Serum Creatinine and Occurrence and Severity of Coronary Artery Disease

Low Levels of IgM Recognizing 4-Hydroxy-2-Nonenal-Modified Apolipoprotein A-I Peptide and Its Association with the Severity of Coronary Artery Disease in Taiwanese Patients

Autoantibodies against apolipoprotein A-I (ApoA-I) are associated with cardiovascular disease risks. We aimed to examine the 4-hydroxy-2-nonenal (HNE) modification of ApoA-I in coronary artery disease (CAD) and evaluate the potential risk of autoantibodies against their unmodified and HNE-modified peptides. We assessed plasma levels of ApoA-I, HNE-protein adducts, and autoantibodies against unmodified and HNE-peptide adducts, and significant correlations and odds ratios (ORs) were examined. Two novel CAD-specific HNE-peptide adducts, ApoA-I251-262 and ApoA-I70-83, were identified. Notably, immunoglobulin G (IgG) anti-ApoA-I251-262 HNE, IgM anti-ApoA-I70-83 HNE, IgG anti-ApoA-I251-262, IgG anti-ApoA-I70-83, and HNE-protein adducts were significantly correlated with triglycerides, creatinine, or high-density lipoprotein in CAD with various degrees of stenosis (<30% or >70%). The HNE-protein adduct (OR = 2.208-fold, p = 0.020) and IgM anti-ApoA-I251-262 HNE (2.046-fold, p = 0.035) showed an increased risk of progression from >30% stenosis in CAD. HNE-protein adducts and IgM anti-ApoA-I251-262 HNE may increase the severity of CAD at high and low levels, respectively.

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients

Cardiovascular disease is the leading cause of mortality among nonalcoholic steatohepatitis (NASH) patients who undergo liver transplants. In the present study, machine learning algorithms were used to identify important risk factors for cardiovascular death and to develop a prediction model. The Standard Transplant Analysis and Research data were gathered from the Organ Procurement and Transplantation Network. After cleaning and preprocessing, the dataset comprised 10,871 patients and 92 features. Recursive feature elimination (RFE) and select from model (SFM) were applied to select relevant features from the dataset and avoid overfitting. Multiple machine learning algorithms, including logistic regression, random forest, decision tree, and XGBoost, were used with RFE and SFM. Additionally, prediction models were developed using a support vector machine, Gaussian naïve Bayes, K-nearest neighbors, random forest, and XGBoost algorithms. Finally, SHapley Additive exPlanations (SHAP) were used to increase interpretability. The findings showed that the best feature selection method was RFE with a random forest estimator, and the most critical features were recipient and donor blood type, body mass index, recipient and donor state of residence, serum creatinine, and year of transplantation. Furthermore, among all the outcomes, the XGBoost model had the highest performance, with an accuracy value of 0.6909 and an area under the curve value of 0.86. The findings also revealed a predictive relationship between features and cardiovascular death after liver transplant among NASH patients. These insights may assist clinical decision-makers in devising strategies to prevent cardiovascular complications in post-liver transplant NASH patients.

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning

OBJECTIVE

This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).

METHODS

Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model.

RESULTS

The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814-0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD.

CONCLUSION

This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.

Metabolome Profiling in the Plasma of Dogs with Idiopathic Dilated Cardiomyopathy: A Multiplatform Mass-Spectrometry-Based Approach

Dilated cardiomyopathy is one of the important diseases in dogs and humans. The second most common cause of heart failure in dogs is idiopathic dilated cardiomyopathy (iDCM), which results in heart failure or sudden cardiac death due to arrhythmia. This study aimed to determine changes in the plasma metabolome of dogs with iDCM compared to healthy dogs. For that purpose, a multiplatform mass-spectrometry-based approach was used. In this study, we included two groups of dogs: 12 dogs with iDCM and 8 healthy dogs. A total of 272 metabolites were detected in the plasma samples of dogs by combining three approaches but four MS-based platforms (GC-MS, LC-MS (untargeted), LC-MS (targeted), and FIA-MS (targeted) methods). Our findings demonstrated changes in the canine plasma metabolome involved in the development of iDCM, including the different concentrations of amino acids, biogenic amines, acylcarnitines, triglycerides and diglycerides, sphingomyelins, and organic acids. The results of this study will enable the detection and monitoring of pathophysiological mechanisms involved in the development of iDCM in the future.

Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension

BACKGROUND

Serum creatinine is associated with cardiovascular risk and cardiovascular events, however, the relationship between serum creatinine levels and cardiovascular risk is not well established in hypertensive population in Jiangsu Province. We aimed to evaluate the association of serum creatinine levels with traditional cardiovascular risk factors and 10-year cardiovascular risk in a Chinese hypertensive population.

METHODS

Participants were patients with hypertension registered and enrolled in health service centers in 5 counties or districts from January 2019 to May 2020 in Jiangsu Province of China followed strict inclusion and exclusion criteria, demographics as well as clinical indicators and disease history and lifestyle were collected. Participants were divided into four groups according to quartiles of serum creatinine levels, then the China-PAR model was used to calculate 10-year cardiovascular risk for each individual.

RESULTS

A total of 9978 participants were enrolled in this study, 4173(41.82%) were males. The blood pressure level and prevalence of dyslipidemia, elderly, current smokers and drinking as well as obesity were higher in the Q4 group than the Q1 group (all P < 0.05). Multivariable logistic regression showed that serum creatinine in the Q4 group compared with that in the Q1 group was positively associated with overweight and obesity (OR=1.432, 95% CI 1.237-1.658, P<0.001), while negatively associated with physical activity (OR=0.189, 95%CI 0.165-0.217, P<0.001), and so on. Multiple linear regression showed 10-year cardiovascular risk is positively associated with serum creatinine levels after adjusting for multiple risk factors (β=0.432, P< 0.001).

CONCLUSION

Serum creatinine was associated with several traditional cardiovascular risk factors and the 10-year cardiovascular risk in hypertensive patients. Creatinine-reduction and kidney-sparing therapy are essential for patients with hypertension to optimize control of cardiovascular risk.

Prognostic value of troponin/creatinine ratio in patients with chest pain

Background

The aim of this study was to investigate the prognostic value of the troponin/creatinine ratio in patients presenting with chest pain and to identify laboratory values affecting mortality.

Methods

Between October 1st, 2016 and April 30th, 2019, a total of 5,079 patients (2,844 males, 2,235 females; median age: 65 years; range, 49 to 83 years) who presented to the emergency department with chest pain and whose troponin and creatinine measurements were done were retrospectively analyzed. Laboratory data and 28-day mortality rates were evaluated. The patients were divided into two groups according to 28-day mortality rates after the initial emergency department admission as survivors (n=4,793) and non-survivors (n=286).

Results

There were statistically significant differences in the white blood cell count, C-reactive protein, creatinine, high-sensitivity troponin I, and troponin/creatinine ratio between the groups (p<0.05). The high-sensitivity troponin I ≥31.96 pg/dL, creatinine ≥1.11 mg/dL, C-reactive protein ≥43.94 mg/L, and troponin/creatinine ratio ≥25.12 were independent predictors of 28-day mortality (p<0.05). A C-reactive protein value of ≥43.94 mg/L was found to be more specific (81.14%) than the other markers for mortality.

Conclusion

Troponin/creatinine ratio is a predictor of mortality in patients presenting to the emergency department with chest pain and high-sensitivity troponin I, creatinine, and C-reactive protein seem to be independent risk factors for 28-day mortality.

Older Women Who Practiced Physical Exercises before the COVID-19 Pandemic Present Metabolic Alterations and Worsened Functional Physical Capacity after One Year of Social Isolation

Background: Because the consequences of the lifestyle changes in older adults associated with the social isolation imposed in response to the COVID-19 pandemic are not fully understood, here, we investigated the effects of one year of social isolation imposed by COVID-19 on the metabolic parameters and functional physical capacity of older women who regularly practiced physical exercises before the pandemic. Methods: Systemic lipid and protein profiles, estimated creatinine clearance (ECC), and functional physical capacity (FPC) were assessed before (January-February 2020) and 12 months after social isolation in 30 older women (mean age 73.77 ± 6.22) who were engaged in a combined-exercise training program for at least 3 years before the COVID-19 pandemic. Results: In this group, we observed increased plasma levels of triglycerides and creatinine, an increase in the time necessary to perform gait speed and time-up-and-go tests, and reduced muscle strength assessed by the handgrip test and ECC post-COVID-19 pandemic relative to values recorded pre-pandemic. In addition, we observed significant correlations (both negative and positive) between anthropometric, some metabolic parameters, and physical tests. Conclusion: One year of interruption of physical exercise practice imposed in response to the COVID-19 pandemic significantly altered some systemic metabolic parameters and worsened ECC and FPC in older women.

Predicting the Risk of Unplanned Readmission at 30 Days After PCI: Development and Validation of a New Predictive Nomogram

Objective

This study aimed to develop and validate a risk prediction model that can be used to identify percutaneous coronary intervention (PCI) patients at high risk for 30-day unplanned readmission.

Patients and Methods

We developed a prediction model based on a training dataset of 1348 patients after PCI. The data were collected from January 2020 to December 2020. Clinical characteristics, laboratory data and risk factors were collected using the hospital database. The LASSO regression method was applied to filter variables and select predictors, and feature selection for a 30-day readmission risk model was optimized using least absolute shrinkage. Multivariate logistic regression was used to construct a nomogram. The performance and clinical utility of the nomogram were evaluated with a receiver operating characteristic (ROC) curve, a calibration curve, and decision curve analysis (DCA). Internal validation of the predictive accuracy was performed using bootstrapping validation.

Results

The predictors included in the prediction nomogram were medical insurance, length of stay, left ventricular ejection fraction on admission, history of hypertension, the presence of chronic lung disease, the presence of anemia, and serum creatinine level on admission. The area under the receiver operating characteristic curve for the predictive model was 0.735 (95% CI: 0.711–0.759). The P value of the Hosmer–Lemeshow goodness of fit test was 0.326, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA also demonstrated that the nomogram was clinically useful. A high c-index value of 0.723 was obtained during the internal validation.

Conclusion

We developed an easy-to-use nomogram model to predict the risk of readmission 30 days after discharge for PCI patients. This risk prediction model may serve as a guide for screening high-risk patients and allocating resources for PCI patients at the time of hospital discharge and may provide a reference for preventive care interventions.

Analysis of the prevalence, risk factors, and clinical characteristics of osteoporosis in patients with essential hypertension

BACKGROUND

The present study investigated the prevalence of osteoporosis (OP) among patients with essential hypertension (EH) in the Changchun community and analysed the correlation between EH and OP.

METHODS

The study included 425 subjects with EH and 425 age- and sex-matched healthy controls. Bone mineral density (BMD) and serum creatinine (CR) levels were measured, and the subjects' current EH and OP statuses were surveyed to analyse the correlation between EH and OP.

RESULTS

The EH group exhibited lower BMD and a higher rate of having OP than the control group, and this difference was statistically significant (p < 0.05). A significant sex difference in the BMD T-score was observed among the subjects (male: - 1.19 ± 1.55, female: - 1.70 ± 1.34). In both the EH group and the control group, the rate of having OP in females was greater than that in males. However, the OP prevalence among subjects with EH varied significantly by age, body weight, fracture history, nocturnal urination frequency, depression and anxiety status, duration of hypertension, and antihypertensive medication use (p < 0.05). Two-way analysis of variance suggested an effect of the interaction between different EH statuses and bone mass conditions on the serum CR values (F = 3.584, p = 0.028, bias η² = 0.008).

CONCLUSIONS

The prevalence of OP and low BMD were significantly higher among subjects with EH than among healthy controls. Additionally, the findings indicate that age, weight, fracture history, nocturnal urination frequency, depression and anxiety, duration of hypertension and antihypertensive drug use may be correlated to having OP in EH subjects, requiring further studies. Moreover, serum CR levels in subjects with different bone mass profiles were strongly influenced by the presence or absence of EH, and the serum CR levels differed significantly with the interaction of these two factors.

D-dimer to Creatinine Ratio: A Novel Biomarker Associated with Gensini Score in ST-Segment Elevation Myocardial Infarction Patients

Objective

We propose for the first time that D-dimer to creatinine ratio (DCR) may serve as a new clinical biomarker and explore its association with ST-segment elevation myocardial infarction (STEMI).

Methods

347 STEMI patients with complete D-dimer and creatinine were included in the analysis. According to the median of DCR value, patients were divided into the lower DCR group (DCR < 1.402, n = 173) and the higher DCR group (DCR ≥ 1.402, n = 174), and the differences between the two groups were compared. In addition, patients were divided into four groups according to the quartiles of Gensini score: Group 1(Gensini score ≤ 34, n = 88); Group 2(34 < Gensini score ≤ 65, n = 88); Group 3(65 < Gensini score ≤100, n = 87); Group 4(Gensini score ＞100, n = 84). Multivariate linear and multivariate logistic regression analyzes were performed to determine independent predictors of the Gensini score.

Results

High DCR group had higher Gensini score compared with the low DCR group ( P < .05). DCR was positively correlated with Gensini score (r = 0.493, P < .001). Multiple linear regression analysis showed that Previous MI (r = 11.312, P = .035) and DCR (r = 5.129, P < .001) were independent risk factors associated with the Gensini score. Multivariate logistic regression analysis showed that, compared to Group 1, DCR was an independent risk factor in Group 2, Group 3, Group 4 ( P < .001).

Conclusions

As a new and useful clinical biomarker, DCR was positively correlated with coronary Gensini score in STEMI patients.

MELD-XI score predict no-reflow phenomenon and short-term mortality in patient with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention

INTRODUCTION

No-reflow phenomenon (NRP) is one of the complications that mostly occur during percutaneous coronary intervention (PCI). In this study, we comprehensively examined the relationship between the model for end-stage liver disease-XI (MELD-XI) score and NRP. Moreover, we discussed whether the MELD-XI score could be considered as an accurate risk assessment score of patients with ST-segment elevation myocardial infarction (STEMI) who are candidates for PCI.

METHODS

This retrospective study involved 693 patients with acute STEMI and who underwent an emergency PCI. They were divided into a normal reflow group or a no-reflow group on the basis of the flow rate of post-interventional thrombolysis in myocardial infarction. Univariate, multivariate logistic regression, and Cox regression analyses were performed to identify the independent predictors of NRP in both groups. Receiver operator characteristic (ROC) curves and Kaplan-Meier curves were plotted to estimate the predictive values of the MELD-XI score.

RESULTS

MELD-XI score was found to be an independent indicator of NRP (odds ratio: 1.247, 95% CI: 1.144-1.360, P < 0.001). Multivariate Cox regression analysis also revealed that the MELD-XI score is an independent prognostic factor for 30-day all-cause mortality (hazard ratio: 1.155, 95% CI: 1.077-1.239, P < 0.001). Moreover, according to the ROC curves, the cutoff value of the MELD-XI score to predict NRP was 9.47 (area under ROC curve: 0.739, P < 0.001). The Kaplan-Meier curves for 30-day all-cause mortality revealed lower survival rate in the group with a MELD-XI score of > 9.78 (P < 0.001).

CONCLUSION

The MELD-XI score can be used to predict NRP and the 30-day prognosis in patients with STEMI who are candidates for primary PCI. It could be adopted as an inexpensive and a readily available tool for risk stratification.

Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach

Introduction

Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with high-fat or high-carbohydrate diets. Generally, increased triglyceride levels occur in conjunction with other symptoms that are difficult to notice and recognize.

Objectives

The study's goal was to develop and predict the model that could be used to explain the relationship between triglycerides and waist circumference, high-density lipoprotein (HDL), and hypertension status by determining the relationship between triglycerides and waist circumference, HDL, and hypertension status. This model was developed using qualitative predictor variables and incorporated data bootstrapping multilayer perceptron neural networks and fuzzy linear regression. Materials and procedures. This was a public health study that combined retrospective data analysis with methodology development. The medical records of patients who attended outpatient clinics at Hospital Universiti Sains Malaysia (USM) were collected and analyzed. This was to provide a more extensive illustration of the methods developed. Screening and selection of patient data were necessary following the inclusion and exclusion criteria. The patient's medical record was used to obtain triglycerides, high-density lipoprotein (HDL), waist circumference, and hypertension status. Due to the critical nature of the variable, it was chosen to aid the clinical expert. The R-Studio software was used to develop the associated syntax for the hybrid model, which would define the association between the examined variables. The purpose of this study is to create a technique for the clinical trial design that utilizes bootstrapping, Qualitative Predictor Variables (QPV), Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Fuzzy Regression (FR). All analyses were performed using the newly introduced R syntax. The research developed a fuzzy linear model that increased modelling performance by incorporating clinically significant factors and validated variables via Multilayer Perceptron (MLP).

Conclusion

The proposed technique for modelling and prediction appeared to be the ideal combination of bootstrap, Multilayer Feed Forward (MLFF) neural network, and fuzzy linear regression. The created syntax is currently being evaluated and validated clinically. For modelling and prediction, the proposed technique looked to be the best, as it incorporated bootstrap, MLFF neural network, and fuzzy linear regression. The established syntax is now being utilized in the clinic to evaluate and validate the outcome. In terms of variable selection, modelling, and model validation, this strategy was superior to earlier approaches for fuzzy regression modelling.

A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression

Background and Objective:

Dyslipidemia is one of the most important risk factors for coronary heart disease with diabetes mellitus. Diabetic dyslipidemia is correlated with reduced concentrations of high-density lipoprotein cholesterol, elevated concentrations of plasma triglycerides, and increased concentrations of dense small particles of low-density lipoprotein cholesterol. Furthermore, dyslipidemia is one of the factors that accelerate renal failure in patients with nephropathy that is observed to be higher in these patients. This paper aims to propose the variable selection using the multilayer perceptron (MLP) neural network methodology before performing the multiple linear regression (MLR) modeling. Dataset consists of patient with Dyslipidemia, and Type 2 Diabetes Mellitus was selected to illustrate the design-build methodology. According to clinical expert's opinion and based on their assessment, these variables were chosen, which comprises the level of creatinine, urea, total cholesterol, uric acid, sodium, and HbA1c.

Materials and Methods:

At the first stage, all the selected variables will be a screen for their clinical important point of view, and it was found that creatinine has a significant relationship to the level of urea reading, a total of cholesterol reading, and the level of uric acid reading. By considering the level of significance, α = 0.05, these three variables are being selected and used for the input of the MLP model. Then, the MLR is being applied according to the best variable obtained through MLP process.

Results:

Through the testing/out-sample mean squared error (MSE), the performance of MLP was assessed. MSE is an indication of the distance from the actual findings from our estimates. The smallest MSE of the MLP shows the best variable selection combination in the model.

Conclusion:

In this research paper, we also provide the R syntax for MLP better illustration. The key factors associated with creatinine were urea, total cholesterol, and uric acid in patients with dyslipidemia and type 2 diabetes mellitus.

Association of Kidney Disease, Potassium, and Cardiovascular Risk Factor Prevalence with Coronary Arteriosclerotic Burden, by Sex

The present study aimed to determine the relationship between the prevalence of cardiovascular risk factors and the number and severity of coronary artery atherosclerotic lesions obtained by coronary angiography. We reviewed and analyzed 1642 records from consecutive patients at the Catheter Laboratory of Talca Regional Hospital in Chile between March 2018 and May 2019. Patients were stratified according to the presence and severity of atherosclerotic lesions: 632 (38.5%) had no lesions or <30% stenosis and 1010 (61.5%) had at least one coronary atherosclerotic lesion with ≥30% stenosis (CALS-30). CALS-30 was more frequent in males, smokers, and patients with diabetes and/or hypertension (all p-values < 0.02). Serum potassium, glycaemia, creatinine and glomerular filtration rates were also associated with CALS-30 (all p-values < 0.01) in males. The age and the proportion of males with CALS-30 increased with the number of risk factors (p-values for trends < 0.001). Our results showed a stronger association between the accumulation of risk factors and CALS-30 in women than in men. Serum potassium levels were inversely associated with CALS-30 in men but not in women.

Clinical characteristics in patients with coronary slow flow phenomenon: A retrospective study

ABSTRACT

Coronary slow flow phenomenon (CSFP) is a coronary artery disease in which coronary angiography shows no obvious stenosis, but there is a delay in blood flow perfusion. The etiopathogenic mechanisms of CSFP are still unclear. The aim of the present study was to investigate the role of clinical characteristics in patients with CSFP, and to provide a reference for exploring the potential mechanisms of CSFP. Patients with angiographically normal epicardial arteries were enrolled (145 patients with CSFP and 145 normal controls). Collected clinical information and laboratory indexes, which measured by peripheral venous blood samples before coronary angiography. Logistic regression analysis was performed for statistical analysis. The present study found 19 clinical and laboratory indexes with statistical differences between the two groups in univariate analysis. Multivariate analysis showed that monocyte count, haemoglobin, serum creatinine and globulin were independent predictors of CSFP. Moreover, the monocyte count, haemoglobin, creatinine and globulin levels were significantly higher in the CSFP patients than the controls, with positive associations between these parameters and the extent of CSFP. In addition, ROC analysis showed the diagnostic value of the above indexes for CSFP.

The risk factors of adverse pregnancy outcome for pre-pregnancy couples in Hunan, China: A cross-sectional study based on population

To identify the prevalence of chronic disease and behavior risk factors of adverse pregnancy outcome for pre-pregnancy couples in Hunan province, China.A retrospective study including cross-sectional datas from the Free Pre-pregnancy Health Check (FPHC) surveillance system of Hunan, China in 2019 was conducted. Microsoft Excel 2010 was used for preliminary data analysis. The fowllowing descriptive analysis, t-tests, and Chi-Squared tests were carried out using SPSS 25.0.Among 419,971 couples, 182,450 (21.72%) individuals were older than 35 years, 257,471(69.48%) couples planned to have a second or additional child and 114,892 (27.36%) individuals had a history of adverse pregnancy outcomes. The mean number of risk factors of adverse pregnancy outcome was higher in males than that in females (2.17 per male vs 1.92 per female). The prevalence/proportions of hypertension, increased psychological pressure, high alanine transaminase or creatinine, smoking, passive smoke exposure, alcohol use, and exposure to environmental risk factors were higher in males than that in females (2.43% vs 1.35%, 0.68% vs 0.54%, 12.80% vs 5.93%, 2.52% vs 1.47%, 27.70% vs 0.24%, 10.94% vs 3.58%, 15.62% vs 1.07%, and 1.46% vs 1.15%, respectively). The proportion of females with an abnormal cervix was 3.35%, and the proportion of males with abnormal wrapping was 1.90%. The prevalence/proportions of anemia and work-related pressure or social tensions were higher in females than that in males (5.53% vs 0.51%, 15.39% vs 13.61%, and 8.22% vs 7.88%, respectively).History of adverse pregnancy outcomes and age olderthan 35 years were important risk factors for pre-pregnancy couples in Hunan province. The mean number of risk factors was higher in males than that in females. Hypertension, increased psychological pressure, high alanine transaminase and creatinine levels, smoking, passive smoke exposure, alcohol use, and exposure to dangerous environmental factors were the major risk factors for males. Anemia, work-related pressure and social tensions were the major risk factors for females.