Association of Diabetes and Cancers: An analysis of the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2005-2015 Dataset

Association of Diabetes and Cancers: An analysis of the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2005-2015 Dataset | 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 Association of Diabetes and Cancers: An analysis of the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2005-2015 Dataset Shahryar Zeighami, Ali Seifi, Mohammad Hossein Sharifi, Zahra Gheibi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7414347/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 Background There is a growing body of evidence on the relationship between diabetes and cancer. However, the literature is limited and inconclusive about whether diabetes is associated with an increased or decreased risk of developing each cancer. Method In this retrospective cohort study, 44,262,443 patients aged ≥ 50 years who were discharged from community hospitals between 2005–2015 were obtained from the Healthcare Cost and Utilization Project - Nationwide Inpatient Sample (HCUP-NIS) database. Univariate and multivariate logistic regression analyses were used to study the association between diabetes and the 33 selected cancers. Results The prevalences of diabetes and cancer were 29.48 and 20.57%, respectively. After adjusting for age, sex, race, smoking, alcohol consumption, and obesity, the odds of association between selected cancers and diabetes represented diabetes as a protective factor for cancer, except for pancreatic cancer (OR [odds ratio]: 1.20; 95% CI: 1.18, 1.21) and liver and intrahepatic cancers (OR: 1.42; 95% CI: 1.40, 1.43). Conclusion Using a large amount of individualized data, this study implied the likelihood of a protective role of diabetes in most of the 33 selected cancers. Further research is needed to elucidate the mechanisms linking diabetes with cancer development or progression. Association Diabetes Cancer HCUP-NIS Figures Figure 1 Introduction In recent decades, evidence has emerged regarding the association between diabetes and cancer( 1 ). These findings remain limited and contradictory; that is, diabetes has been associated with an increased risk of some cancers (e.g., liver, pancreas, breast, endometrium, kidney, colon, and rectum) and decreased risk of other cancers (e.g., non-aggressive forms of prostate cancer)( 2 – 8 ). Nonetheless, while early evidence has recognized such a correlation, more recent prospective studies have found no strong evidence to support an association between diabetes and overall cancer risk or site-specific cancer risk( 3 ). While the risk factors of many cancers are not well defined, the potential shared risk factors between cancer and diabetes are still not well understood( 9 ). There are complex interactions between non-modifiable factors (e.g., age and genetic susceptibility) and modifiable factors (e.g., diet and physical activity)( 10 ). One such linking factor might be the medications used for glycemic control( 11 ). Anti-hyperglycemic medications, such as metformin, may have a protective effect on the progression of cancer and may even play a role in the treatment of certain cancers( 12 ). Conversely, it appears that high cumulative doses of insulin and pioglitazone (> 24 months and > 28,000 mg cumulative dose) may be associated with an increased risk of certain cancers( 13 ). Other proposed linking factors include microangiopathic complications of diabetes and, lifestyle modifications( 14 – 17 ). By and large, there is currently no definitive biological mechanism explaining the link between diabetes and cancer risk( 3 ). In addition, a report by the American Diabetes Association and the American Cancer Society on the relationship between diabetes and certain cancers was published in December 2009 and addressed some unanswered questions about these relationships( 8 ). In this study, we aimed to assess the association between diabetes and its treatment with cancers in a large in-patient sample. This research might help answer questions regarding the association between diabetes and cancer risk. Methods In this retrospective cohort study, data from 44,262,443 patients who were discharged from community hospitals and aged ≥ 50 years during 2005–2015 were obtained from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) database. The HCUP-NIS dataset contains anonymized and de-identified data from approximately 20% of all community hospital discharges in the United States obtained by applying a weighted sampling design, which is expected to yield national estimates from the NIS database. The database is considered one of the most comprehensive and reliable sources of inpatient care and outcomes in the United States and includes International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for procedures and diagnoses, patient demographics (i.e., age, sex, race/ethnicity [White, Black, Hispanic, Asian or Pacific islanders, Native American, or other]), hospital characteristics (i.e., size, location/teaching status, region, and control type [government, nonprofit, or private]), primary payer, disease severity at admission, risk of in-hospital mortality, in-hospital mortality, length of hospital stay, total charges, and comorbidities (i.e., alcohol consumption, cigarette smoking, obesity). Additional information on HCUP-NIS is available at https://www.hcup-us.ahrq.gov/nisoverview.jsp . The Institutional Review Board of the University of Texas Health at San Antonio exempted this analysis from a full review. It is worth noting that the study utilized a secondary, de-identified dataset and therefore informed consent was not required. Also, these data were data from hospitalized patients and no animal data were used. All data from diabetes and cancer patients were analyzed and there was no attrition. This study was not a randomized clinical trial and there was no need for randomization, blinding, and clinical trial protocols. In this study, we did not use any Antibodies, Cell Lines, Oligonucleotides, Organisms Plasmids and related software. We included patients aged ≥ 50 years, male and female for three reasons: first, to exclude all women who were admitted for delivery and could have gestational diabetes; second, to capture individuals beyond the average age of diabetes diagnosis, allowing for the assessment of disease progression and long-term management; and third, to include older subjects being at a higher risk of developing cancer. In the pre-analytical step, based on ICD-9-CM codes, each type of cancer was included in the analysis as the principal (1st, as the reason for admission) diagnosis or as a subsequent (2-to-30, as a comorbidity) diagnosis. Trends in the prevalence of diabetes and selected cancers were shown. In addition, the frequency of diabetes stratified by demographic variables and some HCUP-NIS characteristics was also analyzed using the Wald χ 2 test. The main analysis was conducted in two steps. In the first step, 99 univariable analyses were performed between diabetes and 33 selected cancers (diagnosed as 1st, 2nd-to-30th and 1st-to-30th diagnosis) – A total of 3 univariable analyses for each cancer were performed – using the Wald χ 2 test. In the second step, 99 multivariable logistic regressions – 3 multivariable logistic regressions for each cancer – were carried out to adjust for probable confounders, including age, sex, race, obesity, smoking and alcohol consumption. Finally, the estimated correlation coefficients were reported using the prevalence ratio (95% confidence interval [CI]) as a proxy for relative risk. The results of the univariable and multivariable analyses are shown in Supplementary File 1, Table S1 , and Supplementary File 2, S2, S3, and S4, respectively. All analyses were performed using Stata version 12.0 software (Stata, College Station, TX, USA). Results The study cohort primarily consisted of female patients (53.95%), individuals identified as white (75.38%), discharges from urban hospitals (86.195), and older adults (51.72% of patients aged ≥ 70 years; mean age, 70.37 years). A total of 13,048,961 patients with diabetes were detected out of 44,262,443 discharged patients during the study period, resulting in a diabetes prevalence of 29.48% among hospitalized adults aged ≥ 50 years. Higher prevalence rates were found in men, individuals aged 60–69 years, Hispanics and Native Americans, and patients in the southern region (Table 1). A total of 9,103,889 (20.57%) patients had at least one out of 33 different cancers, with an aggregated count of 11,752,871. There was a notable increasing trend of 25.79% in 2005 to 32.31% in 2015 among hospitalized patients with diabetes in the United States. The data also showed an increase in the proportion of hospitalized patients who were defined and used consistently across the time points–from 4.47% and 21.32% in 2005 to 7.14% and 25.17% in 2015, respectively (Supplementary File 3: Fig. S1). In addition, the prevalence of most of the 33 selected cancers increased compared to that in the baseline year (2005). According to the 11-year average prevalence rates, breast cancer (32.3%); prostate cancer (29.5%); cancers of the lung, bronchus, and trachea (26.5%); and colon cancer (21.4%) were the most prevalent cancers among hospitalized patients aged ≥ 50 years in the United States (Supplementary File 3: Table S5). Table 2 shows the odds of association between 33 cancers (1st, 2nd-to-30th and 1st-to-30th diagnosis) and diabetes, using multivariable logistic regression adjusted for age, sex, race, smoking, alcohol consumption, and obesity among hospitalized patients in the United States between 2005 and 2015. We found that diabetes as a 1st-to-30th diagnosis had a protective role for 33 selected cancers, except pancreatic cancer (OR: 1.20; 95% CI: 1.18, 1.21) and liver and intrahepatic cancers (OR: 1.42; 95% CI: 1.40, 1.43) (Fig. 1). Table 2 Summary correlation coefficients, odds ratio (95% CI) between diabetes and cancers (as 1st, 2nd-to-30th and 1st-to-30th diagnosis) using multivariable logistic regression amongst hospitalized patients aged ≥ 50 years in the United States, 2005–2015 Cancer/Cancer group Cancer diagnosed as: Reason of admission [1st diagnosis] Comorbidity [2nd-to-30th ] Any [1st-to-30th] Head and neck 0.50 [0.49, 0.52] 0.59 [0.59, 0.60] 0.58 [0.57, 0.58] Esophagus 0.59 [0.57, 0.61] 0.67 [0.66, 0.68] 0.65 [0.64, 0.66] Stomach 0.69 [0.68, 0.71] 0.73 [0.72, 0.75] 0.72 [0.71, 0.73] Colon 0.71 [0.70, 0.71] 0.94 [0.93, 0.94] 0.88 [0.88, 0.89] Rectum and anus 0.60 [0.59, 0.61] 0.70 [0.69, 0.71] 0.67 [0.66, 0.67] Liver, intrahepatic 1.07 [1.05, 1.10] 1.25 [1.23, 1.27] 1.20 [1.18, 1.21] Pancreas 1.39 [1.37, 1.42] 1.43 [1.41, 1.45] 1.42 [1.40, 1.43] Gastrointestinal, peritoneum 0.70 [0.69, 0.72] 1.06 [1.05, 1.08] 0.94 [0.93, 0.96] Lung, Bronchus 0.65 [0.64, 0.65] 0.75 [0.75, 0.76] 0.73 [0.72, 0.73] Other respiratory tissue 0.58 [0.53, 0.62] 0.64 [0.61, 0.67] 0.62 [0.59, 0.65] Bone tissue 0.55 [0.53, 0.57] 0.61 [0.59, 0.63] 0.59 [0.57, 0.60] Skin, Melanomas 0.61 [0.57, 0.65] 0.73 [0.72, 0.74] 0.73 [0.72, 0.73] Skin, non-epithelial 0.69 [0.66, 0.72] 0.79 [0.78, 0.80] 0.79 [0.78, 0.79] Breast 0.54 [0.53, 0.55] 0.89 [0.89, 0.89] 0.85 [0.85, 0.86] Uterus 0.70 [0.69, 0.71] 1.01 [1.00, 1.03] 0.92 [0.91, 0.93] Cervix 0.50 [0.47, 0.52] 0.80 [0.79, 0.81] 0.76 [0.75, 0.77] Ovary 0.47 [0.46, 0.48] 0.66 [0.65, 0.67] 0.62 [0.61, 0.62] Other female cancers 0.72 [0.69, 0.76] 0.91 [0.89, 0.93] 0.87 [0.85, 0.88] Prostate 0.38 [0.37, 0.39] 0.83 [0.83, 0.83] 0.74 [0.74, 0.75] Testis 0.47 [0.35, 0.63] 0.82 [0.79, 0.85] 0.81 [0.79, 0.84] Other male cancers 0.84 [0.75, 0.94] 0.96 [0.91, 1.00] 0.93 [0.89, 0.98] Bladder 0.74 [0.72, 0.95] 0.96 [0.95, 0.96] 0.92 [0.91, 0.92] Kidney (Renal) 0.78 [0.77, 0.80] 1.02 [1.02, 1.03] 0.96 [0.95, 0.97] Other urinary track 0.69 [0.65, 0.73] 0.82 [0.79, 0.85] 0.77 [0.75, 0.80] Brain 0.67 [0.66, 0.68] 0.70 [0.69, 0.72] 0.70 [0.69, 0.71] Thyroid 0.60 [0.58, 0.62] 0.87 [0.86, 0.89] 0.80 [0.79, 0.82] Hodgkin 0.69 [0.63, 0.75] 0.81 [0.79, 0.83] 0.80 [0.78, 0.82] Non-Hodgkin 0.69 [0.67, 0.71] 0.79 [0.78, 0.80] 0.77 [0.77, 0.78] Leukemia 0.66 [0.65, 0.67] 0.85 [0.85, 0.86] 0.82 [0.81, 0.83] Multiple myeloma 0.60 [0.58, 0.62] 0.78 [0.77, 0.79] 0.74 [0.73, 0.75] Other unspecified 0.60 [0.57, 0.63] 0.74 [0.73, 0.75] 0.73 [0.72, 0.74] Secondary malignancies 0.64 [0.63, 0.64] 0.68 [0.68, 0.69] 0.68 [0.68, 0.68] Neoplasms unspecified 0.72 [0.71, 0.74] 0.90 [0.90, 0.91] 0.87 [0.87, 0.88] Note: [1] Gray cells indicate diabetes as a risk factor for that specific cancer; [2] Cloud gray cells indicate diabetes as a weak risk factor for that specific cancer. Discussion Although some studies have shown that there might be some mechanisms linking diabetes with the incidence of many site-specific cancers, significant debate remains because of the limitations of these studies for a conclusive interpretation of the outcome( 3 – 5 , 8 ). In this retrospective cohort of more than 40 million hospitalized adult patients, we investigated the association between diabetes and cancer. Our major finding was that diabetes may be associated with a decreased risk of developing most selected cancers, except for pancreatic cancer and liver and intrahepatic cancers. Several epidemiological studies have reported increased cancer incidence in some medical conditions, including obesity, metabolic syndrome, polycystic ovary syndrome, insulin-resistant states, and diabetes( 7 , 18 ). Hyperinsulinemia, hyperglycemia, inflammation, and oxidative stress have been proposed as some of the possible mechanisms link these diseases to cancer( 5 , 19 ). Furthermore, the oncogenic effects of some anti-hyperglycemic drugs represent an important pathway that might have either increasing or decreasing effect on cancer incidence( 20 – 22 ). According to the American Association of Clinical Endocrinologists and the American College of Endocrinology (AACE/ACE) consensus statement, metformin is thought to play a protective role in cancer development, whereas exogenous insulin use appears to be associated with an increased risk of cancer( 13 ). Anti-hyperglycemic medications have been suggested to affect cancer risk through several direct and indirect mechanisms, including serum insulin levels, growth factors, chronic inflammation, estrogen, testosterone, and weight loss( 6 , 13 , 18 , 20 , 23 ). In addition, a cohort study conducted by Dankner et al. ( 24 ), showed that poor glycemic control was weakly correlated with cancer risk in patients with diabetes. Therefore, it can be postulated that diabetes and cancer are linked through medications using for glycemic control rather than per se. However, the effects of newer diabetes medications on cancer progression remains uncertain( 13 ). In addition, specific linking factors for diabetes and each type of cancer have not yet been thoroughly established, and future research is required. Another linking factor could be lifestyle modification, as supported by several studies( 15 , 16 ). Obesity, physical inactivity, and an unhealthy diet can influence both diabetes and several cancers. Compared to normal-weight individuals, obese patients with poorly controlled insulin-treated type 2 diabetes mellitus are at a higher risk for malignancies, particularly breast cancer in women and colorectal cancer in men( 22 ). A diagnosis of diabetes, especially when accompanied by cardiovascular disease, often prompts changes in lifestyle behaviors through consultation with a health care provider( 25 , 26 ). As a consequence of behavioral changes, the physiology of the body may influence the risk of developing cancer, particularly at specific cancer sites. Furthermore, there were differences in the average age of diabetes and cancer diagnosis. Over time, the average age at diabetes diagnosis has declined to 47.9 years of age( 27 ). However, half of all cancers are diagnosed after 65 years of age( 28 ). It is possible that improvements in early diagnosis, medical management, lifestyle modifications, and increased life expectancy of patients with diabetes in middle age, which can reduce the risk factors shared with certain cancers, have affected the prevalence of some cancers. Furthermore, clinical evidence has shown prolonged survival in patients with diabetes and non-small cell lung cancer. Hanbali et al. ( 14 ), showed that this survival benefit was likely related to the protective effect of diabetes-associated microangiopathic complications on slowing the progression of metastatic lesions. The results showed that patients with diabetes were more likely to have pancreatic, hepatic and intrahepatic cancers than those without diabetes. This finding broadly supports previous research linking diabetes to these cancers( 29 – 33 ). Li et al. ( 34 ), conducted a multivariable pooled analysis of three large case-control studies and found that diabetes had an odds ratio of 1.8 (95% CI: 1.5–2.1) for pancreatic cancer. In addition, in a meta-analysis of 21 studies, Ren et al. ( 35 ) found that diabetes was associated with an increased risk of biliary tract cancer with a relative risk of 1.43 (95% CI: 1.18, 1.72). Moreover, a population-based cohort study found that patients with diabetes had a higher risk of developing hepatocellular carcinoma (adjusted hazard ratio,1.73 ; 95% CI: 1.47, 2.03), compare to those without diabetes. These findings underscore the need for further high-quality studies to elucidate the mechanisms that underlie these relationships( 36 ). Another finding of this study was a modest association between diabetes and kidney cancer, peritoneal cancer, uterine cancer, or bladder cancer; however, these associations are not consistently supported by previous research. A recent meta-analysis showed a positive association between diabetes and the risk of kidney cancer; however, future studies should determine whether this association is causative( 37 ). A linkage study using two statistical databases in England found significantly high odds ratios for uterine cancers in patients with diabetes( 38 ). The National Health Insurance Study in Taiwan showed that patients with diabetes are at an increased risk of bladder cancer( 39 ). In addition, a cohort study in the United States showed that bladder cancer incidence was increased with anti-hyperglycemic medication (pioglitazone) in patients with diabetes aged ≥ 65 years, but not in the incident cohorts( 40 ). However, a meta-analysis showed a modest, insignificant positive association between bladder cancer incidence and type 1 diabetes( 41 ). In summary, more studies are needed to determine a positive, null, or negative association between diabetes and cancer. This study had some limitations. It was not possible to extract data regarding the time interval between the incidence of cancer and diabetes from the HCUP-NIS database. In addition, while we included confounding factors such as age, sex, race, smoking, alcohol consumption, and obesity, the models lacked other possible confounding factors, such as occupational exposure, environmental exposure, and anti-hyperglycemic medication use. Conclusion This large-scale, retrospective cohort study provides new insights into the direction of association between diabetes and cancer. We found that diabetes may have a protective effect against most of the 33 selected cancers, except for pancreatic, liver, and intrahepatic cancers. Further research is needed to elucidate the mechanisms that link diabetes to certain cancers. Declarations Ethical Approval and consent to participate Based on the retrospective nature of this study and the use of de-identified data from the Healthcare Cost and Utilization Project (HCUP) database, the requirement for informed consent was waived. In accordance with U.S. federal regulations (45 CFR 46.102) and institutional policy at the University of Texas Health Science Center at San Antonio, analyses of such de-identified data are considered exempt from Institutional Review Board (IRB) review. The study was conducted in accordance with the ethical principles set forth in the Declaration of Helsinki (1964) and its later amendments. Availability of data and materials The dataset used in this study was obtained with a certificate of completion for HCUP data use from the corresponding author, (HCUP Certification Code: HCUP-4T78I53DU), and was not publicly available. Data may be made available from the corresponding author upon reasonable request and with permission from H-Cup (Email: [email protected] ). Funding This study did not receive any financial support. Competing interests The authors declare that there is no competing interest. Author contributions A.M, S.Z. and A.S. conceptualized and designed the study. M.H.S. was responsible for data acquisition and formal analysis. Z.G. provided expertise in methodology and supervision. A.R. and A.H. contributed to the data interpretation and manuscript writing. S.S. and H.S. reviewed and edited the manuscript, providing critical intellectual feedback. A.M. oversaw the project and validated the research findings. All authors read and approved the final manuscript. Acknowledgement The authors would like to thank H-CUP for providing the dataset used in this study. References Tsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. Bmj. 2015;350. Wojciechowska J, Krajewski W, Bolanowski M, Kręcicki T, Zatoński T. Diabetes and cancer: a review of current knowledge. Experimental and Clinical Endocrinology & Diabetes. 2016;124(05):263-75. Goto A, Yamaji T, Sawada N, Momozawa Y, Kamatani Y, Kubo M, et al. Diabetes and cancer risk: a Mendelian randomization study. 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Oskoui HB, Bogumil DD, Kysh L, Barrett M, Siegmund K, Cortessis VK. 1685-P: Type 1 Diabetes Mellitus and Incident Bladder Cancer: A Systematic Review and Meta-analysis. Diabetes. 2019;68(Supplement_1):1685-P. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files suppl.file.1.docx suppl.file.2.docx suppl.file.3.docx Table1.docx 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. We do this by developing innovative software and high quality services for the global research community. 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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-7414347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514841647,"identity":"c4493d85-ba20-4330-9e94-21e8363f8992","order_by":0,"name":"Shahryar Zeighami","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shahryar","middleName":"","lastName":"Zeighami","suffix":""},{"id":514841648,"identity":"f20e0757-bd28-43ac-a8b6-4ac73154b2e6","order_by":1,"name":"Ali Seifi","email":"","orcid":"","institution":"University of Texas Health Science Center at San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Seifi","suffix":""},{"id":514841649,"identity":"0724de2e-cb4d-408b-9bbd-ce0171772d8b","order_by":2,"name":"Mohammad Hossein Sharifi","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hossein","lastName":"Sharifi","suffix":""},{"id":514841650,"identity":"1d62c166-409d-46a9-b44f-812ffb2bab04","order_by":3,"name":"Zahra Gheibi","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Gheibi","suffix":""},{"id":514841651,"identity":"5a280f07-47e3-4547-bc86-e5c5670c0330","order_by":4,"name":"Alireza Rezvani","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Rezvani","suffix":""},{"id":514841652,"identity":"d59abfd8-6367-4f84-8551-6464164b97ea","order_by":5,"name":"Alireza Heiran","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Heiran","suffix":""},{"id":514841653,"identity":"94b47bd5-c0bf-4b80-b5dc-b186fe808ae0","order_by":6,"name":"Saba Sahraian","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Saba","middleName":"","lastName":"Sahraian","suffix":""},{"id":514841654,"identity":"6d9b8ab5-8749-4201-97cc-bb141115fee8","order_by":7,"name":"Hadis Salimi","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hadis","middleName":"","lastName":"Salimi","suffix":""},{"id":514841655,"identity":"dcc228f8-b0bd-4240-8f26-65d1fa5442eb","order_by":8,"name":"Alireza Mirahmadizadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZjBiYGxjYD4AFzyASzWaFrYEIrUwQLU0MPAYEOcu3Xbeh58Lc+xk+9jPfP7Mm1PHwN9+gPFwBR4tZofZjaVnbks2buPJ3SbNu+0wg8SZBIaDZ/BqYWMAqmRObGPI3cbMuw3oixsMDAcb8Gth/s27rT6xjf/N48+82+oY5InQwgZyT2KbRA7YOgYDYrRY8247btwm8cxMcu62wzyGZxIb8Gs5f4z5Nu+2atn5/cmPP7zdVicnd/zw4Y/4tGAAHnAcjYJRMApGwSigDAAANTFJ0PjVe/oAAAAASUVORK5CYII=","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Mirahmadizadeh","suffix":""}],"badges":[],"createdAt":"2025-08-20 07:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7414347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7414347/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91509564,"identity":"235b827d-2ae2-474e-a176-b5da9e99ed48","added_by":"auto","created_at":"2025-09-17 08:39:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102001,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted odds ratios obtained by multivariable logistic regression on the association between diabetes and 33 selected cancers (as 1st-to-30th diagnosis) amongst hospitalized patients aged ≥ 50 years in the United States, 2005–15\u003c/p\u003e","description":"","filename":"drawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/98ac84e70023351bfb053da3.png"},{"id":96611265,"identity":"f420667f-b6ea-4f45-9291-c39201997010","added_by":"auto","created_at":"2025-11-24 09:33:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":740306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/6672bfc8-a1a2-483a-a790-61f2c1dca56d.pdf"},{"id":91509559,"identity":"bb305856-971a-4ac3-aa64-c4659ceb1955","added_by":"auto","created_at":"2025-09-17 08:39:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35869,"visible":true,"origin":"","legend":"","description":"","filename":"suppl.file.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/8d6bf893039de0bd278a2c56.docx"},{"id":91509549,"identity":"ff1eadb7-37f7-4dc0-bcd0-d5cc0469a858","added_by":"auto","created_at":"2025-09-17 08:39:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":51481,"visible":true,"origin":"","legend":"","description":"","filename":"suppl.file.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/5c98f83aa1c9f1a306ee6b0c.docx"},{"id":91511256,"identity":"f0457eb1-4d47-4ab1-ba0a-b86af2323e89","added_by":"auto","created_at":"2025-09-17 08:47:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":45959,"visible":true,"origin":"","legend":"","description":"","filename":"suppl.file.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/ba2405c52d73937e2a3f0b64.docx"},{"id":91509552,"identity":"bb795aea-4fdd-46b3-91d4-fb5665b17aa8","added_by":"auto","created_at":"2025-09-17 08:39:37","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18655,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7414347/v1/06aafcfb8ef96865393a7e8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Diabetes and Cancers: An analysis of the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2005-2015 Dataset","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, evidence has emerged regarding the association between diabetes and cancer(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These findings remain limited and contradictory; that is, diabetes has been associated with an increased risk of some cancers (e.g., liver, pancreas, breast, endometrium, kidney, colon, and rectum) and decreased risk of other cancers (e.g., non-aggressive forms of prostate cancer)(\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Nonetheless, while early evidence has recognized such a correlation, more recent prospective studies have found no strong evidence to support an association between diabetes and overall cancer risk or site-specific cancer risk(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile the risk factors of many cancers are not well defined, the potential shared risk factors between cancer and diabetes are still not well understood(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). There are complex interactions between non-modifiable factors (e.g., age and genetic susceptibility) and modifiable factors (e.g., diet and physical activity)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). One such linking factor might be the medications used for glycemic control(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Anti-hyperglycemic medications, such as metformin, may have a protective effect on the progression of cancer and may even play a role in the treatment of certain cancers(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Conversely, it appears that high cumulative doses of insulin and pioglitazone (\u0026gt;\u0026thinsp;24 months and \u0026gt;\u0026thinsp;28,000 mg cumulative dose) may be associated with an increased risk of certain cancers(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Other proposed linking factors include microangiopathic complications of diabetes and, lifestyle modifications(\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy and large, there is currently no definitive biological mechanism explaining the link between diabetes and cancer risk(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In addition, a report by the American Diabetes Association and the American Cancer Society on the relationship between diabetes and certain cancers was published in December 2009 and addressed some unanswered questions about these relationships(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In this study, we aimed to assess the association between diabetes and its treatment with cancers in a large in-patient sample. This research might help answer questions regarding the association between diabetes and cancer risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this retrospective cohort study, data from 44,262,443 patients who were discharged from community hospitals and aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years during 2005\u0026ndash;2015 were obtained from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) database. The HCUP-NIS dataset contains anonymized and de-identified data from approximately 20% of all community hospital discharges in the United States obtained by applying a weighted sampling design, which is expected to yield national estimates from the NIS database. The database is considered one of the most comprehensive and reliable sources of inpatient care and outcomes in the United States and includes International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for procedures and diagnoses, patient demographics (i.e., age, sex, race/ethnicity [White, Black, Hispanic, Asian or Pacific islanders, Native American, or other]), hospital characteristics (i.e., size, location/teaching status, region, and control type [government, nonprofit, or private]), primary payer, disease severity at admission, risk of in-hospital mortality, in-hospital mortality, length of hospital stay, total charges, and comorbidities (i.e., alcohol consumption, cigarette smoking, obesity). Additional information on HCUP-NIS is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hcup-us.ahrq.gov/nisoverview.jsp\u003c/span\u003e\u003cspan address=\"https://www.hcup-us.ahrq.gov/nisoverview.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The Institutional Review Board of the University of Texas Health at San Antonio exempted this analysis from a full review. It is worth noting that the study utilized a secondary, de-identified dataset and therefore informed consent was not required. Also, these data were data from hospitalized patients and no animal data were used. All data from diabetes and cancer patients were analyzed and there was no attrition. This study was not a randomized clinical trial and there was no need for randomization, blinding, and clinical trial protocols. In this study, we did not use any Antibodies, Cell Lines, Oligonucleotides, Organisms Plasmids and related software.\u003c/p\u003e\u003cp\u003eWe included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years, male and female for three reasons: first, to exclude all women who were admitted for delivery and could have gestational diabetes; second, to capture individuals beyond the average age of diabetes diagnosis, allowing for the assessment of disease progression and long-term management; and third, to include older subjects being at a higher risk of developing cancer. In the pre-analytical step, based on ICD-9-CM codes, each type of cancer was included in the analysis as the principal (1st, as the reason for admission) diagnosis or as a subsequent (2-to-30, as a comorbidity) diagnosis.\u003c/p\u003e\u003cp\u003eTrends in the prevalence of diabetes and selected cancers were shown. In addition, the frequency of diabetes stratified by demographic variables and some HCUP-NIS characteristics was also analyzed using the Wald χ\u003csup\u003e2\u003c/sup\u003e test. The main analysis was conducted in two steps. In the first step, 99 univariable analyses were performed between diabetes and 33 selected cancers (diagnosed as 1st, 2nd-to-30th and 1st-to-30th diagnosis) \u0026ndash; A total of 3 univariable analyses for each cancer were performed \u0026ndash; using the Wald χ\u003csup\u003e2\u003c/sup\u003e test. In the second step, 99 multivariable logistic regressions \u0026ndash; 3 multivariable logistic regressions for each cancer \u0026ndash; were carried out to adjust for probable confounders, including age, sex, race, obesity, smoking and alcohol consumption. Finally, the estimated correlation coefficients were reported using the prevalence ratio (95% confidence interval [CI]) as a proxy for relative risk. The results of the univariable and multivariable analyses are shown in Supplementary File 1, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and Supplementary File 2, S2, S3, and S4, respectively. All analyses were performed using Stata version 12.0 software (Stata, College Station, TX, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study cohort primarily consisted of female patients (53.95%), individuals identified as white (75.38%), discharges from urban hospitals (86.195), and older adults (51.72% of patients aged ≥ 70 years; mean age, 70.37 years). A total of 13,048,961 patients with diabetes were detected out of 44,262,443 discharged patients during the study period, resulting in a diabetes prevalence of 29.48% among hospitalized adults aged ≥ 50 years. Higher prevalence rates were found in men, individuals aged 60–69 years, Hispanics and Native Americans, and patients in the southern region (Table 1).\u003c/p\u003e\n\u003cp\u003eA total of 9,103,889 (20.57%) patients had at least one out of 33 different cancers, with an aggregated count of 11,752,871. There was a notable increasing trend of 25.79% in 2005 to 32.31% in 2015 among hospitalized patients with diabetes in the United States. The data also showed an increase in the proportion of hospitalized patients who were defined and used consistently across the time points–from 4.47% and 21.32% in 2005 to 7.14% and 25.17% in 2015, respectively (Supplementary File 3: Fig. S1). In addition, the prevalence of most of the 33 selected cancers increased compared to that in the baseline year (2005). According to the 11-year average prevalence rates, breast cancer (32.3%); prostate cancer (29.5%); cancers of the lung, bronchus, and trachea (26.5%); and colon cancer (21.4%) were the most prevalent cancers among hospitalized patients aged ≥ 50 years in the United States (Supplementary File 3: Table S5).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2 shows the odds of association between 33 cancers (1st, 2nd-to-30th and 1st-to-30th diagnosis) and diabetes, using multivariable logistic regression adjusted for age, sex, race, smoking, alcohol consumption, and obesity among hospitalized patients in the United States between 2005 and 2015. We found that diabetes as a 1st-to-30th diagnosis had a protective role for 33 selected cancers, except pancreatic cancer (OR: 1.20; 95% CI: 1.18, 1.21) and liver and intrahepatic cancers (OR: 1.42; 95% CI: 1.40, 1.43) (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSummary correlation coefficients, odds ratio (95% CI) between diabetes and cancers (as 1st, 2nd-to-30th and 1st-to-30th diagnosis) using multivariable logistic regression amongst hospitalized patients aged ≥ 50 years in the United States, 2005–2015\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCancer/Cancer group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCancer diagnosed as:\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReason of admission [1st diagnosis]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComorbidity [2nd-to-30th ]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAny [1st-to-30th]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead and neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 [0.49, 0.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 [0.59, 0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 [0.57, 0.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsophagus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 [0.57, 0.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 [0.66, 0.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 [0.64, 0.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 [0.68, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 [0.72, 0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 [0.71, 0.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71 [0.70, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 [0.93, 0.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 [0.88, 0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRectum and anus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 [0.59, 0.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.69, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 [0.66, 0.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver, intrahepatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 [1.05, 1.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 [1.23, 1.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 [1.18, 1.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39 [1.37, 1.42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 [1.41, 1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 [1.40, 1.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGastrointestinal, peritoneum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.69, 0.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 [1.05, 1.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 [0.93, 0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung, Bronchus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 [0.64, 0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 [0.75, 0.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 [0.72, 0.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther respiratory tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 [0.53, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 [0.61, 0.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 [0.59, 0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBone tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 [0.53, 0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 [0.59, 0.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 [0.57, 0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin, Melanomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 [0.57, 0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 [0.72, 0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 [0.72, 0.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin, non-epithelial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 [0.66, 0.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 [0.78, 0.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 [0.78, 0.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54 [0.53, 0.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 [0.89, 0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 [0.85, 0.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.69, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 [1.00, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 [0.91, 0.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 [0.47, 0.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 [0.79, 0.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76 [0.75, 0.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 [0.46, 0.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66 [0.65, 0.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 [0.61, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther female cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 [0.69, 0.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 [0.89, 0.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 [0.85, 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38 [0.37, 0.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 [0.83, 0.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 [0.74, 0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTestis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 [0.35, 0.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 [0.79, 0.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 [0.79, 0.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther male cancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 [0.75, 0.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 [0.91, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 [0.89, 0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 [0.72, 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 [0.95, 0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 [0.91, 0.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney (Renal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 [0.77, 0.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 [1.02, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 [0.95, 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther urinary track\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 [0.65, 0.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 [0.79, 0.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 [0.75, 0.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 [0.66, 0.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.69, 0.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.69, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 [0.58, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 [0.86, 0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 [0.79, 0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHodgkin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 [0.63, 0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 [0.79, 0.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 [0.78, 0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hodgkin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 [0.67, 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 [0.78, 0.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 [0.77, 0.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66 [0.65, 0.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 [0.85, 0.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 [0.81, 0.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple myeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 [0.58, 0.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 [0.77, 0.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 [0.73, 0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 [0.57, 0.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 [0.73, 0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 [0.72, 0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 [0.63, 0.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 [0.68, 0.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 [0.68, 0.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeoplasms unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 [0.71, 0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 [0.90, 0.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 [0.87, 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNote: [1] Gray cells indicate diabetes as a risk factor for that specific cancer; [2] Cloud gray cells indicate diabetes as a weak risk factor for that specific cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough some studies have shown that there might be some mechanisms linking diabetes with the incidence of many site-specific cancers, significant debate remains because of the limitations of these studies for a conclusive interpretation of the outcome(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In this retrospective cohort of more than 40\u0026nbsp;million hospitalized adult patients, we investigated the association between diabetes and cancer. Our major finding was that diabetes may be associated with a decreased risk of developing most selected cancers, except for pancreatic cancer and liver and intrahepatic cancers.\u003c/p\u003e\u003cp\u003eSeveral epidemiological studies have reported increased cancer incidence in some medical conditions, including obesity, metabolic syndrome, polycystic ovary syndrome, insulin-resistant states, and diabetes(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Hyperinsulinemia, hyperglycemia, inflammation, and oxidative stress have been proposed as some of the possible mechanisms link these diseases to cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, the oncogenic effects of some anti-hyperglycemic drugs represent an important pathway that might have either increasing or decreasing effect on cancer incidence(\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). According to the American Association of Clinical Endocrinologists and the American College of Endocrinology (AACE/ACE) consensus statement, metformin is thought to play a protective role in cancer development, whereas exogenous insulin use appears to be associated with an increased risk of cancer(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Anti-hyperglycemic medications have been suggested to affect cancer risk through several direct and indirect mechanisms, including serum insulin levels, growth factors, chronic inflammation, estrogen, testosterone, and weight loss(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In addition, a cohort study conducted by Dankner et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), showed that poor glycemic control was weakly correlated with cancer risk in patients with diabetes. Therefore, it can be postulated that diabetes and cancer are linked through medications using for glycemic control rather than per se. However, the effects of newer diabetes medications on cancer progression remains uncertain(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In addition, specific linking factors for diabetes and each type of cancer have not yet been thoroughly established, and future research is required.\u003c/p\u003e\u003cp\u003eAnother linking factor could be lifestyle modification, as supported by several studies(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Obesity, physical inactivity, and an unhealthy diet can influence both diabetes and several cancers. Compared to normal-weight individuals, obese patients with poorly controlled insulin-treated type 2 diabetes mellitus are at a higher risk for malignancies, particularly breast cancer in women and colorectal cancer in men(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A diagnosis of diabetes, especially when accompanied by cardiovascular disease, often prompts changes in lifestyle behaviors through consultation with a health care provider(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). As a consequence of behavioral changes, the physiology of the body may influence the risk of developing cancer, particularly at specific cancer sites. Furthermore, there were differences in the average age of diabetes and cancer diagnosis. Over time, the average age at diabetes diagnosis has declined to 47.9 years of age(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, half of all cancers are diagnosed after 65 years of age(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). It is possible that improvements in early diagnosis, medical management, lifestyle modifications, and increased life expectancy of patients with diabetes in middle age, which can reduce the risk factors shared with certain cancers, have affected the prevalence of some cancers.\u003c/p\u003e\u003cp\u003eFurthermore, clinical evidence has shown prolonged survival in patients with diabetes and non-small cell lung cancer. Hanbali et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), showed that this survival benefit was likely related to the protective effect of diabetes-associated microangiopathic complications on slowing the progression of metastatic lesions.\u003c/p\u003e\u003cp\u003eThe results showed that patients with diabetes were more likely to have pancreatic, hepatic and intrahepatic cancers than those without diabetes. This finding broadly supports previous research linking diabetes to these cancers(\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), conducted a multivariable pooled analysis of three large case-control studies and found that diabetes had an odds ratio of 1.8 (95% CI: 1.5\u0026ndash;2.1) for pancreatic cancer. In addition, in a meta-analysis of 21 studies, Ren et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) found that diabetes was associated with an increased risk of biliary tract cancer with a relative risk of 1.43 (95% CI: 1.18, 1.72). Moreover, a population-based cohort study found that patients with diabetes had a higher risk of developing hepatocellular carcinoma (adjusted hazard ratio,1.73 ; 95% CI: 1.47, 2.03), compare to those without diabetes. These findings underscore the need for further high-quality studies to elucidate the mechanisms that underlie these relationships(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother finding of this study was a modest association between diabetes and kidney cancer, peritoneal cancer, uterine cancer, or bladder cancer; however, these associations are not consistently supported by previous research. A recent meta-analysis showed a positive association between diabetes and the risk of kidney cancer; however, future studies should determine whether this association is causative(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). A linkage study using two statistical databases in England found significantly high odds ratios for uterine cancers in patients with diabetes(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The National Health Insurance Study in Taiwan showed that patients with diabetes are at an increased risk of bladder cancer(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In addition, a cohort study in the United States showed that bladder cancer incidence was increased with anti-hyperglycemic medication (pioglitazone) in patients with diabetes aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, but not in the incident cohorts(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, a meta-analysis showed a modest, insignificant positive association between bladder cancer incidence and type 1 diabetes(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In summary, more studies are needed to determine a positive, null, or negative association between diabetes and cancer.\u003c/p\u003e\u003cp\u003eThis study had some limitations. It was not possible to extract data regarding the time interval between the incidence of cancer and diabetes from the HCUP-NIS database. In addition, while we included confounding factors such as age, sex, race, smoking, alcohol consumption, and obesity, the models lacked other possible confounding factors, such as occupational exposure, environmental exposure, and anti-hyperglycemic medication use.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis large-scale, retrospective cohort study provides new insights into the direction of association between diabetes and cancer. We found that diabetes may have a protective effect against most of the 33 selected cancers, except for pancreatic, liver, and intrahepatic cancers. Further research is needed to elucidate the mechanisms that link diabetes to certain cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthical Approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the retrospective nature of this study and the use of de-identified data from the Healthcare Cost and Utilization Project (HCUP) database, the requirement for informed consent was waived. In accordance with U.S. federal regulations (45 CFR 46.102) and institutional policy at the University of Texas Health Science Center at San Antonio, analyses of such de-identified data are considered exempt from Institutional Review Board (IRB) review. The study was conducted in accordance with the ethical principles set forth in the Declaration of Helsinki (1964) and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study was obtained with a certificate of completion for HCUP data use from the corresponding author, (HCUP Certification Code: HCUP-4T78I53DU), and was not publicly available. Data may be made available from the corresponding author upon reasonable request and with permission from H-Cup (Email: \u003cu\[email protected]\u003c/u\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M, S.Z. and A.S. conceptualized and designed the study. M.H.S. was responsible for data acquisition and formal analysis. Z.G. provided expertise in methodology and supervision. A.R. and A.H. contributed to the data interpretation and manuscript writing. S.S. and H.S. reviewed and edited the manuscript, providing critical intellectual feedback. A.M. oversaw the project and validated the research findings. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank H-CUP for providing the dataset used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTsilidis KK, Kasimis JC, Lopez DS, Ntzani EE, Ioannidis JPA. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. Bmj. 2015;350.\u003c/li\u003e\n\u003cli\u003eWojciechowska J, Krajewski W, Bolanowski M, Kręcicki T, Zatoński T. Diabetes and cancer: a review of current knowledge. Experimental and Clinical Endocrinology \u0026amp; Diabetes. 2016;124(05):263-75.\u003c/li\u003e\n\u003cli\u003eGoto A, Yamaji T, Sawada N, Momozawa Y, Kamatani Y, Kubo M, et al. Diabetes and cancer risk: a Mendelian randomization study. International journal of cancer. 2020;146(3):712-9.\u003c/li\u003e\n\u003cli\u003eHarding JL, Shaw JE, Peeters A, Cartensen B, Magliano DJ. Cancer risk among people with type 1 and type 2 diabetes: disentangling true associations, detection bias, and reverse causation. Diabetes care. 2015;38(2):264-70.\u003c/li\u003e\n\u003cli\u003eAbudawood M. Diabetes and cancer: A comprehensive review. Journal of Research in Medical Sciences. 2019;24(1):94.\u003c/li\u003e\n\u003cli\u003eCollins KK. The diabetes-cancer link. Diabetes spectrum: a publication of the American Diabetes Association. 2014;27(4):276.\u003c/li\u003e\n\u003cli\u003eWang M, Yang Y, Liao Z. Diabetes and cancer: Epidemiological and biological links. World journal of diabetes. 2020;11(6):227.\u003c/li\u003e\n\u003cli\u003eGiovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, et al. Diabetes and cancer: a consensus report. CA: a cancer journal for clinicians. 2010;60(4):207-21.\u003c/li\u003e\n\u003cli\u003eVigneri P, Frasca F, Sciacca L, Pandini G, Vigneri R. Diabetes and cancer. Endocrine-related cancer. 2009;16(4):1103-23.\u003c/li\u003e\n\u003cli\u003eHua F, Yu J-J, Hu Z-W. Diabetes and cancer, common threads and missing links. Cancer letters. 2016;374(1):54-61.\u003c/li\u003e\n\u003cli\u003eWu L, Zhu J, Prokop LJ, Murad MH. Pharmacologic therapy of diabetes and overall cancer risk and mortality: a meta-analysis of 265 studies. Scientific reports. 2015;5(1):10147.\u003c/li\u003e\n\u003cli\u003eZi F, Zi H, Li Y, He J, Shi Q, Cai Z. Metformin and cancer: An existing drug for cancer prevention and therapy. Oncology letters. 2018;15(1):683-90.\u003c/li\u003e\n\u003cli\u003eHandelsman Y, Leroith D, Bloomgarden ZT, Dagogo-Jack S, Einhorn D, Garber AJ, et al. Diabetes and cancer--an AACE/ACE consensus statement. Endocr Pract. 2013;19(4):675-93.\u003c/li\u003e\n\u003cli\u003eHanbali A, Al-Khasawneh K, Cole-Johnson C, Divine G, Ali H. Protective effect of diabetes against metastasis in patients with non\u0026ndash;small cell lung cancer. Archives of internal medicine. 2007;167(5):513-.\u003c/li\u003e\n\u003cli\u003eDartois L, Fagherazzi G, Boutron-Ruault M-C, Mesrine S, Clavel-Chapelon F. Association between five lifestyle habits and cancer risk: results from the E3N cohort. Cancer Prevention Research. 2014;7(5):516-25.\u003c/li\u003e\n\u003cli\u003eFeldman AL, Long GH, Johansson I, Weinehall L, Fh\u0026auml;rm E, Wennberg P, et al. Change in lifestyle behaviors and diabetes risk: evidence from a population-based cohort study with 10 year follow-up. International Journal of Behavioral Nutrition and Physical Activity. 2017;14(1):39.\u003c/li\u003e\n\u003cli\u003eTseng YH, Tsan YT, Chen PC. Association between severity of diabetic complications and risk of cancer in middle‐aged patients with type 2 diabetes. Journal of Diabetes Investigation. 2025;16(1):16-24.\u003c/li\u003e\n\u003cli\u003eCohen DH, LeRoith D. Obesity, type 2 diabetes, and cancer: the insulin and IGF connection. Endocrine-related cancer. 2012;19(5):F27-F45.\u003c/li\u003e\n\u003cli\u003eZhu B, Qu S. The relationship between diabetes mellitus and cancers and its underlying mechanisms. Frontiers in Endocrinology. 2022;13:800995.\u003c/li\u003e\n\u003cli\u003eGallagher EJ, LeRoith D. Diabetes, cancer, and metformin: connections of metabolism and cell proliferation. Annals of the New York Academy of Sciences. 2011;1243(1):54-68.\u003c/li\u003e\n\u003cli\u003eGarczorz W, Kosowska A, Francuz T. Antidiabetic drugs in breast cancer patients. Cancers. 2024;16(2):299.\u003c/li\u003e\n\u003cli\u003eDąbrowski M, Szymańska-Garbacz E, Miszczyszyn Z, Dereziński T, Czupryniak L. Risk factors for cancer development in type 2 diabetes: A retrospective case-control study. BMC cancer. 2016;16(1):785.\u003c/li\u003e\n\u003cli\u003eCannata D, Fierz Y, Vijayakumar A, LeRoith D. Type 2 diabetes and cancer: what is the connection? Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine: A Journal of Translational and Personalized Medicine. 2010;77(2):197-213.\u003c/li\u003e\n\u003cli\u003eDankner R, Boker LK, Boffetta P, Balicer RD, Murad H, Berlin A, et al. A historical cohort study on glycemic-control and cancer-risk among patients with diabetes. Cancer epidemiology. 2018;57:104-9.\u003c/li\u003e\n\u003cli\u003ePe\u0026ntilde;a-Longobardo LM, Rodr\u0026iacute;guez-S\u0026aacute;nchez B, Mata-Cases M, Rodr\u0026iacute;guez-Ma\u0026ntilde;as L, Capel M, Oliva-Moreno J. Is quality of life different between diabetic and non-diabetic people? The importance of cardiovascular risks. PLoS One. 2017;12(12):e0189505.\u003c/li\u003e\n\u003cli\u003eJuntarawijit C, Juntarawijit Y. Comparison of sleep and health behaviors among diabetic patients and non-diabetics in Phitsanulok, Thailand: a cross-sectional study. 2021.\u003c/li\u003e\n\u003cli\u003eKoopman RJ, Mainous AG, Diaz VA, Geesey ME. Changes in age at diagnosis of type 2 diabetes mellitus in the United States, 1988 to 2000. The Annals of Family Medicine. 2005;3(1):60-3.\u003c/li\u003e\n\u003cli\u003eWhite MC, Holman DM, Boehm JE, Peipins LA, Grossman M, Henley SJ. Age and cancer risk: a potentially modifiable relationship. American journal of preventive medicine. 2014;46(3):S7-S15.\u003c/li\u003e\n\u003cli\u003ePizzato M, Turati F, Rosato V, La Vecchia C. Exploring the link between diabetes and pancreatic cancer. Expert review of anticancer therapy. 2019;19(8):681-7.\u003c/li\u003e\n\u003cli\u003eRisch HA. Diabetes and pancreatic cancer: both cause and effect. Oxford University Press; 2019. p. 1-2.\u003c/li\u003e\n\u003cli\u003eSharma A, Kandlakunta H, Nagpal SJS, Feng Z, Hoos W, Petersen GM, et al. Model to determine risk of pancreatic cancer in patients with new-onset diabetes. Gastroenterology. 2018;155(3):730-9.\u003c/li\u003e\n\u003cli\u003eSetiawan VW, Stram DO, Porcel J, Chari ST, Maskarinec G, Le Marchand L, et al. Pancreatic cancer following incident diabetes in African Americans and Latinos: the multiethnic cohort. JNCI: Journal of the National Cancer Institute. 2019;111(1):27-33.\u003c/li\u003e\n\u003cli\u003eChari ST, Leibson CL, Rabe KG, Ransom J, De Andrade M, Petersen GM. Probability of pancreatic cancer following diabetes: a population-based study. Gastroenterology. 2005;129(2):504-11.\u003c/li\u003e\n\u003cli\u003eLi D, Tang H, Hassan MM, Holly EA, Bracci PM, Silverman DT. Diabetes and risk of pancreatic cancer: a pooled analysis of three large case-control studies. Cancer Causes Control. 2011;22(2):189-97.\u003c/li\u003e\n\u003cli\u003eRen H-B, Yu T, Liu C, Li Y-Q. Diabetes mellitus and increased risk of biliary tract cancer: systematic review and meta-analysis. Cancer Causes \u0026amp; Control. 2011;22(6):837-47.\u003c/li\u003e\n\u003cli\u003eLai S-W, Chen P-C, Liao K-F, Muo C-H, Lin C-C, Sung F-C. Risk of hepatocellular carcinoma in diabetic patients and risk reduction associated with anti-diabetic therapy: a population-based cohort study. Official journal of the American College of Gastroenterology| ACG. 2012;107(1):46-52.\u003c/li\u003e\n\u003cli\u003eLarsson SC, Wolk A. Diabetes mellitus and incidence of kidney cancer: a meta-analysis of cohort studies. Diabetologia. 2011;54(5):1013-8.\u003c/li\u003e\n\u003cli\u003eWotton CJ, Yeates DGR, Goldacre MJ. Cancer in patients admitted to hospital with diabetes mellitus aged 30 years and over: record linkage studies. Diabetologia. 2011;54(3):527-34.\u003c/li\u003e\n\u003cli\u003eTseng CH. Diabetes and risk of bladder cancer: a study using the National Health Insurance database in Taiwan. Diabetologia. 2011;54(8):2009-15.\u003c/li\u003e\n\u003cli\u003eMackenzie TA, Zaha R, Smith J, Karagas MR, Morden NE. Diabetes pharmacotherapies and bladder cancer: a medicare epidemiologic study. Diabetes Therapy. 2016;7(1):61-73.\u003c/li\u003e\n\u003cli\u003eOskoui HB, Bogumil DD, Kysh L, Barrett M, Siegmund K, Cortessis VK. 1685-P: Type 1 Diabetes Mellitus and Incident Bladder Cancer: A Systematic Review and Meta-analysis. Diabetes. 2019;68(Supplement_1):1685-P.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\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":"Association, Diabetes, Cancer, HCUP-NIS","lastPublishedDoi":"10.21203/rs.3.rs-7414347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7414347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThere is a growing body of evidence on the relationship between diabetes and cancer. However, the literature is limited and inconclusive about whether diabetes is associated with an increased or decreased risk of developing each cancer.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eIn this retrospective cohort study, 44,262,443 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years who were discharged from community hospitals between 2005\u0026ndash;2015 were obtained from the Healthcare Cost and Utilization Project - Nationwide Inpatient Sample (HCUP-NIS) database. Univariate and multivariate logistic regression analyses were used to study the association between diabetes and the 33 selected cancers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe prevalences of diabetes and cancer were 29.48 and 20.57%, respectively. After adjusting for age, sex, race, smoking, alcohol consumption, and obesity, the odds of association between selected cancers and diabetes represented diabetes as a protective factor for cancer, except for pancreatic cancer (OR [odds ratio]: 1.20; 95% CI: 1.18, 1.21) and liver and intrahepatic cancers (OR: 1.42; 95% CI: 1.40, 1.43).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eUsing a large amount of individualized data, this study implied the likelihood of a protective role of diabetes in most of the 33 selected cancers. Further research is needed to elucidate the mechanisms linking diabetes with cancer development or progression.\u003c/p\u003e","manuscriptTitle":"Association of Diabetes and Cancers: An analysis of the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2005-2015 Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:39:18","doi":"10.21203/rs.3.rs-7414347/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":"3f575b3f-55ec-47b2-aa23-ec917e02c025","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T09:31:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 08:39:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7414347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7414347","identity":"rs-7414347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}