
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261530
71908
10.1038/s41598-024-71908-z
Article
Blood PAI-1 and cardiovascular and metabolic risk factors among the middle-aged women from SWAN study
Xu Zhenyan 2
Huang Ying hynanchang8888@163.com

1
1 https://ror.org/042v6xz23 grid.260463.5 0000 0001 2182 8825 Rehabilitation Department, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang City, 330006 Jiangxi China
2 https://ror.org/042v6xz23 grid.260463.5 0000 0001 2182 8825 Department of Cardiovascular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang City, 330006 Jiangxi China
11 9 2024
11 9 2024
2024
14 2120719 11 2023
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The research on the role of plasminogen activator inhibitor-1 (PAI-1) in cardiovascular and metabolic diseases is insufficient. We aimed to explore whether elevated blood PAI-1 levels are significantly related to increased cardiovascular and metabolic risk factors in a midlife women population. Data were obtained from baseline characteristics in Study of Women’s Health Across the Nation (SWAN) study. Multivariable linear regression models were performed to examine for the trends of associations between PAI-1 and cardiovascular and metabolic risk factors (systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC), respectively. Smooth curve demonstrated gradual upward trends on associations of blood PAI-1 levels with LDL-C, TG, TC, fasting blood glucose, insulin, systolic BP and diastolic BP (all P < 0.05) and a gradual downward trend of PAI-1 levels with HDL-C (P < 0.05). Multivariable linear regression models still indicated that increased blood PAI-1 levels were associated with higher cardiovascular and metabolic risk after confounding factors including age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status, total family income and BMI were controlled for. Moreover, we observed that the independent associations between blood levels of PAI-1 and cardiovascular and metabolic risk factors examined by stratified analysis were not influenced by age, smoking status, menopausal status and BMI, respectively. Our analysis showed that increased blood PAI-1 levels were associated with higher level for cardiovascular and metabolic risk factors which mainly causes to higher possibility of cardio-cerebrovascular diseases in a large-sample midlife women subjects.

Keywords

PAI-1
Cardiovascular
Metabolic syndrome
Middle-aged women
SWAN study
Subject terms

Biochemistry
Biomarkers
Diseases
Risk factors
Jiangxi Provincial Natural Science Foundation20224BAB216019 Huang Ying National Natural Science Foundation Incubation Program of the Second Affiliated Hospital of Nanchang University2022YNFY12010 Huang Ying General Science and Technology Plan of Jiangxi Provincial Health Commission202410034 Huang Ying issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Cardiovascular diseases (CVDs) such as hypertension, coronary heart disease and heart failure, and metabolic diseases such as diabetes mellitus, dyslipidemia and obesity, have been recognized as the main cause of death worldwide1–4. Although great progress has been made on the treating and/or preventing CVDs and metabolic diseases in the last few decades, the pathogenesis of these diseases has not been fully clarified yet due to the interactions of various pathological factors5,6. Hence, accumulating evidence is of great clinical significance to further confirm the potential risk factors of cardiovascular and metabolic diseases.

Plasminogen activator inhibitor-1 (PAI-1), the primary physiological inhibitor of tissue plasminogen activator (tPA) and urokinase-type plasminogen activator (uPA), is expressed in various tissues and strongly regulated by multiple cytokines like inflammatory cytokines, growth factors, glucose, hormones and others6–9. As the main inhibitor in the fibrinolytic system, highly expressed PAI-1 level can contribute a prothrombotic or hypofibrinolytic state, which might promote CVDs progress. Vascular blockage is often resulted from the thrombosis formation or stenotic atherosclerotic plaque in ischemic heart disease or stroke, which tend to coincide with elevated blood PAI-1 levels. Several studies have provided evidence that PAI-1 may be considered as an independent factor for CVDs such as stroke, coronary heart disease, myocardial infarction and venous thrombosis previously10–13, but some other studies did not enable to validate these independent associations or there was no significance for these relations after risk factors including age, gender and other confounding factors were adjusted for14–16. Also, several observational studies have confirmed that increased blood levels of PAI-1 are an important biomarker for the developing metabolic disorders in metabolicsyndrome and diabetes mellitus17,18. A small amount of clinical evidenc also suggested that PAI-1 inhibition or PAI-1 deficiency can provide protective effects on metabolic disturbances19–21. However, the research evidence is relatively insufficient for the relationships between PAI-1 and metabolic metabolic disordersd due to lack of high-quality prospective cohort studies or clinical trials.

Therefore, considering the insufficient evidence or controversy between PAI-1 and cardiovascular and metabolic risk factors, performing a large-sample and well-designed clinical investigations are necessary to better confirm the associations of blood PAI-1 with cardiovascular and metabolic risk factors among various general populations. To this end, we analyzed the baseline data from Study of Women’s Health Across the Nation (SWAN) and evaluate associations between blood PAI-1 and cardiovascular and metabolic risk factors among the midlife women population.

Materials and methods

Study population

Data from SWAN study is for public use that contains baseline and follow-up data, which provides free channels for researchers. Our study data in this analysis are mainly from the ICPSR public database (https://www.icpsr.umich.edu/web/ICPSR/search/studies?q=SWAN). The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

SWAN study is a community-based, multi-ethnic, cohort study among 3302 women subjects who were enrolled at 7 field sites of the United States (Davis, CA; Los Angeles, CA; Boston, MA; Newark, NJ; Detroit, MI; Pittsburgh, PA; and Chicago, IL) as previously described22. At baseline subjects from this SWAN study (1996–1997), all of the included women who were aged 42–52 years and non-pregnant did not use any hormone therapy in the preceding 3 months and had at least 1 menstrual period and an intact uterus with at least 1 ovary. The SWAN study provided detailed ethnic group, lifestyle, self-reported health, physicals, cardiovascular-related risk factors and blood indicators. Study subjects from the SWAN baseline with missing blood indicators data were excluded (N = 674). Our study finally included 2628 women in final sample. The study protocol was approved by The Institutional Review Board (IRB) at each SWAN site and Data Coordinating Center, and informed consent was obtained from all included individuals. All methods were performed in accordance with the relevant guidelines and regulations based on the Declaration of Helsinki.

Cardiovascular and metabolic risk factors

The measurement of cardiovascular and metabolic risk factors were collected in all SWAN participants at baseline. After at least a 5-min rest, systolic and diastolic blood pressure (BP) were measured in a seated position. The average values of three BP readings were used for analyses. Direct low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglyceride (TG) and total cholesterol (TC) were tested by coupled enzymatic methods. Blood glucose was tested by using a 2-step enzymatic reaction and insulin was tested by a 2-site sandwich immunoassay. Glucose, insulin and lipid profile assays were performed by a Siemens ADVIA 2400 automated chemistry analyzer. Specific experimental methods for blood indicators was saved on the relevant website22.

Covariates

All SWAN subjects have underwent interviewer-administered questionnaires at baseline to obtain smoking status, drinking status, physical measures, medication use, fasting blood draw and others. Included population characteristics in our study contained age, ethnicity (Black/African American, Japanese/Japanese American, Chinese/Chinese American, Caucasian/White Non-Hispanic and Hispanic) and total family income. Lifestyle characteristics contained smoking status, alcohol consumption and body mass index (BMI). The race/ethnicity were self-reported at the SWAN baseline. Alcohol consumption was categorized as alcohol in last 24 h (yes or no). Smoking was classified as ever smoked regularly (yes or no) based on self-reported smoking status. Current medications and menopausal status was categorized as “yes” or “no”. Height and weight were measured using standardized protocols and then BMI (kg/m2) was calculated via weight divided by height squared. Covariates were added into their reported association between independent variable (PAI-1) and dependent variables (systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC).

Statistical analysis

Each continuous variable was tested for normality. Descriptive statistics (continuous variables described as mean or median values, and percentages described as categorical variables) were used for summing up subject characteristics or cardiovascular and metabolic risk factors. Multivariable linear regression models were performed to examine for the trends of associations between PAI-1 and cardiovascular and metabolic risk factors (systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC), respectively. Linear regression models were performed with systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC as the dependent variables in separate model with PAI-1 as the predictor. Within models, the model 1 adjusted for age and race/ethnicity; the model 2 added covariates for ever smoked regularly and alcohol in last 24 h; the model 3 added covariates for menopausal status and total family income; and model 4 continued to added BMI as the covariate. Subgroup analysis was also examined these associations by using age, smoking status, menopausal status and BMI as a stratification variable respectively. EmpowerStats 3.0. was used for all analyse. P value (≤ 0.05) was considered to be statistically significant.

Results

Characteristics of participants

As shown in Table 1, the characteristics of the participants were presented. The median age from all included female subjects is 46 year old and most of them are white and overweight. Blood median PAI-1 and tPA levels of them were 20.35 and 7.25 ng/mL. Cardiovascular and metabolic risk factors including HDL-C, LDL-C, TG, TC, fasting blood glucose, insulin, systolic BP and diastolic BP were 54.00 mg/dl, 114.00 mg/dl, 90.00 mg/dl, 191.00 mg/dl, 91.00 mg/dl, 8.40 mg/dl, 115 mmHg and 74 mmHg, respectively. More information for blood biomarkers was also presented in detail in Table 1. Table 1 Characteristics of participants.

Variables	Descriptive statistics	
Age	46.00 (44.00–48.00)	
Race/ethnicity	
 Black/African American, n (%)	741 (28.20%)	
 Chinese/Chinese American, n (%)	203 (7.72%)	
 Japanese/Japanese American, n (%)	225 (8.56%)	
 Caucasian/White Non-Hispanic, n (%)	1268 (48.25%)	
 Hispanic, n (%)	191 (7.27%)	
Total family income	
 Less Than $19,999, n (%)	364 (13.85%)	
 $20,000–$49,999, n (%)	895 (34.06%)	
 $50,000–$99,999, n (%)	972 (36.99%)	
 $100,000 or More, n (%)	397 (15.11%)	
 Ever smoked regularly, n (%)	1113 (42.35%)	
 Alcohol in last 24 h, n (%)	348 (13.24%)	
Menopausal status	
 Early Peri, n (%)	1190 (45.28%)	
 Pre-menopausal, n (%)	1438 (54.72%)	
 BMI (kg/m2)	26.47 (22.77–32.04)	
 Hip circumference (cm)	103.70 (96.00–114.53)	
 Waist circumference (cm)	82.45 (74.00–95.00)	
 Pulse (per 30 s)	35.00 (32.00–38.00)	
 Systolic BP (mmHg)	115.00 (106.00–126.00)	
 Diastolic BP (mmHg)	74.00 (69.00–81.00)	
Laboratory blood test	
 Glucose (mg/dL)	91.00 (86.00–98.00)	
 Insulin (uIU/mL)	8.40 (6.10–12.80)	
 Triglycerides (mg/dL)	90.00 (67.00–128.25)	
 Total cholesterol (mg/dL)	191.00 (171.00–214.00)	
 LDL-C (mg/dL)	114.00 (95.00–135.00)	
 HDL-C (mg/dL)	54.00 (46.00–64.00)	
 PAI-1 (ng/mL)	20.35 (12.10–33.80)	
 tPA (ng/mL)	7.25 (5.20–9.60)	
 C-reactive protein (mg/L)	1.50 (0.60–4.40)	
 Dehydroepiandrosterone sulfate (ug/dL)	113.95 (75.30–168.12)	
 Estradiol (pg/mL)	55.02 (32.69–87.86)	
 Follicle-stimulating hormone (mIU/mL)	15.90 (11.07–26.40)	
 Sex hormone-binding globulin (nmol/L)	41.45 (28.50–57.62)	
 Testosterone (ng/dL)	41.40 (29.75–55.82)	
Current medications	
 Anticoagulants, n (%)	16 (0.61%)	
 Heart medication, n (%)	47 (1.79%)	
 Cholesterol medications, n (%)	21 (0.80%)	
 Blood pressures medications, n (%)	298 (11.34%)	
 Insulin medications, n (%)	71 (2.70%)	
BMI body mass index, BP blood pressure, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, PAI-1 plasminogen activator inhibitor-1, tPA tissue type plasminogenactivator.

Correlations analysis between blood PAI-1 and cardiovascular and metabolic risk factors

Supplementary materials Table 1 described Spearman analysis for associations between blood levels of PAI-1 were significantly associated cardiovascular and metabolic risk factors including HDL-C (r = − 0.398, P < 0.01), LDL-C (r = 0.196, P < 0.01), TG (r = 0.481, P < 0.01), TC (r = 0.152, P < 0.01), fasting blood glucose (r = 0.366, P < 0.01), insulin (r = 0.473, P < 0.01), systolic BP (r = 0.290, P < 0.01) and diastolic BP (r = 0.210, P < 0.01). Consistently, we also observed similar trends through smooth curve analysis (Fig. 1). Moreover, the associaitons between blood tPA and these cardiovascular and metabolic risk factors also has consistent trends in Supplementary materials Table 1 and Fig. 1.Fig. 1 Smooth curve on relations between blood PAI-1 and tPA levels and cardiovascular and metabolic risk factors.

Independent association between PAI-1 and cardiovascular and metabolic risk factors

Consistently, the linear regression coefficient in Model 1 with 95% confidence intervals (CIs) of cardiovascular and metabolic risk factors (Tables 2, 3, 4) indicated that blood PAI-1 were significantly and independently associated with systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC (all P < 0.01) when age and race/ethnicity were adjusted. After controlling for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status, total family income and BMI (Model 4), these associations remained statistically significant and was little changed (all P < 0.01). Also, these associations between blood tPA and these cardiovascular and metabolic risk factors has consistent trends (all P < 0.01). Table 2 Linear regression analysis for associations between PAI-1, tPA and BP.

Variables	Systolic BP (mmHg)	Diastolic BP (mmHg)	
B (95%CI)	P-Value	B (95%CI)	P-Value	
Model 1	
 PAI-1 (ng/mL)	0.096 (0.075, 0.117)	 < 0.001	0.042 (0.028, 0.055)	 < 0.001	
 tPA (ng/mL)	1.209 (1.023, 1.395)	 < 0.001	0.650 (0.530, 0.770)	 < 0.001	
Model 2	
 PAI-1 (ng/mL)	0.094 (0.073, 0.116)	 < 0.001	0.041 (0.028, 0.055)	 < 0.001	
 tPA (ng/mL)	1.195 (1.009, 1.382)	 < 0.001	0.647 (0.527, 0.767)	 < 0.001	
Model 3	
 PAI-1 (ng/mL)	0.094 (0.072, 0.115)	 < 0.001	0.042 (0.028, 0.056)	 < 0.001	
 tPA (ng/mL)	1.193 (1.005, 1.381)	 < 0.001	0.656 (0.535, 0.777)	 < 0.001	
Model 4	
 PAI-1 (ng/mL)	0.050 (0.028, 0.072)	 < 0.001	0.026 (0.011, 0.040)	 < 0.001	
 tPA (ng/mL)	0.733 (0.523, 0.943)	 < 0.001	0.537 (0.400, 0.674)	 < 0.001	
Model 1: Adjusted for age and race/ethnicity.

Model 2: Adjusted for age, race/ethnicity, ever smoked regularly and alcohol in last 24 h.

Model 3: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status and total family income.

Model 4: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status, total family income and BMI.

BP blood pressure, PAI-1 plasminogen activator inhibitor-1, tPA tissue type plasminogenactivator, BMI body mass index.

Table 3 Linear regression analysis for associations between PAI-1, tPA and insulin and blood glucose.

Variables	Insulin (uIU/ml)	Glucose (mg/dL)	
B (95%CI)	P-Value	B (95%CI)	P-Value	
Model 1	
 PAI-1 (ng/mL)	0.096 (0.077, 0.114)	 < 0.001	0.199 (0.159, 0.239)	 < 0.001	
 tPA (ng/mL)	1.120 (0.961, 1.279)	 < 0.001	2.861 (2.516, 3.207)	 < 0.001	
Model 2	
 PAI-1 (ng/mL)	0.094 (0.076, 0.113)	 < 0.001	0.197 (0.156, 0.237)	 < 0.001	
 tPA (ng/mL)	1.120 (0.960, 1.279)	 < 0.001	2.862 (2.516, 3.207)	 < 0.001	
Model 3	
 PAI-1 (ng/mL)	0.093 (0.075, 0.112)	 < 0.001	0.189 (0.149, 0.230)	 < 0.001	
 tPA (ng/mL)	1.115 (0.955, 1.276)	 < 0.001	2.794 (2.446, 3.141)	 < 0.001	
Model 4	
 PAI-1 (ng/mL)	0.052 (0.033, 0.071)	 < 0.001	0.118 (0.076, 0.160)	 < 0.001	
 tPA (ng/mL)	0.668 (0.489, 0.847)	 < 0.001	2.226 (1.833, 2.618)	 < 0.001	
Model 1: Adjusted for age and race/ethnicity.

Model 2: Adjusted for age, race/ethnicity, ever smoked regularly and alcohol in last 24 h.

Model 3: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status and total family income.

Model 4: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status, total family income and BMI.

PAI-1 plasminogen activator inhibitor-1, tPA tissue type plasminogenactivator, BMI body mass index.

Table 4 Linear regression analysis for associations between PAI-1, tPA and blood lipid.

Variables	Triglycerides (mg/dL)	Total cholesterol (mg/dL)	LDL-C (mg/dL)	HDL-C (mg/dL)	
B (95%CI)	P-Value	B (95%CI)	P-Value	B (95%CI)	P-Value	B (95%CI)	P-Value	
Model 1	
 PAI-1 (ng/mL)	0.605 (0.529, 0.680)	 < 0.001	0.113 (0.068, 0.159)	 < 0.001	0.117 (0.076, 0.159)	 < 0.001	− 0.125 (− 0.143, − 0.106)	 < 0.001	
 tPA (ng/mL)	7.934 (7.301, 8.566)	 < 0.001	1.801 (1.398, 2.203)	 < 0.001	1.701 (1.337, 2.066)	 < 0.001	− 1.487 (− 1.648, − 1.325)	 < 0.001	
Model 2	
 PAI-1 (ng/mL)	0.593 (0.518, 0.669)	 < 0.001	0.113 (0.067, 0.159)	 < 0.001	0.113 (0.072, 0.155)	 < 0.001	− 0.119 (− 0.137, − 0.101)	 < 0.001	
 tP A (ng/mL)	7.860 (7.230, 8.491)	 < 0.001	1.798 (1.394, 2.201)	 < 0.001	1.692 (1.327, 2.056)	 < 0.001	− 1.466 (− 1.623, − 1.309)	 < 0.001	
Model 3	
 PAI-1 (ng/mL)	0.583 (0.507, 0.659)	 < 0.001	0.108 (0.062, 0.154)	 < 0.001	0.109 (0.068, 0.151)	 < 0.001	− 0.118 (− 0.137, − 0.100)	 < 0.001	
 tPA (ng/ml)	7.799 (7.163, 8.434)	 < 0.001	1.750 (1.344, 2.156)	 < 0.001	1.659 (1.292, 2.026)	 < 0.001	− 1.469 (− 1.627, − 1.310)	 < 0.001	
Model 4	
 PAI-1 (ng/mL)	0.429 (0.350, 0.507)	 < 0.001	0.086 (0.037, 0.135)	 < 0.001	0.072 (0.028, 0.117)	0.001	− 0.072 (− 0.091, − 0.053)	 < 0.001	
 tPA (ng/mL)	6.778 (6.060, 7.496)	 < 0.001	1.749 (1.287, 2.211)	 < 0.001	1.409 (0.991, 1.826)	 < 0.001	− 1.016 (− 1.193, − 0.840)	 < 0.001	
Model 1: Adjusted for age and race/ethnicity.

Model 2: Adjusted for age, race/ethnicity, ever smoked regularly and alcohol in last 24 h.

Model 3: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status and total family income.

Model 4: Adjusted for age, race/ethnicity, ever smoked regularly, alcohol in last 24 h, menopausal status, total family income and BMI.

LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, PAI-1 plasminogen activator inhibitor-1, tPA tissue type plasminogenactivator, BMI body mass index.

Stratified analysis for association between blood PAI-1 and cardiovascular and metabolic risk factors

Furthermore, the associations between blood levels of PAI-1 and cardiovascular and metabolic risk factors were examined through stratified analysis using age, smoking status, menopausal status and BMI as the stratification variable, respectively (Tables 5 and 6). The present results reported that elevated PAI-1 levels, as well as blood tPA, were still almost associated with systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC in all subgroups (age ≥ 46 years and age < 46 years; BMI ≥ 24 and BMI < 24; ever smoked regularly and not ever smoked regularly; Early peri and pre-menopausal). These results are consistent with the above analysis. Table 5 Stratified analysis for associations between PAI-1, tPA and BP, glucose and insulin.

Variables	Systolic BP (mmHg)	Diastolic BP (mmHg)	Insulin (uIU/ml)	Glucose (mg/dL)	
B (95%CI)	P− Value	B (95%CI)	P− Value	B (95%CI)	P− Value	B (95%CI)	P− Value	
PAI− 1 (ng/mL)	
 Age < 46	0.047 (0.020, 0.075)	0.001	0.029 (0.010, 0.048)	0.003	0.033 (0.013, 0.054)	0.002	0.069 (0.016, 0.122)	0.011	
 Age ≥ 46	0.051 (0.014, 0.087)	0.006	0.020 (− 0.003, 0.042)	0.092	0.076 (0.043, 0.110)	 < 0.001	0.190 (0.123, 0.257)	 < 0.001	
 NO ever smoked	0.055 (0.026, 0.083)	 < 0.001	0.024 (0.005, 0.042)	0.015	0.043 (0.016, 0.071)	0.002	0.106 (0.050, 0.162)	 < 0.001	
 Ever smoked regularly	0.048 (0.012, 0.083)	0.009	0.031 (0.008, 0.054)	0.009	0.062 (0.038, 0.086)	 < 0.001	0.127 (0.062, 0.191)	 < 0.001	
 Early Peri	0.046 (0.012, 0.080)	0.009	0.030 (0.008, 0.052)	0.009	0.057 (0.020, 0.095)	0.003	0.173 (0.106, 0.241)	 < 0.001	
 Pre− menopausal	0.053 (0.024, 0.082)	 < 0.001	0.023 (0.003, 0.042)	0.022	0.045 (0.028, 0.062)	 < 0.001	0.069 (0.016, 0.122)	0.011	
 BMI < 24	0.078 (0.023, 0.133)	0.005	0.028 (− 0.010, 0.066)	0.145	0.032 (0.011, 0.053)	0.003	0.097 (0.030, 0.164)	0.005	
 BMI ≥ 24	0.073 (0.049, 0.098)	 < 0.001	0.034 (0.018, 0.049)	 < 0.001	0.084 (0.060, 0.108)	 < 0.001	0.177 (0.126, 0.228)	 < 0.001	
tPA (ng/mL)	
 Age < 46	0.827 (0.541, 1.113)	 < 0.001	0.616 (0.419, 0.812)	 < 0.001	0.593 (0.376, 0.809)	 < 0.001	1.969 (1.418, 2.519)	 < 0.001	
 Age ≥ 46	0.664 (0.356, 0.973)	 < 0.001	0.483 (0.290, 0.675)	 < 0.001	0.735 (0.452, 1.018)	 < 0.001	2.486 (1.926, 3.046)	 < 0.001	
 NO ever smoked	0.696 (0.425, 0.966)	 < 0.001	0.515 (0.335, 0.694)	 < 0.001	0.562 (0.296, 0.829)	 < 0.001	2.396 (1.872, 2.920)	 < 0.001	
 Ever smoked regularly	0.760 (0.428, 1.093)	 < 0.001	0.546 (0.331, 0.760)	 < 0.001	0.797 (0.573, 1.021)	 < 0.001	2.003 (1.405, 2.601)	 < 0.001	
 Early Peri	0.718 (0.393, 1.043)	 < 0.001	0.615 (0.404, 0.826)	 < 0.001	0.744 (0.388, 1.100)	 < 0.001	2.312 (1.676, 2.948)	 < 0.001	
 Pre− menopausal	0.742 (0.465, 1.019)	 < 0.001	0.469 (0.287, 0.651)	 < 0.001	0.603 (0.443, 0.763)	 < 0.001	2.104 (1.610, 2.597)	 < 0.001	
 BMI < 24	0.799 (0.440, 1.159)	 < 0.001	0.558 (0.312, 0.805)	 < 0.001	0.284 (0.147, 0.421)	 < 0.001	1.103 (0.666, 1.539)	 < 0.001	
 BMI ≥ 24	1.047 (0.807, 1.287)	 < 0.001	0.576 (0.425, 0.728)	 < 0.001	1.193 (0.960, 1.426)	 < 0.001	3.187 (2.698, 3.676)	 < 0.001	
BP blood pressure, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, PAI-1 plasminogen activator inhibitor-1, tPA tissue type plasminogenactivator, BMI body mass index.

Table 6 Stratified analysis for associations between PAI-1, tPA and blood lipid.

Variables	Triglycerides (mg/dL)	Total cholesterol (mg/dL)	LDL-C (mg/dL)	HDL-C (mg/dL)	
B (95%CI)	P-Value	B (95%CI)	P-Value	B (95%CI)	P-Value	B (95%CI)	P-Value	
PAI-1 (ng/mL)	
 Age < 46	0.324 (0.222, 0.425)	 < 0.001	0.081 (0.017, 0.145)	0.013	0.071 (0.013, 0.128)	0.016	− 0.055 (− 0.080, − 0.030)	 < 0.001	
 Age ≥ 46	0.572 (0.449, 0.695)	 < 0.001	0.085 (0.008, 0.162)	0.030	0.068 (− 0.001, 0.138)	0.055	− 0.097 (− 0.126, − 0.068)	 < 0.001	
 NO ever smoked	0.392 (0.290, 0.494)	 < 0.001	0.075 (0.010, 0.140)	0.024	0.055 (− 0.003, 0.114)	0.063	− 0.058 (− 0.082, − 0.035)	 < 0.001	
 Ever smoked regularly	0.484 (0.360, 0.608)	 < 0.001	0.107 (0.032, 0.181)	0.005	0.099 (0.031, 0.167)	0.005	− 0.090 (− 0.121, − 0.058)	 < 0.001	
 Early Peri	0.521 (0.398, 0.643)	 < 0.001	0.094 (0.017, 0.171)	0.016	0.078 (0.010, 0.147)	0.026	− 0.088 (− 0.116, − 0.060)	 < 0.001	
 Pre− menopausal,	0.358 (0.255, 0.460)	 < 0.001	0.080 (0.017, 0.144)	0.013	0.068 (0.011, 0.126)	0.020	− 0.060 (− 0.085, − 0.035)	 < 0.001	
 BMI < 24	0.764 (0.605, 0.924)	 < 0.001	0.223 (0.099, 0.347)	 < 0.001	0.188 (0.075, 0.302)	0.001	− 0.117 (− 0.172, − 0.063)	 < 0.001	
 BMI ≥ 24	0.429 (0.339, 0.518)	 < 0.001	0.062 (0.009, 0.114)	0.021	0.058 (0.011, 0.105)	0.015	− 0.082 (− 0.101, − 0.064)	 < 0.001	
tPA (ng/mL)	
 Age < 46	6.426 (5.402, 7.450)	 < 0.001	1.942 (1.279, 2.606)	 < 0.001	1.497 (0.898, 2.096)	 < 0.001	− 0.839 (− 1.097, − 0.580)	 < 0.001	
 Age ≥ 46	7.165 (6.159, 8.171)	 < 0.001	1.679 (1.030, 2.328)	 < 0.001	1.406 (0.820, 1.993)	 < 0.001	− 1.163 (− 1.406, − 0.920)	 < 0.001	
 NO ever smoked	6.011 (5.059, 6.962)	 < 0.001	1.500 (0.878, 2.122)	 < 0.001	1.427 (0.872, 1.983)	 < 0.001	− 1.130 (− 1.350, − 0.911)	 < 0.001	
 Ever smoked regularly	7.735 (6.633, 8.838)	 < 0.001	2.089 (1.396, 2.782)	 < 0.001	1.427 (0.791, 2.064)	 < 0.001	− 0.887 (− 1.178, − 0.596)	 < 0.001	
 Early Peri	7.068 (5.934, 8.202)	 < 0.001	2.106 (1.380, 2.831)	 < 0.001	1.665 (1.016, 2.313)	 < 0.001	− 0.975 (− 1.244, − 0.706)	 < 0.001	
 Pre− menopausal,	6.531 (5.599, 7.463)	 < 0.001	1.493 (0.893, 2.094)	 < 0.001	1.231 (0.683, 1.780)	 < 0.001	− 1.044 (− 1.280, − 0.808)	 < 0.001	
 BMI < 24	6.701 (5.692, 7.710)	 < 0.001	2.431 (1.622, 3.239)	 < 0.001	1.913 (1.170, 2.656)	 < 0.001	− 0.818 (− 1.176, − 0.459)	 < 0.001	
 BMI ≥ 24	6.821 (5.972, 7.670)	 < 0.001	1.248 (0.734, 1.763)	 < 0.001	1.113 (0.652, 1.573)	 < 0.001	− 1.232 (− 1.414, − 1.050)	 < 0.001	
LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; PAI-1: plasminogen activator inhibitor-1; tPA: tissue type plasminogenactivator; BMI: body mass index.

Discussion

In this study, we used baseline data of SWAN (1996–1997) and a total of 2628 women subjects with 42–52 year old were enrolled for analysis. The present study examined the associations of blood PAI-1 levels with cardiovascular and metabolic risk factors (systolic BP, diastolic BP, fasting blood glucose, insulin, HDL-C, LDL-C, TG and TC). We found that a high blood PAI-1 levels were significantly and strongly associated with cardiovascular and metabolic risk factors after adjusting for potential confounders.

PAI-1, a 45-kDa single-chain glycoprotein, is involved in various pathophysiological processes18 which has been extensively investigated in humans and mice with overexpress or knockout PAI-1. A large amount of existing evidence has confirmed significant associations between PAI-1 and different diseases including CVDs, metabolic disorder, inflammation, aging, tissue fibrosis, cancer and neurodegenerative disorder. For example, after the rupture of atherosclerotic plaque in the coronary arteries, occlusive thrombus rapidly forms and then causes myocardial infarction accompanied by with elevated blood PAI-1 levels23. There was sufficient evidence reporting that atherosclerotic plaques have higher overexpression of PAI-1 in human coronary arteries, and the highest PAI-1 levels were observed in the vulnerable part of the plaque24–26, which suggested that PAI may be an important biomarker for acute cardiovascular events. However, PAI-1 can also potentially stabilize the formed fibrous plaques by locally inhibiting plasmin generation, which inconsistent with the former study from the perspective of pathological mechanisms that as the main inhibitor in the fibrinolytic system, PAI-1 can inhibit the dissolution of fibrous plaques6–9,24–26. These conclusions may seem completely inconsistent, but there may be some reasons to explain them. Firstly, the occurrence and development of CVDs inherently involve multiple complex mechanisms, and existing evidence does not support the primary role of PAI in this process. Secondly, there is currently insufficient evidence to suggest that PAI plays a driving role in cardiovascular events or is a reactive expression of adverse pathological outcomes. Thirdly, from a physiological mechanism perspective, elevated PAI does have a stabilizing effect on plaques, but sustained elevated PAI may lead to arterial stenosis. Therefore, these previous clinical studies even suggested that PAI-1 can be act as a predictive factor for CVDs including ischemic stroke, myocardial infarction and venous thrombosis10–13. Interestingly, some other studies could not even confirm it again because there was no significance for these relations after confounding factors including age, gender and other factors were controlled for14–16. It can be seen that these controversial conclusions have always existed, because these factors such as statistical analysis and other unknown factors cannot be ruled out. In our results, we observed that elevated blood PAI-1 levels still independently contributed to higher blood levels of LDL-C, TG, TC, fasting blood glucose, insulin, systolic BP and diastolic BP and lower blood LDL-C levels when adjusting demographic characteristics, life habits (smoking status, drinking status, BMI), menopausal status and total family income were made. Our research conclusion is consistent with previous clinical and basic research10–13,23–26.

Moreover, several clinical investigations have also suggested that elevated blood PAI-1 level is a valuable biomarker for the developing metabolic disorder in metabolic syndrome and diabetes mellitus17,18 and inhibiting PAI-1 can alleviate the progression19–21. As a multifactorial disease, metabolic syndrome is mainly manifested as a cluster of co-occurring metabolic abnormalitie, including impaired glucose tolerance, central obesity, dyslipidemia, hyperinsulinemia and hypertension, which are important dangerous factors of diabetes mellitus and CVDs27. Crucially, some studies have shown that increased insulin28, blood glucose29 and free fatty acids30 levels enable to alleviate the mRNA degradation of PAI-131 and promote PAI-1 expression. Furthermore, it has been found that adipocytes contribute an important source of PAI-1 and its high expression increases an important part to circulating levels of PAI-1 in obese mice and human adipose tissue32,33. The other side of the shield, metabolic syndrome and type 2 diabetes are also related to chronic inflammation by overexpressing inflammatory adipokines including tumor necrosis factor-α and interleukin-6, which can promote PAI-1 expression in adipose cells34. There were research evidence suggesting an association of PAI-1 with lipid metabolism in obesity that elevated PAI-1 levels were linked with a increased amounts of small-dense LDL lipoprotein fraction, contributing to increased CVDs risk in obesity35. Our study results were also consistent with these previous conclusions. Unfortunately, it is still unclear whether PAI is the cause or outcome of CVDs and metabolic syndrome.

There were several advantages that should be emphasized. The large sample from SWAN study containing enough female population in a middle age, which provides sufficient data for analyzing the relationships between cardiovascular and metabolic risk factors (systolic and diastolic BP, HDL-C, LDL-C, TG, TC, fasting blood glucose and insulin) and coagulation function (PAI-1 and tPA). We investigated baseline data (1996–1997) with a total of 2628 women subjects with 42–52 year old, which enable to estimate prognostic value of PAI-1/tPA for health status in middle-aged women. This is a rare study to evaluate the relationship among in a group of middle-aged women. Another advantage is that our study subjects were from various races from US, increasing the generalizability of other different races. Importantly, confounding factors for these independent associations including demographic characteristics, lifestyle and others were adjusted, which further contributed more reliability to our research conclusions. Naturally, some weaknesses in this study need to be noted. Due to evaluating women’s health study from the ICPSR public database, research samples included in the present analysis were referred to middle-aged women, rather than men, which might has biased the demographic characteristics of the study participants and limited the data generalisability. Blood parameters for cardiovascular and metabolic risk factors were not available in all subjects from baseline data of SWAN. Fortunately, sufficient correction and stratified analysis for all included participants did not contribute to significant influence on our results. Additionally, the cross-sectional study did not fully support this causal relationship between PAI-1 and cardiovascular and metabolic risk factors. There were many missing variables such as lipid-lowering drugs, antihypertensive drugs and hypoglycemic drugs that should be analyzed by our study. However, including these variables can significantly reduce our sample size, which may lead to further result bias. Finally, due to the inherent limitations of clinical research, we really could not clearly define the specific role of PAI in CVD events. We only understood that PAI was closely related to the risk factors of cardiovascular adverse events, let alone whether it is just a manifestation of adverse events. This required further validation through more basic research in the future.

Conclusion

On the whole, our observations indicated that elevated blood levels of PAI-1 were associated with higher levels in blood parameters of cardiovascular and metabolic risk factors that contributed to higher risk of cardio-cerebrovascular disease in a large-sample women subjects with 42–52 year old.

Supplementary Information

Supplementary Table 1.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71908-z.

Author contributions

Z.X. completed the main manuscript text, data collation and validation; Ying Huang revised the final draft and supervised it. All authors reviewed the manuscript.

Funding

The study is supported by the Jiangxi Provincial Natural Science Foundation [20224BAB216019], Science and Technology Plan of Jiangxi Provincial Health Commission [202410034] and the National Natural Science Foundation Incubation Program of the Second Affiliated Hospital of Nanchang University [2022YNFY12010].

Data availability

Our study data in this analysis are mainly from the ICPSR public database (https://www.icpsr.umich.edu/web/ICPSR/search/studies?q=SWAN). The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

Competing interests

The authors declare no competing interests.

Ethics approval

The study protocol was approved by The Institutional Review Board (IRB) at each Study of Women’s Health Across the Nation (SWAN) site and Data Coordinating Center, and informed consent was obtained from all included individuals.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Huxley RR Perkovic V The modifiable burden of worldwide mortality from cardiovascular diseases Lancet Diabetes Endocrinol. 2014 2 8 604 606 10.1016/S2213-8587(14)70040-3 24842600
Huxley, R. R. & Perkovic, V. The modifiable burden of worldwide mortality from cardiovascular diseases. Lancet Diabetes Endocrinol. 2(8), 604–606. 10.1016/S2213-8587(14)70040-3 (2014).24842600 10.1016/S2213-8587(14)70040-3
2. Petrie JR Sattar N excess cardiovascular risk in type 1 diabetes mellitus Circulation 2019 139 6 744 747 10.1161/CIRCULATIONAHA.118.038137 30715940
Petrie, J. R. & Sattar, N. excess cardiovascular risk in type 1 diabetes mellitus. Circulation 139(6), 744–747. 10.1161/CIRCULATIONAHA.118.038137 (2019).30715940 10.1161/CIRCULATIONAHA.118.038137
3. Kane AE Sinclair DA Sirtuins and NAD+ in the development and treatment of metabolic and cardiovascular diseases Circ. Res. 2018 123 7 868 885 10.1161/CIRCRESAHA.118.312498 30355082
Kane, A. E. & Sinclair, D. A. Sirtuins and NAD+ in the development and treatment of metabolic and cardiovascular diseases. Circ. Res. 123(7), 868–885. 10.1161/CIRCRESAHA.118.312498 (2018).30355082 10.1161/CIRCRESAHA.118.312498
4. Agarwal I Glazer NL Barasch E Djousse L Gottdiener JS Ix JH Kizer JR Rimm EB Siscovick DS King GL Mukamal KJ Associations between metabolic dysregulation and circulating biomarkers of fibrosis: The Cardiovascular Health Study Metabolism 2015 64 10 1316 1323 10.1016/j.metabol.2015.07.013 26282733
Agarwal, I. et al. Associations between metabolic dysregulation and circulating biomarkers of fibrosis: The Cardiovascular Health Study. Metabolism 64(10), 1316–1323. 10.1016/j.metabol.2015.07.013 (2015).26282733 10.1016/j.metabol.2015.07.013
5. Benjamin EJ Virani SS Callaway CW Chamberlain AM Chang AR Cheng S Chiuve SE Cushman M Delling FN Deo R de Ferranti SD Ferguson JF Fornage M Gillespie C Isasi CR Jiménez MC Jordan LC Judd SE Lackland D Lichtman JH Lisabeth L Liu S Longenecker CT Lutsey PL Mackey JS Matchar DB Matsushita K Mussolino ME Nasir K O'Flaherty M Palaniappan LP Pandey A Pandey DK Reeves MJ Ritchey MD Rodriguez CJ Roth GA Rosamond WD Sampson UKA Satou GM Shah SH Spartano NL Tirschwell DL Tsao CW Voeks JH Willey JZ Wilkins JT Wu JH Alger HM Wong SS Muntner P American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee Heart disease and stroke statistics-2018 update: A report from the American Heart Association Circulation 2018 137 12 e67 e492 10.1161/CIR.0000000000000558 29386200
Benjamin, E. J. et al. Heart disease and stroke statistics-2018 update: A report from the American Heart Association. Circulation 137(12), e67–e492. 10.1161/CIR.0000000000000558 (2018).29386200 10.1161/CIR.0000000000000558
6. Saklayen MG The global epidemic of the metabolic syndrome Curr. Hypertens Rep. 2018 20 2 12 10.1007/s11906-018-0812-z 29480368
Saklayen, M. G. The global epidemic of the metabolic syndrome. Curr. Hypertens Rep. 20(2), 12. 10.1007/s11906-018-0812-z (2018).29480368 10.1007/s11906-018-0812-z
7. Crandall DL Quinet EM Morgan GA Busler DE McHendry-Rinde B Kral JG Synthesis and secretion of plasminogen activator inhibitor-1 by human preadipocytes J. Clin. Endocrinol. Metab. 1999 84 9 3222 3227 10.1210/jcem.84.9.5987 10487691
Crandall, D. L. et al. Synthesis and secretion of plasminogen activator inhibitor-1 by human preadipocytes. J. Clin. Endocrinol. Metab. 84(9), 3222–3227. 10.1210/jcem.84.9.5987 (1999).10487691 10.1210/jcem.84.9.5987
8. Simpson AJ Booth NA Moore NR Bennett B Distribution of plasminogen activator inhibitor (PAI-1) in tissues J. Clin. Pathol. 1991 44 2 139 143 10.1136/jcp.44.2.139 1864986
Simpson, A. J., Booth, N. A., Moore, N. R. & Bennett, B. Distribution of plasminogen activator inhibitor (PAI-1) in tissues. J. Clin. Pathol. 44(2), 139–143. 10.1136/jcp.44.2.139 (1991).1864986 10.1136/jcp.44.2.139
9. Yamamoto K Takeshita K Shimokawa T Yi H Isobe K Loskutoff DJ Saito H Plasminogen activator inhibitor-1 is a major stress-regulated gene: Implications for stress-induced thrombosis in aged individuals Proc. Natl. Acad. Sci. USA 2002 99 2 890 895 10.1073/pnas.022608799 11792849
Yamamoto, K. et al. Plasminogen activator inhibitor-1 is a major stress-regulated gene: Implications for stress-induced thrombosis in aged individuals. Proc. Natl. Acad. Sci. USA 99(2), 890–895. 10.1073/pnas.022608799 (2002).11792849 10.1073/pnas.022608799
10. Jung RG Motazedian P Ramirez FD Simard T Di Santo P Visintini S Faraz MA Labinaz A Jung Y Hibbert B Association between plasminogen activator inhibitor-1 and cardiovascular events: A systematic review and meta-analysis Thromb. J. 2018 5 16 12 10.1186/s12959-018-0166-4
Jung, R. G. et al. Association between plasminogen activator inhibitor-1 and cardiovascular events: A systematic review and meta-analysis. Thromb. J. 5(16), 12. 10.1186/s12959-018-0166-4 (2018).10.1186/s12959-018-0166-4
11. Song C Burgess S Eicher JD O'Donnell CJ Johnson AD Causal effect of plasminogen activator inhibitor type 1 on coronary heart disease J. Am. Heart Assoc. 2017 6 6 e004918 10.1161/JAHA.116.004918 28550093
Song, C., Burgess, S., Eicher, J. D., O’Donnell, C. J. & Johnson, A. D. Causal effect of plasminogen activator inhibitor type 1 on coronary heart disease. J. Am. Heart Assoc. 6(6), e004918. 10.1161/JAHA.116.004918 (2017).28550093 10.1161/JAHA.116.004918
12. Hamsten A Wiman B de Faire U Blombäck M Increased plasma levels of a rapid inhibitor of tissue plasminogen activator in young survivors of myocardial infarction N. Engl. J. Med. 1985 313 25 1557 1563 10.1056/NEJM198512193132501 3934538
Hamsten, A., Wiman, B., de Faire, U. & Blombäck, M. Increased plasma levels of a rapid inhibitor of tissue plasminogen activator in young survivors of myocardial infarction. N. Engl. J. Med. 313(25), 1557–1563. 10.1056/NEJM198512193132501 (1985).3934538 10.1056/NEJM198512193132501
13. Meltzer ME Lisman T de Groot PG Meijers JC le Cessie S Doggen CJ Rosendaal FR Venous thrombosis risk associated with plasma hypofibrinolysis is explained by elevated plasma levels of TAFI and PAI-1 Blood 2010 116 1 113 121 10.1182/blood-2010-02-267740 20385790
Meltzer, M. E. et al. Venous thrombosis risk associated with plasma hypofibrinolysis is explained by elevated plasma levels of TAFI and PAI-1. Blood 116(1), 113–121. 10.1182/blood-2010-02-267740 (2010).20385790 10.1182/blood-2010-02-267740
14. Carratala A Martinez-Hervas S Rodriguez-Borja E Benito E Real JT Saez GT Carmena R Ascaso JF PAI-1 levels are related to insulin resistance and carotid atherosclerosis in subjects with familial combined hyperlipidemia J. Investig. Med. 2018 66 1 17 21 10.1136/jim-2017-000468 28822973
Carratala, A. et al. PAI-1 levels are related to insulin resistance and carotid atherosclerosis in subjects with familial combined hyperlipidemia. J. Investig. Med. 66(1), 17–21. 10.1136/jim-2017-000468 (2018).28822973 10.1136/jim-2017-000468
15. Ploplis VA Effects of altered plasminogen activator inhibitor-1 expression on cardiovascular disease Curr. Drug Targets. 2011 12 12 1782 1789 10.2174/138945011797635803 21707474
Ploplis, V. A. Effects of altered plasminogen activator inhibitor-1 expression on cardiovascular disease. Curr. Drug Targets. 12(12), 1782–1789. 10.2174/138945011797635803 (2011).21707474 10.2174/138945011797635803
16. Juhan-Vague I Alessi MC Vague P Increased plasma plasminogen activator inhibitor 1 levels: A possible link between insulin resistance and atherothrombosis Diabetologia 1991 34 7 457 462 10.1007/BF00403280 1916049
Juhan-Vague, I., Alessi, M. C. & Vague, P. Increased plasma plasminogen activator inhibitor 1 levels: A possible link between insulin resistance and atherothrombosis. Diabetologia 34(7), 457–462. 10.1007/BF00403280 (1991).1916049 10.1007/BF00403280
17. Yarmolinsky J Bordin Barbieri N Weinmann T Ziegelmann PK Duncan BB Inês SM Plasminogen activator inhibitor-1 and type 2 diabetes: A systematic review and meta-analysis of observational studies Sci. Rep. 2016 27 6 17714 10.1038/srep17714
Yarmolinsky, J. et al. Plasminogen activator inhibitor-1 and type 2 diabetes: A systematic review and meta-analysis of observational studies. Sci. Rep. 27(6), 17714. 10.1038/srep17714 (2016).10.1038/srep17714
18. Festa A D'Agostino R Jr Tracy RP Haffner SM Insulin Resistance Atherosclerosis Study Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: The insulin resistance atherosclerosis study Diabetes 2002 51 4 1131 1137 10.2337/diabetes.51.4.1131 11916936
Festa, A., D’Agostino, R. Jr., Tracy, R. P., Haffner, S. M., Insulin Resistance Atherosclerosis Study. Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: The insulin resistance atherosclerosis study. Diabetes 51(4), 1131–1137. 10.2337/diabetes.51.4.1131 (2002).11916936 10.2337/diabetes.51.4.1131
19. Henkel AS Khan SS Olivares S Miyata T Vaughan DE Inhibition of plasminogen activator inhibitor 1 attenuates hepatic steatosis but does not prevent progressive nonalcoholic steatohepatitis in mice Hepatol. Commun. 2018 2 12 1479 1492 10.1002/hep4.1259 30556037
Henkel, A. S., Khan, S. S., Olivares, S., Miyata, T. & Vaughan, D. E. Inhibition of plasminogen activator inhibitor 1 attenuates hepatic steatosis but does not prevent progressive nonalcoholic steatohepatitis in mice. Hepatol. Commun. 2(12), 1479–1492. 10.1002/hep4.1259 (2018).30556037 10.1002/hep4.1259
20. Schäfer K Fujisawa K Konstantinides S Loskutoff DJ Disruption of the plasminogen activator inhibitor 1 gene reduces the adiposity and improves the metabolic profile of genetically obese and diabetic ob/ob mice FASEB J. 2001 15 10 1840 1842 10.1096/fj.00-0750fje 11481248
Schäfer, K., Fujisawa, K., Konstantinides, S. & Loskutoff, D. J. Disruption of the plasminogen activator inhibitor 1 gene reduces the adiposity and improves the metabolic profile of genetically obese and diabetic ob/ob mice. FASEB J. 15(10), 1840–1842. 10.1096/fj.00-0750fje (2001).11481248 10.1096/fj.00-0750fje
21. Ma LJ Mao SL Taylor KL Kanjanabuch T Guan Y Zhang Y Brown NJ Swift LL McGuinness OP Wasserman DH Vaughan DE Fogo AB Prevention of obesity and insulin resistance in mice lacking plasminogen activator inhibitor 1 Diabetes 2004 53 2 336 346 10.2337/diabetes.53.2.336 14747283
Ma, L. J. et al. Prevention of obesity and insulin resistance in mice lacking plasminogen activator inhibitor 1. Diabetes 53(2), 336–346. 10.2337/diabetes.53.2.336 (2004).14747283 10.2337/diabetes.53.2.336
22. Sowers MF Crawford S Sternfeld B Morganstein D Gold EB Greendale GA Evans D Neer R Matthews K Sherman S Lobo R Kelsey J Marcus R SWAN: A multicenter, multiethnic, community-based cohort study of women and the menopausal transition Menopause: Biology and Pathobiology 2000 Academic Press 175 188
Sowers, M. F. et al. SWAN: A multicenter, multiethnic, community-based cohort study of women and the menopausal transition. In Menopause: Biology and Pathobiology (eds Lobo, R. et al.) 175–188 (Academic Press, 2000).
23. Frangogiannis NG Pathophysiology of myocardial infarction Compr. Physiol. 2015 5 4 1841 1875 10.1002/cphy.c150006 26426469
Frangogiannis, N. G. Pathophysiology of myocardial infarction. Compr. Physiol. 5(4), 1841–1875. 10.1002/cphy.c150006 (2015).26426469 10.1002/cphy.c150006
24. Jönsson Rylander AC Lindgren A Deinum J Bergström GM Böttcher G Kalies I Wåhlander K Fibrinolysis inhibitors in plaque stability: a morphological association of PAI-1 and TAFI in advanced carotid plaque J. Thromb. Haemost. 2017 15 4 758 769 10.1111/jth.13641 28135035
Jönsson Rylander, A. C. et al. Fibrinolysis inhibitors in plaque stability: a morphological association of PAI-1 and TAFI in advanced carotid plaque. J. Thromb. Haemost. 15(4), 758–769. 10.1111/jth.13641 (2017).28135035 10.1111/jth.13641
25. Padró T Steins M Li CX Mesters RM Hammel D Scheld HH Kienast J Comparative analysis of plasminogen activator inhibitor-1 expression in different types of atherosclerotic lesions in coronary arteries from human heart explants Cardiovasc. Res. 1997 36 1 28 36 10.1016/s0008-6363(97)00144-2 9415269
Padró, T. et al. Comparative analysis of plasminogen activator inhibitor-1 expression in different types of atherosclerotic lesions in coronary arteries from human heart explants. Cardiovasc. Res. 36(1), 28–36. 10.1016/s0008-6363(97)00144-2 (1997).9415269 10.1016/s0008-6363(97)00144-2
26. Schneiderman J Sawdey MS Keeton MR Bordin GM Bernstein EF Dilley RB Loskutoff DJ Increased type 1 plasminogen activator inhibitor gene expression in atherosclerotic human arteries Proc. Natl. Acad. Sci. USA 1992 89 15 6998 7002 10.1073/pnas.89.15.6998 1495992
Schneiderman, J. et al. Increased type 1 plasminogen activator inhibitor gene expression in atherosclerotic human arteries. Proc. Natl. Acad. Sci. USA 89(15), 6998–7002. 10.1073/pnas.89.15.6998 (1992).1495992 10.1073/pnas.89.15.6998
27. Ninomiya T Kubo M Doi Y Yonemoto K Tanizaki Y Rahman M Arima H Tsuryuya K Iida M Kiyohara Y Impact of metabolic syndrome on the development of cardiovascular disease in a general Japanese population: the Hisayama study Stroke 2007 38 7 2063 2069 10.1161/STROKEAHA.106.479642 17525396
Ninomiya, T. et al. Impact of metabolic syndrome on the development of cardiovascular disease in a general Japanese population: the Hisayama study. Stroke 38(7), 2063–2069. 10.1161/STROKEAHA.106.479642 (2007).17525396 10.1161/STROKEAHA.106.479642
28. Alessi MC Juhan-Vague I Kooistra T Declerck PJ Collen D Insulin stimulates the synthesis of plasminogen activator inhibitor 1 by the human hepatocellular cell line Hep G2 Thromb. Haemost. 1988 60 3 491 494 10.1055/s-0038-1646997 3149048
Alessi, M. C., Juhan-Vague, I., Kooistra, T., Declerck, P. J. & Collen, D. Insulin stimulates the synthesis of plasminogen activator inhibitor 1 by the human hepatocellular cell line Hep G2. Thromb. Haemost. 60(3), 491–494 (1988).3149048 10.1055/s-0038-1646997
29. Iwasaki H Okamoto R Kato S Konishi K Mizutani H Yamada N Isaka N Nakano T Ito M High glucose induces plasminogen activator inhibitor-1 expression through Rho/Rho-kinase-mediated NF-kappaB activation in bovine aortic endothelial cells Atherosclerosis 2008 196 1 22 28 10.1016/j.atherosclerosis.2006.12.025 17275007
Iwasaki, H. et al. High glucose induces plasminogen activator inhibitor-1 expression through Rho/Rho-kinase-mediated NF-kappaB activation in bovine aortic endothelial cells. Atherosclerosis 196(1), 22–28. 10.1016/j.atherosclerosis.2006.12.025 (2008).17275007 10.1016/j.atherosclerosis.2006.12.025
30. Chen Y Billadello JJ Schneider DJ Identification and localization of a fatty acid response region in the human plasminogen activator inhibitor-1 gene Arterioscler. Thromb. Vasc. Biol. 2000 20 12 2696 2701 10.1161/01.atv.20.12.2696 11116074
Chen, Y., Billadello, J. J. & Schneider, D. J. Identification and localization of a fatty acid response region in the human plasminogen activator inhibitor-1 gene. Arterioscler. Thromb. Vasc. Biol. 20(12), 2696–2701. 10.1161/01.atv.20.12.2696 (2000).11116074 10.1161/01.atv.20.12.2696
31. Fattal PG Schneider DJ Sobel BE Billadello JJ Post-transcriptional regulation of expression of plasminogen activator inhibitor type 1 mRNA by insulin and insulin-like growth factor 1 J. Biol. Chem. 1992 267 18 12412 12415 10.1016/S0021-9258(18)42289-2 1618746
Fattal, P. G., Schneider, D. J., Sobel, B. E. & Billadello, J. J. Post-transcriptional regulation of expression of plasminogen activator inhibitor type 1 mRNA by insulin and insulin-like growth factor 1. J. Biol. Chem. 267(18), 12412–12415 (1992).1618746 10.1016/S0021-9258(18)42289-2
32. Alessi MC Peiretti F Morange P Henry M Nalbone G Juhan-Vague I Production of plasminogen activator inhibitor 1 by human adipose tissue: possible link between visceral fat accumulation and vascular disease Diabetes 1997 46 5 860 867 10.2337/diab.46.5.860 9133556
Alessi, M. C. et al. Production of plasminogen activator inhibitor 1 by human adipose tissue: possible link between visceral fat accumulation and vascular disease. Diabetes 46(5), 860–867. 10.2337/diab.46.5.860 (1997).9133556 10.2337/diab.46.5.860
33. Sawdey MS Loskutoff DJ Regulation of murine type 1 plasminogen activator inhibitor gene expression in vivo: Tissue specificity and induction by lipopolysaccharide, tumor necrosis factor-alpha, and transforming growth factor-beta J. Clin. Invest. 1991 88 4 1346 1353 10.1172/JCI115440 1918385
Sawdey, M. S. & Loskutoff, D. J. Regulation of murine type 1 plasminogen activator inhibitor gene expression in vivo: Tissue specificity and induction by lipopolysaccharide, tumor necrosis factor-alpha, and transforming growth factor-beta. J. Clin. Invest. 88(4), 1346–1353. 10.1172/JCI115440 (1991).1918385 10.1172/JCI115440
34. Rega G Kaun C Weiss TW Demyanets S Zorn G Kastl SP Steiner S Seidinger D Kopp CW Frey M Roehle R Maurer G Huber K Wojta J Inflammatory cytokines interleukin-6 and oncostatin m induce plasminogen activator inhibitor-1 in human adipose tissue Circulation 2005 111 15 1938 1945 10.1161/01.CIR.0000161823.55935.BE 15837947
Rega, G. et al. Inflammatory cytokines interleukin-6 and oncostatin m induce plasminogen activator inhibitor-1 in human adipose tissue. Circulation 111(15), 1938–1945. 10.1161/01.CIR.0000161823.55935.BE (2005).15837947 10.1161/01.CIR.0000161823.55935.BE
35. Somodi S Seres I Lőrincz H Harangi M Fülöp P Paragh G Plasminogen activator inhibitor-1 level correlates with lipoprotein subfractions in obese nondiabetic subjects Int. J. Endocrinol. 2018 30 2018 9596054 10.1155/2018/9596054
Somodi, S. et al. Plasminogen activator inhibitor-1 level correlates with lipoprotein subfractions in obese nondiabetic subjects. Int. J. Endocrinol. 30(2018), 9596054. 10.1155/2018/9596054 (2018).10.1155/2018/9596054
