Correlation Analysis Between Uric Acid and Metabolic Syndrome in the Elderly Population

Abstract Background：The prevalence of metabolic syndrome in the elderly is gradually increasing，which accounts for the largest burden of non-communicable diseases worldwide and has direct effects on health. Research on the relationship between uric acid and metabolic syndrome in the elderly is relatively lacking. The purpose of this study is to explore the diagnostic value of uric acid levels for metabolic syndrome, compared to other components of metabolic syndrome.Materials and methods：We collected the physical examination data of 1,267 elderly people in the community in Wuhan, and used SPSS IBM 22.0 for data analysis. Perform correlation analysis, logistic regression analysis and draw ROC curve. Results：The prevalence of hyperuricemia was 28.1%, and metabolic syndrome was 18.6%; the uric acid level of the non-metabolic syndrome group was lower than that of the metabolic syndrome group (337.31 vs 381.91 µmol/ L; P<0.05); Pearson analysis revealed uric acid levels are correlated with blood pressure, BMI, triglyceride, high-density lipoprotein cholesterol. Logistic regression analysis results suggest that uric acid is a risk factor for metabolic syndrome. Metabolic syndrome components TG and HDL levels are also related to uric acid levels. The result is described as OR value and 95% CI (OR 1.003 [1.001, 1.005]). By drawing the ROC curve, we found that the area under the curve for uric acid to diagnose metabolic syndrome is 0.64 (sensitivity: 79.3%, specificity: 45.1%), which is similar to other components of metabolic syndrome. Conclusion：We confirmed the correlation between uric acid levels and metabolic syndrome in the elderly Chinese population.

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A study of correlations between metabolic syndrome factors and osteosarcopenic adiposity

BMC Endocrine Disorders ◽

10.1186/s12902-021-00880-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yu-Hsiang Su ◽

Yu-Ming Chang ◽

Chih-Ying Kung ◽

Chiu-Kuei Sung ◽

Wei-Shin Foo ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Health Management ◽

Multinomial Logistic Regression ◽

The Elderly ◽

Simultaneous Occurrence ◽

Multinomial Logistic Regression Analysis ◽

Significant Difference

Abstract Background Aging reduces the quality and strength of bones and muscles and increases body fat, which can lead to the simultaneous occurrence of sarcopenia, osteopenia, and adiposity, a condition referred to as OsteoSarcopenic Adiposity (OSA). While previous studies have demonstrated that metabolic syndrome is associated with sarcopenia, osteopenia, and adiposity, the relationship between metabolic syndrome and OSA remains largely unknown. Methods We analyzed data for a sample of middle-aged individuals from a Health Management Center database, which was collected in 2016–2018. There are 2991 cases of people over 50 years from a physical examination center in a hospital in Taiwan during 2016–2018. In addition to descriptive statistics, chi-squared test, analysis of variance, and multinomial logistic regression analysis were conducted to examine OSA risk and associated factors. Results Based on multinomial logistic regression analysis, in different OSA severity level (1–3 more serious), those who are with metabolic syndrome has increased the 2.49–2.57 times risk of OSA (p < 0.001) in OSA = 2 and 3 groups while there is no significant difference in OSA =1 group. Conclusion The prevalence of OSA may impair the health and quality of life in the elderly group, especially those diagnosed with metabolic syndrome, increasing the risk of OSA. These results can help promote early diagnosis and treatment of OSA in clinical settings, particularly among aging individuals with abnormal physical function, the group with the highest OSA incidence.

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Association between Hyperuricemia and Metabolic Syndrome: An Epidemiological Study of a Labor Force Population in Taiwan

BioMed Research International ◽

10.1155/2015/369179 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 12

Author(s):

Cheng-Yu Wei ◽

Chia-Cheng Sun ◽

James Cheng-Chung Wei ◽

Hsu-Chih Tai ◽

Chien-An Sun ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Multiple Logistic Regression Analysis ◽

Density Lipoprotein ◽

Cross Sectional Study ◽

Multiple Logistic Regression ◽

Cross Sectional ◽

The Relationship

The increasing prevalence of metabolic syndrome (MetS) has become an important issue worldwide. Metabolic comorbidities of hypertension, obesity, and hyperlipidemia are shown as important risk factors for incident gout. The purpose of this study was to investigate the relationship between hyperuricemia and MetS. This is a cross-sectional study. The effective sample included 21,544 individuals who received worker health examinations at a local teaching hospital in Changhua County from 2008~2012. We used multiple logistic regression analysis to investigate the influences of hyperuricemia on MetS. The results showed that individuals with MetS had significantly higher blood pressure, fasting plasma glucose, triglycerides, waist circumference, and high-density lipoprotein cholesterol than those without MetS(P<0.001). Multiple logistic regression analysis revealed hyperuricemia to be an important factor of MetS. The risk of developing MetS is higher with high levels of serum uric acid (SUA) and the odds ratio (OR) of having MetS is 4.98 times higher for Tertile 3 than for Tertile 1 (95% CI = 4.16–5.97) and 4 times higher for Quartile 4 than for Quartile 1 (95% CI = 3.59–4.46). In conclusion, males are more likely to develop MetS than females, and the risk of having MetS increases with age and SUA concentration.

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Covid -19, An Acute on Chronic Metabolic Syndrome? HbA1c-A Marker of Severe Infection.

10.21203/rs.3.rs-518074/v1 ◽

2021 ◽

Author(s):

Susanne Rysz ◽

Malin Jonsson Fagerlund ◽

Claire Rimes-Stigare ◽

Emma Larsson ◽

Francesca Campoccia Jalde ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Respiratory Failure ◽

Logistic Regression Analysis ◽

Multivariable Logistic Regression Analysis ◽

The Metabolic Syndrome ◽

Multivariable Logistic Regression ◽

Uncontrolled Diabetes ◽

Increased Risk

Abstract Background: The comorbidities commonly observed in severe Covid-19 are diagnoses that comprise the metabolic syndrome. The metabolic status of patients when infected with SARS-Cov-2 and its significance for the severity of the disease is not yet fully understood. We investigated the association between respiratory symptoms and the levels of HbA1c in hospitalized patients infected with SARS-CoV-2. Methods: In this retrospective observational study, we included all inpatients at the Karolinska University Hospital, Sweden who had both a positive SARS-CoV-2 test and who had a HbA1c test obtained within 3 months of admission. The primary reasons for hospitalization included trauma, stroke, myocardial infarction, acute or elective surgery as well as infection. Based on HbA1c level and diabetes history, we classified patients into the following dysglycemia categories: prediabetes, unknown diabetes, controlled diabetes or uncontrolled diabetes. We used multivariable logistic regression analysis adjusted for age, sex and body mass index, to assess the association between dysglycemia categories and development of respiratory failure when infected with SARS-CoV-2. Primary outcome was respiratory failure associated with SARS-CoV-2.Results: Of the 385 study patients, 88 (22.9%) had prediabetes, 68 (17.7%) had unknown diabetes, 36 (9.4%) had controlled diabetes, and 83 (21.6%) had uncontrolled diabetes. Overall, 299 (77.7%) patients were admitted with or developed SARS-CoV-2-assoociated respiratory failure during hospitalization. The proportion of patients in need of intensive care (62.5% vs 26.7%, p<0.001), mechanical ventilation (60.9% vs. 26.7%, p<0.001) and renal replacement therapy (14% vs. 1.2%, p<0.001) were all higher in patients with SARS-CoV-2 associated respiratory failure vs. patients without. In addition, 83% of the ICU patients with SARS-CoV-2 associated respiratory failure had a HbA1c > 42 mmol/mol. In multivariable logistic regression analysis compared with no chronic dysglycemia, prediabetes (OR 14.41, 95% CI 5.27-39.43), unknown diabetes (OR 15.86, 95% CI 4.55-55.36), and uncontrolled diabetes (OR 17.61, 95% CI 5.77-53.74) was independently associated with increased risk of SARS-CoV-2-induced respiratory failure.Conclusion: Metabolic imbalance, reflected by elevated HbA1c with or without previous diagnosed diabetes mellitus, was associated with a more severe course of SARS-CoV-2-respratory infection. We suggest that HbA1c could be used as a marker of risk for severe Covid-19.

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Controlling energetic intake based on a novel logistic regression model for the metabolic syndrome in a Chinese population

British Journal Of Nutrition ◽

10.1017/s0007114510003235 ◽

2010 ◽

Vol 105 (2) ◽

pp. 256-262

Author(s):

Lv Yangmei ◽

Miao Yanxia ◽

Qiao Liangmei ◽

Zhang Jinhui ◽

Hua Yu ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Energy Requirement ◽

Multivariate Logistic Regression Analysis ◽

Control Group ◽

Multivariate Logistic Regression ◽

The Metabolic Syndrome ◽

Requirement Model

The present study was designed to develop a novel method of energy calculation for controlling energetic intake in patients with the metabolic syndrome. Demographics and dietary data were recorded for 2582 obese subjects. Nutritional education was applied to all the patients. One year later, the data on age, sex, activity intensity coefficient, waistline, environmental temperature and BMI in subjects who lost ≥ 5 % body weight were entered into a multivariate logistic regression analysis model. Energy requirement was calculated from the results of multivariate logistic regression. Four hundred and thirty-four metabolic syndrome patients were then randomly divided into the treated group (216) and the control group (218). The energetic intake in the experimental group was controlled based on the new energy requirement model. The traditional energy exchange method was used in the control group. The independent factors predicting metabolic syndrome prognosis, such as age, sex, activity intensity coefficient, waistline, environmental temperature and BMI, were identified by multivariate logistic regression analysis. The energy requirement model was then constructed by logistic regression analysis. After 6 months of energetic intake control based on the new model, the parameters of the experimental group were significantly different from those of the controls (all P < 0·05): waistline, 89·65 (sd 5·54) v. 91·97 (sd 4·78) cm; BMI, 24·67 (sd 3·54) v. 25·87 (sd 2·65) kg/m2; fasting blood glucose, 6·9 (sd 3·6) v. 8·7 (sd 4·6) mmol/l; 2 h PG, 8·7 (sd 5·7) v. 10·7 (sd 4·5) mmol/l; HbA1c, 7·7 (sd 1·6) v. 8·9 (sd 2·6) %; homoeostasis model insulin resistance index, 3·14 (sd 1·62) v. 4·32 (sd 2·25). The new energy requirement model can effectively improve the clinical outcomes of controlling energetic intake in metabolic syndrome patients.

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Relationship Between the Accumulation of Metabolic Syndrome Components and Orthostatic Hypotension

American Journal of Hypertension ◽

10.1093/ajh/hpab012 ◽

2021 ◽

Vol 34 (7) ◽

pp. 775-776

Author(s):

Xiao-qi Cai ◽

Xin-lei Gao ◽

Ting-jun Wang ◽

Yi-hua Shen ◽

Guo-yan Xu ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Orthostatic Hypotension ◽

Logistic Regression Analysis ◽

Middle Age ◽

Binary Logistic Regression ◽

The Elderly ◽

Elderly Subjects ◽

Binary Logistic Regression Analysis

Abstract Background To investigate the relationship between the accumulation of metabolic syndrome (MS) components and orthostatic hypotension (OH). Methods A total of 2,201 subjects were enrolled and divided into 0 component (n = 199), 1–2 components (n = 1,003), and 3–4 components (n = 999) groups based on the number of MS components according to the criteria of 2018 Chinese Guidelines for Prevention and Management of Hypertension. Stratified analyses and binary logistic regression analysis were performed. Results The incidence of OH was significantly increased with the number of MS components (5.0% in 0 component group, 13.5% in 1–2 components group, and 17.9% in 3–4 components group, P < 0.05). Compared with subjects without OH, the incidence of MS in those with OH was significantly elevated (55.2% vs. 43.7%, P < 0.05). The incidence of OH in the elderly subjects was significantly higher than that in the young and middle-aged subjects (22.3% vs. 10.9%, P < 0.01). Binary logistic regression analysis showed that the number of MS components was associated with OH in all subjects, and the risk of OH was increased with the increment of MS components. Compared with the subjects without any MS component, the risk of OH increased by 2.3 times in the subjects with 4 MS components (odds ratio = 3.274, 95% confidence interval 1.626–6.594, P < 0.05). Stratified analyses found that the number of MS components was independently associated with OH in young to middle-age, female and non-MS subjects. Conclusions The incidence of OH is elevated with accumulations of MS components, especially in young to middle-age, female and non-MS subjects.

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Cross-Sectional Association between Hypercholesterolemia and Knee Pain in the Elderly with Radiographic Knee Osteoarthritis: Data from the Korean National Health and Nutritional Examination Survey

Journal of Clinical Medicine ◽

10.3390/jcm10050933 ◽

2021 ◽

Vol 10 (5) ◽

pp. 933

Author(s):

Byung Woo Cho ◽

Du Seong Kim ◽

Hyuck Min Kwon ◽

Ick Hwan Yang ◽

Woo-Suk Lee ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Knee Pain ◽

Logistic Regression Analysis ◽

National Health ◽

Large Scale ◽

Metabolic Diseases ◽

The Elderly ◽

Cross Sectional ◽

Ordinal Logistic Regression Analysis

Few studies have reported the relationship between knee pain and hypercholesterolemia in the elderly population with osteoarthritis (OA), independent of other variables. The aim of this study was to reveal the association between knee pain and metabolic diseases including hypercholesterolemia using a large-scale cohort. A cross-sectional study was conducted using data from the Korea National Health and the Nutrition Examination Survey (KNHANES-V, VI-1; 2010–2013). Among the subjects aged ≥60 years, 7438 subjects (weighted number estimate = 35,524,307) who replied knee pain item and performed the simple radiographs of knee were enrolled. Using multivariable ordinal logistic regression analysis, variables affecting knee pain were identified, and the odds ratio (OR) was calculated. Of the 35,524,307 subjects, 10,630,836 (29.9%) subjects experienced knee pain. Overall, 20,290,421 subjects (56.3%) had radiographic OA, and 8,119,372 (40.0%) of them complained of knee pain. Multivariable ordinal logistic regression analysis showed that among the metabolic diseases, only hypercholesterolemia was positively correlated with knee pain in the OA group (OR 1.24; 95% Confidence Interval 1.02–1.52, p = 0.033). There were no metabolic diseases correlated with knee pain in the non-OA group. This large-scale study revealed that in the elderly, hypercholesterolemia was positively associated with knee pain independent of body mass index and other metabolic diseases in the OA group, but not in the non-OA group. These results will help in understanding the nature of arthritic pain, and may support the need for exploring the longitudinal associations.

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Multivariable Logistic Regression Equation to Predict Prostate Cancer

10.21203/rs.3.rs-923836/v1 ◽

2021 ◽

Author(s):

Lu Ma ◽

Dong Cheng ◽

Qinghua Li ◽

Jingbo Zhu ◽

Yu Wang ◽

...

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Prostate Specific Antigen ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Specific Antigen ◽

Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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Abstract 3553: Effect of Resolution of Metabolic Syndrome on Endothelial Function and Cardiovascular Events after Acute Coronary Syndromes

Circulation ◽

10.1161/circ.116.suppl_16.ii_804-a ◽

2007 ◽

Vol 116 (suppl_16) ◽

Author(s):

Shin-ichiro Miyazaki ◽

Yoshikazu Hiasa ◽

Takefumi Takahashi ◽

Riyo Ogura ◽

Naoki Suzuki ◽

...

Keyword(s):

Metabolic Syndrome ◽

Logistic Regression ◽

Regression Analysis ◽

Endothelial Function ◽

Logistic Regression Analysis ◽

Acute Coronary Syndromes ◽

Cardiovascular Events ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Coronary Syndromes

Background: Metabolic syndrome (MetS) is associated with endothelial dysfunction, and recognized as a risk factor of cardiovascular events after acute coronary syndromes (ACS). We examined whether the resolution of MetS would improve endothelial function and provide a beneficial effect on clinical outcome after ACS. Methods: We studied 60 patients with MetS who underwent a percutaneous revascularization procedure for ACS. MetS was defined using modified International Diabetes Federation criteria. Brachial artery flow-mediated dilation (FMD) and several risk parameters related to metabolic disorders were assessed at baseline and at 6 months. Each patient was given basic spoken advice on lifestyle modification and optimal medications before discharge. Patients were divided into 2 groups according to whether the criteria for MetS were fulfilled at 6 months: resolved MetS (R-MetS, n=35) and persistent MetS (P-MetS, n=25). Cardiovascular events were defined as cardiac death, stroke, myocardial infarction, unstable angina, and target vessel revascularization. Results: During the 1-year follow-up, 3 patients with R-MetS (8.6%) and 14 patients with P-MetS (56%) had cardiovascular events (p=0.0002). The extent of improvement in FMD was significantly greater in patients with R-MetS than those with P-MetS (change in FMD: 1.5 vs −1.2: p=0.007; respectively). In a multivariate logistic regression analysis, P-MetS was an independent predictor of cardiovascular events (odds ratio 18.4, 95%CI 1.67–28.5, p=0.025). Conclusion: The resolution of MetS is associated with the recovery of endothelial function and prevents cardiovascular events after ACS. Multivariate logistic regression analysis

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O-207 Which infertile patients mostly deserve to have a sperm DNA fragmentation index done? Findings from a cross-sectional study

Human Reproduction ◽

10.1093/humrep/deab128.018 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

E Pozzi ◽

L Boeri ◽

L Candela ◽

D Cignoli ◽

G Colandrea ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Dna Fragmentation ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Sperm Concentration ◽

Testicular Volume ◽

Sperm Dna Fragmentation ◽

Sperm Dna ◽

Increased Risk

Abstract Study question Current scientific guidelines do not clearly suggest which patients would benefit the most from a sperm DNA fragmentation (SDF) test. Summary answer We aimed to investigate potential predictive factors for altered SDF in a homogenous cohort of white-European men presenting for primary couple’s infertility. What is known already High SDF has been associated with reduced fertilization rates, reduced chances of natural conception and an increased risk of early pregnancy loss. Study design, size, duration Data from 478 consecutive men with normal or altered SDF were analysed. Infertility was defined according to the WHO criteria. Semen analysis, SDF (according to SCSA) and serum hormones were measured in every patient. Health significant comorbidities were scored with the Charlson Comorbidity Index (CCI). Altered SDF was considered with a threshold of > 30%. Participants/materials, setting, methods Descriptive statistics compared the overall characteristics of patients with normal SDF and altered SDF. Logistic regression analysis tested potential predictors of altered SDF. ROC curve was used to test the accuracy of the model in predicting SDF alteration Main results and the role of chance Of 478 patients, 253 (57.7%) had altered SDF. Median (IQR) age and BMI of the whole cohort were 38 (35-42) years and 25.1 (23.3-27.1) kg/m2 respectively. Patients with altered SDF were older (median (IQR) age: 39 (36-43) vs. 37 (34-38) years, p < 0.0001), had lower sperm concentration (5 (1.1–18) vs. 17 x106/mL (6–38.8), p < 0.0001), testicular volume (15.1 (12 –20) vs. 16.8 (12 – 25) Prader, p = 0.0005), and total motile sperm count (TMSC) (1.8 (0.21–10.71) vs. 11.8x106 (2–37.26), p < 0.0001). Conversely, men with altered SDF had higher FSH (6.1 (3.85–9.7) vs. 4.8 (3.85 – 7.9) mIU/mL, p < 0.0001) and prolactin levels (9.8 (7.43–14.04) vs. 8.3 (6.6–11.3) pg/mL, p = 0.0004) than those with normal SDF. At multivariable logistic regression analysis, patients’ age >35 years (OR: 2.45, p = 0.0009), FSH > 8.0 mIU/mL (OR: 2.23, p < 0.0001) and lower TMSC (OR: 2.04, p = 0.002) were identified as indipendent predictors of altered SDF, after adjusting for testicular volume and CCI≥1. ROC curve (Figure 1) revealed that the model has a good predictive ability to identify patients with SDF alteration (AUC: 0.72, 95%CI: 0.67 - 0.77). Limitations, reasons for caution It is a retrospective analysis at a single, tertiary-referral academic centre, thus raising the possibility of selection biases. In spite of this, all patients have been consistently analysed over time with a rigorous follow-up, thus limiting potential heterogeneity in terms of data reporting Wider implications of the findings Primary infertile men older than 35 years, with high serum FSH and low TMSC at baseline are the ones who mostly deserve a SDF test over their diagnostic work-up and that would potentially benefit the most of certain treatments to improve SDF value, thus increasing chances of conceiving. Trial registration number Not applicable

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Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

Journal of International Medical Research ◽

10.1177/0300060518777173 ◽

2018 ◽

Vol 46 (9) ◽

pp. 3656-3664 ◽

Cited By ~ 5

Author(s):

Wenbo Xu ◽

Yang Zhao ◽

Shiyan Nian ◽

Lei Feng ◽

Xuejing Bai ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Density Lipoprotein ◽

Binary Logistic Regression ◽

Lipoprotein Cholesterol ◽

Binary Logistic Regression Analysis ◽

Confounding Factors ◽

Analysis Strategies ◽

Glutamyl Transferase

Objective To investigate the importance of controlling confounding factors during binary logistic regression analysis. Methods Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. Results 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. Conclusions When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

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