Clinical Characteristics And Associated Factors Of Colonic Polyps In Acromegaly

Clinical Characteristics And Associated Factors Of Colonic Polyps In Acromegaly | 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 Clinical Characteristics And Associated Factors Of Colonic Polyps In Acromegaly Guiliang Peng, Xing Li, Yuanyuan Zhou, Jianying Bai, Pian Hong, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1371353/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 Purpose: This study investigates the clinical characteristics and the associated factors of colonic polyps in patients with acromegaly. Methods: We retrospectively reviewed clinical characteristics and colonoscopy findings of 86 acromegalic patients who received treatment between August 2015 and July 2020 at Xinqiao Hospital. We analyzed colonoscopy findings and correlation with growth hormone (GH)-secreting pituitary adenoma (GHPA) volume and hormonal/metabolic levels. Results: Our analysis revealed that the prevalence of colonic polyps in acromegalic patients was 40.7% and increased significantly with advanced age, especially ≥50 years. Multiple polyps (62.8%) and colonic polyps in the left colon (54.2%) were detected more frequently. Compared to acromegalic patients without polyps, patients with polyps displayed higher IGF-1×ULN levels (P=0.03). IGF-1 levels and GHPA volumes in patients with polyps showed increasing trends, although there were no significant differences. Meanwhile, patients with polyps in the right colon showed higher GH levels and GH nadir (GHnadir) levels in the oral glucose tolerance test (OGTT) than those with polyps in the whole colon (P=0.023 and P=0.037, respectively). GH levels were higher in patients with polyps diameter ≤5mm than those with polyps diameter >5mm (P=0.031). In addition, the univariate and multivariate logistic regression analysis exhibited GHPA volumes (OR: 1.09, 95% CI: 1.01–1.20; P = 0.039) and IGF-1×ULN Q2 levels (OR: 6.51, 95% CI: 1.20–44.60; P =0.038) were independent factors for predicting the risk of colonic polyp occurrence in acromegalic patients. A nomogram was created to evaluate the risk of colonic polyps in acromegalic patients. Conclusion: The acromegalic patients are a high prevalence population of colonic polyps. Colonic polyps in acromegaly were multiple and frequently detected in the sigmoid colon and rectum. GHPA volumes and IGF-1×ULN levels may be predictors of colonic polyp occurrence. Acromegaly Colonic polyps Growth hormone (GH) Colonoscopy Insulin-like growth factor-1(IGF-1) Figures Figure 1 Figure 2 Introduction Acromegaly is a chronic endocrine and metabolic disease, accompanied by excessive secretion of GH and insulin-like growth factor-1 (IGF-1) by growth hormone (GH)-secreting pituitary adenoma (GHPA) [ 1 , 2 ]. The increased mortality rate in acromegaly results from cardiovascular and cerebrovascular diseases, respiratory complications, and neoplastic complications such as colorectal cancer [ 3 , 4 ]. Colonic polyps and diverticula are typical digestive complications in acromegaly. The significantly increased risk of colonic polyps in patients with acromegaly compared with the general population is well recognized [ 5 ]. The prevalence of colonic polyps in acromegalic patients reported a wider range from 7 to 76% [ 6 – 9 ], while there is still a lack of epidemiological data in China. Most colorectal cancer derives from an “adenomatous polyp-carcinoma sequence,” and the process generally takes 10–15 years [ 10 ]. However, according to the current findings, colorectal cancer occurrence remains controversial in acromegaly. A nationwide survey in Italy reported an overall standardized incidence ratio (SIR) for colorectal cancer of 1.67 (95% CI: 1.07–2.58) [ 11 , 12 ]. At the same time, population-based studies did not identify any significant risk of colorectal cancer [ 13 ]. In acromegalic patients, a better understanding of digestive diseases, especially colonic polyp developments, is critical for early diagnosis and clinical intervention of colorectal cancer. To gain insights into the clinical characteristics and the associated factors of colonic polyps in acromegaly. In this retrospective study, we collected the clinical data of 86 acromegalic patients who underwent a colonoscopy at diagnosis in our center. We analyzed the prevalence, number, size, and site distribution of colonic polyps and other clinical indicators. Furthermore, we identified the associated risk factors of colonic polyps in patients with acromegaly. Materials And Methods Patients We retrospectively collected data of acromegalic patients followed at the Second Affiliated Army Medical University (Xinqiao Hospital) from August 2015 to July 2020, and 86 patients (44 males and 42 females) who underwent a colonoscopy at diagnosis were included in this study. Acromegaly was diagnosed according to the criteria available at the time of diagnosis as follows [ 14 , 15 ]: 1) evidence of clinical signs and symptoms of the disease; 2) serum IGF-I levels beyond the normal range for age- and sex-matched control individuals, and elevated baseline GH level; 3) maximally suppressed GH levels (GHnadir) during a 75-g oral glucose load test (OGTT) were > 1ug/L; and 4) evidence of a pituitary tumor on imaging. No patient had a family history of colon cancer. This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Army Medical University (No. 2021-035-01) and registered in the Chinese Clinical Trial Registry (No. ChiCTR-1800017714). All the participants provided written informed consent. Data Collection Clinical data, including age, sex, height, weight, body mass index (BMI), histological results, site distribution, size, and number of polyps, were collected. Blood samples were obtained after an overnight fast. Glucose metabolic profiles were determined, including fasting blood glucose (FBG) and glycosylated hemoglobin A1c (HbA1c) levels. The automatic biochemical analyzer measured the creatinine (CREA) level, estimated the glomerular filtration rate (EGFR), and measured the levels of uric acid (UA), cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Growth hormone (GH) and insulin-like growth factor (IGF-1) levels were quantified using chemiluminescent immunoassays. The outcome of the IGF-1 assay in each patient was represented by the IGF-1 index (IGF-1×ULN): serum IGF-1/upper limit of IGF-I for age [ 9 ]. Each patient consumed a 75-g glucose beverage in 5 min, and blood samples were collected before the start of the test (0 min) and 30, 60, 90, 120, and 180 minutes after the 75-g glucose intake. GHnadir levels during OGTT corresponded to maximally suppressed GH levels during the 180 min-OGTT. The pituitary was imaged with a 3T MRI scanner with or without gadolinium-DTPA (1.0 mmol/kg). The anteroposterior diameter (AD), vertical diameter (VD), and transverse diameter (TD), as well as the Knosp classifications of the GHPA, were evaluated by two experienced radiologists using precision calipers. The GHPA volume was calculated using the formula for approximating the volume of an ellipsoid: π/6 × AD× VD × TD [ 16 ]. Experienced gastroenterologists performed colonoscopies in acromegalic patients after careful bowel preparation with a 2 L dose of polyethylene glycol electrolyte-based solution (Shenzhen Wanhe Pharmaceutical Co., Ltd., Shenzhen, China). All colonic polyps on colonoscopy were recorded, and if possible, removed for histological examination. Age was divided into a categorical variable with four groups, namely, ≤ 39, 40–49, 50–59, and ≥ 60 years. The site distribution of polyps was defined as the right colon (the cecum, ascending colon, and hepatic flexure), the left colon (the splenic flexure, descending colon, sigmoid colon, and rectum), and the whole colon (both the right colon and the left colon) [ 10 ]. All colonic polyps detected at colonoscopy were grouped into size categories (≤ 0.5, 0.6–0.9, and ≥ 1.0 cm) depending on endoscopic measurement by the diameter of open biopsy forceps [ 17 ]. The number of polyps was divided into a categorical variable (single or multiple (≥ 2)). Statistical analysis Analyses were conducted by R studio (version 1.3.1093). The Shapiro-Wilk W test verified the normality of the variable distribution: variable distribution was considered normal if P ≥ 0.05. According to the distribution of the variables, variables were described either as means ± SD or medians with interquartile ranges (IQR). For normally distributed variables, we used an independent samples t -test to compare variables between two groups, whereas one-way ANOVA, followed by Tukey’s multiple comparison test, was used to compare variables among three or more groups. For non-normally-distributed variables, the Mann-Whitney U test was used to compare variables between two groups, whereas the Kruskal-Wallis test, followed by pairwise comparisons using the BWS all-pairs test, was used for multiple subgroups. The relationships between variables were examined using Spearman’s correlation analysis. Univariate and multivariate logistic regression analyses were performed to assess the associations of the variables with the diagnosis. A nomogram was created using the predictors from the multivariate analysis for relating the risk of polyp occurrence. The tests were considered statistically significant at P < 0.05. Results Clinical features of acromegalic patients with colonic polyps A total of 86 acromegalic patients (mean age, 43.53 ± 11.78 years; sex, 48.8% females) underwent complete colonoscopy. Of the 86 patients, 35 patients (18 females, 17 males) were found to have one or more polyps, with a higher prevalence (40.7%) than a general Asian population (17.6–23.9%) of comparable age [17-20]. The prevalence of polyps increased with age, reaching a peak at ≥60 years. The prevalence rates of polyps in acromegalic patients aged ≤39, 40–49, 50–59, and ≥60 years were 30, 32, 50, and 77.8%, respectively (Fig. 1). Of the 35 patients, 13 (37.2 %) and 22 (62.8%) patients had a single polyp and multiple polyps, respectively. The mean diameter of the polyps was in most cases ≤5 mm (71.4%), and the maximum diameter of the polyps was ≥10 mm. With respect to the different site distributions of the colonic polyps, 4 (11.4%) patients had polyps in the right colon, 19 (54.3%) patients had polyps in the left colon, and 12 (34.3%) patients had polyps in the whole colon. Colonic polyps were more frequently detected in the sigmoid colon and rectum. In addition, only 5 cases of colonic polyps were biopsied, and all of them were histologically confirmed to be adenomas. Furthermore, 21 patients were observed to have hemorrhoids, and 1 patient was observed to have chronic colitis. There were no patients with colorectal carcinoma (Table 1). At diagnosis, 79 (92%) patients had pituitary macroadenoma, 3 (3%) patients had pituitary microadenoma, and 4 (5%) patients had no data on the pituitary tumor diameter. Hypertension, impaired glucose metabolism (diabetes mellitus and impaired glucose tolerance), and dyslipidemia were separately diagnosed in 24 (28%), 47 (55%), and 34 (40%) patients, respectively (Table 1). Characteristics of colonic polyps associated with hormone levels The results showed that acromegalic patients with colonic polyps were older and had higher IGF-1×ULN levels than those without colonic polyps (P=0.005 and P=0.03, respectively, Table 1). No significant differences were found between acromegalic patients with or without colonic polyps regarding sex, BMI, FBG levels, dyslipidemia, hypertension, diabetes mellitus, GHPA volumes, Knosp classifications, UA levels, GH levels, GHnadir levels during OGTT, and IGF-1 levels (Table 1 and Supplementary Table 1). Although GH levels and GHnadir levels during OGTT were similar in patients with or without colonic polyps, GHPA volumes and IGF-1 levels tended to be higher in those with polyps, although there were no statistical differences between the two groups (Table 1). As shown in Table 2, with respect to the different site distribution of polyps, GH levels, and GHnadir levels during OGTT in patients with polyps in the right colon were higher than those in patients with polyps in the whole colon (P=0.023 and P=0.037, respectively). Interestingly, compared with polyp diameters >5 mm, GH levels were significantly higher in patients with polyp diameters ≤5 mm (P=0.031, Table 2). Apart from the BMI (P=0.013), we did not identify relevant clinical indicators between the two groups with single polyps and multiple polyps (Table 2). GHPA volumes, IGF-1 levels, and IGF-1× ULN levels were similar among subjects with different site distribution, size, and number of polyps. In addition, with respect to the different site distribution of polyps, hypertension and diabetes mellitus had a significant association with the polyps in the right colon, left colon, and whole colon (P = 0.003 and P = 0.008, respectively, Table 2). However, FBG and HbA1c had observed no difference with respect to three different site distributions of polyps (Supplementary Table 2). Next, to investigate the effect of IGF-1×ULN in acromegalic patients with polyps, we further divided the subjects into IGF-1×ULN-quantile subgroups according to the IGF-1×ULN level (Table 3). Age, GH levels, and GHnadir levels during OGTT were significantly higher in acromegalic patients with the highest IGF-1×ULN levels than those with the lowest IGF-1×ULN levels in the first quantile (P<0.05 for Q1 vs Q4). Compared to patients with lower IGF-1×ULN levels in the second quantile, UA levels were significantly higher in patients with the highest IGF-1×ULN levels (P<0.05 for Q2 vs Q4). Notably, there were changes in the IGF-1×ULN-quantile subgroups with respect to acromegalic patients with or without colonic polyps, GHPA volumes groups, and IGF-1 levels (P<0.05, Table 3). Prognostic model for colonic polyps in acromegalic patients In univariate analysis, age, especially 60 years and older, and the IGF-1×ULN level predicted polyp occurrence in acromegalic patients as shown in Table 4. Furthermore, in multivariate analysis, these variables showed that GHPA volumes (OR: 1.09, 95% CI: 1.01–1.20; P=0.039) and IGF-1×ULN Q2 levels (OR: 6.51, 95% CI: 1.20–44.60; P =0.038) were independently associated with polyp occurrence (Table 4). IGF-1×ULN Q3 and IGF-1×ULN Q4 cloud also are independent risk predictors, although no significant difference was found. Thus, univariate and multivariate analyses revealed that GHPA volumes and IGF-1×ULN levels could be independent risk factors for polyps in acromegalic patients. In addition, a nomogram for predicting acromegalic patients with polyp risk was constructed using the variables (Fig 2). According to the prognostic model, a man with a GHPA volume of 3.5 cm³ and an IGF-1×ULN level of 5.5 was predicted to have an 80% probability of polyps. Discussion According to several population-based studies, the incidence of colon cancer in patients with acromegaly ranges from 0.9 to 2.4% [ 12 , 21 , 22 ]. However, the risk of colon cancer in acromegaly is still controversial. Colonoscopy screening can reduce the mortality from colorectal cancer by early detection and removal of pre-existing adenomatous polyps. In this study, we ascertained a high prevalence of colonic polyps was 40.7% in acromegalic patients. IGF-1×ULN levels were higher in patients with polyps than in those without polyps. GHPA volumes and IGF-1×ULN levels were found to be independent risk factors for polyps. Colonic polyps had a high prevalence in acromegalic patients. In this study, the overall prevalence of colonic polyps was 40.7%. The prevalence of colonic polyps in the acromegalic population was significantly higher than in the Asian general population. In the non-acromegalic population, the prevalence of polyps ranged from 17.6 to 23.9% [ 17 – 20 ]. The prevalence of colonic polyps also varied in different acromegalic populations. One of the largest datasets from 14 centers across Europe indicated that the prevalence of polyps in a fourth of acromegalic patients (820/3173) who had a colonoscopy was 13% [ 23 ]. Furthermore, the prevalence of polyps in patients with acromegaly was 32% in an Italian single-center study [ 24 ]. In contrast, French national registry data reported that the prevalence of colonic polyps ranged from 27–55% [ 22 ]. The different prevalence of colonic polyps may be due to differences in genetic predisposition, environmental backgrounds, and lifestyle (including dietary habits). The prevalence of multiple polyps was 62.8% compared to earlier reports by Bogazzi (50%) [ 9 ]and Colao (72.1%) [ 8 ]. A colonic polyp diameter > 10 mm is generally considered a high risk of colorectal carcinoma and thought to take more than ten years. A recent study reported the frequency of polyps ≥ 10 mm was 15.2%, and 5 patients were detected colorectal cancer [ 25 ]. However, the maximum diameter of colonic polyps was < 10 mm, and most colonic polyps were at early stages ≤ 5mm (71.4%), which may explain why no colorectal cancer was found in our study. We found a significant association between polyp occurrence and hormone values. In detail, IGF-1×ULN levels in patients with polyps were higher than those without polyps, consistent with previous findings [ 22 ]. Although GH and GHnadir levels during OGTT were similar in patients with or without colonic polyps, IGF-1 levels and GHPA volumes tended to be higher in those with polyps. Nevertheless, few studies have consistently suggested that IGF-1 levels are significantly related to polyp prevalence [ 24 , 26 ]. This study also indicated that GH and GHnadir levels during OGTT were higher in patients with polyps in the right colon and a diameter ≤ 5 mm. As a result, we hypothesize that GH levels may play different roles in different stages of polyp development. The findings in patients with acromegaly suggest a higher prevalence of polyps in the left colon, similar to Battistone et al. [ 27 ]. In the general population, previous literature also reported that the site distribution of polyps was detected mainly in the left colon [ 17 , 28 ]; however, several studies observed polyps in the cecum and ascending colon [ 29 – 31 ]. Additionally, the acromegalic patients with multiple polyps were higher BMI levels than those with single polyps. Compared to patients without polyps, the age in patients with polyps was higher. Simultaneously, the prevalence of colonic polyps increased significantly with age ≥ 50 years (> 50%). Bogazzi [ 9 ] and Parolin [ 24 ] also reported similar trends in polyp prevalence when patients were ≥ 50 years old. Although the recent guidelines recommend the initiation of colonoscopy screening at 50 years of age for the average-risk non-acromegalic population [ 32 , 33 ], this may be inadequate for patients with acromegaly. A few guidelines have suggested that the initial colonoscopy should be performed at the age of 40 years to early detect precancerous polyps in acromegaly [ 31 , 34 , 35 ]. However, we observed that several patients younger than 40 years of age had colonic polyps. Similarly, Terzolo et al. reported younger acromegalic patients had a higher risk of colonic neoplasia than those with age-matched controls [ 7 ]. Therefore, colonoscopic surveillance should be performed at the time of diagnosis in acromegaly as recent guideline recommendations [ 36 – 39 ]. GHPA volumes may be a reliable predictor for polyp occurrence in acromegaly. According to univariate analysis and clinical parameters, the final diagnostic model was decided using age, height, GHPA volumes, and IGF-1×ULN levels. To our knowledge, this is the first study reporting that GHPA volumes (OR: 1.09, 95% CI: 1.01–1.20; P = 0.039) were an independent risk factor for colonic polyp occurrence. However, GHPA volumes did not differ significantly between patients with or without polyps. We speculate that the larger GHPA volumes were indicative of long-term and uncontrolled secretion of GH and IGF-1 contributing to the higher risk of polyp occurrence. Meanwhile, we found that IGF-1×ULN levels (OR: 6.51, 95% CI: 1.20–44.60; P = 0.038) were a predictor for polyp occurrence similar to the results of Gonzalez [ 26 ]. Therefore, we established a polyp risk prediction model according to GHPA volumes and IGF-1×ULN levels. For example, a woman with a GHPA volume of 3.5 cm³ and an IGF-1×ULN level of 5.5 was predicted to have a probability of polyps by approximately 80%. In this study, alone standard intestinal preparation resulted in inadequate intestinal cleansing in most acromegalic patients, seriously affecting the detection of colonoscopy (data not shown). Previous studies have suggested two consecutive bowel preparations or an increased dose of PEG solution for acromegalic patients [ 40 ], and the time required to reach the cecum during colonoscopy was significantly prolonged [ 41 ]. Furthermore, we acknowledge several limitations in our study. First, this study was relatively small, and the absence of a control group. Second, this was a retrospective analysis, and selectivity bias was impossible to avoid. Third, most polyps with a diameter > 5 mm were not biopsied. Further long-term prospective studies involving acromegalic patients may determine whether colonic polyps have a similar tendency to develop into colon cancer as in the general population. In conclusion, we confirmed the high prevalence of colonic polyps in patients with acromegaly. Older age, multiple, and usually occurring in the left colon were the clinical features of acromegalic patients with colonic polyps. In addition, GHPA volumes and IGF-1×ULN levels might predict colonic polyp occurrence in the acromegalic population. Declarations Ethics declarations Disclosure Statement: There were no conflicts of interest to declare. Statement of Ethics: This study was approved by the Medical Ethics Committee of The Second Affiliated Hospital of Army Medical University. Funding Sources: This work was supported by grants from the Clinical Research Project of Army Medical University (2019XLC2009 and 2018XLC3049). Informed consent: Informed consent was obtained from all individual participants included in the study. Author Contributions GL.P., P.H., and YL.Z.: acquired the data; RF.S. and L.Z.: M.R. imaging assessment; YY.Z., JY.B., and GL.P.: colonoscopy assessment; GL.P., X.L., RF.S.: did the statistical data analysis and draft the manuscript; L.Z., MY.L., YL. Z. And WX.L.: interpreted the data, contributed to the methods, and performed the laboratory analyses; M.L., HT.Z., and Z.S.: revised the manuscript; RF.S. and M.L.: obtained the study funding and supervised the study. All authors read and approved the manuscript for publication. References A. Ben-Shlomo, S. Melmed.(2008) Acromegaly. Endocrinol Metab Clin North Am.371:101 – 22, viii. https://doi.org/10.1016/j.ecl.2007.10.002 S. Melmed(2009) Acromegaly pathogenesis and treatment. J. Clin. Invest..11911:3189 – 202. https://doi.org/10.1172/jci39375 M. Sherlock, J. Ayuk, J.W. Tomlinson, A.A. Toogood, A. 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Wu, Prevalence of advanced colonic polyps in asymptomatic Chinese. World J. Gastroenterol. 1130 , 4731–4734 (2005). https://doi.org/10.3748/wjg.v11.i30.4731 B. Delhougne, C. Deneux, R. Abs, P. Chanson, H. Fierens et al., The prevalence of colonic polyps in acromegaly: a colonoscopic and pathological study in 103 patients. J. Clin. Endocrinol. Metab. 8011 , 3223–3226 (1995). https://doi.org/10.1210/jcem.80.11.7593429 A.G. Renehan, P. Bhaskar, J.E. Painter, S.T. O'Dwyer, N. Haboubi et al., The prevalence and characteristics of colorectal neoplasia in acromegaly. J. Clin. Endocrinol. Metab. 859 , 3417–3424 (2000). https://doi.org/10.1210/jcem.85.9.6775 K. Lois, J. Bukowczan, P. Perros, S. Jones, M. Gunn et al.(2015) The role of colonoscopic screening in acromegaly revisited: review of current literature and practice guidelines. Pituitary.184:568 – 74. https://doi.org/10.1007/s11102-014-0586-5 A. Qaseem, T.D. Denberg, R.H. Hopkins Jr., L.L. Humphrey, J. 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Takahashi et al.(2015) The prevalence and associated factors of colorectal neoplasms in acromegaly: a single center based study. Pituitary.183:343 – 51. https://doi.org/10.1007/s11102-014-0580-y Tables Table 1. Clinical characteristics of the study patients with acromegaly. Total Patients (n = 86) Patients without polyps (n = 51) Patients with polyps (n = 35) P value Age (years) 43.53 ±11.78 40.63 ±11.58 47.77 ±10.88 0.005* Sex, female, n (%) 42 (49) 24 (47) 18 (51) 0.858 BMI (Kg/m2) 26.1 ±3.05 25.96 ±3.05 26.31 ±3.09 0.602 GHPA Volumes (cm³) 2.47 (1.15, 6.44) 2.2 (1.17, 4.87) 3.19 (1.15, 7.31) 0.305 Basal GH levels (ug/L) 18.3 (8.08, 34.5) 17.8 (5.94, 29.8) 19.3 (9.7, 52.5) 0.153 GHnadir levels during OGTT (ug/L) 12.1 (5.24, 30.9) 12 (4.41, 25.2) 13 (6.54, 40.85) 0.18 Basal IGF-1 levels (ng/mL) 762.56 ±259.66 733.73 ±259.89 804.57 ±257.24 0.215 IGF-1×ULN 2.53 (1.94, 3.01) 2.22 (1.79, 2.88) 2.73 (2.1, 3.2) 0.03* FBG (mmol/L) 5.07 (4.4, 5.96) 4.95 (4.38, 5.82) 5.33 (4.54, 6.29) 0.34 Hemorrhoids,n (%) 21(24) 7(20) 14(27) 0.403 Hypertension, n (%) 24 (28) 15 (29) 9 (26) 0.896 Diabetes mellitus, n (%) 0.708 non-diabetes mellitus 25 (29) 14 (27) 11 (31) IGT 39 (45) 25 (49) 14 (40) DM 22 (26) 12 (24) 10 (29) Data are shown as mean ± SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables. BMI: body mass index; GHPA Volume: growth hormone (GH)-secreting pituitary adenoma volume; GH: growth hormone; GHnadir levels during OGTT: the maximal suppression of GH levels during 180-OGTT, IGF-1: insulin-like growth factor; ULN = upper limit of normal; FBG: fasting blood glucose; IGT: impaired glucose tolerance; DM: diabetes mellitus. P value for Patients without polyps vs Patients with polyps, * p＜0.05. Table 2. Clinical characteristics of acromegalic patients with polyps. the site distribution of polyps the size of polyps the number of polyps right colon (n = 4) left colon (n = 19) whole colon (n = 12) P value ≤5mm (n = 25) >5mm (n = 10) P value Single polyp (n = 13) Multiple polyps (n = 22) P value Age (years) 49.25 ±13 44.58 ±10.47 52.33 ±9.95 0.148 47 ± 11.43 49.7 ± 9.62 0.486 43.23 ±11.73 50.45 ±9.62 0.056 Sex (Female), n (%) 4 (100) 8 (42) 6 (50) 0.164 13 (52) 5 (50) 1 7 (54) 11 (50) 1 BMI (kg/m2) 27.09 ±3.74 26.19 ±3.56 26.24 ±2.19 0.873 25.95 ± 3.42 27.2 ± 1.91 0.182 24.66 ±3.25 27.28 ±2.6 0.013* GHPA Volumes (cm³) 4.83 (3.55, 5.88) 3.48 (1.3, 12.63) 1.83 (0.96, 6.91) 0.531 3.48 (1.25, 6.75) 2.22 (0.54, 8.81) 0.615 3.59 (1.81, 7.46) 2.95 (1.05, 6.86) 0.366 Basal GH levels (ug/L) 127.5 (70.33, 189.5) c 17 (13.05, 33.95) 11.95 (7.8, 33.08) 0.023* 26.6 (14.5, 61.4) 9.7 (8.08, 19.1) 0.031* 35.3 (14, 82) 16.45 (8.2, 27.57) 0.142 GHnadir levels during OGTT (ug/L) 73.9 (44.7, 111.1) c 12.4 (8.59, 37.2) 8.75 (5.02, 24.25) 0.037* 21.2 (8.76, 48) 8.7 (4.52, 12.32) 0.05 21.2 (10.3, 48) 11.25 (4.43, 28.23) 0.124 Basal IGF-1 (ng/mL) 731.75 ±261.15 869 ±267.81 726.83 ±229.95 0.279 802.56 ± 271.42 809.6 ± 231.27 0.939 808.54 ±247.11 802.23 ±268.75 0.945 IGF-1×ULN 2.92 (1.84, 3.93) 2.83 (2.22, 3.2) 2.59 (2.36, 2.97) 0.764 2.84 ± 1.05 2.94 ± 0.85 0.773 2.72 (2.08, 3.16) 2.78 (2.42, 3.22) 0.585 Hypertension, n (%) 1 (25) a,b 1 (5) c 7 (58) 0.003* 6 (24) 3 (30) 0.694 1 (8) 8 (36) 0.109 DM, n (%) a,b, c 0.008* 0.33 0.455 Non-DM 0 (0) 11 (58) 3 (25) 12 (48) 2 (20) 6 (46) 8 (36) IGT 0 (0) 6 (32) 4 (33) 6 (24) 4 (40) 2 (15) 8 (36) DM 4 (100) 2 (11) 5 (42) 7 (28) 4 (40) 5 (38) 6 (27) Data are shown as mean ± SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables. BMI: body mass index; GHPA Volume: growth hormone (GH)-secreting pituitary adenoma volume; GH: growth hormone; GHnadir levels during OGTT: the maximal suppression of GH levels during 180-OGTT, IGF-1: insulin-like growth factor; ULN = upper limit of normal; *: P<0.05. a: P<0.05 for right colon vs left colon. b: P<0.05 for right colon vs whole colon. c: P<0.05 for left colon vs whole colon. Table 3. Clinical characteristics and biochemical variables in acromegalic patients of different IGF-1×ULN quartiles. IGF-1×ULN quartiles Q1 (n = 21) Q2 (n = 21) Q3 (n = 22) Q4 (n = 22) P value Age (years) 37.38 ± 13.35 c 45.19 ± 12.02 45 ± 10.18 46.36 ± 9.96 0.048* Sex, n (%) 0.72 Male 9 (43) 10 (48) 12 (55) 13 (59) Female 12 (57) 11 (52) 10 (45) 9 (41) BMI (Kg/m2) 25.11 ± 2.92 25.76 ± 3.46 26.37 ± 2.88 27.1 ± 2.78 0.174 GHPA Volumes (cm³) 5.19 (0.83, 7.95) 2.33 (1.19, 3.38) 2.73 (1.72, 6.5) 1.81 (1.12, 4.33) 0.779 Basal GH levels (ug/L) 9.12 (4.87, 21.8) c 17 (10.5, 29.5) 17.15 (8.08, 29.42) 27.45 (19.25, 55.95) 0.018* GHnadir levels during OGTT (ug/L) 5.85 (2.83, 12.5) c 13.8 (11.3, 26.5) 8.59 (5.53, 22.78) 27.25 (10.83, 43.38) 0.023* Basal IGF-1 levels (ng/mL) 501.24 ± 133.91 a,b,c 653.19 ± 131.4 d,e 807.36 ± 134.64 f 1071.59 ± 197.13 < 0.001* IGF-1×ULN 1.59 (1.33, 1.8) a,b,c 2.11 (2.03, 2.32) d,e 2.81 (2.72, 2.88) f 3.85 (3.16, 4.2) < 0.001* UA (umol/L) 259.4 (220.7, 348.8) 259.1 (221.6, 299.5) e 301.2 (238.48, 389.7) 332.6 (279.68, 389.88) 0.048* Patients with polyps, n (%) 3 (14) a,b,c 10 (48) d,e 10 (45) f 12 (55) 0.038* GHPA Volume group(cm³), n (%) a,b,c, d,e, f 0.018* <1 7 (33) 3 (14) 3 (14) 4 (18) 1–4 2 (10) 13 (62) 11 (50) 11 (50) ≥ 4 12 (57) 5 (24) 8 (36) 7 (32) Data are shown as mean ± SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables. BMI: body mass index; GHPA Volume: growth hormone (GH)-secreting pituitary adenoma volume; GH: growth hormone; GHnadir levels during OGTT: the maximal suppression of GH levels during 180-OGTT, IGF-1: insulin-like growth factor; ULN: upper limit of normal. *: P<0.05. a. P<0.05 for Q1 vs Q2; b. P<0.05 for Q1 vs Q3; c. P<0.05 for Q1 vs Q4; d. P<0.05 for Q2 vs Q3; e. P<0.05 for Q2 vs Q4; f. P<0.05 for Q3 vs Q4. Table 4. Univariate and multivariable logistic regression analysis of associations between clinical and biochemical variables and polyps. Univariate analysis Multivariate analysis OR (CI 95%) P value OR (CI 95%) P value Age 1.06 (1.02, 1.11) 0.007 1.05 (0.99, 1.12) 0.11 sex 1.19 (0.50, 2.84) 0.691 - - Height 0.02 (0.00, 2.03) 0.102 0.03 (0.00, 16.03) 0.286 Weights 0.98 (0.94, 1.02) 0.397 - - BMI 1.04 (0.90, 1.20) 0.597 - - GHPA Volumes 1.06 (0.99, 1.15) 0.136 1.09 (1.01, 1.20) 0.039 Basal GH levels 1.00 (1.00, 1.01) 0.278 - - GHnadir levels during OGTT 1.00 (1.00, 1.01) 0.22 - - Basal IGF-1 levels 1.00 (1.00, 1.00) 0.216 - - IGF-1×ULN 1.65 (1.04, 2.75) 0.04 1.47 (0.88, 2.55) 0.151 FBG 1.07 (0.86, 1.34) 0.548 - - TG 0.84 (0.50, 1.10) 0.394 - - TC 0.77 (0.49, 1.10) 0.21 - - HDL 1.96 (0.33, 12.46) 0.459 - - LDL 0.89 (0.47, 1.64) 0.71 - - UA 1.00 (1.00, 1.01) 0.702 - - Age Group 0.011 ≤39 1 1 40–50 1.10 (0.34, 3.48) 0.873 - - 50–60 2.33 (0.75, 7.53) 0.147 - - >60 8.17 (1.61, 62.58) 0.019 - - IGF-1×ULN Quartile 0.015 Q1 1 1 Q2 5.45 (1.34, 28.54) 0.026 6.51 (1.20, 44.60) 0.038 Q3 5.00 (1.24, 25.93) 0.033 5.82 (0.71, 55.33) 0.106 Q4 7.20 (1.80, 37.50) 0.009 10.52 (0.42, 300.62) 0.152 OR, odd ratio; CI, confidence interval. 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Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2022-02-18 03:06:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1371353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1371353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18586853,"identity":"de95be8f-7875-4e1f-a86a-6d72435c8998","added_by":"auto","created_at":"2022-02-24 21:37:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226985,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between age (years) and Prevalence of polyps (%). Age was parsed into a categorical variable with four groups: ≤39, 40–49, 50–59, and ≥60 years. Prevalence (%) of polyposis according to Renehan, et al. [30]; Matano, et al. [42]; Yamamoto, et al. [43] and this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1371353/v1/191c2e5fad4ce3bb03eb37ca.png"},{"id":18586652,"identity":"eea1467d-f4f9-4d28-8dc4-adba27b517b3","added_by":"auto","created_at":"2022-02-24 21:34:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131973,"visible":true,"origin":"","legend":"\u003cp\u003eConstructed colonic polyp incidence risk nomogram. The colonic polyp risk was constructed with the features, including GHPA volume, IGF-1× ULN. GHPA Volume: growth hormone (GH)-secreting pituitary adenoma volume; IGF-1: insulin-like growth factor; ULN: upper limit of normal.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1371353/v1/bbef9d2278b8f3ff0c4eb730.png"},{"id":19227833,"identity":"686d4098-e5c5-4e9c-9caf-02c6b6478b0e","added_by":"auto","created_at":"2022-03-15 07:41:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1371353/v1/92f5da4f-2a68-48a6-808c-57cc9a5a5745.pdf"},{"id":18586653,"identity":"ea2f41b0-33a1-4805-99f2-e888903814c1","added_by":"auto","created_at":"2022-02-24 21:34:56","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":36393,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables2022.docx","url":"https://assets-eu.researchsquare.com/files/rs-1371353/v1/0006e4c7c2330930b674725e.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eClinical Characteristics And Associated Factors Of Colonic Polyps In Acromegaly\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcromegaly is a chronic endocrine and metabolic disease, accompanied by excessive secretion of GH and insulin-like growth factor-1 (IGF-1) by growth hormone (GH)-secreting pituitary adenoma (GHPA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The increased mortality rate in acromegaly results from cardiovascular and cerebrovascular diseases, respiratory complications, and neoplastic complications such as colorectal cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eColonic polyps and diverticula are typical digestive complications in acromegaly. The significantly increased risk of colonic polyps in patients with acromegaly compared with the general population is well recognized [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The prevalence of colonic polyps in acromegalic patients reported a wider range from 7 to 76% [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while there is still a lack of epidemiological data in China. Most colorectal cancer derives from an \u0026ldquo;adenomatous polyp-carcinoma sequence,\u0026rdquo; and the process generally takes 10\u0026ndash;15 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, according to the current findings, colorectal cancer occurrence remains controversial in acromegaly. A nationwide survey in Italy reported an overall standardized incidence ratio (SIR) for colorectal cancer of 1.67 (95% CI: 1.07\u0026ndash;2.58) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. At the same time, population-based studies did not identify any significant risk of colorectal cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In acromegalic patients, a better understanding of digestive diseases, especially colonic polyp developments, is critical for early diagnosis and clinical intervention of colorectal cancer.\u003c/p\u003e \u003cp\u003eTo gain insights into the clinical characteristics and the associated factors of colonic polyps in acromegaly. In this retrospective study, we collected the clinical data of 86 acromegalic patients who underwent a colonoscopy at diagnosis in our center. We analyzed the prevalence, number, size, and site distribution of colonic polyps and other clinical indicators. Furthermore, we identified the associated risk factors of colonic polyps in patients with acromegaly.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e We retrospectively collected data of acromegalic patients followed at the Second Affiliated Army Medical University (Xinqiao Hospital) from August 2015 to July 2020, and 86 patients (44 males and 42 females) who underwent a colonoscopy at diagnosis were included in this study. Acromegaly was diagnosed according to the criteria available at the time of diagnosis as follows [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]: 1) evidence of clinical signs and symptoms of the disease; 2) serum IGF-I levels beyond the normal range for age- and sex-matched control individuals, and elevated baseline GH level; 3) maximally suppressed GH levels (GHnadir) during a 75-g oral glucose load test (OGTT) were \u0026gt;\u0026thinsp;1ug/L; and 4) evidence of a pituitary tumor on imaging. No patient had a family history of colon cancer.\u003c/p\u003e \u003cp\u003eThis study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Army Medical University (No. 2021-035-01) and registered in the Chinese Clinical Trial Registry (No. ChiCTR-1800017714). All the participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003eClinical data, including age, sex, height, weight, body mass index (BMI), histological results, site distribution, size, and number of polyps, were collected. Blood samples were obtained after an overnight fast. Glucose metabolic profiles were determined, including fasting blood glucose (FBG) and glycosylated hemoglobin A1c (HbA1c) levels. The automatic biochemical analyzer measured the creatinine (CREA) level, estimated the glomerular filtration rate (EGFR), and measured the levels of uric acid (UA), cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Growth hormone (GH) and insulin-like growth factor (IGF-1) levels were quantified using chemiluminescent immunoassays. The outcome of the IGF-1 assay in each patient was represented by the IGF-1 index (IGF-1\u0026times;ULN): serum IGF-1/upper limit of IGF-I for age [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Each patient consumed a 75-g glucose beverage in 5 min, and blood samples were collected before the start of the test (0 min) and 30, 60, 90, 120, and 180 minutes after the 75-g glucose intake. GHnadir levels during OGTT corresponded to maximally suppressed GH levels during the 180 min-OGTT. The pituitary was imaged with a 3T MRI scanner with or without gadolinium-DTPA (1.0 mmol/kg). The anteroposterior diameter (AD), vertical diameter (VD), and transverse diameter (TD), as well as the Knosp classifications of the GHPA, were evaluated by two experienced radiologists using precision calipers. The GHPA volume was calculated using the formula for approximating the volume of an ellipsoid: π/6 \u0026times; AD\u0026times; VD \u0026times; TD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExperienced gastroenterologists performed colonoscopies in acromegalic patients after careful bowel preparation with a 2 L dose of polyethylene glycol electrolyte-based solution (Shenzhen Wanhe Pharmaceutical Co., Ltd., Shenzhen, China). All colonic polyps on colonoscopy were recorded, and if possible, removed for histological examination. Age was divided into a categorical variable with four groups, namely, \u0026le;\u0026thinsp;39, 40\u0026ndash;49, 50\u0026ndash;59, and \u0026ge;\u0026thinsp;60 years. The site distribution of polyps was defined as the right colon (the cecum, ascending colon, and hepatic flexure), the left colon (the splenic flexure, descending colon, sigmoid colon, and rectum), and the whole colon (both the right colon and the left colon) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All colonic polyps detected at colonoscopy were grouped into size categories (\u0026le;\u0026thinsp;0.5, 0.6\u0026ndash;0.9, and \u0026ge;\u0026thinsp;1.0 cm) depending on endoscopic measurement by the diameter of open biopsy forceps [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The number of polyps was divided into a categorical variable (single or multiple (\u0026ge;\u0026thinsp;2)).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted by R studio (version 1.3.1093). The Shapiro-Wilk W test verified the normality of the variable distribution: variable distribution was considered normal if \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05. According to the distribution of the variables, variables were described either as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or medians with interquartile ranges (IQR). For normally distributed variables, we used an independent samples \u003cem\u003et\u003c/em\u003e-test to compare variables between two groups, whereas one-way ANOVA, followed by Tukey\u0026rsquo;s multiple comparison test, was used to compare variables among three or more groups. For non-normally-distributed variables, the Mann-Whitney U test was used to compare variables between two groups, whereas the Kruskal-Wallis test, followed by pairwise comparisons using the BWS all-pairs test, was used for multiple subgroups. The relationships between variables were examined using Spearman\u0026rsquo;s correlation analysis. Univariate and multivariate logistic regression analyses were performed to assess the associations of the variables with the diagnosis. A nomogram was created using the predictors from the multivariate analysis for relating the risk of polyp occurrence. The tests were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"Clinical features of acromegalic patients with colonic polyps\nA total of 86 acromegalic patients (mean age, 43.53 ± 11.78 years; sex, 48.8% females) underwent complete colonoscopy. Of the 86 patients, 35 patients (18 females, 17 males) were found to have one or more polyps, with a higher prevalence (40.7%) than a general Asian population (17.6–23.9%) of comparable age [17-20]. The prevalence of polyps increased with age, reaching a peak at ≥60 years. The prevalence rates of polyps in acromegalic patients aged ≤39, 40–49, 50–59, and ≥60 years were 30, 32, 50, and 77.8%, respectively (Fig. 1). Of the 35 patients, 13 (37.2 %) and 22 (62.8%) patients had a single polyp and multiple polyps, respectively. The mean diameter of the polyps was in most cases ≤5 mm (71.4%), and the maximum diameter of the polyps was ≥10 mm. With respect to the different site distributions of the colonic polyps, 4 (11.4%) patients had polyps in the right colon, 19 (54.3%) patients had polyps in the left colon, and 12 (34.3%) patients had polyps in the whole colon. Colonic polyps were more frequently detected in the sigmoid colon and rectum. In addition, only 5 cases of colonic polyps were biopsied, and all of them were histologically confirmed to be adenomas. Furthermore, 21 patients were observed to have hemorrhoids, and 1 patient was observed to have chronic colitis. There were no patients with colorectal carcinoma (Table 1).\nAt diagnosis, 79 (92%) patients had pituitary macroadenoma, 3 (3%) patients had pituitary microadenoma, and 4 (5%) patients had no data on the pituitary tumor diameter. Hypertension, impaired glucose metabolism (diabetes mellitus and impaired glucose tolerance), and dyslipidemia were separately diagnosed in 24 (28%), 47 (55%), and 34 (40%) patients, respectively (Table 1).\n\nCharacteristics of colonic polyps associated with hormone levels\nThe results showed that acromegalic patients with colonic polyps were older and had higher IGF-1×ULN levels than those without colonic polyps (P=0.005 and P=0.03, respectively, Table 1). No significant differences were found between acromegalic patients with or without colonic polyps regarding sex, BMI, FBG levels, dyslipidemia, hypertension, diabetes mellitus, GHPA volumes, Knosp classifications, UA levels, GH levels, GHnadir levels during OGTT, and IGF-1 levels (Table 1 and Supplementary Table 1). Although GH levels and GHnadir levels during OGTT were similar in patients with or without colonic polyps, GHPA volumes and IGF-1 levels tended to be higher in those with polyps, although there were no statistical differences between the two groups (Table 1). As shown in Table 2, with respect to the different site distribution of polyps, GH levels, and GHnadir levels during OGTT in patients with polyps in the right colon were higher than those in patients with polyps in the whole colon (P=0.023 and P=0.037, respectively). Interestingly, compared with polyp diameters \u003e5 mm, GH levels were significantly higher in patients with polyp diameters ≤5 mm (P=0.031, Table 2). Apart from the BMI (P=0.013), we did not identify relevant clinical indicators between the two groups with single polyps and multiple polyps (Table 2). GHPA volumes, IGF-1 levels, and IGF-1× ULN levels were similar among subjects with different site distribution, size, and number of polyps.\nIn addition, with respect to the different site distribution of polyps, hypertension and diabetes mellitus had a significant association with the polyps in the right colon, left colon, and whole colon (P = 0.003 and P = 0.008, respectively, Table 2). However, FBG and HbA1c had observed no difference with respect to three different site distributions of polyps (Supplementary Table 2). Next, to investigate the effect of IGF-1×ULN in acromegalic patients with polyps, we further divided the subjects into IGF-1×ULN-quantile subgroups according to the IGF-1×ULN level (Table 3). Age, GH levels, and GHnadir levels during OGTT were significantly higher in acromegalic patients with the highest IGF-1×ULN levels than those with the lowest IGF-1×ULN levels in the first quantile (P\u003c0.05 for Q1 vs Q4). Compared to patients with lower IGF-1×ULN levels in the second quantile, UA levels were significantly higher in patients with the highest IGF-1×ULN levels (P\u003c0.05 for Q2 vs Q4). Notably, there were changes in the IGF-1×ULN-quantile subgroups with respect to acromegalic patients with or without colonic polyps, GHPA volumes groups, and IGF-1 levels (P\u003c0.05, Table 3).\n\nPrognostic model for colonic polyps in acromegalic patients\nIn univariate analysis, age, especially 60 years and older, and the IGF-1×ULN level predicted polyp occurrence in acromegalic patients as shown in Table 4. Furthermore, in multivariate analysis, these variables showed that GHPA volumes (OR: 1.09, 95% CI: 1.01–1.20; P=0.039) and IGF-1×ULN Q2 levels (OR: 6.51, 95% CI: 1.20–44.60; P =0.038) were independently associated with polyp occurrence (Table 4). IGF-1×ULN Q3 and IGF-1×ULN Q4 cloud also are independent risk predictors, although no significant difference was found. Thus, univariate and multivariate analyses revealed that GHPA volumes and IGF-1×ULN levels could be independent risk factors for polyps in acromegalic patients. In addition, a nomogram for predicting acromegalic patients with polyp risk was constructed using the variables (Fig 2). According to the prognostic model, a man with a GHPA volume of 3.5 cm³ and an IGF-1×ULN level of 5.5 was predicted to have an 80% probability of polyps. "},{"header":"Discussion","content":"\u003cp\u003eAccording to several population-based studies, the incidence of colon cancer in patients with acromegaly ranges from 0.9 to 2.4% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the risk of colon cancer in acromegaly is still controversial. Colonoscopy screening can reduce the mortality from colorectal cancer by early detection and removal of pre-existing adenomatous polyps. In this study, we ascertained a high prevalence of colonic polyps was 40.7% in acromegalic patients. IGF-1\u0026times;ULN levels were higher in patients with polyps than in those without polyps. GHPA volumes and IGF-1\u0026times;ULN levels were found to be independent risk factors for polyps.\u003c/p\u003e \u003cp\u003eColonic polyps had a high prevalence in acromegalic patients. In this study, the overall prevalence of colonic polyps was 40.7%. The prevalence of colonic polyps in the acromegalic population was significantly higher than in the Asian general population. In the non-acromegalic population, the prevalence of polyps ranged from 17.6 to 23.9% [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The prevalence of colonic polyps also varied in different acromegalic populations. One of the largest datasets from 14 centers across Europe indicated that the prevalence of polyps in a fourth of acromegalic patients (820/3173) who had a colonoscopy was 13% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the prevalence of polyps in patients with acromegaly was 32% in an Italian single-center study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, French national registry data reported that the prevalence of colonic polyps ranged from 27\u0026ndash;55% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The different prevalence of colonic polyps may be due to differences in genetic predisposition, environmental backgrounds, and lifestyle (including dietary habits). The prevalence of multiple polyps was 62.8% compared to earlier reports by Bogazzi (50%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]and Colao (72.1%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A colonic polyp diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 mm is generally considered a high risk of colorectal carcinoma and thought to take more than ten years. A recent study reported the frequency of polyps\u0026thinsp;\u0026ge;\u0026thinsp;10 mm was 15.2%, and 5 patients were detected colorectal cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the maximum diameter of colonic polyps was \u0026lt;\u0026thinsp;10 mm, and most colonic polyps were at early stages\u0026thinsp;\u0026le;\u0026thinsp;5mm (71.4%), which may explain why no colorectal cancer was found in our study.\u003c/p\u003e \u003cp\u003eWe found a significant association between polyp occurrence and hormone values. In detail, IGF-1\u0026times;ULN levels in patients with polyps were higher than those without polyps, consistent with previous findings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although GH and GHnadir levels during OGTT were similar in patients with or without colonic polyps, IGF-1 levels and GHPA volumes tended to be higher in those with polyps. Nevertheless, few studies have consistently suggested that IGF-1 levels are significantly related to polyp prevalence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This study also indicated that GH and GHnadir levels during OGTT were higher in patients with polyps in the right colon and a diameter\u0026thinsp;\u0026le;\u0026thinsp;5 mm. As a result, we hypothesize that GH levels may play different roles in different stages of polyp development. The findings in patients with acromegaly suggest a higher prevalence of polyps in the left colon, similar to Battistone et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the general population, previous literature also reported that the site distribution of polyps was detected mainly in the left colon [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; however, several studies observed polyps in the cecum and ascending colon [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, the acromegalic patients with multiple polyps were higher BMI levels than those with single polyps. Compared to patients without polyps, the age in patients with polyps was higher. Simultaneously, the prevalence of colonic polyps increased significantly with age\u0026thinsp;\u0026ge;\u0026thinsp;50 years (\u0026gt;\u0026thinsp;50%). Bogazzi [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Parolin [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] also reported similar trends in polyp prevalence when patients were \u0026ge;\u0026thinsp;50 years old. Although the recent guidelines recommend the initiation of colonoscopy screening at 50 years of age for the average-risk non-acromegalic population [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], this may be inadequate for patients with acromegaly. A few guidelines have suggested that the initial colonoscopy should be performed at the age of 40 years to early detect precancerous polyps in acromegaly [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, we observed that several patients younger than 40 years of age had colonic polyps. Similarly, Terzolo et al. reported younger acromegalic patients had a higher risk of colonic neoplasia than those with age-matched controls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, colonoscopic surveillance should be performed at the time of diagnosis in acromegaly as recent guideline recommendations [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGHPA volumes may be a reliable predictor for polyp occurrence in acromegaly. According to univariate analysis and clinical parameters, the final diagnostic model was decided using age, height, GHPA volumes, and IGF-1\u0026times;ULN levels. To our knowledge, this is the first study reporting that GHPA volumes (OR: 1.09, 95% CI: 1.01\u0026ndash;1.20; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) were an independent risk factor for colonic polyp occurrence. However, GHPA volumes did not differ significantly between patients with or without polyps. We speculate that the larger GHPA volumes were indicative of long-term and uncontrolled secretion of GH and IGF-1 contributing to the higher risk of polyp occurrence. Meanwhile, we found that IGF-1\u0026times;ULN levels (OR: 6.51, 95% CI: 1.20\u0026ndash;44.60; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) were a predictor for polyp occurrence similar to the results of Gonzalez [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, we established a polyp risk prediction model according to GHPA volumes and IGF-1\u0026times;ULN levels. For example, a woman with a GHPA volume of 3.5 cm\u0026sup3; and an IGF-1\u0026times;ULN level of 5.5 was predicted to have a probability of polyps by approximately 80%.\u003c/p\u003e \u003cp\u003eIn this study, alone standard intestinal preparation resulted in inadequate intestinal cleansing in most acromegalic patients, seriously affecting the detection of colonoscopy (data not shown). Previous studies have suggested two consecutive bowel preparations or an increased dose of PEG solution for acromegalic patients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and the time required to reach the cecum during colonoscopy was significantly prolonged [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, we acknowledge several limitations in our study. First, this study was relatively small, and the absence of a control group. Second, this was a retrospective analysis, and selectivity bias was impossible to avoid. Third, most polyps with a diameter\u0026thinsp;\u0026gt;\u0026thinsp;5 mm were not biopsied. Further long-term prospective studies involving acromegalic patients may determine whether colonic polyps have a similar tendency to develop into colon cancer as in the general population.\u003c/p\u003e \u003cp\u003eIn conclusion, we confirmed the high prevalence of colonic polyps in patients with acromegaly. Older age, multiple, and usually occurring in the left colon were the clinical features of acromegalic patients with colonic polyps. In addition, GHPA volumes and IGF-1\u0026times;ULN levels might predict colonic polyp occurrence in the acromegalic population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u0026nbsp;\u003c/strong\u003eThere were no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics:\u003c/strong\u003e This study was approved by the Medical Ethics Committee of The Second Affiliated Hospital of Army Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources:\u0026nbsp;\u003c/strong\u003eThis work was supported by grants from the Clinical Research Project of Army Medical University (2019XLC2009 and 2018XLC3049).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGL.P., P.H., and YL.Z.: acquired the data; RF.S. and L.Z.: M.R. imaging assessment; YY.Z., JY.B., and GL.P.: colonoscopy assessment; GL.P., X.L., RF.S.: did the statistical data analysis and draft the manuscript; L.Z., MY.L., YL. Z. And WX.L.: interpreted the data, contributed to the methods, and performed the laboratory analyses; M.L., HT.Z., and Z.S.: revised the manuscript; RF.S. and M.L.: obtained the study funding and supervised the study. All authors read and approved the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eA. Ben-Shlomo, S. Melmed.(2008) Acromegaly. Endocrinol Metab Clin North Am.371:101 \u0026ndash; 22, viii. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecl.2007.10.002\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Melmed(2009) Acromegaly pathogenesis and treatment. J. Clin. Invest..11911:3189 \u0026ndash; 202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/jci39375\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. 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CA Cancer J Clin.684:250 \u0026ndash; 81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21457\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP.J. Jenkins, P.D. Fairclough.(2002) Screening guidelines for colorectal cancer and polyps in patients with acromegaly. Gut.51 Suppl 5Suppl 5:V13\u0026ndash;V14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gut.51.suppl_5.v13\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Iwamuro, M. Yasuda, K. Hasegawa, S. Fujisawa, K. Ogura-Ochi et al., Colonoscopy examination requires a longer time in patients with acromegaly than in other individuals. Endocr. J. \u003cb\u003e652\u003c/b\u003e, 151\u0026ndash;157 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1507/endocrj.EJ17-0322\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Matano, T. Okada, A. Suzuki, T. Yoneda, Y. Takeda et al., Risk of colorectal neoplasm in patients with acromegaly and its relationship with serum growth hormone levels. Am. J. Gastroenterol. \u003cb\u003e1005\u003c/b\u003e, 1154\u0026ndash;1160 (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1572-0241.2005.40808.x\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Yamamoto, H. Fukuoka, G. Iguchi, R. Matsumoto, M. Takahashi et al.(2015) The prevalence and associated factors of colorectal neoplasms in acromegaly: a single center based study. Pituitary.183:343 \u0026ndash; 51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11102-014-0580-y\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Clinical characteristics of the study patients with acromegaly.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" style=\"margin-right: calc(9%); width: 91%;\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003eTotal Patients\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n = 86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003ePatients without polyps \u0026nbsp; \u0026nbsp; \u0026nbsp;(n = 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003ePatients with polyps \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(n = 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e43.53 \u0026plusmn;11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e40.63 \u0026plusmn;11.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e47.77 \u0026plusmn;10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eSex, female, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e42 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e24 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e18 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e26.1 \u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e25.96 \u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e26.31 \u0026plusmn;3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eGHPA Volumes (cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e2.47 (1.15, 6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e2.2 (1.17, 4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e3.19 (1.15, 7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eBasal GH levels (ug/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e18.3 (8.08, 34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e17.8 (5.94, 29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e19.3 (9.7, 52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eGHnadir levels during OGTT (ug/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e12.1 (5.24, 30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e12 (4.41, 25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e13 (6.54, 40.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eBasal IGF-1 levels (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e762.56 \u0026plusmn;259.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e733.73 \u0026plusmn;259.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e804.57 \u0026plusmn;257.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIGF-1\u0026times;ULN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e2.53 (1.94, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e2.22 (1.79, 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e2.73 (2.1, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e5.07 (4.4, 5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e4.95 (4.38, 5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e5.33 (4.54, 6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eHemorrhoids,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e21(24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e7(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e14(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e24 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e15 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e9 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e\u0026nbsp;non-diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e25 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e14 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e11 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e\u0026nbsp;IGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e39 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e25 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e14 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.1935%;\" width=\"28.800988875154513%\"\u003e\n \u003cp\u003e\u0026nbsp;DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e22 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4839%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e12 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6452%;\" width=\"20.148331273176762%\"\u003e\n \u003cp\u003e10 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0323%;\" width=\"8.899876390605685%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are shown as mean \u0026plusmn; SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: body mass index;\u0026nbsp;GHPA Volume:\u0026nbsp;growth hormone (GH)-secreting pituitary adenoma\u0026nbsp;volume;\u0026nbsp;GH: growth hormone; GHnadir levels during OGTT:\u0026nbsp;the maximal suppression of GH levels during 180-OGTT,\u0026nbsp;IGF-1: insulin-like growth factor; ULN = upper limit of normal; FBG: fasting blood glucose;\u0026nbsp;IGT: impaired glucose tolerance;\u0026nbsp;DM: diabetes mellitus.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue for Patients without polyps vs\u0026nbsp;Patients with polyps, * p＜0.05.\u003c/p\u003e\n\u003cp\u003eTable 2. Clinical characteristics of acromegalic patients with polyps.\u003c/p\u003e\n\u003ctable align=\"left\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"29.423459244532804%\"\u003e\n \u003cp\u003ethe site distribution of polyps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"23.55864811133201%\"\u003e\n \u003cp\u003ethe size of polyps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"26.73956262425447%\"\u003e\n \u003cp\u003ethe number of polyps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003eright colon\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003eleft colon\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003ewhole colon\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e\u0026le;5mm\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e\u0026gt;5mm\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003eSingle polyp\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n = 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003eMultiple polyps (n = 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e49.25 \u0026plusmn;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e44.58 \u0026plusmn;10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e52.33 \u0026plusmn;9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e47 \u0026plusmn; 11.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e49.7 \u0026plusmn; 9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e43.23 \u0026plusmn;11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e50.45 \u0026plusmn;9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eSex (Female),\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e8 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e13 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e5 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e7 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e11 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e27.09 \u0026plusmn;3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e26.19 \u0026plusmn;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e26.24 \u0026plusmn;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e25.95 \u0026plusmn; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e27.2 \u0026plusmn; 1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e24.66 \u0026plusmn;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e27.28 \u0026plusmn;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eGHPA Volumes (cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e4.83 (3.55, 5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e3.48 (1.3, 12.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e1.83 (0.96, 6.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e3.48 (1.25, 6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e2.22 (0.54, 8.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e3.59 (1.81, 7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e2.95 (1.05, 6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eBasal GH levels (ug/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e127.5 (70.33, 189.5)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e17 (13.05, 33.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e11.95 (7.8, 33.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e26.6 (14.5, 61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e9.7 (8.08, 19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e35.3 (14, 82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e16.45 (8.2, 27.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eGHnadir levels during OGTT (ug/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e73.9 (44.7, 111.1)\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e12.4 (8.59, 37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e8.75 (5.02, 24.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e21.2 (8.76, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e8.7 (4.52, 12.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e21.2 (10.3, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e11.25 (4.43, 28.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eBasal IGF-1 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e731.75 \u0026plusmn;261.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e869 \u0026plusmn;267.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e726.83 \u0026plusmn;229.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e802.56 \u0026plusmn; 271.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e809.6 \u0026plusmn; 231.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e808.54 \u0026plusmn;247.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e802.23 \u0026plusmn;268.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eIGF-1\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e2.92 (1.84, 3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e2.83 (2.22, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e2.59 (2.36, 2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e2.84 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e2.94 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e2.72 (2.08, 3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e2.78 (2.42, 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e1 (25)\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e1 (5)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e7 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e6 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e3 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e1 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e8 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eDM, n (%)\u003csup\u003e\u0026nbsp;a,b,\u003c/sup\u003e\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eNon-DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e11 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e3 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e12 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e2 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e6 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e8 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eIGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e6 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e4 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e6 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e4 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e2 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e8 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.630218687872764%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.636182902584492%\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.244532803180915%\"\u003e\n \u003cp\u003e2 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.542743538767395%\"\u003e\n \u003cp\u003e5 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e7 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.946322067594433%\"\u003e\n \u003cp\u003e4 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4910536779324055%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.139165009940358%\"\u003e\n \u003cp\u003e5 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934393638170974%\"\u003e\n \u003cp\u003e6 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.666003976143141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are shown as mean \u0026plusmn; SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: body mass index;\u0026nbsp;GHPA Volume:\u0026nbsp;growth hormone (GH)-secreting pituitary adenoma\u0026nbsp;volume;\u0026nbsp;GH: growth hormone; GHnadir levels during OGTT:\u0026nbsp;the maximal suppression of GH levels during 180-OGTT,\u0026nbsp;IGF-1: insulin-like growth factor; ULN = upper limit of normal;\u003c/p\u003e\n\u003cp\u003e*:\u0026nbsp;P\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003ea: P\u0026lt;0.05 for right colon vs left colon. b: P\u0026lt;0.05 for right colon vs whole colon. c: P\u0026lt;0.05 for left colon vs whole colon.\u003c/p\u003e\n\u003cp\u003eTable 3. Clinical characteristics and biochemical variables in acromegalic patients of different IGF-1\u0026times;ULN quartiles.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eIGF-1\u0026times;ULN\u0026nbsp;quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003eQ1 (n = 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003eQ2 (n = 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003eQ3 (n = 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003eQ4 (n = 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e37.38 \u0026plusmn; 13.35\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e45.19 \u0026plusmn; 12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e45 \u0026plusmn; 10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e46.36 \u0026plusmn; 9.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e9 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e10 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e12 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e13 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e12 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e11 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e10 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e9 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e25.11 \u0026plusmn; 2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e25.76 \u0026plusmn; 3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e26.37 \u0026plusmn; 2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e27.1 \u0026plusmn; 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eGHPA Volumes (cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e5.19 (0.83, 7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e2.33 (1.19, 3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e2.73 (1.72, 6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e1.81 (1.12, 4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eBasal GH levels (ug/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e9.12 (4.87, 21.8)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e17 (10.5, 29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e17.15 (8.08, 29.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e27.45 (19.25, 55.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eGHnadir levels during OGTT (ug/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e5.85 (2.83, 12.5)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e13.8 (11.3, 26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e8.59 (5.53, 22.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e27.25 (10.83, 43.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eBasal IGF-1 levels (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e501.24 \u0026plusmn; 133.91\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e653.19 \u0026plusmn; 131.4\u003csup\u003ed,e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e807.36 \u0026plusmn; 134.64\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e1071.59 \u0026plusmn; 197.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eIGF-1\u0026times;ULN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e1.59 (1.33, 1.8)\u003csup\u003e\u0026nbsp;a,b,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e2.11 (2.03, 2.32)\u003csup\u003e\u0026nbsp;d,e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e2.81 (2.72, 2.88)\u003csup\u003e\u0026nbsp;f\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e3.85 (3.16, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eUA (umol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e259.4 (220.7, 348.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e259.1 (221.6, 299.5)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e301.2 (238.48, 389.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e332.6 (279.68, 389.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003ePatients with polyps, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e3 (14)\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e10 (48)\u003csup\u003ed,e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e10 (45)\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e12 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003eGHPA Volume group(cm\u0026sup3;), n (%)\u003csup\u003ea,b,c, d,e, f\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e7 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e3 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e3 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e4 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003e\u0026nbsp; 1\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e2 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e13 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e11 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e11 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.020431328036324%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.869466515323497%\"\u003e\n \u003cp\u003e12 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.274687854710557%\"\u003e\n \u003cp\u003e5 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.955732122587968%\"\u003e\n \u003cp\u003e8 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.480136208853576%\"\u003e\n \u003cp\u003e7 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.399545970488083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are shown as mean \u0026plusmn; SD for variables of normal distribution, and median with the interquartile range (25-75%) for skewed variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: body mass index;\u0026nbsp;GHPA Volume:\u0026nbsp;growth hormone (GH)-secreting pituitary adenoma\u0026nbsp;volume;\u0026nbsp;GH: growth hormone; GHnadir levels during OGTT:\u0026nbsp;the maximal suppression of GH levels during 180-OGTT,\u0026nbsp;IGF-1: insulin-like growth factor; ULN: upper limit of normal.\u003c/p\u003e\n\u003cp\u003e*:\u0026nbsp;P\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;a. P\u0026lt;0.05 for Q1 vs Q2; b. P\u0026lt;0.05 for Q1 vs Q3; c. P\u0026lt;0.05 for Q1 vs Q4; d. P\u0026lt;0.05 for Q2 vs Q3; e. P\u0026lt;0.05 for Q2 vs Q4; f. P\u0026lt;0.05 for Q3 vs Q4.\u003c/p\u003e\n\u003cp\u003eTable 4. Univariate and multivariable logistic regression analysis of associations between clinical and biochemical variables and polyps.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"34.97884344146686%\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"34.97884344146686%\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003eOR (CI 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003eOR (CI 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.06 (1.02, 1.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.05 (0.99, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003esex \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.19 (0.50, 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.02 (0.00, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.03 (0.00, 16.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eWeights \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.98 (0.94, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.04 (0.90, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHPA Volumes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.06 (0.99, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.01, 1.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eBasal GH levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eGHnadir levels during OGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eBasal IGF-1 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIGF-1\u0026times;ULN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.65 (1.04, 2.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.47 (0.88, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eFBG \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.07 (0.86, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.84 (0.50, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.77 (0.49, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eHDL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.96 (0.33, 12.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eLDL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e0.89 (0.47, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003eUA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026le;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u0026nbsp;40\u0026ndash;50 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e1.10 (0.34, 3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.118476727785612%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.860366713681241%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.04231311706629%\"\u003e\n \u003cp\u003e\u0026nbsp;50\u0026ndash;60 \u0026nbsp; 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CI, confidence interval.\u003c/p\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":"Acromegaly, Colonic polyps, Growth hormone (GH), Colonoscopy, Insulin-like growth factor-1(IGF-1)","lastPublishedDoi":"10.21203/rs.3.rs-1371353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1371353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study investigates the clinical characteristics and the associated factors of colonic polyps in patients with acromegaly.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We retrospectively reviewed clinical characteristics and colonoscopy findings of 86 acromegalic patients who received treatment between August 2015 and July 2020 at Xinqiao Hospital. We analyzed colonoscopy findings and correlation with growth hormone (GH)-secreting pituitary adenoma (GHPA) volume and hormonal/metabolic levels.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our analysis revealed that the prevalence of colonic polyps in acromegalic patients was 40.7% and increased significantly with advanced age, especially ≥50 years. Multiple polyps (62.8%) and colonic polyps in the left colon (54.2%) were detected more frequently. Compared to acromegalic patients without polyps, patients with polyps displayed higher IGF-1×ULN levels (P=0.03). IGF-1 levels and GHPA volumes in patients with polyps showed increasing trends, although there were no significant differences. Meanwhile, patients with polyps in the right colon showed higher GH levels and GH nadir (GHnadir) levels in the oral glucose tolerance test (OGTT) than those with polyps in the whole colon (P=0.023 and P=0.037, respectively). \u0026nbsp;GH levels were higher in patients with polyps diameter ≤5mm than those with polyps diameter \u0026gt;5mm (P=0.031). In addition, the univariate and multivariate logistic regression analysis exhibited GHPA volumes (OR: 1.09, 95% CI: 1.01–1.20; \u003cem\u003eP\u003c/em\u003e = 0.039) and IGF-1×ULN Q2 levels (OR: 6.51, 95% CI: 1.20–44.60; \u003cem\u003eP\u003c/em\u003e=0.038) were independent factors for predicting the risk of colonic polyp occurrence in acromegalic patients. A nomogram was created to evaluate the risk of colonic polyps in acromegalic patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eacromegalic patients are a high prevalence population of colonic polyps. Colonic polyps in acromegaly were multiple and frequently detected\u0026nbsp;in the sigmoid colon and rectum. GHPA volumes and IGF-1×ULN levels may be predictors of colonic polyp occurrence.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Clinical Characteristics And Associated Factors Of Colonic Polyps In Acromegaly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-24 21:34:54","doi":"10.21203/rs.3.rs-1371353/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":"194dccbb-6e36-42ab-9de9-f75af24e9392","owner":[],"postedDate":"February 24th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-15T07:41:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-24 21:34:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1371353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1371353","identity":"rs-1371353","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}