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Sun Y, Lu B, Hu Y, Lv Y, Zhong S. Glycemic Variability in Pancreatogenic Diabetes Mellitus: characteristics, Risks, Potential Mechanisms, and Treatment Possibilities. Int J Gen Med 2024; 17:4297-4309. [PMID: 39324147 PMCID: PMC11423834 DOI: 10.2147/ijgm.s477497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 09/03/2024] [Indexed: 09/27/2024] Open
Abstract
In recent years, pancreatogenic diabetes mellitus has garnered significant attention due to its high incidence, complications, and mortality rates. Glycemic variability (GV) can increase the risk of pancreatogenic diabetes mellitus and its associated complications; however, the precise mechanism remains unclear. The effective control of GV is crucial for preventing the onset of pancreatic diabetes mellitus and improving prognosis. Both diet and antidiabetic medications have substantial effects on GV. However, many patients are prescribed suboptimal or even harmful drugs. Therefore, to provide a comprehensive treatment basis for clinicians to prevent and treat pancreatogenic diabetes mellitus, this study aimed to elucidate the relationship between GV and pancreatogenic diabetes mellitus; investigate the potential mechanisms (such as oxidative stress, inflammatory response, insulin resistance, and lipid metabolism disorders); provide lifestyle guidance; and recommend drug selections to reduce the GV in patients with pancreatogenic diabetes mellitus.
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Affiliation(s)
- Yuyan Sun
- Department of Endocrinology, Gusu School, Nanjing Medical University, The First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China
| | - Bing Lu
- Department of Endocrinology, Gusu School, Nanjing Medical University, The First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China
| | - Yuanwen Hu
- Department of Gastroenterology, The First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China
| | - Yingqi Lv
- Division of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
| | - Shao Zhong
- Department of Endocrinology, Gusu School, Nanjing Medical University, The First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China
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2
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Löhr JM, Öhlund D, Söreskog E, Andersson E, Vujasinovic M, Zethraeus N, Sund M. Can our experience with surveillance for inherited pancreatic cancer help to identify early pancreatic cancer in the general population? Fam Cancer 2024; 23:399-403. [PMID: 38441833 PMCID: PMC11255073 DOI: 10.1007/s10689-024-00363-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 02/05/2024] [Indexed: 07/18/2024]
Abstract
Screening of the general population for cancer is a matter of primary prevention reducing the burden of disease. Whilst this is successful for several cancers including breast, colon and prostate, the situation to screen and hence prevent pancreatic cancer is different. The organ is not as accessible to simple physical exam or biological samples (fecal or blood test). Neither exists a blood test such as PSA that is cost-effective. Reviewing the evidence from screening risk groups for pancreatic cancer, one must conclude that there is no rational at present to screen the general population, for a lack of appropriate tests.
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Affiliation(s)
- J-Matthias Löhr
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden.
- Div. of Surgery & Oncology, Dept. of Upper Abdominal Diseases, CLINTEC Karolinska Institutet, Karolinska Comprehensive Cancer Center, Stockholm, SE-141 86, Sweden.
| | - Daniel Öhlund
- Department of Radiation Sciences and Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden
| | - Emma Söreskog
- Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Stockholm, Sweden
| | - Emil Andersson
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden
| | - Miroslav Vujasinovic
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden
| | - Niklas Zethraeus
- Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Stockholm, Sweden
| | - Malin Sund
- Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Surgical and Perioperative Sciences/ Surgery, Umeå University, Umeå, Sweden
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3
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Gao L, Ugalde A, Livingston PM, White V, Watts JJ, Jongebloed H, McCaffrey N, Menzies D, Robinson S. Simulating the healthcare workforce impact and capacity for pancreatic cancer care in Victoria: a model-based analysis. BMC Health Serv Res 2024; 24:239. [PMID: 38395852 PMCID: PMC10893744 DOI: 10.1186/s12913-024-10722-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The incidence of pancreatic cancer is rising. With improvements in knowledge for screening and early detection, earlier detection of pancreatic cancer will continue to be more common. To support workforce planning, our aim is to perform a model-based analysis that simulates the potential impact on the healthcare workforce, assuming an earlier diagnosis of pancreatic cancer. METHODS We developed a simulation model to estimate the demand (i.e. new cases of pancreatic cancer) and supply (i.e. the healthcare workforce including general surgeons, medical oncologists, radiation oncologists, pain medicine physicians, and palliative care physicians) between 2023 and 2027 in Victoria, Australia. The model compares the current scenario to one in which pancreatic cancer is diagnosed at an earlier stage. The incidence of pancreatic cancer in Victoria, five-year survival rates, and Victoria's population size were obtained from Victorian Cancer Registry, Cancer Council NSW, and Australian Bureau of Statistics respectively. The healthcare workforce data were sourced from the Australian Government Department of Health and Aged Care's Health Workforce Data. The model was constructed at the remoteness level. We analysed the new cases and the number of healthcare workforce by profession together to assess the impact on the healthcare workforce. RESULTS In the status quo, over the next five years, there will be 198 to 220 stages I-II, 297 to 330 stage III, and 495 to 550 stage IV pancreatic cancer cases diagnosed annually, respectively. Assuming 20-70% of the shift towards pancreatic cancer's earlier diagnosis (shifting from stage IV to stages I-II pancreatic cancer within one year), the stages I-II cases could increase to 351 to 390 or 598 to 665 per year. The shift to early diagnosis led to substantial survival gains, translating into an additional 284 or 795 out of 5246 patients with pancreatic cancer remaining alive up to year 5 post-diagnosis. Workforce supply decreases significantly by the remoteness levels, and remote areas face a shortage of key medical professionals registered in delivering pancreatic cancer care, suggesting travel necessities by patients or clinicians. CONCLUSION Improving the early detection and diagnosis of pancreatic cancer is expected to bring significant survival benefits, although there are workforce distribution imbalances in Victoria that may affect the ability to achieve the anticipated survival gain.
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Affiliation(s)
- Lan Gao
- Deakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia.
| | - Anna Ugalde
- School of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia
| | - Patricia M Livingston
- School of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia
| | - Victoria White
- School of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia
| | - Jennifer J Watts
- Deakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia
| | - Hannah Jongebloed
- School of Nursing & Midwifery, Institute of Health Transformation, Faculty of Health, Deakin University, Melbourne, Australia
| | - Nikki McCaffrey
- Deakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia
| | | | - Suzanne Robinson
- Deakin Health Economics, Institute of Health Transformation, Faculty of Health, Deakin University, 1 Gheringhap St, 3220, Geelong, Australia
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Cichosz SL, Jensen MH, Hejlesen O, Henriksen SD, Drewes AM, Olesen SS. Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107965. [PMID: 38070389 DOI: 10.1016/j.cmpb.2023.107965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 01/26/2024]
Abstract
OBJECTIVE To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD). METHODS From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores. RESULTS The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79). CONCLUSION In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
| | | | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Stine Dam Henriksen
- Department of Gastrointestinal Surgery and Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
| | - Asbjørn Mohr Drewes
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark; Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Søren Schou Olesen
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark; Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
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Claridge H, Price CA, Ali R, Cooke EA, de Lusignan S, Harvey-Sullivan A, Hodges C, Khalaf N, O'Callaghan D, Stunt A, Thomas SA, Thomson J, Lemanska A. Determining the feasibility of calculating pancreatic cancer risk scores for people with new-onset diabetes in primary care (DEFEND PRIME): study protocol. BMJ Open 2024; 14:e079863. [PMID: 38262635 PMCID: PMC10806670 DOI: 10.1136/bmjopen-2023-079863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024] Open
Abstract
INTRODUCTION Worldwide, pancreatic cancer has a poor prognosis. Early diagnosis may improve survival by enabling curative treatment. Statistical and machine learning diagnostic prediction models using risk factors such as patient demographics and blood tests are being developed for clinical use to improve early diagnosis. One example is the Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) model, which employs patients' age, blood glucose and weight changes to provide pancreatic cancer risk scores. These values are routinely collected in primary care in the UK. Primary care's central role in cancer diagnosis makes it an ideal setting to implement ENDPAC but it has yet to be used in clinical settings. This study aims to determine the feasibility of applying ENDPAC to data held by UK primary care practices. METHODS AND ANALYSIS This will be a multicentre observational study with a cohort design, determining the feasibility of applying ENDPAC in UK primary care. We will develop software to search, extract and process anonymised data from 20 primary care providers' electronic patient record management systems on participants aged 50+ years, with a glycated haemoglobin (HbA1c) test result of ≥48 mmol/mol (6.5%) and no previous abnormal HbA1c results. Software to calculate ENDPAC scores will be developed, and descriptive statistics used to summarise the cohort's demographics and assess data quality. Findings will inform the development of a future UK clinical trial to test ENDPAC's effectiveness for the early detection of pancreatic cancer. ETHICS AND DISSEMINATION This project has been reviewed by the University of Surrey University Ethics Committee and received a favourable ethical opinion (FHMS 22-23151 EGA). Study findings will be presented at scientific meetings and published in international peer-reviewed journals. Participating primary care practices, clinical leads and policy makers will be provided with summaries of the findings.
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Affiliation(s)
- Hugh Claridge
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- National Physical Laboratory, Teddington, UK
| | - Claire A Price
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- National Physical Laboratory, Teddington, UK
| | - Rofique Ali
- Tower Hamlets Network 1 Primary Care Network, London, UK
| | | | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Adam Harvey-Sullivan
- Tower Hamlets Network 1 Primary Care Network, London, UK
- Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Natalia Khalaf
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
| | | | - Ali Stunt
- Pancreatic Cancer Action, Oakhanger, Hampshire, UK
| | | | | | - Agnieszka Lemanska
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- National Physical Laboratory, Teddington, UK
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Ke TM, Lophatananon A, Muir KR. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank. Biomedicines 2023; 11:3206. [PMID: 38137427 PMCID: PMC10740416 DOI: 10.3390/biomedicines11123206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/24/2023] Open
Abstract
Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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Jensen MH, Cichosz SL, Hejlesen O, Henriksen SD, Drewes AM, Olesen SS. Risk of pancreatic cancer in people with new-onset diabetes: A Danish nationwide population-based cohort study. Pancreatology 2023; 23:642-649. [PMID: 37422338 DOI: 10.1016/j.pan.2023.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/29/2023] [Accepted: 07/01/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level. METHODS This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group. RESULTS During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels. CONCLUSIONS The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels.
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Affiliation(s)
- Morten Hasselstrøm Jensen
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Stine Dam Henriksen
- Department of Gastrointestinal Surgery and Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark; Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Asbjørn Mohr Drewes
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark; Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Søren Schou Olesen
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark; Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark.
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Bures J, Kohoutova D, Skrha J, Bunganic B, Ngo O, Suchanek S, Skrha P, Zavoral M. Diabetes Mellitus in Pancreatic Cancer: A Distinct Approach to Older Subjects with New-Onset Diabetes Mellitus. Cancers (Basel) 2023; 15:3669. [PMID: 37509329 PMCID: PMC10377806 DOI: 10.3390/cancers15143669] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/02/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis, with near-identical incidence and mortality. According to the World Health Organization Globocan Database, the estimated number of new cases worldwide will rise by 70% between 2020 and 2040. There are no effective screening methods available so far, even for high-risk individuals. The prognosis of PDAC, even at its early stages, is still mostly unsatisfactory. Impaired glucose metabolism is present in about 3/4 of PDAC cases. METHODS Available literature on pancreatic cancer and diabetes mellitus was reviewed using a PubMed database. Data from a national oncology registry (on PDAC) and information from a registry of healthcare providers (on diabetes mellitus and a number of abdominal ultrasound investigations) were obtained. RESULTS New-onset diabetes mellitus in subjects older than 60 years should be an incentive for a prompt and detailed investigation to exclude PDAC. Type 2 diabetes mellitus, diabetes mellitus associated with chronic non-malignant diseases of the exocrine pancreas, and PDAC-associated type 3c diabetes mellitus are the most frequent types. Proper differentiation of particular types of new-onset diabetes mellitus is a starting point for a population-based program. An algorithm for subsequent steps of the workup was proposed. CONCLUSIONS The structured, well-differentiated, and elaborately designed approach to the elderly with a new onset of diabetes mellitus could improve the current situation in diagnostics and subsequent poor outcomes of therapy of PDAC.
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Affiliation(s)
- Jan Bures
- Institute of Gastrointestinal Oncology, Military University Hospital Prague, 169 02 Prague, Czech Republic
- Department of Medicine, First Faculty of Medicine, Charles University, Prague and Military University Hospital Prague, 169 02 Prague, Czech Republic
- Biomedical Research Centre, University Hospital Hradec Kralove, 500 03 Hradec Kralove, Czech Republic
| | - Darina Kohoutova
- Biomedical Research Centre, University Hospital Hradec Kralove, 500 03 Hradec Kralove, Czech Republic
- The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Jan Skrha
- Third Department of Internal Medicine-Endocrinology and Metabolism, First Faculty of Medicine, Charles University, Prague and General University Hospital in Prague, 128 08 Prague, Czech Republic
| | - Bohus Bunganic
- Department of Medicine, First Faculty of Medicine, Charles University, Prague and Military University Hospital Prague, 169 02 Prague, Czech Republic
| | - Ondrej Ngo
- Institute of Health Information and Statistics of the Czech Republic, 128 01 Prague, Czech Republic
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, 602 00 Brno, Czech Republic
| | - Stepan Suchanek
- Institute of Gastrointestinal Oncology, Military University Hospital Prague, 169 02 Prague, Czech Republic
- Department of Medicine, First Faculty of Medicine, Charles University, Prague and Military University Hospital Prague, 169 02 Prague, Czech Republic
| | - Pavel Skrha
- Department of Medicine, Third Faculty of Medicine, Charles University, Prague and University Hospital Kralovske Vinohrady, 100 00 Prague, Czech Republic
| | - Miroslav Zavoral
- Institute of Gastrointestinal Oncology, Military University Hospital Prague, 169 02 Prague, Czech Republic
- Department of Medicine, First Faculty of Medicine, Charles University, Prague and Military University Hospital Prague, 169 02 Prague, Czech Republic
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Goodarzi MO, Petrov MS. Diabetes of the Exocrine Pancreas: Implications for Pharmacological Management. Drugs 2023:10.1007/s40265-023-01913-5. [PMID: 37410209 PMCID: PMC10361873 DOI: 10.1007/s40265-023-01913-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/12/2023] [Indexed: 07/07/2023]
Abstract
Post-pancreatitis diabetes mellitus, pancreatic cancer-related diabetes, and cystic fibrosis-related diabetes are often underappreciated. As a result, a substantial proportion of people with these sub-types of diabetes receive antidiabetic medications that may be suboptimal, if not harmful, in the context of their underlying disease of the exocrine pancreas. The present article delineates both classical (biguanides, insulin, sulfonylureas, α-glucosidase inhibitors, thiazolidinediones, and meglitinides) and newer (glucagon-like peptide-1 receptor agonists, amylin analogs, dipeptidyl peptidase-4 inhibitors, sodium-glucose co-transporter-2 inhibitors, D2 receptor agonists, bile acid sequestrants, and dual glucagon-like peptide-1 receptor and glucose-dependent insulinotropic polypeptide receptor co-agonists) therapies and provides recommendations for managing people with diabetes of the exocrine pancreas based on the most up-to-date clinical evidence. Also, several emerging directions (lipid-enriched pathways, Y4 receptor agonism, glucagon-like peptide-1 and glucagon receptor co-agonism) are presented with a view to informing the process of new drug discovery and development.
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Affiliation(s)
- Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maxim S Petrov
- School of Medicine, University of Auckland, Auckland, New Zealand.
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Zhang Y, Wang QL, Yuan C, Lee AA, Babic A, Ng K, Perez K, Nowak JA, Lagergren J, Stampfer MJ, Giovannucci EL, Sander C, Rosenthal MH, Kraft P, Wolpin BM. Pancreatic cancer is associated with medication changes prior to clinical diagnosis. Nat Commun 2023; 14:2437. [PMID: 37117188 PMCID: PMC10147931 DOI: 10.1038/s41467-023-38088-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 04/11/2023] [Indexed: 04/30/2023] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) commonly develop symptoms and signs in the 1-2 years before diagnosis that can result in changes to medications. We investigate recent medication changes and PDAC diagnosis in Nurses' Health Study (NHS; females) and Health Professionals Follow-up Study (HPFS; males), including up to 148,973 U.S. participants followed for 2,994,057 person-years and 991 incident PDAC cases. Here we show recent initiation of antidiabetic (NHS) or anticoagulant (NHS, HFS) medications and cessation of antihypertensive medications (NHS, HPFS) are associated with pancreatic cancer diagnosis in the next 2 years. Two-year PDAC risk increases as number of relevant medication changes increases (P-trend <1 × 10-5), with participants who recently start antidiabetic and stop antihypertensive medications having multivariable-adjusted hazard ratio of 4.86 (95%CI, 1.74-13.6). These changes are not associated with diagnosis of other digestive system cancers. Recent medication changes should be considered as candidate features in multi-factor risk models for PDAC, though they are not causally implicated in development of PDAC.
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Affiliation(s)
- Yin Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Qiao-Li Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Department of Clinical Science, Intervention and Technology, Karolinka Institutet, Stockholm, Sweden
| | - Chen Yuan
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Alice A Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Kimberly Perez
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jesper Lagergren
- Upper Gastrointestinal Surgery, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Meir J Stampfer
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Chris Sander
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael H Rosenthal
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
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