 281 women with a history of GH or PE. Among them, 75 had prevalent CH at enrollment. Of the remaining 206 women without CH at baseline, 27 developed incident CH during follow-up. Nine metabolites were significantly associated with CH, including glycine and lipid components in lipoprotein subclasses. Notably, adding the metabolic profile to a conventional prediction model significantly improved the predictive performance for CH.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings suggest that metabolic dysregulation, particularly in lipid metabolism, is involved in the progression to CH following GH or PE. The identified metabolic profile may enhance CH risk prediction in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Development of chronic hypertension among women with a history of de novo hypertension disorders during pregnancy: the role of metabolites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 16:30:22","doi":"10.21203/rs.3.rs-9229260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-02T14:29:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T13:54:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T07:04:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T07:04:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-26T04:46:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a583669a-f0c2-47f5-95c5-fb6771300449","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T16:30:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 16:30:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9229260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9229260","identity":"rs-9229260","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}