The Predictive Value of Laboratory Parameters in Diagnosing Gastrointestinal Malignancy in Older Adults

The Predictive Value of Laboratory Parameters in Diagnosing Gastrointestinal Malignancy in Older Adults | 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 The Predictive Value of Laboratory Parameters in Diagnosing Gastrointestinal Malignancy in Older Adults Funda Yildirim Borazan, Meryem Yilmaz, Barış Tuzcu, Ozlem Gulbahar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5354042/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 Introduction: Early diagnosis and treatment through screening tests to identify and remove precancerous lesions or detect cancer at a curable stage are crucial for managing gastrointestinal (GI) cancers. However, achieving early detection in older adults, particularly those who are frail, can be challenging. This retrospective study aimed to evaluate the predictive value of pre-endoscopic biochemical parameters for detecting malignant lesions in older adults undergoing upper and lower GI endoscopies. Material and Methods: We retrospectively analyzed 419 individuals aged 60 and above. Of these, 109 older adult patients who underwent both upper and lower GI endoscopies were included in the study. Patients with a prior history of GI cancer or those who could not complete the procedure due to intolerance were excluded. Patients were categorized based on the presence of benign or malignant lesions. Results: Malignant lesions were identified in 10.1% (11/109) of patients. Statistically significant differences were observed between the benign and malignant groups in terms of hemoglobin (Hb), neutrophil count, mean corpuscular volume (MCV), neutrophil-lymphocyte ratio (NLR), iron (Fe), 25-hydroxyvitamin D [25(OH)D], C-reactive protein (CRP), total protein, albumin (Alb), blood urea nitrogen (BUN), CRP/albumin ratio (CAR), and aspartate aminotransferase (AST). The ROC curve analysis suggests that MCV, NLR, 25(OH)D, Fe, and CAR are valuable indicators for predicting malignant lesions in older adults, with optimal cut-off values of 79.5 fL, 3.28, 12 µg/L, 22 µg/dL, and 5.93, respectively. Conclusion: These findings underscore the predictive value of CAR, NLR, MCV, Fe, and 25(OH)D in identifying GI neoplasms in older adults. The study suggests that patients should undergo an endoscopic evaluation to investigate potential GI malignancies when they have MCV ≤79.5 fL, NLR 5.93. Gastrointestinal Malignancy Gastrointestinal endoscopy Predictive Older adult Laboratory parameters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastrointestinal (GI) cancers represent over a quarter of all cancer diagnoses and are responsible for one-third of cancer-related deaths globally [ 1 ]. In our country, colorectal cancer (CRC) ranks as the third most common cancer in men and the second most common cancer in women aged 70 and over [ 2 ]. Moreover, gastric cancer is the second leading cause of cancer-related mortality, along with CRC. [ 3 ]. Early diagnosis and treatment are critical in improving survival and longevity for GI cancers, as with most cancers[ 3 ]. Therefore, early diagnosis and treatment through screening tests to identify and remove precancerous lesions or to identify cancer at a curable stage is essential in managing GI cancers. Guidelines recommend colonoscopy screening for asymptomatic individuals, with recent recommendations suggesting that screening started at age 45 rather than 50 [ 4 ]. However, there is no universally recommended screening program for gastric cancer except in populations with a high incidence. [ 5 , 6 ]. Nevertheless, performing combined upper and lower endoscopy may be preferred in geriatric patients, where screening is necessary due to advanced age. Iron deficiency anemia (IDA) can manifest gastrointestinal malignancies in older adults [ 7 ]. Consequently, when anemia is detected in this population, upper and lower gastrointestinal endoscopes are performed to identify potential malignancies or bleeding lesions [ 8 , 9 ]. Laboratory parameters, including serum ferritin, transferrin saturation (TSAT), sedimentation rate, albumin (Alb), and lactate dehydrogenase (LDH), may also be associated with gastrointestinal malignancies [ 10 – 13 ]. Given the well-established link between inflammation and cancer, recent studies have explored the relationship between certain cancers and systemic inflammation markers such as C-reactive protein (CRP)/Alb ratio (CAR) and the neutrophil-lymphocyte ratio (NLR) [ 14 – 17 ]. However, research explicitly focusing on older adult patients remains limited. This study aims to evaluate whether the pre-investigation biochemical parameters can predict the presence of premalignant/malignant disease in patients undergoing upper and lower gastrointestinal endoscopy. Identifying a potential biochemical predictor of malignancy in older adults could offer valuable insights for clinicians and enhance early detection and intervention strategies. Material and Methods Study Population This retrospective study included 419 patients aged 60 and older who presented to our geriatric outpatient clinic with iron deficiency anemia (IDA) or persistent gastrointestinal symptoms. These patients, for whom upper and lower gastrointestinal endoscopies were planned, were seen between September 2016 and January 2021. A total of 109 older adult patients who underwent both upper and lower gastrointestinal endoscopies were included in the final analysis. Patients with a previous history of gastrointestinal malignancy (n = 14) or those who could not tolerate the procedure and withdrew (n = 18) were excluded from the study. This study was approved by the local ethics committee (05. 10.2020/663 ) and conducted according to the Declarations of Helsinki. Baseline characteristics Demographic data, comorbidities, indications for endoscopy, and presenting complaints were collected retrospectively from outpatient clinic records. Routine laboratory parameters measured before endoscopy were recorded, including complete blood count, iron (Fe) (µg/dL), total iron-binding capacity (TIBC) (µg/dL), serum ferritin (ng/mL), transferrin saturation (TS) (%), vitamin B12 (ng/L), folate (µg/L), Aspartate Aminotransferase (AST) (U/L), Alanine Transaminase (ALT) (U/L), Lactate dehydrogenase (LDH) (U/L), electrolytes, total protein (TP) (g/dL), albumin (Alb) (g/dL), Blood Urea Nitrogen (BUN) (mg/dL), Creatinine (Cr) (mg/dL), 25-hydroxyvitamin D [25(OH)D] (µg/L), Erythrocyte sedimentation rate (ESR) (mm/h), and C-reactive protein (CRP) (mg/L). The recorded values represented the most recent laboratory assessments conducted before the endoscopic procedures. Anemia was defined as hemoglobin concentrations < 13g/dL for men and < 12g/dL for women [ 18 ]. The neutrophil-lymphocyte ratio (NLR) was calculated by dividing the neutrophil count (*10^9/L) by the lymphocyte count (*10^9/L). The C-reactive protein/albumin ratio (CAR) was calculated by dividing CRP (mg/L) by albumin (g/dL). Gastrointestinal Endoscopies Endoscopic imaging and, when available, biopsy results were reviewed for all patients. In upper gastrointestinal endoscopy, gastritis, ulcers, and the presence of Helicobacter pylori were classified as benign conditions, while intestinal metaplasia was considered premalignant. Colonoscopy findings categorized hemorrhoids, diverticula, tubular adenoma, tubulovillous adenoma, villous adenoma, and angiodysplasia as benign conditions. Malignancy was confirmed in cases where cancerous lesions were detected. Statistical analyses Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 22.0 (IBM Corp., Armonk, NY, USA). Categorical variables were presented as frequencies and percentages. The normality of the distribution of continuous variables was assessed using the Kolmogorov-Smirnov test and visualized with histograms. Normally distributed continuous variables were expressed as mean ± standard deviation (SD), while non-normally distributed variables were presented as median (minimum-maximum). Comparisons between parametric variables were made using Student’s t-test, while the Mann-Whitney U test was used for non-normally distributed variables. The chi-square test was applied for categorical variables. A p-value of < 0.05 was considered statistically significant. Receiver Operating Characteristic (ROC) curve analysis was conducted using MedCalc Software Version 22.023 to assess the ability of biochemical parameters to predict the presence of malignant lesions for significant cut-off values, sensitivity, specificity, and positive and negative predictive values reported. The area under the curve (AUC) was evaluated with a 5% type I error threshold, and a p-value < 0.05 was considered statistically significant. Results A total of 109 older adult patients were included in the study. The mean age was 77.7 ± 6.5 years, and 58.7% of the participants were female. Baseline characteristics and laboratory results stratified by benign and malignant lesion status are presented in Table 1 . Malignant lesions were identified in 10.1% of the study population (11/109). Significant differences between the benign and malignant groups were observed in Hb, MCV, NLR, Fe, 25(OH)D, CRP, TP, Alb, BUN, CAR, and AST levels. Table 1 Demographics and baseline characteristics of the patients Parameters All participants (n = 109) Malignant (n = 11) No Malignant (n = 98) P value Age, year, (SD) 77.9 ± 6.6 79.6 ± 7 77.7 ± 6.5 0.356 Gender, female, n (%) 64 (58.7%) 5 (45.5%) 59 (60.2%) 0.346 DM, n (%) 41 (36%) 5 (45.5%) 36 (36.7%) 0.706 HTN, n (%) 60 (55%) 6 (54.5%) 54 (55.1%) 0.728 CKD, n (%) 8 (7.3%) 0 8 (8.2%) 0.303 CHF, n (%) 7 (7.5%) 1 (9.1%) 6 (6.1%) 0.754 Dementia, n(%) 7 (6.4%) 0 7 (7.1%) 0.369 Anemia, n (%) 73 (67%) 8 (72.7%) 65 (66.3%) 0.093 Weight loss, n (%) 52 (47.7%) 6 (54.5%) 46 (46.9%) 0.725 Hb (g/dL) 10.7 (6.3–18.4) 8.8 (6.3–18.4) 11 (7.1–15.7) 0.050 WBC, *10^9/L 7.06 (3.03–26.6) 8,17 (5.11–16.4) 7.01 (3.03–26.6) 0.075 Neut, *10^9/L 4.7 (1.9–13.1) 5.27 (2.91–12.2) 4.5 (1.9–13.1) 0.030 Lymph, *10^9/L 1.52 (0.58–3.23) 1.4 (0.8–3.2) 1.53 (0.58–3.23) 0.903 NLR 3.13 (1.14–14.56) 3.81 (1.72–8.75) 3 (1.14–14.56) 0.044 MCV (fL) 84.3 (26.5–129) 74 (26.5–96.1) 85.5 (31.6–129) 0.009 Fe (µg/dL) 41 (3-182) 21 (3–93) 41 (11.7–182) 0.028 TIBC (µg/dL) 246 (81–509) 247 (116–509) 245.5 (81–494) 0.794 Ferritin (ng/mL) 51 (5-1083) 26 (5-458) 53.5 (5-1083) 0.288 TSAT (%) 16.3 (0.91–224.7) 8.2 (0.91–41.4) 16.7 (3.7-224.7) 0.278 Vitamin B12 (ng/L) 341 (50-2804) 158 (92–894) 345.5 (50-2804) 0.141 Folic acid (µg/L) 8 (2.9–24) 7.4 (4.7–13) 8 (2.9–24) 0.866 25(OH)D (µg/L) 22 (5.4–63) 10 (8.3–51) 23.5 (5.4–63) 0.011 ESR (mm/h) 42 (1-140) 48.5 (13–106) 42 (1-140) 0.254 CRP (mg/L) 8 (1-341) 42 (3.1–206) 6.9 (1-341) 0.020 TP (g/dL) 7 (3.8–8.4) 6.2 (3.8–7.1) 7 (5-8.4) 0.006 Alb (g/dL) 3.8 (2-5.1) 3.4 (2-4.5) 3.8 (2.3–5.1) 0.031 BUN (mg/dL) 23 (7–73) 30 (16–44) 22 (7–73) 0.008 Cr (mg/dL) 0.95 (0.43-7) 1.15 (0.6–3.34) 0.92 (0.43-7) 0.364 CAR 2.25 (0.22–92.2) 12.35 (0.91–55.7) 1.85 (0.22–92.2) 0.015 ALT (U/L) 15 (1–68) 15 (4–26) 15 (1–68) 0.251 AST (U/L) 20 (7–85) 13 (8–35) 21 (7–85) 0.011 LDH (U/L) 209 (70–646) 219 (134–326) 203 (70–646) 0.535 Data were given as a number and percentages for categorical variables, mean ± SD* for normally distributed continuous variables, and median (min-max) for non-normally distributed continuous variables. *SD: standard deviation DM: Diabetes Mellitus, HTN: Hypertension, CKD: Chronic Kidney Disease, CHF: Congestive heart failure, Hb: Hemoglobin, NLR: neutrophil/lymphocyte ratio, MCV: Mean Corpuscular Volume, Fe: Iron, TIBC: Total Iron Binding Capacity, TSAT: Transferrin Saturation, 25(OH)D: 25-hydroxyvitamin D, ESR: Erythrocyte sedimentation rate, CRP: C-Reactive protein, TP: Total Protein, Alb: Albumin, BUN: Blood urea nitrogen, Cr: Creatinine, CAR: C-reactive protein/albumin ratio, AST: Aspartate Aminotransferase, ALT: Alanine Transaminase, LDH: Lactate dehydrogenase Table 2 presents the endoscopic findings of the study participants. Gastric cancer was identified in 4 patients (3.7%), while intestinal metaplasia was detected in 21 patients (19.2%). Colonic polyps, including hyperplastic polyps, tubular, tubulovillous, and villous adenomas, were found in 31 patients (28.4%), and colorectal cancer was identified in 7 patients (6.4%). Table 2 Endoscopic data of the patients Upper Gastrointestinal Endoscopies All participants (n = 109) Chronic Gastitis, n (%) 69 (63.3%) Peptic Ulcer, n (%) 7 (6.4%) Helicobacter Pylori, n (%) 11 (12.4%) Intestinal Metaplasia, n (%) 21 (19.2%) Gastric Cancer, n (%) 4 (3.7%) Lower Gastrointestinal Endoscopies All participants (n = 109) Colonic Polyps, n (%) 31 (28.4%) Angiodysplasia, n (%) 3 (2.8%) Hemoroid, n (%) 20 (18.3%) Colitis, n (%) 5 (4.6%) Colorectal Cancer, n (%) 7 (6.4%) Data were given as a number and percentages for categorical variables. Malignancy was detected in 8 out of the 73 patients with anemia (10.9%). Of these, three patients had gastric malignancies, and 5 had colorectal malignancies. The prevalence of anemia at the time of malignancy diagnosis among these patients was 88.9%. Figures 1 , 2, 3, 4, and 5 display the Receiver Operating Characteristic (ROC) curves for MCV, NLR, 25(OH)D, CAR, and Fe in diagnosing gastrointestinal malignant lesions. Table 3 presents the predictive values of these parameters for malignant lesions in older adults, with optimal cut-off values set at ≤ 79.5 fL for MCV, > 3.28 for NLR, 5.93 for CAR, and ≤ 22 µg/L for Fe. Table 3 ROC curve of laboratory parameters in the diagnosis of malignant lesions in older adults Parameters Cut-off AUC SE P 95%CI Sensitivity Specificity +PV -PV MCV (fL) ≤ 79.5 0.740 0.102 0.018 0.647–0.820 72.73 78.35 27.6 96.2 NLR > 3.28 0.686 0.080 0.021 0.589–0.772 81.82 59.79 18.8 96.7 25(OH)D (µg/L) ≤ 12 0.759 0.107 0.015 0.661–0.840 77.78 79.55 28.0 97.2 CAR > 5.93 0.723 0.067 0.001 0.629–0.806 72.73 67.71 20.5 95.6 Fe (µg/dL) ≤ 22 0.702 0.105 0.029 0.606–0.787 63.64 88.54 38.9 95.5 MCV: Mean Corpuscular Volume, NLR: neutrophil/lymphocyte ratio, 25(OH)D: 25-hydroxyvitamin D, CAR: C-reactive protein/albumin ratio, Fe: Iron, AUC: area under the curve, SE: Standard Error, CI: Confidence interval, PV: Predictive Value Discussion The present study identified significant differences between the malignant and benign groups concerning NLR, MCV, 25(OH)D, Fe, and C-reactive protein/albumin ratio (CAR) levels before diagnosis. Notably, the ROC curve analysis suggests that NLR and CAR are valuable indicators for predicting malignant lesions in older adults, with optimal cut-off values of 3.28 and 5.93, respectively. To our knowledge, this is the first study to investigate pre-diagnosis laboratory parameters for predicting malignant gastrointestinal lesions, specifically in older adults. In recent years, systemic inflammatory response markers such as NLR, CRP, and CAR have been shown to predict prognosis in patients diagnosed with cancer.[ 19 , 20 ]. Additionally, studies have demonstrated that The Glasgow Prognostic Score (derived from serum CRP and Alb levels) is a reliable prognostic factor in individuals with non-small cell lung cancer and gastric and colorectal cancer [ 21 , 22 ]. While these studies emphasize the prognostic value of inflammatory markers in diagnosed patients, our study demonstrates that NLR and CAR can also serve as predictive markers for malignancy in undiagnosed patients. A study involving 544 adults suggested that male patients with anemia should undergo comprehensive endoscopic evaluation for gastrointestinal neoplasms when serum ferritin levels are below 44 ng/mL or transferrin saturation (TSAT) is less than 9% [ 23 ]. This study is particularly noteworthy as it identified a ferritin cut-off within the normal range despite being conducted in anemic individuals. In our study, while no significant difference in ferritin levels was observed between patients with and without malignancy, a significant difference was noted in Fe levels. Additionally, their study population included a broad age range (20–92 years), whereas our research specifically focused on older adults[ 23 ]. A study of 222 adult patients with unintentional weight loss, 22 of whom were diagnosed with gastrointestinal cancer, demonstrated that ferritin above 100 mcg/L could rule out colon cancer but not gastric or rectal cancer [ 24 ]. In our study, ferritin was > 100 mcg/L in 18% of patients with malignancies, including cases of colon and gastric cancer. However, as ferritin is an acute-phase reactant that can rise with inflammation and its prevalence increases with age, relying on elevated ferritin to exclude cancer may lead to missed diagnoses[ 25 , 26 ]. Some findings also suggest that ferritin levels in many elderly individuals with iron deficiency anemia may be normal or elevated. [ 27 ]. Therefore, we recommend using other laboratory parameters, such as Fe, alongside ferritin to provide a more reliable prediction of gastrointestinal cancer, particularly in older adults. We hypothesize that Fe may be a more appropriate biomarker than ferritin in this population. In another study, upper gastrointestinal endoscopy and colonoscopy were performed on 96 older adults with iron deficiency anemia. They diagnosed gastrointestinal malignancy in 15 (15.6%) patients (8 colon, one esophageal and six gastric cancers) [ 9 ]. Their study's malignancy rate was higher than ours (10.9%). This may be because the mean Hb value of their study population was 9.1 ± 3.0 g/dl [ 9 ], while the median Hb of our anemic group was 10.2 (6.3–12.7) g/dl. It is also known that cancer prevalence varies from region to region in the world and in our country. It is known that gastric cancer is much more common in the eastern areas of country [ 28 ], and according to some epidemiological studies, colorectal carcinoma is more common in western regions of our country [ 29 , 30 ]. A retrospective study of 333 patients over 85 years old who underwent colonoscopy examined the role of MCV as a predictor of CRC [ 31 ]. The study revealed that decreased MCV was an independent predictor of CRC. Preoperative MCV has also been suggested to be a prognostic factor for esophageal squamous cell carcinoma and colorectal cancer [ 32 , 33 ]. Our study's ROC curve indicates that MCV could be a helpful indicator in predicting gastrointestinal malignant lesions in older adults, with a cut-off value of ≤ 79.5 fL. Numerous studies have demonstrated the association between 25(OH)D and various cancers' incidence, prognosis, and mortality [ 34 ]. One randomized placebo-controlled trial involving 25,871 participants evaluated vitamin D 3 (cholecalciferol) supplementation of 2000 IU per day and omega-3 fatty acids (1 g/ day) for the prevention of cancer and cardiovascular disease in adults aged 50 and older in the United States [ 35 ]. After a median follow-up period of 5.3 years, cancer was diagnosed in 1,617 participants, 793 in the vitamin D group and 824 in the placebo group— showing no significant reduction in invasive cancer incidence with vitamin D supplementation [ 35 ]. However, 25(OH)D plays a crucial role in the development of GI malignancies through the Vitamin D receptor (VDR), influencing processes such as differentiation, proliferation, metastasis, invasion, angiogenesis, and apoptosis [ 36 , 37 ] In our study, significantly lower levels of 25(OH)D in patients with malignancy further support these findings. In conclusion, this study suggests that older adults with MCV ≤ 79.5 fL, NLR 5.93 should undergo endoscopic evaluation for potential GI neoplasms. Early identification of cancer using these laboratory parameters could help lower disease-specific mortality by facilitating the detection and removal of precursor lesions. Declarations Ethical approval and consent to participate : This study was approved by the Gazi University Ethics Committee (05.10.2020/663) and conducted according to the Declarations of Helsinki. Written informed consent was obtained from all participants. Conflicts of Interest: The authors declare no conflicts of interest. Consent for publication: Not applicable Funding Source : The authors declare no funding for this study. Statement of data and materials: All data generated or analyzed during this study are available upon request and can be obtained from the corresponding author. Authors’ Contributions : F.Y.B. contributed to the concept and design of the study, acquisition, analysis, and drafting of the manuscript; F.Y.B., M.Y., B.T., and O.G. equally contributed to the acquisition and analysis of data; H.D.V. contributed to the statistical analysis with data interpretation, H.D.V and B.G. equally auditing, and reviewing the manuscript. All authors critically revised the manuscript, agreed to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript. References WHO [https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer] T.C. 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Manson JE, Cook NR, Lee IM, Christen W, Bassuk SS, Mora S, Gibson H, Gordon D, Copeland T, D'Agostino D et al : Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease. N Engl J Med 2019, 380(1):33-44. Mahendra A, Karishma, Choudhury BK, Sharma T, Bansal N, Bansal R, Gupta S: Vitamin D and gastrointestinal cancer. J Lab Physicians 2018, 10(1):1-5. Bao A, Li Y, Tong Y, Zheng H, Wu W, Wei C: Tumor-suppressive effects of 1, 25-dihydroxyvitamin D3 in gastric cancer cells. Hepatogastroenterology 2013, 60(124):943-948. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5354042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374866019,"identity":"2871de47-b0f4-4f1a-ad24-d84ab83f286b","order_by":0,"name":"Funda Yildirim Borazan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCA0D0gE2CgY2B8QEDkJQDCz4gpCUBrIXZAKTFGCyYQEALQwIbiAXRktgAYuPTwnf7jOGBhDKLfD6xw2zSBWV26fPDDj8E2mInp9uAXYvkuRyDAwnnJCzbpJPZpGecS87deDsNKMKQbGx2ALsWgzM8BgcS2yQM2KTzj0nztjHnbpydANJyIHEbYS1AW3jb6tMNZ6d/IEnL4QR56Rz8tkieYSsA+QWkhdma59xxww3SOUARA9x+4TvDvPnDh7I6A/nZyYy3ecqq5eVnpwNFKuzkcGlhYOAwQHMqWKUBFpVwwP4AlS/fgE/1KBgFo2AUjEQAAEXIXcYwSnj9AAAAAElFTkSuQmCC","orcid":"","institution":"Gazi University","correspondingAuthor":true,"prefix":"","firstName":"Funda","middleName":"Yildirim","lastName":"Borazan","suffix":""},{"id":374866020,"identity":"6caad294-6ce1-4a9f-810e-269699d7e25b","order_by":1,"name":"Meryem Yilmaz","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Meryem","middleName":"","lastName":"Yilmaz","suffix":""},{"id":374866021,"identity":"11f814a3-452b-4958-9484-e1d879039df2","order_by":2,"name":"Barış Tuzcu","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Barış","middleName":"","lastName":"Tuzcu","suffix":""},{"id":374866022,"identity":"ea500bad-2738-4d47-9ad9-31c7ce151561","order_by":3,"name":"Ozlem Gulbahar","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Ozlem","middleName":"","lastName":"Gulbahar","suffix":""},{"id":374866023,"identity":"53deac29-384a-4234-b6a9-3fb55eec5a00","order_by":4,"name":"Berna Göker","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Berna","middleName":"","lastName":"Göker","suffix":""},{"id":374866024,"identity":"1b0de37e-68fc-4792-93a2-05771a2d4d3c","order_by":5,"name":"Hacer Dogan Varan","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Hacer","middleName":"Dogan","lastName":"Varan","suffix":""}],"badges":[],"createdAt":"2024-10-29 11:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5354042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5354042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70386581,"identity":"9c633e3f-ea8b-4d2b-9542-808a1d046f0a","added_by":"auto","created_at":"2024-12-02 17:21:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of MCV level for diagnosing malignancy in all patients with 95% confidence intervals. AUC is the area under the curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.MCVroc.png","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/32a42e293cfbccaf43e2595d.png"},{"id":70386557,"identity":"c101dd2e-2192-4cfe-ab6a-57f1677fb9b2","added_by":"auto","created_at":"2024-12-02 17:21:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of NLR level for diagnosing malignancy in all patients with 95% confidence intervals. AUC is the area under the curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.NLRroc.png","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/878237dfb611840c4f8bb778.png"},{"id":70386571,"identity":"2c55de39-43a6-44ed-b65e-b405185a73f5","added_by":"auto","created_at":"2024-12-02 17:21:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of 25(OH)D level for diagnosing malignancy in all patients with 95% confidence intervals. AUC is the area under the curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.25OHDroc.png","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/669e8bdc229df6d3e4fa2ddc.png"},{"id":70386556,"identity":"153ee9eb-f2a2-4615-aa13-448227fabed4","added_by":"auto","created_at":"2024-12-02 17:21:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of CAR level for diagnosing malignancy in all patients with 95% confidence intervals. AUC is the area under the curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.carroc.png","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/a5ae415fde9012d2a20b3878.png"},{"id":70386562,"identity":"a0583188-be9e-4159-a69b-2535fa73d712","added_by":"auto","created_at":"2024-12-02 17:21:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of Fe level for diagnosing malignancy in all patients with 95% confidence intervals. AUC is the area under the curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.Feroc.png","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/47d7f3a0850c16505716dbdc.png"},{"id":80303455,"identity":"0bc04edd-e1f4-4716-9dc1-df964b1c09d2","added_by":"auto","created_at":"2025-04-10 09:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5354042/v1/341dc87c-154a-41a5-a000-4ad264364413.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Predictive Value of Laboratory Parameters in Diagnosing Gastrointestinal Malignancy in Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastrointestinal (GI) cancers represent over a quarter of all cancer diagnoses and are responsible for one-third of cancer-related deaths globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In our country, colorectal cancer (CRC) ranks as the third most common cancer in men and the second most common cancer in women aged 70 and over [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, gastric cancer is the second leading cause of cancer-related mortality, along with CRC. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly diagnosis and treatment are critical in improving survival and longevity for GI cancers, as with most cancers[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, early diagnosis and treatment through screening tests to identify and remove precancerous lesions or to identify cancer at a curable stage is essential in managing GI cancers. Guidelines recommend colonoscopy screening for asymptomatic individuals, with recent recommendations suggesting that screening started at age 45 rather than 50 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there is no universally recommended screening program for gastric cancer except in populations with a high incidence. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nevertheless, performing combined upper and lower endoscopy may be preferred in geriatric patients, where screening is necessary due to advanced age.\u003c/p\u003e \u003cp\u003eIron deficiency anemia (IDA) can manifest gastrointestinal malignancies in older adults [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, when anemia is detected in this population, upper and lower gastrointestinal endoscopes are performed to identify potential malignancies or bleeding lesions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Laboratory parameters, including serum ferritin, transferrin saturation (TSAT), sedimentation rate, albumin (Alb), and lactate dehydrogenase (LDH), may also be associated with gastrointestinal malignancies [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given the well-established link between inflammation and cancer, recent studies have explored the relationship between certain cancers and systemic inflammation markers such as C-reactive protein (CRP)/Alb ratio (CAR) and the neutrophil-lymphocyte ratio (NLR) [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, research explicitly focusing on older adult patients remains limited.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate whether the pre-investigation biochemical parameters can predict the presence of premalignant/malignant disease in patients undergoing upper and lower gastrointestinal endoscopy. Identifying a potential biochemical predictor of malignancy in older adults could offer valuable insights for clinicians and enhance early detection and intervention strategies.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis retrospective study included 419 patients aged 60 and older who presented to our geriatric outpatient clinic with iron deficiency anemia (IDA) or persistent gastrointestinal symptoms. These patients, for whom upper and lower gastrointestinal endoscopies were planned, were seen between September 2016 and January 2021. A total of 109 older adult patients who underwent both upper and lower gastrointestinal endoscopies were included in the final analysis. Patients with a previous history of gastrointestinal malignancy (n\u0026thinsp;=\u0026thinsp;14) or those who could not tolerate the procedure and withdrew (n\u0026thinsp;=\u0026thinsp;18) were excluded from the study. This study was approved by the local ethics committee (05.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2020/663\u003c/span\u003e\u003cspan address=\"10.2020/663\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and conducted according to the Declarations of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline characteristics\u003c/h3\u003e\n\u003cp\u003eDemographic data, comorbidities, indications for endoscopy, and presenting complaints were collected retrospectively from outpatient clinic records. Routine laboratory parameters measured before endoscopy were recorded, including complete blood count, iron (Fe) (\u0026micro;g/dL), total iron-binding capacity (TIBC) (\u0026micro;g/dL), serum ferritin (ng/mL), transferrin saturation (TS) (%), vitamin B12 (ng/L), folate (\u0026micro;g/L), Aspartate Aminotransferase (AST) (U/L), Alanine Transaminase (ALT) (U/L), Lactate dehydrogenase (LDH) (U/L), electrolytes, total protein (TP) (g/dL), albumin (Alb) (g/dL), Blood Urea Nitrogen (BUN) (mg/dL), Creatinine (Cr) (mg/dL), 25-hydroxyvitamin D [25(OH)D] (\u0026micro;g/L), Erythrocyte sedimentation rate (ESR) (mm/h), and C-reactive protein (CRP) (mg/L). The recorded values represented the most recent laboratory assessments conducted before the endoscopic procedures. Anemia was defined as hemoglobin concentrations\u0026thinsp;\u0026lt;\u0026thinsp;13g/dL for men and \u0026lt;\u0026thinsp;12g/dL for women [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The neutrophil-lymphocyte ratio (NLR) was calculated by dividing the neutrophil count (*10^9/L) by the lymphocyte count (*10^9/L). The C-reactive protein/albumin ratio (CAR) was calculated by dividing CRP (mg/L) by albumin (g/dL).\u003c/p\u003e\n\u003ch3\u003eGastrointestinal Endoscopies\u003c/h3\u003e\n\u003cp\u003eEndoscopic imaging and, when available, biopsy results were reviewed for all patients. In upper gastrointestinal endoscopy, gastritis, ulcers, and the presence of \u003cem\u003eHelicobacter pylori\u003c/em\u003e were classified as benign conditions, while intestinal metaplasia was considered premalignant.\u003c/p\u003e \u003cp\u003eColonoscopy findings categorized hemorrhoids, diverticula, tubular adenoma, tubulovillous adenoma, villous adenoma, and angiodysplasia as benign conditions. Malignancy was confirmed in cases where cancerous lesions were detected.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 22.0 (IBM Corp., Armonk, NY, USA). Categorical variables were presented as frequencies and percentages. The normality of the distribution of continuous variables was assessed using the Kolmogorov-Smirnov test and visualized with histograms. Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed variables were presented as median (minimum-maximum).\u003c/p\u003e \u003cp\u003eComparisons between parametric variables were made using Student\u0026rsquo;s t-test, while the Mann-Whitney U test was used for non-normally distributed variables. The chi-square test was applied for categorical variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eReceiver Operating Characteristic (ROC) curve analysis was conducted using MedCalc Software Version 22.023 to assess the ability of biochemical parameters to predict the presence of malignant lesions for significant cut-off values, sensitivity, specificity, and positive and negative predictive values reported. The area under the curve (AUC) was evaluated with a 5% type I error threshold, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 109 older adult patients were included in the study. The mean age was 77.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 years, and 58.7% of the participants were female. Baseline characteristics and laboratory results stratified by benign and malignant lesion status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Malignant lesions were identified in 10.1% of the study population (11/109). Significant differences between the benign and malignant groups were observed in Hb, MCV, NLR, Fe, 25(OH)D, CRP, TP, Alb, BUN, CAR, and AST levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and baseline characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll participants (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Malignant (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year, (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (6.3\u0026ndash;18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8 (6.3\u0026ndash;18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (7.1\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, *10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.06 (3.03\u0026ndash;26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,17 (5.11\u0026ndash;16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.01 (3.03\u0026ndash;26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeut, *10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 (1.9\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.27 (2.91\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (1.9\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph, *10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (0.58\u0026ndash;3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (0.8\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (0.58\u0026ndash;3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13 (1.14\u0026ndash;14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81 (1.72\u0026ndash;8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.14\u0026ndash;14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.3 (26.5\u0026ndash;129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (26.5\u0026ndash;96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.5 (31.6\u0026ndash;129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (3-182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (3\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (11.7\u0026ndash;182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIBC (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246 (81\u0026ndash;509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (116\u0026ndash;509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245.5 (81\u0026ndash;494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (5-1083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (5-458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.5 (5-1083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSAT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3 (0.91\u0026ndash;224.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (0.91\u0026ndash;41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7 (3.7-224.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B12 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (50-2804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (92\u0026ndash;894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e345.5 (50-2804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic acid (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.9\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4 (4.7\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.9\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (5.4\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.3\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5 (5.4\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (1-140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.5 (13\u0026ndash;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (1-140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1-341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (3.1\u0026ndash;206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9 (1-341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.8\u0026ndash;8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (3.8\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (5-8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (2-5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (2-4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8 (2.3\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (7\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (16\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (7\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.43-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.6\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.43-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25 (0.22\u0026ndash;92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.35 (0.91\u0026ndash;55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85 (0.22\u0026ndash;92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (1\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (4\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (1\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (7\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (70\u0026ndash;646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (134\u0026ndash;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (70\u0026ndash;646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData were given as a number and percentages for categorical variables, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD* for normally distributed continuous variables, and median (min-max) for non-normally distributed continuous variables. *SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDM: Diabetes Mellitus, HTN: Hypertension, CKD: Chronic Kidney Disease, CHF: Congestive heart failure, Hb: Hemoglobin, NLR: neutrophil/lymphocyte ratio, MCV: Mean Corpuscular Volume, Fe: Iron, TIBC: Total Iron Binding Capacity, TSAT: Transferrin Saturation, 25(OH)D: 25-hydroxyvitamin D, ESR: Erythrocyte sedimentation rate, CRP: C-Reactive protein, TP: Total Protein, Alb: Albumin, BUN: Blood urea nitrogen, Cr: Creatinine, CAR: C-reactive protein/albumin ratio, AST: Aspartate Aminotransferase, ALT: Alanine Transaminase, LDH: Lactate dehydrogenase\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the endoscopic findings of the study participants. Gastric cancer was identified in 4 patients (3.7%), while intestinal metaplasia was detected in 21 patients (19.2%). Colonic polyps, including hyperplastic polyps, tubular, tubulovillous, and villous adenomas, were found in 31 patients (28.4%), and colorectal cancer was identified in 7 patients (6.4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEndoscopic data of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Gastrointestinal Endoscopies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll participants (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Gastitis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic Ulcer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelicobacter Pylori, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal Metaplasia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric Cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLower Gastrointestinal Endoscopies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll participants (n\u0026thinsp;=\u0026thinsp;109)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColonic Polyps, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiodysplasia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoroid, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColitis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal Cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData were given as a number and percentages for categorical variables.\u003c/p\u003e \u003cp\u003eMalignancy was detected in 8 out of the 73 patients with anemia (10.9%). Of these, three patients had gastric malignancies, and 5 had colorectal malignancies. The prevalence of anemia at the time of malignancy diagnosis among these patients was 88.9%.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2, 3, 4, and 5 display the Receiver Operating Characteristic (ROC) curves for MCV, NLR, 25(OH)D, CAR, and Fe in diagnosing gastrointestinal malignant lesions. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the predictive values of these parameters for malignant lesions in older adults, with optimal cut-off values set at \u0026le;\u0026thinsp;79.5 fL for MCV, \u0026gt;\u0026thinsp;3.28 for NLR, \u0026lt;\u0026thinsp;12 \u0026micro;g/L for 25(OH)D, \u0026gt;\u0026thinsp;5.93 for CAR, and \u0026le;\u0026thinsp;22 \u0026micro;g/L for Fe.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve of laboratory parameters in the diagnosis of malignant lesions in older adults\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParameters\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+PV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-PV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.647\u0026ndash;0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.589\u0026ndash;0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.661\u0026ndash;0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e97.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.629\u0026ndash;0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.606\u0026ndash;0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eMCV: Mean Corpuscular Volume, NLR: neutrophil/lymphocyte ratio, 25(OH)D: 25-hydroxyvitamin D, CAR: C-reactive protein/albumin ratio, Fe: Iron, AUC: area under the curve, SE: Standard Error, CI: Confidence interval, PV: Predictive Value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study identified significant differences between the malignant and benign groups concerning NLR, MCV, 25(OH)D, Fe, and C-reactive protein/albumin ratio (CAR) levels before diagnosis. Notably, the ROC curve analysis suggests that NLR and CAR are valuable indicators for predicting malignant lesions in older adults, with optimal cut-off values of 3.28 and 5.93, respectively. To our knowledge, this is the first study to investigate pre-diagnosis laboratory parameters for predicting malignant gastrointestinal lesions, specifically in older adults.\u003c/p\u003e \u003cp\u003eIn recent years, systemic inflammatory response markers such as NLR, CRP, and CAR have been shown to predict prognosis in patients diagnosed with cancer.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, studies have demonstrated that The Glasgow Prognostic Score (derived from serum CRP and Alb levels) is a reliable prognostic factor in individuals with non-small cell lung cancer and gastric and colorectal cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While these studies emphasize the prognostic value of inflammatory markers in diagnosed patients, our study demonstrates that NLR and CAR can also serve as predictive markers for malignancy in undiagnosed patients.\u003c/p\u003e \u003cp\u003eA study involving 544 adults suggested that male patients with anemia should undergo comprehensive endoscopic evaluation for gastrointestinal neoplasms when serum ferritin levels are below 44 ng/mL or transferrin saturation (TSAT) is less than 9% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study is particularly noteworthy as it identified a ferritin cut-off within the normal range despite being conducted in anemic individuals. In our study, while no significant difference in ferritin levels was observed between patients with and without malignancy, a significant difference was noted in Fe levels. Additionally, their study population included a broad age range (20\u0026ndash;92 years), whereas our research specifically focused on older adults[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA study of 222 adult patients with unintentional weight loss, 22 of whom were diagnosed with gastrointestinal cancer, demonstrated that ferritin above 100 mcg/L could rule out colon cancer but not gastric or rectal cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, ferritin was \u0026gt;\u0026thinsp;100 mcg/L in 18% of patients with malignancies, including cases of colon and gastric cancer. However, as ferritin is an acute-phase reactant that can rise with inflammation and its prevalence increases with age, relying on elevated ferritin to exclude cancer may lead to missed diagnoses[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Some findings also suggest that ferritin levels in many elderly individuals with iron deficiency anemia may be normal or elevated. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, we recommend using other laboratory parameters, such as Fe, alongside ferritin to provide a more reliable prediction of gastrointestinal cancer, particularly in older adults. We hypothesize that Fe may be a more appropriate biomarker than ferritin in this population.\u003c/p\u003e \u003cp\u003eIn another study, upper gastrointestinal endoscopy and colonoscopy were performed on 96 older adults with iron deficiency anemia. They diagnosed gastrointestinal malignancy in 15 (15.6%) patients (8 colon, one esophageal and six gastric cancers) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Their study's malignancy rate was higher than ours (10.9%). This may be because the mean Hb value of their study population was 9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 g/dl [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while the median Hb of our anemic group was 10.2 (6.3\u0026ndash;12.7) g/dl. It is also known that cancer prevalence varies from region to region in the world and in our country. It is known that gastric cancer is much more common in the eastern areas of country [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and according to some epidemiological studies, colorectal carcinoma is more common in western regions of our country [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA retrospective study of 333 patients over 85 years old who underwent colonoscopy examined the role of MCV as a predictor of CRC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study revealed that decreased MCV was an independent predictor of CRC. Preoperative MCV has also been suggested to be a prognostic factor for esophageal squamous cell carcinoma and colorectal cancer [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study's ROC curve indicates that MCV could be a helpful indicator in predicting gastrointestinal malignant lesions in older adults, with a cut-off value of \u0026le;\u0026thinsp;79.5 fL.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated the association between 25(OH)D and various cancers' incidence, prognosis, and mortality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. One randomized placebo-controlled trial involving 25,871 participants evaluated vitamin D\u003csub\u003e3\u003c/sub\u003e (cholecalciferol) supplementation of 2000 IU per day and omega-3 fatty acids (1 g/ day) for the prevention of cancer and cardiovascular disease in adults aged 50 and older in the United States [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. After a median follow-up period of 5.3 years, cancer was diagnosed in 1,617 participants, 793 in the vitamin D group and 824 in the placebo group\u0026mdash; showing no significant reduction in invasive cancer incidence with vitamin D supplementation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, 25(OH)D plays a crucial role in the development of GI malignancies through the Vitamin D receptor (VDR), influencing processes such as differentiation, proliferation, metastasis, invasion, angiogenesis, and apoptosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In our study, significantly lower levels of 25(OH)D in patients with malignancy further support these findings.\u003c/p\u003e \u003cp\u003eIn conclusion, this study suggests that older adults with MCV\u0026thinsp;\u0026le;\u0026thinsp;79.5 fL, NLR\u0026thinsp;\u0026lt;\u0026thinsp;3.28, 25(OH)D\u0026thinsp;\u0026le;\u0026thinsp;12 \u0026micro;g/L, Fe\u0026thinsp;\u0026le;\u0026thinsp;22 \u0026micro;g/dL, and CAR\u0026thinsp;\u0026gt;\u0026thinsp;5.93 should undergo endoscopic evaluation for potential GI neoplasms. Early identification of cancer using these laboratory parameters could help lower disease-specific mortality by facilitating the detection and removal of precursor lesions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e:\u0026nbsp;This study was approved by the Gazi University Ethics Committee\u0026nbsp;(05.10.2020/663) and conducted according to the Declarations of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors declare no funding for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of data and materials:\u003c/strong\u003e All data generated or analyzed during this study are available upon request and can be obtained from the corresponding author.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e: F.Y.B. contributed to the concept and design of the study, acquisition, analysis, and drafting of the manuscript; F.Y.B., M.Y., B.T., and O.G. equally contributed to the acquisition and analysis of data; H.D.V. contributed to the statistical analysis with data interpretation, H.D.V and B.G. equally auditing, and reviewing the manuscript. All authors critically revised the manuscript, agreed to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO [https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer]\u003c/li\u003e\n \u003cli\u003eT.C. 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M\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: Epidemiology of colorectal cancer in Turkey: A cross-sectional disease registry study (A Turkish Oncology Group trial). \u003cem\u003eTurk J Gastroenterol\u0026nbsp;\u003c/em\u003e2015, 26(2):145-153.\u003c/li\u003e\n \u003cli\u003e\u0026Ccedil;ağlar E BK, Atasoy D, Sezgin \u0026Ccedil;ağlar A, Akay A.: The incidences of upper gastrointestinal and colorectal malignancies in Ağrı city of eastern Anatolia. \u003cem\u003eCMJ\u0026nbsp;\u003c/em\u003e2019, 41(3):537-543.\u003c/li\u003e\n \u003cli\u003eKato M, Kubosawa Y, Hiarai Y, Abe K, Hirata T, Takada Y, Wada M, Takatori Y, Kinoshita S, Takabayashi K\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: Usefulness of Mean Corpuscular Volume for Detection of Advanced Colorectal Cancer in Patients Older than 85 Years. \u003cem\u003eDigestion\u0026nbsp;\u003c/em\u003e2018, 97(2):177-182.\u003c/li\u003e\n \u003cli\u003eZheng YZ, Dai SQ, Li W, Cao X, Li Y, Zhang LJ, Fu JH, Wang JY: Prognostic value of preoperative mean corpuscular volume in esophageal squamous cell carcinoma. \u003cem\u003eWorld J Gastroenterol\u0026nbsp;\u003c/em\u003e2013, 19(18):2811-2817.\u003c/li\u003e\n \u003cli\u003eNagai H, Yuasa N, Takeuchi E, Miyake H, Yoshioka Y, Miyata K: The mean corpuscular volume as a prognostic factor for colorectal cancer. \u003cem\u003eSurg Today\u0026nbsp;\u003c/em\u003e2018, 48(2):186-194.\u003c/li\u003e\n \u003cli\u003eGiovannucci E: The epidemiology of vitamin D and cancer incidence and mortality: a review (United States). \u003cem\u003eCancer Causes Control\u0026nbsp;\u003c/em\u003e2005, 16(2):83-95.\u003c/li\u003e\n \u003cli\u003eManson JE, Cook NR, Lee IM, Christen W, Bassuk SS, Mora S, Gibson H, Gordon D, Copeland T, D\u0026apos;Agostino D\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease. \u003cem\u003eN Engl J Med\u0026nbsp;\u003c/em\u003e2019, 380(1):33-44.\u003c/li\u003e\n \u003cli\u003eMahendra A, Karishma, Choudhury BK, Sharma T, Bansal N, Bansal R, Gupta S: Vitamin D and gastrointestinal cancer. \u003cem\u003eJ Lab Physicians\u0026nbsp;\u003c/em\u003e2018, 10(1):1-5.\u003c/li\u003e\n \u003cli\u003eBao A, Li Y, Tong Y, Zheng H, Wu W, Wei C: Tumor-suppressive effects of 1, 25-dihydroxyvitamin D3 in gastric cancer cells. \u003cem\u003eHepatogastroenterology\u0026nbsp;\u003c/em\u003e2013, 60(124):943-948.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastrointestinal Malignancy, Gastrointestinal endoscopy, Predictive, Older adult, Laboratory parameters","lastPublishedDoi":"10.21203/rs.3.rs-5354042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5354042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eEarly diagnosis and treatment through screening tests to identify and remove precancerous lesions or detect cancer at a curable stage are crucial for managing gastrointestinal (GI) cancers. However, achieving early detection in older adults, particularly those who are frail, can be challenging. This retrospective study aimed to evaluate the predictive value of pre-endoscopic biochemical parameters for detecting malignant lesions in older adults undergoing upper and lower GI endoscopies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and Methods: \u003c/strong\u003eWe retrospectively analyzed 419 individuals aged 60 and above. Of these, 109 older adult patients who underwent both upper and lower GI endoscopies were included in the study. Patients with a prior history of GI cancer or those who could not complete the procedure due to intolerance were excluded. Patients were categorized based on the presence of benign or malignant lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Malignant lesions were identified in 10.1% (11/109) of patients. Statistically significant differences were observed between the benign and malignant groups in terms of hemoglobin (Hb), neutrophil count, mean corpuscular volume (MCV), neutrophil-lymphocyte ratio (NLR), iron (Fe), 25-hydroxyvitamin D [25(OH)D], C-reactive protein (CRP), total protein, albumin (Alb), blood urea nitrogen (BUN), CRP/albumin ratio (CAR), and aspartate aminotransferase (AST). The ROC curve analysis suggests that MCV, NLR, 25(OH)D, Fe, and CAR are valuable indicators for predicting malignant lesions in older adults, with optimal cut-off values of 79.5 fL, 3.28, 12 µg/L, 22 µg/dL, and 5.93, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings underscore the predictive value of CAR, NLR, MCV, Fe, and 25(OH)D in identifying GI neoplasms in older adults. The study suggests that patients should undergo an endoscopic evaluation to investigate potential GI malignancies when they have MCV ≤79.5 fL, NLR \u0026lt;3.28, 25(OH)D ≤12 µg/L, Fe≤22 µg/dL and CAR \u0026gt;5.93.\u003c/p\u003e","manuscriptTitle":"The Predictive Value of Laboratory Parameters in Diagnosing Gastrointestinal Malignancy in Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 16:28:25","doi":"10.21203/rs.3.rs-5354042/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":"19e5bf5a-4e02-411a-9f88-3d3083bd8374","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-10T09:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 16:28:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5354042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5354042","identity":"rs-5354042","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}