Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions

Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions Wenli Li, Xiao Liu, Xiahong Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7580576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To develop a risk prediction model for colorectal polyps in asymptomatic individuals and provide evidence-based guidance for colonoscopy screening prioritization within health examination cohorts. Methods: We retrospectively analyzed 1,619 participants undergoing health examinations at Ningbo Zhenhai Lianhua Hospital (October 2022-December 2024). Based on endoscopic findings, 847 individuals with colorectal polyps were assigned to the case group, and 772 polyp-free individuals comprised the control group. Risk factors were identified through univariate and multivariate logistic regression analyses. A predictive nomogram was constructed using significant multivariate predictors. Model discrimination was evaluated via the receiver operating characteristic (ROC) curve and concordance index (C-index), while clinical utility was assessed using decision curve analysis (DCA). Results: Multivariate analysis identified the following independent risk factors (all P<0.05): male sex (OR=2.587; 95% CI: 1.932-3.466), age ≥40 years (OR=4.821; 95% CI: 3.285-7.076) , elevated fasting plasma glucose (OR=1.441; 95% CI: 1.125-1.846) , hyperhomocysteinemia (OR=1.402; 95% CI: 1.031-1.908), carotid plaque (OR=1.614; 95% CI: 1.268-2.054) and thyroid nodules (OR=1.575; 95% CI: 1.243-1.996) . The nomogram exhibited discriminative ability for predicting polyps, with an AUC of 0.752 (95% CI: 0.729-0.776) and a C-index of 0.752 (95% CI: 0.732-0.776). Calibration curves indicated satisfactory agreement between predicted and observed probabilities. DCA confirmed significant net clinical benefit across threshold probabilities. Conclusion: This study establishes a clinically applicable nomogram with moderate predictive performance for colorectal polyps, which may facilitate risk-stratified colonoscopy screening decisions in health examination populations. Health examination colorectal polyps risk factors predictive model nomogram Figures Figure 1 Figure 2 Figure 3 Introduction Colorectal cancer (CRC) is a highly prevalent and lethal malignancy of the digestive system. Globally, it ranks as the third most common cancer and the second leading cause of cancer-related deaths [ 1 ].Approximately 80% of CRC arise from the neoplastic progression of adenomatous polyps, a well-established pathogenic cascade substantiated by longitudinal cohort studies. This evidence underscores the clinical imperative for targeted surveillance and early intervention in high-risk cohorts to mitigate CRC-associated morbidity and mortality [ 2 ]. Colorectal adenomatous polyps, representing the predominant precursor lesions for colorectal carcinogenesis, are detectable and resectable during colonoscopy. Nevertheless, their typically asymptomatic presentation in affected individuals poses significant diagnostic challenges. Current screening modalities—primarily colonoscopy—entail invasive procedures, substantial resource allocation, and stringent bowel preparation protocols that impact patient compliance [ 3 ].While advancing age and modifiable lifestyle factors constitute well-documented risk determinants for colorectal polyp development, extant prediction instruments fail to incorporate multidimensional clinico-epidemiological parameters necessary for individualized risk stratification to early diagnosis. This study aims to develop and validate a multivariable logistic regression-based risk prediction model for stratifying individuals at high risk of colorectal polyp progression. This instrument may enhance evidence-based colonoscopy surveillance allocation, thereby improving early detection efficacy while rationalizing healthcare resource utilization. Materials and methods Study population We retrospectively analyzed 2,418 individuals who underwent health examinations and colonoscopies at Ningbo Zhenhai Lianhua Hospital between October 2022 and December 2024. The study protocol was approved by the Ethics Committee of Ningbo Zhenhai People’s Hospital (Approval No. 2025-010; Date:03/28/2025). Inclusion criteria:Asymptomatic adults (≥18 years) undergoing routine preventative health evaluations, with complete colonoscopy examinations documenting adequate bowel preparation (Boston Bowel Preparation Scale [BBPS] total score ≥6). Exclusion criteria:1. History of gastrointestinal diseases requiring surveillance colonoscopy (e.g., inflammatory bowel disease, colorectal polyps,hereditary polyposis syndromes); 2. Active malignancy under treatment; 3. Current use of corticosteroids or thyroid hormones (within 3 months prior to examination);4. Inadequate bowel preparation (Boston Bowel Preparation Scale <6); 5. Repeated examinations (only the first record was analyzed); 6. Incomplete clinical data; 7. Failed cecum intubation (incomplete colonoscopy). Following sequential application of exclusion criteria (Figure 1), the final analytical cohort comprised 1,619 eligible subjects were classified into the colorectal polyp group (n=847) and the non-polyp group (n=772). Data collection The study comprehensively evaluated three domains of clinical parameters as follow: ① Demographic and Anthropometric Parameters: Age, Sex ,Height, weight, Body Mass Index (BMI) , Systolic and diastolic blood pressure (SBP/DBP) ② Systematic Laboratory Assays: Hematological profiles such as white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin(Hb) and platelet count (PLT). Renal/hepatic function index such as serum creatinine (Scr), uric acid (UA), alanine transaminase (ALT), aspartate aminotransferase (AST) ,total/direct/indirect bilirubin (TBIL/DBIL/IBIL), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) . Metabolic panels such as fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC),high-density/low-density lipoprotein cholesterol (HDL-C/LDL-C) . Inflammation and cancer biomarkers such as homocysteine (Hcy), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA) carbohydrate antigen 19-9 (CA19-9) and cancer antigen 125 (CA125). Thyroid function biomarkers as thyroid-stimulating hormone (TSH), total triiodothyronine (T3) and total thyroxine (T4) . Excretion markers include urinary protein and fecal occult blood (FOB) . ③ Multimodal Imaging Indicators : Abdominal ultrasonography (hepatobiliary system, renal architecture) ,Carotid artery ultrasonography (intima-media thickness, plaque presence) ,Thyroid ultrasonography (nodule characteristics per TI-RADS) and Colonoscopy findings (polyp morphology, location, histopathology). Statistical Analysis Statistical analyses were performed using SPSS 17.0 (IBM Corp.) and R software (version 4.4.3). Normally distributed continuous variables with homogeneous variance were expressed as mean ± standard deviation and compared using independent t-test. Non-normally distributed or heteroscedastic variables were presented as median (interquartile range, IQR) and analyzed with Mann-Whitney U test. Categorical variables were compared using chi-square test. Variables with P<0.05 in univariate analysis were entered into multivariate logistic regression to identify independent risk factors for colorectal polyps. According to the multivariate logistic regression analysis, we constructed a predictive nomogram model on the “rms” package of the R software. The discriminative ability of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and concordance index (C-index). Internal validation was performed through 1000 bootstrap resamples. Calibration curves assessed the agreement between predicted and observed probabilities, while decision curve analysis (DCA) evaluated clinical utility. A two-tailed P value <0.05 was considered statistically significant. Results Study population characteristics As presented in Table 1, compared to subjects without polyps, individuals with colorectal polyps demonstrated significantly higher levels of age, BMI, SBP/DBP, WBC, ANC, AST, DBIL, ALP, GGT,FPG, TG, Hcy, Scr, UA, T4, AFP, and CEA (Z = -14.744 to -2.209; all P < 0.05). Additionally, the polyp group had a significantly greater proportion of males and a higher prevalence of comorbidities, including fatty liver, carotid plaque, thyroid nodules, and a history of cholecystectomy (χ² = 4.877 to 96.074; all P < 0.05). Conversely, this group exhibited a lower rate of FOB+ and reduced levels of Hb, PLT, total protein, albumin, and HDL-C (χ² = 4.691; Z = -3.619 to -2.080; all P 0.05). Table 1 Comparison of baseline characteristics of subjects Variable colorectal polyp group non-polyp group t/χ2/Z P value Age, years 62.0 (54.0, 70.0) 52.0 (39.0, 61.0) Z =-14.744 <0.001 Sex, male/female, n (%) 642 (75.8) /205 (24.2) 460 (59.6) /312 (40.4) χ2 = 48.434 <0.001 BMI, kg/m 2 23.71 (21.85, 25.76) 22.79 (20.96, 24.91) Z =-5.416 <0.001 SBP, mmHg 134.0 (121.0, 146.0) 126.0 (116.0, 137.0) Z =-8.839 <0.001 DBP, mmHg 81.0 (74.0, 88.0) 78.0 (70.0, 85.0) Z =-5.837 <0.001 WBC, ×10^ 9 /L 5.80 (4.90, 6.70) 5.40 (4.60, 6.50) Z=- 3.412 0.001 ANC, ×10^ 9 /L 3.20 (2.70, 4.00) 3.10 (2.60, 3.90) Z=- 3.502 <0.001 hemoglobin, g/L 148.00 (138.00, 156.00) 151.00 (136.00, 158.00) Z=- 3.619 <0.001 PLT, ×10^ 9 /L 220.00 (185.00, 256.00) 239.00 (204.00, 280.00) Z=- 3.162 0.002 urinary protein, positive/ negative, n (%) 25 (3.0) /822 (97.0) 20 (2.6) /752 (97.4) χ2 = 0.195 0.659 FOB, positive/ negative, n (%) 49 (15.3) /271 (84.7) 64 (22.1) /225 (77.9) χ2 = 4.691 0.030 total protein, g/L 73.80 (71.60, 76.50) 74.20 (71.80, 76.98) Z=- 2.080 0.038 Albumin, g/L 45.70 (44.30, 47.00) 46.20 (44.70, 47.50) Z=- 4.284 <0.001 ALT, U/L 22.00 (16.00, 30.00) 21.00 (15.00, 30.00) Z=- 1.571 0.116 AST, U/L 24.00 (21.00, 29.00) 24.00 (20.00, 28.00) Z=- 2.113 0.035 TBIL, umol/L 13.60 (10.60, 17.70) 13.15 (10.50, 16.58) Z=- 1.796 0.073 DBIL, umol/L 3.00 (2.20, 3.90) 2.80 (2.10, 3.60) Z=- 3.125 0.002 IBIL, umol/L 10.60 (8.10, 13.90) 10.30 (8.10, 13.48) Z=- 1.332 0.183 ALP, U/L 78.00 (66.00, 93.00) 75.00 (63.25, 89.00) Z=- 2.989 0.003 GGT, U/L 26.00 (19.00, 42.00) 22.00 (16.00, 32.00) Z=- 5.404 <0.001 FPG, mmol/L 5.47 (5.11, 6.0) 5.21 (4.92, 5.56) Z =-9.194 <0.001 TG, mmol/L 1.38 (1.00, 1.97) 1.22 (0.87, 1.74) Z =-4.589 <0.001 TC, mmol/L 4.97 (4.30, 5.66) 5.02 (4.37, 5.63) Z =-0.376 0.707 HDL-C, mmol/L 1.18 (0.99, 1.47) 1.24 (1.01, 1.54) Z =-2.497 0.013 LDL-C, mmol/L 2.91 (2.33, 3.45) 2.92 (2.39, 3.43) Z =-0.058 0.954 Hcy, umol/L 11.80 (10.20, 14.40) 11.30 (9.70, 13.48) Z =-4.465 <0.001 Scr, umol/L 71.00 (63.00, 79.00) 67.00 (58.00, 77.00) Z =-5.776 <0.001 UA, umol/L 364.00 (305.00, 417.00) 348.00 (290.00, 404.75) Z =-3.507 <0.001 TSH, mIU/L 1.77 (1.28, 2.50) 1.84 (1.34, 2.55) Z =-1.222 0.222 T3, nmol/L 1.62 (1.46, 1.81) 1.63 (1.44, 1.82) Z =-0.502 0.615 T4, nmol/L 118.92 (105.32, 134.20) 114.39 (102.19, 127.29) Z =-4.673 <0.001 AFP, ng/mL 2.20 (1.60, 3.00) 2.10 (1.50, 2.80) Z =-2.209 0.027 CEA, ng/mL 1.40 (0.90, 2.10) 1.10 (0.70, 1.70) Z =-5.757 <0.001 CA19-9, U/mL 4.70 (2.50, 8.80) 4.40 (2.50, 8.38) Z =-0.770 0.442 CA125, U/mL 7.20 (5.60, 9.30) 7.40 (5.70, 9.60) Z =-1.514 0.130 fatty liver, n(%) 433 (51.1) 293 (38.0) χ2 = 28.315 <0.001 carotid plaque, n(%) 489 (57.7) 258 (33.4) χ2 = 96.074 <0.001 thyroid nodules, n(%) 608 (71.8) 430 (55.7) χ2 = 45.407 <0.001 cholecystectomy history, n(%) 38 (4.5) 19 (2.5) χ2 = 4.877 0.027 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell count; ANC, absolute neutrophil count; PLT, platelet count; FOB, fecal occult blood; ALT, alanine transaminase; AST, aspartate aminotransferase; TBIL, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Hcy, homocysteine; Scr, serum creatinine; UA, uric acid; TSH, thyroid-stimulating hormone; T3, total triiodothyronine; T4, total thyroxine; AFP, alpha-fetoprotein; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19-9; CA125, cancer antigen 125. Independent risk factors for colorectal polyps Univariable Analysis showed significant associations with colorectal polyps were identified for 17 covariates (P<0.05) including sex, age, BMI, SBP, DBP, FOB+, GGT, DBIL, FPG, TG, Hcy, Scr, UA, CEA, fatty liver, carotid plaque, thyroid nodules, and cholecystectomy history (all P<0.05)(Table 2) . Multivariable logistic regression revealed six independent risk factors in the final logistic regression model: male sex (OR=2.587, 95% CI=1.932-3.466, P<0.001), age ( OR=4.821, 95% CI=3.285-7.076, P<0.001), FPG (OR=1.441, 95% CI=1.125-1.846, P=0.004), Hcy (OR=1.402, 95% CI=1.031-1.908, P=0.031), carotid plaque (OR=1.614, 95% CI=1.268-2.054, P<0.001), and thyroid nodules (OR=1.575, 95% CI=1.243-1.996, P<0.001) (Table 2). Table 2 Univariate and multivariate analysis of factors for predicting colorectal polyps Variables Univariate analysis Multivariate analysis OR 95% CI P value OR 95% CI P value Sex 2.124 1.717-2.628 <0.001 2.532 1.895-3.383 <0.001 Age 6.616 4.711-9.292 <0.001 4.765 3.252-6.980 <0.001 BMI 1.596 1.310-1.943 <0.001 1.283 0.999-1.647 0.051 SBP 2.138 1.753-2.607 <0.001 1.258 0.971-1.629 0.082 DBP 1.606 1.300-1.983 <0.001 0.981 0.749-1.286 0.891 FOB+ 0.638 0.421-0.960 0.031 0.790 0.501-1.243 0.308 GGT 1.454 1.124-1.879 0.004 0.989 0.729-1.340 0.941 DBIL 1.402 1.099-1.788 0.007 1.107 0.839-1.461 0.471 FPG 2.497 2.121-3.099 <0.001 1.458 1.140-1.866 0.003 TG 1.431 1.155-1.774 0.001 1.149 0.883-1.496 0.302 Hcy 1.458 1.127-1.886 0.004 1.391 1.023-1.890 0.035 Scr 1.443 1.179-1.767 <0.001 0.819 0.631-1.063 0.133 UA 1.388 1.141-1.688 0.001 1.152 0.898-1.477 0.266 CEA 2.292 1.194-4.401 0.013 1.479 0.715-3.061 0.291 fatty liver 1.710 1.402-2.085 <0.001 1.027 0.803-1.313 0.835 carotid plaque 2.721 2.223-3.332 <0.001 1.642 1.292-2.088 <0.001 thyroid nodules 2.023 1.646-2.487 <0.001 1.583 1.251-2.005 <0.001 cholecystectomy history 1.862 1.064-3.258 0.030 1.034 0.571-1.873 0.913 OR, odds ratio; CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FOB+, fecal occult blood positivity; GGT, gamma-glutamyl transferase; DBIL, direct bilirubin; FPG, fasting plasma glucose; TG, triglycerides; Hcy, homocysteine; Scr, serum creatinine; UA, uric acid; CEA, carcinoembryonic antigen. Predictive nomogram model Based on the six independent risk factors identified by multivariate logistic regression analysis, a predictive nomogram for colorectal polyps in health examination population was constructed (Figure 2). The nomogram's scoring system assigned points for each variable (sex, age, FPG, Hcy, carotid plaque, and thyroid nodules), with summed total scores corresponding to predicted risk probabilities on the bottom scale. A higher total score indicated greater risk. The model demonstrated good discrimination (AUC=0.752, 95% CI: 0.729-0.776; C-index=0.752, 95% CI: 0.732-0.776)(Figure 3A) and was internally validated with 1000 bootstrap resamples, showing favorable calibration (Figure 3B) and clinically meaningful net benefit on decision curve analysis at threshold probabilities of 20%-80% (Figure 3C). Discussion Current evidence confirms that comprehensive health examinations significantly contribute to disease prevention through holistic health assessments, risk factor screening, and early detection of disease indicators [4]. For asymptomatic individuals, this early detection-intervention paradigm necessitates replacing uniform screening with risk-stratified protocols to avoid excessive testing. Regarding colorectal cancer (CRC) prevention specifically, polypectomy has proven highly effective. Consequently, accurate risk stratification for colorectal polyps is essential to guide personalized colonoscopy screening decisions within asymptomatic populations undergoing periodic health assessments. Relevant studies [5] have indicated that sex and age are high-risk factors for colorectal polyps, which is consistent with the findings of our study. Males exhibit a higher risk of developing colorectal polyps compared to females, potentially attributed to the higher levels of estrogen and progesterone in females. These hormones exert an antagonistic effect on cytokine receptors, thereby protecting the colorectum and reducing the incidence of colorectal polyps in females [6]. Alternatively, this disparity may be linked to the higher prevalence of smoking and alcohol consumption among males. Previous studies have demonstrated that the detection rate of colorectal polyps via colonoscopy increases with age, accompanied by a corresponding rise in malignant transformation risk, likely due to age-related declines in physiological function, weakened immunity, and increased susceptibility to intestinal inflammation in older adults [7]. The Chinese expert consensus on CRC screening for individuals aged 40-74 years [8]. Our results strongly support this recommendation, showing a 4.77-fold higher risk of colorectal polyps in adults aged ≥40 years compared to those under 40. The nomogram illustrates a progressive increase in risk scores with advancing age, indicating a gradual elevation in the likelihood of colorectal polyp development. According to the 2024 American Diabetes Association criteria [9], impaired fasting glucose (IFG) is defined as FPG levels of 5.6-6.9 mmol/L, representing a prediabetic state. In our study, individuals with FPG ≥5.6 mmol/L exhibited a 1.44-fold increased risk of developing colorectal polyps compared to those with FPG <5.6 mmol/L, confirming elevated FPG level as an independent risk factor for colorectal polyps in health examination population. These findings align with previous studies by Wu et al. [10], which demonstrated a linear correlation between FPG and CRC risk in individuals without self-reported diabetes. Additionally, diabetes and prediabetes have been closely linked to the presence of non-advanced adenomatous polyps [11]. Hyperhomocysteinemia, defined as blood Hcy levels >15 μmol/L, is closely linked to CRC [12]. In the colon, Hcy potentiates hydrogen sulfide (H2S)-driven carcinogenesis, a process amplified by sulfate-reducing bacteria abundant in this region [13]. Moreover, Hcy can serve as an indicator of chronic inflammation, and its elevated levels may contribute to the occurrence or progression of CRC through inflammatory mechanisms[14,15].Additionally, hyperhomocysteinemia has been significantly associated with an increased risk of colorectal polyps [16-18]. In line with these findings, our study demonstrated significantly higher Hcy levels in the colorectal polyp group compared to the non-polyp group, identifying elevated Hcy levels as an independent risk factor for colorectal polyps in healthy individuals. Kim et al. [19] found a significant correlation between colorectal adenomas and carotid atherosclerosis in Korean adults. Consistent with this, our study identified carotid plaque as an independent risk factor for colorectal polyps in health examination population. Additionally, previous research has reported that thyroid nodules are more prevalent in subjects with colon polyps compared to those without, independent of insulin resistance [20]. Although no significant differences in T3 or TSH levels were detected between the colorectal polyp and non-polyp groups in our study, T4 levels were significantly elevated in the colorectal polyp group, and thyroid nodules were more prevalent. Thyroid nodules were identified as an independent risk factor for colorectal polyps in health examination population. The underlying mechanisms of these associations remain unclear but may be related to the high consumption of seafood in coastal regions, with dietary habits and metabolic disturbances potentially playing partial roles. Unlike previous studies [5,21], our univariate logistic analysis identified BMI and UA were associated with colorectal polyps, but multivariate logistic regression did not confirm them as independent risk factors. This discrepancy may stem from differences in the study populations. In this study, the stool sample submission rate was 37.6% in the health examinaion cohort, indicating suboptimal compliance. FOB+ can result from diverse factors or underlying conditions [22] , and our analysis did not establish FOB+ as an independent risk factor for colorectal polyps. While several risk predictive models for colorectal polyps or neoplastic polyps exist, most were developed using hospitalized patient data, with few focusing on health examination population. Based on the identified risk factors, we established a nomogram predictive model for colorectal polyps in a health examination population. The model demonstrated good discriminatory ability (AUC=0.752) and fine calibration. Additionally, DCA indicated favorable clinical meaningful net benefit of the model. However, this study has several limitations that must be acknowledged. First, its single-center, retrospective design inherently introduces the potential for selection bias and limits the control over confounding variables. Second, the lack of external validation means that the generalizability of our findings to broader populations and different clinical settings remains uncertain. Third, although variables such as lifestyle data, insulin use and aspirin use are important in the evaluation of polyps, these data were not available in our dataset. Their absence may limit the comprehensiveness and predictive power of our nomogram. Furthermore, We were unable to obtain the detailed histopathology reports for each polyp, which precluded a more nuanced analysis of risk factors based on polyp subtype.Therefore, to address these limitations, future research should involve large-scale, multi-center prospective studies that incorporate these critical variables. External validation of our nomogram in such cohorts, along with stratified subgroup analyses based on polyp pathology, is essential to confirm and extend our findings. Conclusion This study demonstrates gender, age, fasting blood glucose, hyperhomocysteinemia, carotid artery plaque, and thyroid nodules are independent risk factors for colorectal polyps in asymptomatic populations. The developed nomogram demonstrates moderate discriminative performance and clinical utility, offering a practical tool to inform colorectal endoscopic screening decisions in health examination settings. Abbreviations CRC Colorectal cancer BMI Body Mass Index SBP Systolic blood pressure DBP Diastolic blood pressure WBC White blood cell count ANC Absolute neutrophil count Hb Hemoglobin PLT Platelet count Scr Serum creatinine UA Uric acid ALT Alanine transaminase AST Aspartate aminotransferase TBIL Total bilirubin DBIL Direct bilirubin IBIL Indirect bilirubin ALP Alkaline phosphatase GGT Gamma-glutamyl transferase FPG Fasting plasma glucose TG Triglycerides TC Total cholesterol HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol Hcy Homocysteine AFP Alpha-fetoprotein CEA Carcinoembryonic antigen CA19-9 Carbohydrate antigen 19 − 9 CA125 Cancer antigen 125 TSH Thyroid-stimulating hormone T3 Total triiodothyronine T4 Total thyroxine FOB Fecal occult blood AUC Receiver operating characteristic curve C-index Concordance index DCA Decision curve analysis IFG Impaired fasting glucose Declarations Ethical approval and consent to participate This study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee of Ningbo Zhenhai People’s Hospital (Approval No. 2025-010; Date:03/28/2025). The need for informed consent was waived due to the retrospective nature of this study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the correspongding author on reasonable request. Competing Interests The authors have declared that no competing interest exists. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author contributions WLL and XL designed the research. WLL collected data, created the figures and wrote the manuscript. XL helped analyze and process the data. XHD reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. 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Kim H, Cho YJ, Kim YA, et al. Association between colorectal adenoma and carotid atherosclerosis in Korean adults. Int J Environ Res Public Health. 2018;15(12):2762. Mousa U, Anil C, Demir CC, et al. Thyroid nodules are more prevalent in subjects with colon polyps, independent of insulin resistance. Med Princ Pract. 2019;28(5):418-424. Yu LL, Jin HY. Construction of a risk prediction model for colorectal polyps and its clinical value. Chin J Colorec Dis ( Electronic Edition ). 2022;11(5):409-414. Chen YW,Li YC.Examining the factors that affect the diagnosis of patients with positive fecal occult blood test results. Int J Environ Res Public Health. 2022;19:7569. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7580576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515259541,"identity":"a1c8be35-0152-4cea-881d-0a2415bf7fad","order_by":0,"name":"Wenli Li","email":"","orcid":"","institution":"Ningbo Zhenhai People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Li","suffix":""},{"id":515259543,"identity":"7b6e14cc-e239-4e91-bc38-468fc3f7a0b8","order_by":1,"name":"Xiao Liu","email":"","orcid":"","institution":"Ningbo Zhenhai Lianhua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Liu","suffix":""},{"id":515259545,"identity":"0ae9cb46-bf57-4144-9bf3-c988cdb3fdc8","order_by":2,"name":"Xiahong Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCA4wNzAwGNnZs7I2NDz8Qr6UiLZmf53CzsQRxWhgYmBnOHGKcOSO9TYCHGB18xw+3SRe2HWA2uPmwjUGCwU5Ot4GAFskzic3GM9vu8BncTmx7UMCQbGx2gIAWgwOJjY95254xA7W0G0gwHEjcRlDL+YcNh3nbDjNuuHmwTYKHKC03gLbwnDkM9D4jkVokbzxsNuYBBzLQUxIGRPiF73z6M2kecFQef/jwQ4WdHEEt6O4kTfkoGAWjYBSMAhwAAD8pSR1zxfFoAAAAAElFTkSuQmCC","orcid":"","institution":"Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","correspondingAuthor":true,"prefix":"","firstName":"Xiahong","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2025-09-10 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03:05:20","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94053,"visible":true,"origin":"","legend":"","description":"","filename":"8f6a92eb48e54cf5b96ec8bfd35bdaaa1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/22276edc3ea53e4136878d18.xml"},{"id":91938195,"identity":"7f4b2d9a-fd44-4eb3-8de7-5d536d8b671d","added_by":"auto","created_at":"2025-09-23 03:05:20","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100780,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/3136774b79cb98b0c4393e0d.html"},{"id":91938184,"identity":"df39353c-a7a0-44af-b362-9be6c29e1f70","added_by":"auto","created_at":"2025-09-23 03:05:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80363,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/5dfe9974d866879e6838b8b1.png"},{"id":91938183,"identity":"f781dcec-6828-47a7-a50a-58a74d0baa7b","added_by":"auto","created_at":"2025-09-23 03:05:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41089,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting colorectal polyps. FPG, fasting plasma glucose; Hcy, homocysteine; sex(0:female,1:male); carotid plaque(0:no,1:yes); thyroid nodule(0:no,1:yes).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/7d8eeb097379e30fee047dfa.png"},{"id":91939480,"identity":"ef1c7996-e319-4d44-990c-c60d46387820","added_by":"auto","created_at":"2025-09-23 03:13:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47863,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the nomogram model.(A) ROC curve of the nomogram for predicting colorectal polyps. (B) Calibration curves of the nomogram for predicting colorectal polyps. The 45° dashed line represented a perfect prediction by an ideal model, and the black solid line shows the performance of the model. (C) Decision curve analysis of the nomogram for predicting colorectal polyps, demonstrating the net benefit associated with use of the nomogram for the prediction of colorectal polyps. The red solid line represented the predictive model. The gray solid line represented the screen-all scheme. The black solid line represented the screen-none scheme. ROC, receiver operating characteristic. AUC: area under the curve.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/ef424816ad751f9cec610427.png"},{"id":92225250,"identity":"1c6fe2e9-5386-4e0c-b28b-dc8aaa1a75c6","added_by":"auto","created_at":"2025-09-26 04:31:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":850774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7580576/v1/a74d41d6-58e7-4b85-b4c5-3673158eb6cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is a highly prevalent and lethal malignancy of the digestive system. Globally, it ranks as the third most common cancer and the second leading cause of cancer-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Approximately 80% of CRC arise from the neoplastic progression of adenomatous polyps, a well-established pathogenic cascade substantiated by longitudinal cohort studies. This evidence underscores the clinical imperative for targeted surveillance and early intervention in high-risk cohorts to mitigate CRC-associated morbidity and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eColorectal adenomatous polyps, representing the predominant precursor lesions for colorectal carcinogenesis, are detectable and resectable during colonoscopy. Nevertheless, their typically asymptomatic presentation in affected individuals poses significant diagnostic challenges. Current screening modalities\u0026mdash;primarily colonoscopy\u0026mdash;entail invasive procedures, substantial resource allocation, and stringent bowel preparation protocols that impact patient compliance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].While advancing age and modifiable lifestyle factors constitute well-documented risk determinants for colorectal polyp development, extant prediction instruments fail to incorporate multidimensional clinico-epidemiological parameters necessary for individualized risk stratification to early diagnosis.\u003c/p\u003e\u003cp\u003eThis study aims to develop and validate a multivariable logistic regression-based risk prediction model for stratifying individuals at high risk of colorectal polyp progression. This instrument may enhance evidence-based colonoscopy surveillance allocation, thereby improving early detection efficacy while rationalizing healthcare resource utilization.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively analyzed 2,418 individuals who underwent health examinations and colonoscopies at Ningbo Zhenhai Lianhua Hospital between October 2022 and December 2024. The study protocol was approved by the Ethics Committee of Ningbo Zhenhai People\u0026rsquo;s Hospital (Approval No. 2025-010; Date:03/28/2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInclusion criteria:Asymptomatic adults (\u0026ge;18 years) undergoing routine preventative health evaluations, with complete colonoscopy examinations documenting adequate bowel preparation (Boston Bowel Preparation Scale [BBPS] total score \u0026ge;6).\u003c/p\u003e\n\u003cp\u003eExclusion criteria:1. History of gastrointestinal diseases requiring surveillance colonoscopy (e.g., inflammatory bowel disease, colorectal polyps,hereditary polyposis syndromes); 2. Active malignancy under treatment; 3. Current use of corticosteroids or thyroid hormones (within 3 months prior to examination);4. Inadequate bowel preparation (Boston Bowel Preparation Scale \u0026lt;6); 5. Repeated examinations (only the first record was analyzed); 6. Incomplete clinical data; 7. Failed cecum intubation (incomplete colonoscopy).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing sequential application of exclusion criteria (Figure 1), the final analytical cohort comprised 1,619 eligible subjects were classified into the colorectal polyp group (n=847) and the non-polyp group (n=772).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study comprehensively evaluated three domains of clinical parameters as follow:\u003c/p\u003e\n\u003cp\u003e① Demographic and Anthropometric Parameters: Age, Sex ,Height, weight, Body Mass Index (BMI) , Systolic and diastolic blood pressure (SBP/DBP)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e② Systematic Laboratory Assays: Hematological profiles such as white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin(Hb) and platelet count (PLT). Renal/hepatic function index such as serum creatinine (Scr), uric acid (UA), alanine transaminase (ALT), aspartate aminotransferase (AST) ,total/direct/indirect bilirubin (TBIL/DBIL/IBIL), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) . Metabolic panels such as fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC),high-density/low-density lipoprotein cholesterol (HDL-C/LDL-C) . Inflammation and cancer biomarkers such as \u0026nbsp;homocysteine (Hcy), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA) carbohydrate antigen 19-9 (CA19-9) and cancer antigen 125 (CA125). Thyroid function biomarkers as thyroid-stimulating hormone (TSH), total triiodothyronine (T3) and total thyroxine (T4) . Excretion markers include urinary protein and fecal occult blood (FOB) .\u003c/p\u003e\n\u003cp\u003e③ Multimodal Imaging Indicators : Abdominal ultrasonography (hepatobiliary system, renal architecture) ,Carotid artery ultrasonography (intima-media thickness, plaque presence) ,Thyroid ultrasonography (nodule characteristics per TI-RADS) and Colonoscopy findings (polyp morphology, location, histopathology).\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS 17.0 (IBM Corp.) and R software (version 4.4.3). Normally distributed continuous variables with homogeneous variance were expressed as mean \u0026plusmn; standard deviation and compared using independent t-test. Non-normally distributed or heteroscedastic variables were presented as median (interquartile range, IQR) and analyzed with Mann-Whitney U test. Categorical variables were compared using chi-square test. Variables with P\u0026lt;0.05 in univariate analysis were entered into multivariate logistic regression to identify independent risk factors for colorectal polyps. According to the multivariate logistic regression analysis, we constructed a predictive nomogram model on the \u0026ldquo;rms\u0026rdquo; package of the R software. The discriminative ability of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and concordance index (C-index). Internal validation was performed through 1000 bootstrap resamples. Calibration curves assessed the agreement between predicted and observed probabilities, while decision curve analysis (DCA) evaluated clinical utility. A two-tailed P value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy population characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs presented in Table 1, compared to subjects without polyps, individuals with colorectal polyps demonstrated significantly higher levels of age, BMI, SBP/DBP, WBC, ANC, AST, DBIL, ALP, GGT,FPG, TG, Hcy, Scr, UA, T4, AFP, and CEA (Z = -14.744 to -2.209; all P \u0026lt; 0.05). Additionally, the polyp group had a significantly greater proportion of males and a higher prevalence of comorbidities, including fatty liver, carotid plaque, thyroid nodules, and a history of cholecystectomy (\u0026chi;\u0026sup2; = 4.877 to 96.074; all P \u0026lt; 0.05). Conversely, this group exhibited a lower rate of FOB+ and reduced levels of Hb, PLT, total protein, albumin, and HDL-C (\u0026chi;\u0026sup2; = 4.691; Z = -3.619 to -2.080; all P \u0026lt; 0.05). No significant intergroup differences were observed in urinary protein positivity, or in levels of ALT, TBIL, IBIL, TC, LDL-C, TSH, T3, CA125, or CA19-9 (all P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Comparison of baseline characteristics of subjects\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003ecolorectal polyp group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003enon-polyp group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003et/\u0026chi;2/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e62.0 (54.0, 70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e52.0 (39.0, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-14.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSex, male/female, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e642 (75.8) /205 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e460 (59.6) /312 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e48.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e23.71 (21.85, 25.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e22.79 (20.96, 24.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e134.0 (121.0, 146.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e126.0 (116.0, 137.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-8.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e81.0 (74.0, 88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e78.0 (70.0, 85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eWBC,\u0026nbsp;\u0026times;10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e5.80 (4.90, 6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.40 (4.60, 6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e3.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eANC,\u0026nbsp;\u0026times;10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e3.20 (2.70, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.10 (2.60, 3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e3.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ehemoglobin,\u0026nbsp;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e148.00 (138.00, 156.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e151.00 (136.00, 158.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e3.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePLT,\u0026nbsp;\u0026times;10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e220.00 (185.00, 256.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e239.00 (204.00, 280.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e3.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eurinary protein, positive/ negative, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e25 (3.0) /822 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e20 (2.6) /752 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFOB, positive/ negative, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e49 (15.3) /271 (84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e64 (22.1) /225 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e4.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003etotal protein, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e73.80 (71.60, 76.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e74.20 (71.80, 76.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e2.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e45.70 (44.30, 47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e46.20 (44.70, 47.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e4.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e22.00 (16.00, 30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e21.00 (15.00, 30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e1.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e24.00 (21.00, 29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e24.00 (20.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e2.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTBIL, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e13.60 (10.60, 17.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e13.15 (10.50, 16.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e1.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eDBIL, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e3.00 (2.20, 3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.80 (2.10, 3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e3.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eIBIL, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e10.60 (8.10, 13.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e10.30 (8.10, 13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eALP, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e78.00 (66.00, 93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e75.00 (63.25, 89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e2.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGGT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e26.00 (19.00, 42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e22.00 (16.00, 32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ=-\u003c/em\u003e5.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFPG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e5.47 (5.11, 6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.21 (4.92, 5.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-9.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1.38 (1.00, 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.22 (0.87, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-4.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e4.97 (4.30, 5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.02 (4.37, 5.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHDL-C,\u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1.18 (0.99, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.24 (1.01, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-2.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eLDL-C,\u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e2.91 (2.33, 3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.92 (2.39, 3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHcy,\u0026nbsp;umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e11.80 (10.20, 14.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e11.30 (9.70, 13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-4.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eScr,\u0026nbsp;umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e71.00 (63.00, 79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e67.00 (58.00, 77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eUA, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e364.00 (305.00, 417.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e348.00 (290.00, 404.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-3.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTSH, mIU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1.77 (1.28, 2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.84 (1.34, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-1.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eT3, nmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1.62 (1.46, 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.63 (1.44, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eT4, nmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e118.92 (105.32, 134.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e114.39 (102.19, 127.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-4.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAFP,\u0026nbsp;ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e2.20 (1.60, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.10 (1.50, 2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCEA,\u0026nbsp;ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e1.40 (0.90, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.10 (0.70, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-5.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCA19-9,\u0026nbsp;U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e4.70 (2.50, 8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.40 (2.50, 8.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCA125, U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e7.20 (5.60, 9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e7.40 (5.70, 9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-1.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003efatty liver, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e433 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e293 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e28.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ecarotid plaque, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e489 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e258 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e96.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ethyroid nodules, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e608 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e430 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e45.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003echolecystectomy history, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e38 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e19 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003cem\u003e=\u003c/em\u003e4.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI,\u0026nbsp;body mass index;\u0026nbsp;SBP,\u0026nbsp;systolic blood pressure;\u0026nbsp;DBP,\u0026nbsp;diastolic blood pressure; WBC,\u0026nbsp;white blood cell count;\u0026nbsp;ANC,\u0026nbsp;absolute neutrophil count; PLT, platelet count; FOB, fecal occult blood;\u0026nbsp;ALT,\u0026nbsp;alanine transaminase; AST,\u0026nbsp;aspartate aminotransferase;\u0026nbsp;TBIL,\u0026nbsp;total bilirubin;\u0026nbsp;DBIL,\u0026nbsp;direct bilirubin;\u0026nbsp;IBIL,\u0026nbsp;indirect bilirubin;\u0026nbsp;ALP,\u0026nbsp;alkaline phosphatase;\u0026nbsp;GGT,\u0026nbsp;gamma-glutamyl transferase;\u0026nbsp;FPG,\u0026nbsp;fasting plasma glucose;\u0026nbsp;TG,\u0026nbsp;triglycerides;\u0026nbsp;TC,\u0026nbsp;total cholesterol;\u0026nbsp;HDL-C,\u0026nbsp;high-density lipoprotein cholesterol;\u0026nbsp;LDL-C,\u0026nbsp;low-density\u0026nbsp;lipoprotein\u0026nbsp;cholesterol;\u0026nbsp;Hcy,\u0026nbsp;homocysteine;\u0026nbsp;Scr,\u0026nbsp;serum\u0026nbsp;creatinine;\u0026nbsp;UA,\u0026nbsp;uric acid;\u0026nbsp;TSH,\u0026nbsp;thyroid-stimulating hormone;\u0026nbsp;T3,\u0026nbsp;total triiodothyronine;\u0026nbsp;T4,\u0026nbsp;total thyroxine;\u0026nbsp;AFP,\u0026nbsp;alpha-fetoprotein;\u0026nbsp;CEA,\u0026nbsp;carcinoembryonic antigen;\u0026nbsp;CA19-9,\u0026nbsp;carbohydrate antigen 19-9;\u0026nbsp;CA125,\u0026nbsp;cancer antigen 125.\u003c/p\u003e\n\u003ch3\u003eIndependent risk factors for colorectal polyps\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eUnivariable Analysis showed significant associations with colorectal polyps were identified for 17 covariates (P\u0026lt;0.05) including sex, age, BMI, SBP, DBP, FOB+, GGT, DBIL, FPG, TG, Hcy, Scr, UA, CEA, fatty liver, carotid plaque, thyroid nodules, and cholecystectomy history (all P\u0026lt;0.05)(Table 2) .\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression revealed six independent risk factors in the final logistic regression model: male sex (OR=2.587, 95% CI=1.932-3.466, P\u0026lt;0.001), age ( OR=4.821, 95% CI=3.285-7.076, P\u0026lt;0.001), FPG (OR=1.441, 95% CI=1.125-1.846, P=0.004), Hcy (OR=1.402, 95% CI=1.031-1.908, P=0.031), carotid plaque (OR=1.614, 95% CI=1.268-2.054, P\u0026lt;0.001), and thyroid nodules (OR=1.575, 95% CI=1.243-1.996, P\u0026lt;0.001) \u0026nbsp;(Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Univariate and multivariate analysis of factors for predicting colorectal polyps\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.717-2.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e2.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.895-3.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4.711-9.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e4.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3.252-6.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.310-1.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.999-1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.753-2.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.971-1.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.300-1.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.749-1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFOB+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.421-0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.501-1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.124-1.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.729-1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eDBIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.099-1.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.839-1.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2.121-3.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.140-1.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.155-1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.883-1.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eHcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.127-1.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.023-1.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.179-1.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.631-1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.141-1.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.898-1.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.194-4.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.715-3.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003efatty liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.402-2.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.803-1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003ecarotid plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2.223-3.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.292-2.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003ethyroid nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.646-2.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.251-2.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003echolecystectomy history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.064-3.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.571-1.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval; BMI,\u0026nbsp;body mass index;\u0026nbsp;SBP,\u0026nbsp;systolic blood pressure;\u0026nbsp;DBP,\u0026nbsp;diastolic blood pressure;\u0026nbsp;FOB+, fecal occult blood\u0026nbsp;positivity;\u0026nbsp;GGT,\u0026nbsp;gamma-glutamyl transferase;\u0026nbsp;DBIL,\u0026nbsp;direct bilirubin;\u0026nbsp;FPG,\u0026nbsp;fasting plasma glucose;\u0026nbsp;TG,\u0026nbsp;triglycerides;\u0026nbsp;Hcy,\u0026nbsp;homocysteine;\u0026nbsp;Scr,\u0026nbsp;serum\u0026nbsp;creatinine;\u0026nbsp;UA,\u0026nbsp;uric acid;\u0026nbsp;CEA,\u0026nbsp;carcinoembryonic antigen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive nomogram model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the six independent risk factors identified by multivariate logistic regression analysis, a predictive nomogram for colorectal polyps in health examination population was constructed (Figure 2). The nomogram\u0026apos;s scoring system assigned points for each variable (sex, age, FPG, Hcy, carotid plaque, and thyroid nodules), with summed total scores corresponding to predicted risk probabilities on the bottom scale. A higher total score indicated greater risk. The model demonstrated good discrimination (AUC=0.752, 95% CI: 0.729-0.776; C-index=0.752, 95% CI: 0.732-0.776)(Figure 3A) and was internally validated with 1000 bootstrap resamples, showing favorable calibration (Figure 3B) and clinically meaningful net benefit on decision curve analysis at threshold probabilities of 20%-80% (Figure 3C).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrent evidence confirms that comprehensive health examinations significantly contribute to disease prevention through holistic health assessments, risk factor screening, and early detection of disease indicators [4]. For asymptomatic individuals, this early detection-intervention paradigm necessitates replacing uniform screening with risk-stratified protocols to avoid excessive testing. Regarding colorectal cancer (CRC) prevention specifically, polypectomy has proven highly effective. Consequently, accurate risk stratification for colorectal polyps is essential to guide personalized colonoscopy screening decisions within asymptomatic populations undergoing periodic health assessments.\u003c/p\u003e\n\u003cp\u003eRelevant studies\u0026nbsp;[5]\u0026nbsp;have indicated that\u0026nbsp;sex\u0026nbsp;and age are high-risk factors for colorectal polyps, which is consistent with the findings of our study. Males exhibit a higher risk of developing colorectal polyps compared to females, potentially attributed to the higher levels of estrogen and progesterone in females. These hormones exert an antagonistic effect on cytokine receptors, thereby protecting the colorectum and reducing the incidence of colorectal polyps in females\u0026nbsp;[6]. Alternatively, this disparity may be linked to the higher prevalence of smoking and alcohol consumption among males. Previous studies have demonstrated that the detection rate of colorectal polyps via colonoscopy increases with age, accompanied by a corresponding rise in malignant transformation risk, likely due to age-related declines in physiological function, weakened immunity, and increased susceptibility to intestinal inflammation in older adults\u0026nbsp;[7].\u0026nbsp;The Chinese expert\u0026nbsp;consensus on\u0026nbsp;CRC screening for individuals aged 40-74 years\u0026nbsp;[8]. Our results strongly support this recommendation, showing a 4.77-fold higher risk of colorectal polyps in adults aged ≥40 years compared to those under 40.\u0026nbsp;The nomogram illustrates a progressive increase in risk scores with advancing age, indicating a gradual elevation in the likelihood of colorectal polyp development.\u003c/p\u003e\n\u003cp\u003eAccording to the 2024 American Diabetes Association criteria\u0026nbsp;[9], impaired fasting glucose (IFG)\u0026nbsp;is\u0026nbsp;defined as FPG\u0026nbsp;levels of 5.6-6.9 mmol/L, representing a prediabetic state.\u0026nbsp;In our study, individuals with FPG ≥5.6 mmol/L exhibited a 1.44-fold increased risk\u0026nbsp;of developing colorectal polyps compared to those with FPG \u0026lt;5.6 mmol/L, confirming elevated FPG\u0026nbsp;level\u0026nbsp;as an independent risk factor for colorectal polyps\u0026nbsp;in health\u0026nbsp;examination\u0026nbsp;population. These findings align with previous studies by Wu et al.\u0026nbsp;[10], which demonstrated a linear correlation between FPG and CRC risk in individuals without self-reported diabetes. Additionally, diabetes and prediabetes have been closely linked to the presence of non-advanced adenomatous polyps\u0026nbsp;[11].\u003c/p\u003e\n\u003cp\u003eHyperhomocysteinemia, defined as blood Hcy levels \u0026gt;15 μmol/L, is\u0026nbsp;closely linked to CRC\u0026nbsp;[12]. In the colon, Hcy potentiates hydrogen sulfide (H2S)-driven carcinogenesis, a process amplified by sulfate-reducing bacteria abundant in this region [13].\u0026nbsp;Moreover, Hcy\u0026nbsp;can serve as an indicator of chronic inflammation, and its elevated levels may contribute to the occurrence or progression of\u0026nbsp;CRC\u0026nbsp;through inflammatory mechanisms[14,15].Additionally,\u0026nbsp;hyperhomocysteinemia has been significantly associated with an increased risk of colorectal polyps\u0026nbsp;[16-18].\u0026nbsp;In line with these findings, our study demonstrated significantly higher Hcy levels in the\u0026nbsp;colorectal\u0026nbsp;polyp group compared to the\u0026nbsp;non-polyp\u0026nbsp;group, identifying elevated Hcy\u0026nbsp;levels\u0026nbsp;as an independent risk factor for colorectal polyps\u0026nbsp;in healthy individuals.\u003c/p\u003e\n\u003cp\u003eKim et al.\u0026nbsp;[19]\u0026nbsp;found\u0026nbsp;a significant correlation between colorectal adenomas and carotid atherosclerosis in Korean adults.\u0026nbsp;Consistent with this, our study identified carotid plaque as an independent risk factor for colorectal polyps in health\u0026nbsp;examination\u0026nbsp;population. Additionally,\u0026nbsp;previous research has reported that thyroid nodules are more prevalent in\u0026nbsp;subjects\u0026nbsp;with colon\u0026nbsp;polyps compared to those without, independent of insulin resistance\u0026nbsp;[20]. Although\u0026nbsp;no\u0026nbsp;significant differences in T3 or TSH levels\u0026nbsp;were detected\u0026nbsp;between the colorectal\u0026nbsp;polyp and non-polyp groups in our study, T4 levels\u0026nbsp;were significantly elevated in the colorectal polyp group, and thyroid nodules were more prevalent.\u0026nbsp;Thyroid nodules were identified as an independent risk factor for colorectal polyps in health\u0026nbsp;examination\u0026nbsp;population. The underlying mechanisms of these associations remain unclear but may be related to the high consumption of seafood in coastal regions, with dietary habits and metabolic disturbances potentially playing partial roles.\u003c/p\u003e\n\u003cp\u003eUnlike previous studies\u0026nbsp;[5,21], our univariate logistic analysis identified BMI and\u0026nbsp;UA\u0026nbsp;were\u0026nbsp;associated with colorectal polyps, but multivariate logistic regression did not confirm them as independent risk factors.\u0026nbsp;This discrepancy may stem from\u0026nbsp;differences in the study populations. In this study, the stool sample submission rate was 37.6% in the\u0026nbsp;health\u0026nbsp;examinaion\u0026nbsp;cohort, indicating suboptimal compliance.\u0026nbsp;FOB+\u0026nbsp;can result from diverse factors or underlying conditions\u0026nbsp;[22]\u0026nbsp;, and our analysis did not establish FOB+\u0026nbsp;as an independent risk factor for colorectal polyps. While several risk predictive\u0026nbsp;models for colorectal polyps or neoplastic polyps exist, most were developed using hospitalized patient data, with few focusing on health examination population.\u003c/p\u003e\n\u003cp\u003eBased on the identified risk factors, we established a nomogram predictive model for colorectal polyps in a health examination population. The model demonstrated good discriminatory ability (AUC=0.752) and fine calibration. Additionally, DCA indicated favorable clinical meaningful net benefit of the model.\u003c/p\u003e\n\u003cp\u003eHowever, this study has several limitations that must be acknowledged. First, its single-center, retrospective design inherently introduces the potential for selection bias and limits the control over confounding variables. Second, the lack of external validation means that the generalizability of our findings to broader populations and different clinical settings remains uncertain. Third, although variables such as lifestyle data, insulin use and aspirin use are important in the evaluation of polyps, these data were not available in our dataset. Their absence may limit the comprehensiveness and predictive power of our nomogram. Furthermore, We were unable to obtain the detailed histopathology reports for each polyp, which precluded a more nuanced analysis of risk factors based on polyp subtype.Therefore, to address these limitations, future research should involve large-scale, multi-center prospective studies that incorporate these critical variables. External validation of our nomogram in such cohorts, along with stratified subgroup analyses based on polyp pathology, is essential to confirm and extend our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates gender, age, fasting blood glucose, hyperhomocysteinemia, carotid artery plaque, and thyroid nodules are independent risk factors for colorectal polyps in asymptomatic populations. The developed nomogram demonstrates moderate discriminative performance and clinical utility, offering a practical tool to inform colorectal endoscopic screening decisions in health examination settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite blood cell count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbsolute neutrophil count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eScr\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSerum creatinine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUric acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlanine transaminase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAspartate aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDirect bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIBIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndirect bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlkaline phosphatase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGamma-glutamyl transferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFasting plasma glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHcy\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHomocysteine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAFP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlpha-fetoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCarcinoembryonic antigen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCA19-9\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCarbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCA125\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer antigen 125\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThyroid-stimulating hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal triiodothyronine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT4\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal thyroxine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFOB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFecal occult blood\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConcordance index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIFG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImpaired fasting glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the Declaration of Helsinki and\u0026nbsp;was approved by the Ethics Committee of Ningbo Zhenhai People’s Hospital (Approval No. 2025-010; Date:03/28/2025). The need for informed consent was waived due to the retrospective nature of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the correspongding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWLL and XL designed the research. WLL collected data, created the figures and wrote the manuscript. XL helped analyze and process the data. XHD reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249.\u003c/li\u003e\n \u003cli\u003eSung JJY, Chiu HM, Lieberman D, etal.Third AsiA- Pacific consensus recommendations on colorectal cancer screening and postpolypectomy surveillance. Gut.2022;71(11):2152-2166.\u003c/li\u003e\n \u003cli\u003eTan\u0026nbsp;XJ, Peng X, Qin J, et al. Influencing factors for colonoscopic compliance to colorectal cancer screening in outpatients. Chin J Dig Endosc. 2024;41(2):131-136.\u003c/li\u003e\n \u003cli\u003eLiss DT, Uchida T, Wilkes CL, et al. General health checks in adult primary care: a review. J Am Med Assoc. 2021;325(22):2294-2306.\u003c/li\u003e\n \u003cli\u003eGuo TT, Jia Z, Yu L, et al.Study on risk factors of colorectal polyps and establishment of clinical predictive model. Chin J Dig. 2023;43(9):626-630.\u003c/li\u003e\n \u003cli\u003eWu J, Bai Y, Lu Y, et al. Role of sex steroids in colorectal cancer: pathomechanisms and medical applications. Am J Cancer Res. 2024; 14(7):3200-3221.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang XY, Ji DN, Xiang P,et al.Construction and Validation of Risk Prediction Model for Colorectal Polyps. Chin J Gastroenterol. 2022;27(5):278-283.\u003c/li\u003e\n \u003cli\u003eExpert Group on Early Diagnosis and Treatment of Cancer, Chinese Society of Oncology, Chinese Medical Association. Expert consensus on the early diagnosis and treatment of colorectal cancer in China (2023 edition). Natl Med J China. 2023;103(48):3896-3908.\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes‑2024. Diabetes Care. 2024;47(Suppl 1):S20‑S42.\u003c/li\u003e\n \u003cli\u003eWu JJ, He H, Zhang Q, etal. Fasting blood glucose was linearly associated with colorectal \u0026nbsp; \u0026nbsp;cancer risk in population without self-reported diabetes mellitus history. Medicine (Baltimore). 2021;100(34): e26974.\u003c/li\u003e\n \u003cli\u003eHuang HE, Yang YC,Wu JS,et al.The relationship between different glycemic statuses and colon polyps in a taiwanese population. J Gastroenterol. 2014;49(7):1145-1151.\u003c/li\u003e\n \u003cli\u003eZhou F, He Y, Xie X, et al. Homocysteine and Multiple Health Outcomes: An Outcome-Wide Umbrella Review of Meta-analyses and Mendelian Randomization Studies. Adv Nutr. 2025; 16(6):100434.\u003c/li\u003e\n \u003cli\u003eMajumder A. Targeting homocysteine and hydrogen sulfide balance as future therapeutics in cancer treatment. Antioxidants (Basel) .2023;12(8):1520.\u003c/li\u003e\n \u003cli\u003eXie H, Wei L, Wang Q, et al. Elevated serum homocysteine levels associated with poor recurrence-free and overall survival in patients with colorectal cancer. Sci Rep. 2024; 14(1):10057.\u003c/li\u003e\n \u003cli\u003eWang J, Li L, Chen P, et al. Homocysteine aggravates intestinal inflammation through promotion of 5-LOX and COX-2 in IBD. Eur J Med Res.2024; 29(1): 537.\u003c/li\u003e\n \u003cli\u003eChen FP, Lin CC, Chen TT, et al. Higher plasma homocysteine is associated with increased risk of developing colorectal polyps. Nutr Cancer. 2013;65(2):195-201.\u003c/li\u003e\n \u003cli\u003eSun M, Sun M, Zhang L, et al. Colorectal polyp risk is linked to an elevated level of homocysteine. Biosci Rep. 2018;38(2):BSR20171699.\u003c/li\u003e\n \u003cli\u003eWang L. Clinical significance of homocysteine and folic acid in the diagnosis of colonic polyp and colon cancer. Chin J Prim Med Phar. 2020;27(16):1921-1924.\u003c/li\u003e\n \u003cli\u003eKim H, Cho YJ, Kim YA, et al. Association between colorectal adenoma and carotid atherosclerosis in Korean adults. Int J Environ Res Public Health. 2018;15(12):2762.\u003c/li\u003e\n \u003cli\u003eMousa U, Anil C, Demir CC, et al. Thyroid nodules are more prevalent in subjects with colon polyps, independent of insulin resistance. Med Princ Pract. 2019;28(5):418-424.\u003c/li\u003e\n \u003cli\u003eYu LL, Jin HY. Construction of a risk prediction model for colorectal polyps and its clinical value. Chin J Colorec Dis ( Electronic Edition ). 2022;11(5):409-414.\u003c/li\u003e\n \u003cli\u003eChen YW,Li YC.Examining the factors that affect the diagnosis of patients with positive fecal occult blood test results. Int J Environ Res Public Health. 2022;19:7569.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health examination, colorectal polyps, risk factors, predictive model, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7580576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7580576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo develop a risk prediction model for colorectal polyps in asymptomatic individuals and provide evidence-based guidance for colonoscopy screening prioritization within health examination cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We retrospectively analyzed 1,619 participants undergoing health examinations at Ningbo Zhenhai Lianhua Hospital (October 2022-December 2024). Based on endoscopic findings, 847 individuals with colorectal polyps were assigned to the case group, and 772 polyp-free individuals comprised the control group. Risk factors were identified through univariate and multivariate logistic regression analyses. A predictive nomogram was constructed using significant multivariate predictors. Model discrimination was evaluated via the receiver operating characteristic (ROC) curve and concordance index (C-index), while clinical utility was assessed using decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Multivariate analysis identified the following independent risk factors (all P\u0026lt;0.05): male sex (OR=2.587; 95% CI: 1.932-3.466), age ≥40 years (OR=4.821; 95% CI: 3.285-7.076) , elevated fasting plasma glucose (OR=1.441; 95% CI: 1.125-1.846) , hyperhomocysteinemia (OR=1.402; 95% CI: 1.031-1.908), carotid plaque (OR=1.614; 95% CI: 1.268-2.054) \u0026nbsp;and thyroid nodules (OR=1.575; 95% CI: 1.243-1.996) . The nomogram exhibited discriminative ability for predicting polyps, with an AUC of 0.752 (95% CI: 0.729-0.776) and a C-index of 0.752 (95% CI: 0.732-0.776). Calibration curves indicated satisfactory agreement between predicted and observed probabilities. DCA confirmed significant net clinical benefit across threshold probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study establishes a clinically applicable nomogram with moderate predictive performance for colorectal polyps, which may facilitate risk-stratified colonoscopy screening decisions in health examination populations.\u003c/p\u003e","manuscriptTitle":"Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 03:05:15","doi":"10.21203/rs.3.rs-7580576/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"321799b5-f16c-42b4-92e2-3c0a44a0e839","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-26T04:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 03:05:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7580576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7580576","identity":"rs-7580576","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}