1
|
Pitsiava S, Dimakopoulos G, Tsimihodimos V, Kotsa K, Koufakis T. Association between clinical and laboratory factors and response to sodium-glucose cotransporter 2 inhibitors in patients with type 2 diabetes: a retrospective observational study. Expert Opin Pharmacother 2024; 25:1095-1104. [PMID: 38822807 DOI: 10.1080/14656566.2024.2364054] [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: 04/04/2024] [Accepted: 05/31/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND This study aimed to investigate the association between clinical and laboratory parameters and response to therapy with sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with type 2 diabetes mellitus (T2D). RESEARCH DESIGN AND METHODS We retrospectively analyzed the medical records of people with T2D in whom SGLT2i was started. Clinical and laboratory parameters were recorded before, 3 and 6 months after starting treatment. Specific criteria were applied to classify participants into good and poor responders in terms of weight loss (primary outcome) and glycemic control (secondary outcome), separately. RESULTS Fifty individuals (64% men) with a mean age of 65.8 ± 8.5 years were included in the analysis. 86% and 64% of the participants were classified into good response categories for glycemic control and weight loss, respectively. Good responders in terms of glycemic control had lower high-density lipoprotein cholesterol levels at baseline compared to poor responders (43.3 vs 57.4 mg/dl, p = 0.044). In the logistic regression analysis, a higher baseline weight was associated with a better response to therapy in terms of weight loss (p = 0.04). CONCLUSIONS Our findings suggest that specific clinical and laboratory parameters are associated with response to SGLT2i treatment and can contribute to a more personalized approach to T2D care.
Collapse
Affiliation(s)
- Sofia Pitsiava
- School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Dimakopoulos
- BIOSTATS, Epirus Science and Technology Park Campus of the University of Ioannina, Ioannina, Greece
| | - Vasilis Tsimihodimos
- Department of Internal Medicine, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | - Kalliopi Kotsa
- Division of Endocrinology and Metabolism and Diabetes Centre, First Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Theocharis Koufakis
- Second Propedeutic Department of Internal Medicine, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
2
|
Tao X, Jiang M, Liu Y, Hu Q, Zhu B, Hu J, Guo W, Wu X, Xiong Y, Shi X, Zhang X, Han X, Li W, Tong R, Long E. Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms. Sci Rep 2023; 13:16437. [PMID: 37777593 PMCID: PMC10543442 DOI: 10.1038/s41598-023-43240-5] [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: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.
Collapse
Affiliation(s)
- Xue Tao
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Min Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Yumeng Liu
- Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Qi Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan, China
| | - Baoqiang Zhu
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jiaqiang Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Wenmei Guo
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Yu Xiong
- Institute of Materia Medica, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100050, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Xueli Zhang
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Xu Han
- Sichuan Provincial Health Information Center, Chengdu, 610015, Sichuan, China
| | - Wenyuan Li
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, Sichuan, China.
| |
Collapse
|
3
|
Shazman S. Understanding Type 2 Diabetes Mellitus Risk Parameters through Intermittent Fasting: A Machine Learning Approach. Nutrients 2023; 15:3926. [PMID: 37764710 PMCID: PMC10535779 DOI: 10.3390/nu15183926] [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: 08/06/2023] [Revised: 08/31/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels. Despite the availability of pharmacological treatments, dietary plans, and exercise regimens, T2DM remains a significant global cause of mortality. As a result, there is an increasing interest in exploring lifestyle interventions, such as intermittent fasting (IF). This study aims to identify underlying patterns and principles for effectively improving T2DM risk parameters through IF. By analyzing data from multiple randomized clinical trials investigating various IF interventions in humans, a machine learning algorithm was employed to develop a personalized recommendation system. This system offers guidance tailored to pre-diabetic and diabetic individuals, suggesting the most suitable IF interventions to improve T2DM risk parameters. With a success rate of 95%, this recommendation system provides highly individualized advice, optimizing the benefits of IF for diverse population subgroups. The outcomes of this study lead us to conclude that weight is a crucial feature for females, while age plays a determining role for males in reducing glucose levels in blood. By revealing patterns in diabetes risk parameters among individuals, this study not only offers practical guidance but also sheds light on the underlying mechanisms of T2DM, contributing to a deeper understanding of this complex metabolic disorder.
Collapse
Affiliation(s)
- Shula Shazman
- Department of Information Systems, The Max Stern Yezreel Valley College, Yezreel Valley 1930600, Israel; or ; Tel.: +972-54-6388131
- Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana 4353701, Israel
| |
Collapse
|
4
|
Jia L, Huang S, Sun B, Shang Y, Zhu C. Pharmacomicrobiomics and type 2 diabetes mellitus: A novel perspective towards possible treatment. Front Endocrinol (Lausanne) 2023; 14:1149256. [PMID: 37033254 PMCID: PMC10076675 DOI: 10.3389/fendo.2023.1149256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 03/14/2023] [Indexed: 04/11/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM), a major driver of mortality worldwide, is more likely to develop other cardiometabolic risk factors, ultimately leading to diabetes-related mortality. Although a set of measures including lifestyle intervention and antidiabetic drugs have been proposed to manage T2DM, problems associated with potential side-effects and drug resistance are still unresolved. Pharmacomicrobiomics is an emerging field that investigates the interactions between the gut microbiome and drug response variability or drug toxicity. In recent years, increasing evidence supports that the gut microbiome, as the second genome, can serve as an attractive target for improving drug efficacy and safety by manipulating its composition. In this review, we outline the different composition of gut microbiome in T2DM and highlight how these microbiomes actually play a vital role in its development. Furthermore, we also investigate current state-of-the-art knowledge on pharmacomicrobiomics and microbiome's role in modulating the response to antidiabetic drugs, as well as provide innovative potential personalized treatments, including approaches for predicting response to treatment and for modulating the microbiome to improve drug efficacy or reduce drug toxicity.
Collapse
Affiliation(s)
- Liyang Jia
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shiqiong Huang
- Department of Pharmacy, The First Hospital of Changsha, Changsha, China
| | - Boyu Sun
- Department of Pharmacy, The Third People’s Hospital of Qingdao, Qingdao, China
| | - Yongguang Shang
- Department of Pharmacy, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Yongguang Shang, ; Chunsheng Zhu,
| | - Chunsheng Zhu
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Yongguang Shang, ; Chunsheng Zhu,
| |
Collapse
|
5
|
Miao Y, Chen R, Wang X, Zhang J, Tang W, Zhang Z, Liu Y, Xu Q. Drosophila melanogaster diabetes models and its usage in the research of anti-diabetes management with traditional Chinese medicines. Front Med (Lausanne) 2022; 9:953490. [PMID: 36035393 PMCID: PMC9403128 DOI: 10.3389/fmed.2022.953490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
The prevalence of diabetes mellitus (DM) is increasing rapidly worldwide, but the underlying molecular mechanisms of disease development have not been elucidated, and the current popular anti-diabetic approaches still have non-negligible limitations. In the last decades, several different DM models were established on the classic model animal, the fruit fly (Drosophila melanogaster), which provided a convenient way to study the mechanisms underlying diabetes and to discover and evaluate new anti-diabetic compounds. In this article, we introduce the Drosophila Diabetes model from three aspects, including signal pathways, established methods, and pharmacodynamic evaluations. As a highlight, the progress in the treatments and experimental studies of diabetes with Traditional Chinese Medicine (TCM) based on the Drosophila Diabetes model is reviewed. We believe that the values of TCMs are underrated in DM management, and the Drosophila Diabetes models can provide a much more efficient tool to explore its values of it.
Collapse
Affiliation(s)
- Yaodong Miao
- Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- *Correspondence: Yaodong Miao,
| | - Rui Chen
- School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaolu Wang
- Jimo District Qingdao Hospital of Traditional Chinese Medicine, Qingdao, China
| | - Jie Zhang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
| | - Weina Tang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
| | - Zeyu Zhang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Yaoyuan Liu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Qiang Xu
- Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Qiang Xu,
| |
Collapse
|
6
|
Evaluation of the Clinical Efficacy of the Treatment of Overweight and Obesity in Type 2 Diabetes Mellitus by the Telemedicine Management System Based on the Internet of Things Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8149515. [PMID: 35785080 PMCID: PMC9242767 DOI: 10.1155/2022/8149515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/06/2022] [Accepted: 05/23/2022] [Indexed: 12/20/2022]
Abstract
Objective To explore the application value of medical intelligent electronic system under the background of Internet of Things in the clinical study of the treatment of overweight/obesity in type 2 diabetes mellitus (T2DM) with empagliflozin combined with liraglutide; 50 overweight and obese adult T2DM patients in our hospital were randomly divided into the combined group and the control group, 25 cases in each group. The control group was treated with liraglutide alone, while the combined group was treated with empagliflozin on the basis of liraglutide. Based on the Internet of Things technology, with diabetes management as the core, the functions of information collection, transmission, and storage of T2DM patients are realized. Doctors pass the diabetes management plan to T2DM patients through the platform, supervise the implementation, and finally compare the clinical efficacy of the two groups. Results Compared with before treatment, the body mass index (BMI), fasting blood glucose (FPG), postprandial blood glucose (2hPG), glycosylated hemoglobin (HbAlc), islet beta cell secretion function index (HOMA-β), islet resistance index (HOMA-IR), total cholesterol (TC), and triglyceride (TG) in both groups decreased significantly after treatment. After combined treatment, systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, 2hPG, HbA1c, and HOMA-IR in the combined group were significantly lower than those in the control group (P < 0.05). Hypoglycemia occurred in both groups, with 2 cases in the control group and 4 cases in the combined group. Conclusion The telemedicine management system based on Internet of Things technology can improve patients' self-management ability and provide a new choice for individualized treatment of overweight/obesity T2DM patients. The combination therapy of empagliflozin and liraglutide can effectively reduce blood sugar, weight, blood pressure, blood lipid, and hypoglycemia and effectively improve insulin resistance and secretion function of islet β cells in T2DM patients.
Collapse
|
7
|
Thiruvengadam S, Peter PR. Management of type 2 diabetes without insulin: An update for the PCP. Dis Mon 2021; 68:101290. [PMID: 34563347 DOI: 10.1016/j.disamonth.2021.101290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Sudha Thiruvengadam
- Department of Medicine, Trinity Health of New England, 166 Waterbury Rd, Ste 300, Prospect, Waterbury, CT 06712, USA.
| | - Patricia R Peter
- Section of Endocrinology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| |
Collapse
|
8
|
Scurt FG, Bose K, Canbay A, Mertens PR, Chatzikyrkou C. [Chronic kidney injury in patients with liver diseases - Reappraising pathophysiology and treatment options]. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2021; 59:560-579. [PMID: 33728618 DOI: 10.1055/a-1402-1502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Acute and chronic kidney disease concurs commonly with liver disease and is associated with a wide array of complications including dialysis dependency and increased mortality. Patients with liver disease or liver cirrhosis show a higher prevalence of chronic kidney disease. This is attributed to concomitant comorbidities, such as metabolic syndrome, chronic inflammation, hypercoagulability, hyperfibrinolysis, diabetes mellitus and dyslipidaemias. But chronic progressive kidney disease is not always due to hepatorenal syndrome. Beyond that, other diseases or disease entities should be considered. Among them are diabetic nephropathy, secondary IgA nephropathy, hepatitis C -associated membranoproliferative Glomerulonephritis (MPGN) and hepatitis B-associated membranous nephropathy.Coexisting diseases, similar underlying pathophysiologic mechanisms, or simultaneously concurring pathophysiological processes and overlapping clinical manifestations, impede the etiologic diagnosis and corresponding treatment of chronic kidney disease in the setting of chronic liver disease. In this review, we focus on common and rare pathologies, which can lead to chronic kidney disease in this particular patient group and try to summarize the most recent therapeutic modalities.
Collapse
Affiliation(s)
- Florian Gunnar Scurt
- Klinik für Nieren- und Hochdruckerkrankungen, Diabetologie und Endokrinologie, Medizinische Fakultät der Otto-von-Guericke-Universität, Magdeburg, Deutschland.,Health Campus Immunology, Infectiology and Inflammation, Otto von Guericke University, Magdeburg, Germany
| | - Katrin Bose
- Health Campus Immunology, Infectiology and Inflammation, Otto von Guericke University, Magdeburg, Germany.,Universitätsklinik für Gastroenterologie, Hepatologie und Infektiologie, Medizinische Fakultät der Otto-von-Guericke-Universität, Magdeburg, Deutschland
| | - Ali Canbay
- Ruhr-Universität Bochum, Medizinische Klinik, Universitätsklinikum Knappschaftskrankenhaus Bochum GmbH, Bochum, Deutschland
| | - Peter R Mertens
- Klinik für Nieren- und Hochdruckerkrankungen, Diabetologie und Endokrinologie, Medizinische Fakultät der Otto-von-Guericke-Universität, Magdeburg, Deutschland.,Health Campus Immunology, Infectiology and Inflammation, Otto von Guericke University, Magdeburg, Germany
| | - Christos Chatzikyrkou
- Klinik für Nieren- und Hochdruckerkrankungen, Diabetologie und Endokrinologie, Medizinische Fakultät der Otto-von-Guericke-Universität, Magdeburg, Deutschland.,Health Campus Immunology, Infectiology and Inflammation, Otto von Guericke University, Magdeburg, Germany
| |
Collapse
|
9
|
Sirdah MM, Reading NS. Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes. Clin Genet 2020; 98:525-547. [PMID: 32385895 DOI: 10.1111/cge.13772] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 02/06/2023]
Abstract
Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.
Collapse
Affiliation(s)
- Mahmoud M Sirdah
- Division of Hematology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.,Biology Department, Al Azhar University-Gaza, Gaza, Palestine
| | - N Scott Reading
- Institute for Clinical and Experimental Pathology, ARUP Laboratories, Salt Lake City, Utah, USA.,Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| |
Collapse
|