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Lin WL, Chien MM, Patchara S, Wang W, Faradina A, Huang SY, Tung TH, Tsai CS, Skalny AV, Tinkov AA, Chang CC, Chang JS. Essential trace element and phosphatidylcholine remodeling: Implications for body composition and insulin resistance. J Trace Elem Med Biol 2024; 85:127479. [PMID: 38878466 DOI: 10.1016/j.jtemb.2024.127479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Recent studies indicated that bioactive lipids of phosphatidylcholines (PCs) and lysophosphatidylcholines (LysoPCs) predict unhealthy metabolic phenotypes, but results remain inconsistent. To fill this knowledge gap, we investigated whether essential trace elements affect PC-Lyso PC remodeling pathways and the risk of insulin resistance (IR). METHODS Anthropometric and blood biochemical data (glucose, insulin, and lipoprotein-associated phospholipase A2 (Lp-PLA2)) were obtained from 99 adults. Blood essential/probably essential trace elements and lipid metabolites were respectively measured by inductively coupled plasma mass spectrometry (ICP-MS), and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). RESULT AND CONCLUSION Except for LysoPC (O-18:0/0:0), an inverse V shape was observed between body weight and PC and LysoPC species. A Pearson correlation analysis showed that essential/probably-essential metals (Se, Cu, and Ni: r=-0.4∼-0.7) were negatively correlated with PC metabolites but positively correlated with LysoPC (O-18:0/0:0) (Se, Cu, and Ni: r=0.85-0.64). Quantile-g computation showed that one quantile increase in essential metals was associated with a 2.16-fold increase in serum Lp-PLA2 (β=2.16 (95 % confidence interval (CI): 0.34, 3.98), p=0.023), which are key enzymes involved in PC/Lyso PC metabolism. An interactive analysis showed that compared to those with the lowest levels (reference), individuals with the highest levels of serum PCs (pooled, M2) and the lowest essential/probably essential metals (M1) were associated with a healthier body composition and had a 76 % decreased risk of IR (odds ratio (OR)=0.24 (95 % CI: 0.06, 0.90), p<0.05). In contrast, increased exposure to LysoPC(O-18:0/0:0) (M2) and essential metals (M2) exhibited an 8.22-times highest risk of IR (OR= 8.22 (2.07, 32.57), p<0.05) as well as an altered body composition. In conclusion, overexposure to essential/probably essential trace elements may promote an unhealthy body weight and IR through modulating PC/LysoPC remodeling pathways.
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Affiliation(s)
- Wen-Ling Lin
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC
| | - Mu-Ming Chien
- Department of Pediatrics, Taipei Medical University Hospital, Taipei 11031, Taiwan, ROC
| | - Sangopas Patchara
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC
| | - Weu Wang
- Division of Digestive Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei 11301, Taiwan, ROC; Department of Surgery, College of Medicine, Taipei Medical University, Taipei 11301, Taiwan, ROC
| | - Amelia Faradina
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC
| | - Shih-Yi Huang
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC; Center for Reproductive Medicine & Sciences, Taipei Medical University Hospital, Taipei 11031, Taiwan, ROC; Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC
| | - Te-Hsuan Tung
- Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Tri-service General Hospital, National Defense Medical Center, Taipei 114202, Taiwan, ROC; Department and Graduate Institute of Pharmacology, National Defense Medical Center, Taipei 114202, Taiwan, ROC
| | - Anatoly V Skalny
- Center of Bioelementology and Human Ecology, IM Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia; Department of Medical Elementology, Peoples' Friendship University of Russia (RUDN University), Moscow 117198, Russia
| | - Alexey A Tinkov
- Center of Bioelementology and Human Ecology, IM Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia; Laboratory of Ecobiomonitoring and Quality Control, Yaroslavl State University, Yaroslavl, Russia
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taiwan, ROC; Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, Collage of Medicine, Taipei Medical University, Taipei 11031, Taiwan, ROC
| | - Jung-Su Chang
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan, ROC; Nutrition Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan, ROC; Chinese Taipei Society for the Study of Obesity (CTSSO), Taipei 11031, Taiwan, ROC; TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei 11031, Taiwan, ROC.
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Lin L, Pan X, Feng Y, Yang J. Chronic kidney disease combined with metabolic syndrome is a non-negligible risk factor. Ther Adv Endocrinol Metab 2024; 15:20420188241252309. [PMID: 39071115 PMCID: PMC11273817 DOI: 10.1177/20420188241252309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 04/10/2024] [Indexed: 07/30/2024] Open
Abstract
Metabolic syndrome (MetS) is a group of conditions characterized by hypertension (HTN), hyperglycaemia or insulin resistance (IR), hyperlipidaemia, and abdominal obesity. MetS is associated with a high incidence of cardiovascular events and mortality and is an independent risk factor for chronic kidney disease (CKD). MetS can cause CKD or accelerate the progression of kidney disease. Recent studies have found that MetS and kidney disease have a cause-and-effect relationship. Patients with CKD, those undergoing kidney transplantation, or kidney donors have a significantly higher risk of developing MetS than normal people. The present study reviewed the possible mechanisms of MetS in patients with CKD, including the disorders of glucose and fat metabolism after kidney injury, IR, HTN and the administration of glucocorticoid and calcineurin inhibitors. In addition, this study reviewed the effect of MetS in patients with CKD on important target organs such as the kidney, heart, brain and blood vessels, and the treatment and prevention of CKD combined with MetS. The study aims to provide strategies for the diagnosis, treatment and prevention of CKD in patients with MetS.
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Affiliation(s)
- Lirong Lin
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (General Hospital), Chongqing, China
| | - Xianfeng Pan
- Department of Nephrology, Chongqing Kaizhou District People’s Hospital of Chongqing, Chongqing, China
| | - Yuanjun Feng
- Department of Nephrology, Guizhou Aerospace Hospital, Guizhou 563000, China
| | - Jurong Yang
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (General Hospital), Chongqing 401120, China
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Xing Z, Chen H, Alman AC. Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches. AIMS Public Health 2024; 11:667-687. [PMID: 39027391 PMCID: PMC11252584 DOI: 10.3934/publichealth.2024034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 07/20/2024] Open
Abstract
Objective We employed machine learning algorithms to discriminate insulin resistance (IR) in middle-aged nondiabetic women. Methods The data was from the National Health and Nutrition Examination Survey (2007-2018). The study subjects were 2084 nondiabetic women aged 45-64. The analysis included 48 predictors. We randomly divided the data into training (n = 1667) and testing (n = 417) datasets. Four machine learning techniques were employed to discriminate IR: extreme gradient boosting (XGBoosting), random forest (RF), gradient boosting machine (GBM), and decision tree (DT). The area under the curve (AUC) of receiver operating characteristic (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were compared as performance metrics to select the optimal technique. Results The XGBoosting algorithm achieved a relatively high AUC of 0.93 in the training dataset and 0.86 in the testing dataset to discriminate IR using 48 predictors and was followed by the RF, GBM, and DT models. After selecting the top five predictors to build models, the XGBoost algorithm with the AUC of 0.90 (training dataset) and 0.86 (testing dataset) remained the optimal prediction model. The SHapley Additive exPlanations (SHAP) values revealed the associations between the five predictors and IR, namely BMI (strongly positive impact on IR), fasting glucose (strongly positive), HDL-C (medium negative), triglycerides (medium positive), and glycohemoglobin (medium positive). The threshold values for identifying IR were 29 kg/m2, 100 mg/dL, 54.5 mg/dL, 89 mg/dL, and 5.6% for BMI, glucose, HDL-C, triglycerides, and glycohemoglobin, respectively. Conclusion The XGBoosting algorithm demonstrated superior performance metrics for discriminating IR in middle-aged nondiabetic women, with BMI, glucose, HDL-C, glycohemoglobin, and triglycerides as the top five predictors.
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Affiliation(s)
- Zailing Xing
- College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC 56, Tampa, FL 33612, USA
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Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artif Intell Med 2024; 151:102868. [PMID: 38632030 DOI: 10.1016/j.artmed.2024.102868] [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: 08/18/2023] [Revised: 03/03/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024]
Abstract
Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.
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Affiliation(s)
- Maryam Eghbali-Zarch
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA
| | - Sara Masoud
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA.
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Liu J, Li X, Zhu P. Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective. Biol Trace Elem Res 2024:10.1007/s12011-024-04126-3. [PMID: 38409445 DOI: 10.1007/s12011-024-04126-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/22/2024] [Indexed: 02/28/2024]
Abstract
Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.
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Affiliation(s)
- Jun Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Xingyu Li
- Cardiovascular Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peng Zhu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China.
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Zhang H, Zeng T, Zhang J, Zheng J, Min J, Peng M, Liu G, Zhong X, Wang Y, Qiu K, Tian S, Liu X, Huang H, Surmach M, Wang P, Hu X, Chen L. Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China. Front Endocrinol (Lausanne) 2024; 15:1292346. [PMID: 38332892 PMCID: PMC10850228 DOI: 10.3389/fendo.2024.1292346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/11/2024] [Indexed: 02/10/2024] Open
Abstract
Objective Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. Methods We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. Results The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. Conclusion The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.
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Affiliation(s)
- Hao Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Tianshu Zeng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jiaoyue Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Juan Zheng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jie Min
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Miaomiao Peng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Geng Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xueyu Zhong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Ying Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Kangli Qiu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Shenghua Tian
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xiaohuan Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hantao Huang
- Department of Emergency Medicine, Yichang Yiling Hospital, Yichang, China
| | - Marina Surmach
- Department of Public Health and Health Services, Grodno State Medical University, Grodno, Belarus
| | - Ping Wang
- Precision Health Program, Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Xiang Hu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
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Cao J, Qiu W, Lin Y, Liu T, Dou Z, Chen Z. Appropriate sleep duration modifying the association of insulin resistance and hepatic steatosis is varied in different status of metabolic disturbances among adults from the United States, NHANES 2017-March 2020. Prev Med Rep 2023; 36:102406. [PMID: 37744738 PMCID: PMC10511803 DOI: 10.1016/j.pmedr.2023.102406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/20/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
Steatosis is the hepatic manifestation of metabolic syndrome (MetS) and its developing is closely associated with insulin resistance. Shortened sleep has adverse effects on hepatic steatosis and the underlying mechanism remains unknown. We conceived to evaluate whether sleep duration was a lifestyle factor modifying the association between insulin resistance and hepatic steatosis and whether it was varied in different status of metabolic disturbances. We performed a cross-sectional analysis on 2264 adults of United States representing a population of 138,319,512 with MetS or pre-MetS from National Health and Nutrition Examination Survey (NHANES) 2017-March 2020. Participants underwent hepatic transient elastography and laboratory tests. The sleep duration was obtained from interviews. Results showed that insulin resistance was significantly associated with hepatic steatosis among participants with metabolic disturbances (OR = 1.85, 95% CI: 1.30-2.65). Significant moderation of sleep duration on the association between insulin resistance and hepatic steatosis was observed when sleep duration was dichotomized by 6.5- (P = 0.042) or 9.5-hour (P = 0.031). The risk of hepatic steatosis associated with insulin resistance was increased when sleep duration was ≤ 6.5 h and > 9.5 h. Furthermore, the moderation effect of 6.5-hour sleeping was only significant among participants with pre-MetS while that of 9.5-hour sleeping was only significant among participants with MetS. In conclusion, insufficient or excessive sleep increased the risk of hepatic steatosis associated with insulin resistance. Appropriate sleep duration was advocated and varied in different status of metabolic disturbances. Ensuring adequate sleep should be highlighted before MetS occurs and excessive sleep should be prevented for participants with MetS.
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Affiliation(s)
- Junyan Cao
- Department of Medical Ultrasonics, The Third Affiliated Hospital of Sun Yat-sen University, China
| | - Weihong Qiu
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, China
| | - Yuwei Lin
- Peking University Clinical Research Institute, Peking University, China
| | - Tianyu Liu
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, China
| | - Zulin Dou
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, China
| | - Zhaocong Chen
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, China
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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: 0] [Impact Index Per Article: 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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Tsai SF, Yang CT, Liu WJ, Lee CL. Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study. EClinicalMedicine 2023; 58:101934. [PMID: 37090441 PMCID: PMC10119497 DOI: 10.1016/j.eclinm.2023.101934] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 04/25/2023] Open
Abstract
Background Insulin resistance (IR) is associated with diabetes mellitus, cardiovascular disease (CV), and mortality. Few studies have used machine learning to predict IR in the non-diabetic population. Methods In this prospective cohort study, we trained a predictive model for IR in the non-diabetic populations using the US National Health and Nutrition Examination Survey (NHANES, from JAN 01, 1999 to DEC 31, 2012) database and the Taiwan MAJOR (from JAN 01, 2008 to DEC 31, 2017) database. We analysed participants in the NHANES and MAJOR and participants were excluded if they were aged <18 years old, had incomplete laboratory data, or had DM. To investigate the clinical implications (CV and all-cause mortality) of this trained model, we tested it with the Taiwan biobank (TWB) database from DEC 10, 2008 to NOV 30, 2018. We then used SHapley Additive exPlanation (SHAP) values to explain differences across the machine learning models. Findings Of all participants (combined NHANES and MJ databases), we randomly selected 14,705 participants for the training group, and 4018 participants for the validation group. In the validation group, their areas under the curve (AUC) were all >0.8 (highest being XGboost, 0.87). In the test group, all AUC were also >0.80 (highest being XGboost, 0.88). Among all 9 features (age, gender, race, body mass index, fasting plasma glucose (FPG), glycohemoglobin, triglyceride, total cholesterol and high-density cholesterol), BMI had the highest value of feature importance on IR (0.43 for XGboost and 0.47 for RF algorithms). All participants from the TWB database were separated into the IR group and the non-IR group according to the XGboost algorithm. The Kaplan-Meier survival curve showed a significant difference between the IR and non-IR groups (p < 0.0001 for CV mortality, and p = 0.0006 for all-cause mortality). Therefore, the XGboost model has clear clinical implications for predicting IR, aside from CV and all-cause mortality. Interpretation To predict IR in non-diabetic patients with high accuracy, only 9 easily obtained features are needed for prediction accuracy using our machine learning model. Similarly, the model predicts IR patients with significantly higher CV and all-cause mortality. The model can be applied to both Asian and Caucasian populations in clinical practice. Funding Taichung Veterans General Hospital, Taiwan and Japan Society for the Promotion of Science KAKENHI Grant Number JP21KK0293.
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Affiliation(s)
- Shang-Feng Tsai
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Life Science, Tunghai University, Taichung, Taiwan
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan
| | - Wei-Ju Liu
- Intelligent Data Mining Laboratory, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chia-Lin Lee
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Intelligent Data Mining Laboratory, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
- Corresponding author. Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, No. 1650 Taiwan Boulevard Sect. 4, Taichung, Taiwan 407219, ROC.
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A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques. Healthcare (Basel) 2022; 10:healthcare10122496. [PMID: 36554020 PMCID: PMC9778302 DOI: 10.3390/healthcare10122496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/28/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.
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11
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Mavrogiorgou A, Kiourtis A, Kleftakis S, Mavrogiorgos K, Zafeiropoulos N, Kyriazis D. A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions. SENSORS (BASEL, SWITZERLAND) 2022; 22:8615. [PMID: 36433212 PMCID: PMC9695983 DOI: 10.3390/s22228615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 05/27/2023]
Abstract
Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.
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Affiliation(s)
- Argyro Mavrogiorgou
- Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece
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Zhang Q, Wan NJ. Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning. Diabetes Metab Syndr Obes 2022; 15:2963-2975. [PMID: 36193541 PMCID: PMC9526431 DOI: 10.2147/dmso.s380772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care. PATIENTS AND METHODS Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods-namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected. RESULTS After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model. CONCLUSION The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.
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Affiliation(s)
- Qian Zhang
- Department of Pediatrics, Beijing Jishuitan Hospital, Beijing, People’s Republic of China
| | - Nai-jun Wan
- Department of Pediatrics, Beijing Jishuitan Hospital, Beijing, People’s Republic of China
- Correspondence: Nai-jun Wan, Department of Pediatrics, Beijing Jishuitan Hospital, 31# Xinjiekou Dongjie, West District, Beijing, 100035, People’s Republic of China, Tel +86-10-58398102, Email
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