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Hsieh DY, Lai YR, Huang CC, Chen YN, Wu SY, Chiu WC, Cheng BC, Lin TY, Chiang HC, Lu CH. Baroreflex Sensitivity as a Surrogate Biomarker for Concurrently Assessing the Severity of Arterial Stiffness and Cardiovascular Autonomic Neuropathy in Individuals with Type 2 Diabetes. J Pers Med 2024; 14:491. [PMID: 38793073 PMCID: PMC11122369 DOI: 10.3390/jpm14050491] [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: 04/10/2024] [Revised: 04/22/2024] [Accepted: 04/29/2024] [Indexed: 05/26/2024] Open
Abstract
This study aimed to investigate whether baroreflex sensitivity (BRS) could serve as a reliable metric for assessing cardiovascular autonomic neuropathy (CAN) and concurrently act as a surrogate biomarker for evaluating the severity of arterial stiffness and CAN in individuals diagnosed with type 2 diabetes mellitus (T2DM). Participants underwent brachial-ankle pulse wave velocity (baPWV) as well as autonomic function evaluations encompassing the Sudoscan-based modified composite autonomic scoring scale (CASS), baroreflex sensitivity, and heart rate variability in time domains and frequency domains. Linear regression analysis was performed to evaluate the influence of independent variables on baPWV and modified CASS. Participants with higher baPWV values were older, with longer diabetes duration, lower body weight, body mass index, waist circumference, elevated systolic and diastolic blood pressure, and mean arterial blood pressure. They also exhibited a higher prevalence of retinopathy as the underlying disease and reduced estimated glomerular filtration rate. Multiple linear regression analysis revealed that age and BRS were significantly associated with baPWV while diabetes duration, UACR, and BRS were significantly associated with modified CASS. Our study confirms the significant association of BRS with baPWV and modified CASS in T2DM, highlighting its pivotal role in linking microvascular and macrovascular complications. This supports BRS as a surrogate marker for assessing both the severity of arterial stiffness and cardiovascular autonomic neuropathy in T2DM, enabling the early identification of complications.
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Affiliation(s)
- Dong-Yi Hsieh
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (D.-Y.H.); (Y.-R.L.); (H.-C.C.)
| | - Yun-Ru Lai
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (D.-Y.H.); (Y.-R.L.); (H.-C.C.)
- Department of Hyperbaric Oxygen Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan
| | - Chih-Cheng Huang
- Department of Neurology, Chi-Mei Medical Center, Tainan City 73657, Taiwan;
| | - Yung-Nien Chen
- Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (Y.-N.C.); (W.-C.C.); (B.-C.C.)
| | - Szu-Ying Wu
- Department of Chinese Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan;
| | - Wen-Chan Chiu
- Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (Y.-N.C.); (W.-C.C.); (B.-C.C.)
| | - Ben-Chung Cheng
- Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (Y.-N.C.); (W.-C.C.); (B.-C.C.)
| | - Ting-Yin Lin
- Department of Nursing, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan;
| | - Hui-Ching Chiang
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (D.-Y.H.); (Y.-R.L.); (H.-C.C.)
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan; (D.-Y.H.); (Y.-R.L.); (H.-C.C.)
- Department of Center for Shockwave Medicine and Tissue Engineering, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City 83301, Taiwan
- Department of Biological Science, National Sun Yat-Sen University, Kaohsiung City 80424, Taiwan
- Department of Neurology, Xiamen Chang Gung Memorial Hospital, Xiamen 361126, China
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Pichot V, Corbier C, Chouchou F, Barthélémy JC, Roche F. CVRanalysis: a free software for analyzing cardiac, vascular and respiratory interactions. Front Physiol 2024; 14:1224440. [PMID: 38250656 PMCID: PMC10797906 DOI: 10.3389/fphys.2023.1224440] [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: 05/17/2023] [Accepted: 12/13/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction: Simultaneous beat-to-beat R-R intervals, blood pressure and respiration signals are routinely analyzed for the evaluation of autonomic cardiovascular and cardiorespiratory regulations for research or clinical purposes. The more recognized analyses are i) heart rate variability and cardiac coherence, which provides an evaluation of autonomic nervous system activity and more particularly parasympathetic and sympathetic autonomic arms; ii) blood pressure variability which is mainly linked to sympathetic modulation and myogenic vascular function; iii) baroreflex sensitivity; iv) time-frequency analyses to identify fast modifications of autonomic activity; and more recently, v) time and frequency domain Granger causality analyses were introduced for assessing bidirectional causal links between each considered signal, thus allowing the scrutiny of many physiological regulatory mechanisms. Methods: These analyses are commonly applied in various populations and conditions, including mortality and morbidity predictions, cardiac and respiratory rehabilitation, training and overtraining, diabetes, autonomic status of newborns, anesthesia, or neurophysiological studies. Results: We developed CVRanalysis, a free software to analyze cardiac, vascular and respiratory interactions, with a friendly graphical interface designed to meet laboratory requirements. The main strength of CVRanalysis resides in its wide scope of applications: recordings can arise from beat-to-beat preprocessed data (R-R, systolic, diastolic and mean blood pressure, respiration) or raw data (ECG, continuous blood pressure and respiratory waveforms). It has several tools for beat detection and correction, as well as setting of specific areas or events. In addition to the wide possibility of analyses cited above, the interface is also designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in text files that are easily employable in statistical softwares. Conclusion: CVRanalysis is freely available at this website: anslabtools.univ-st-etienne.fr. It has been developed using MATLAB® and works on Windows 64-bit operating systems. The software is a standalone application avoiding to have programming skills and to install MATLAB. The aims of this paper area are to describe the physiological, research and clinical contexts of CVRanalysis, to introduce the methodological approach of the different techniques used, and to show an overview of the software with the aid of screenshots.
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Affiliation(s)
- Vincent Pichot
- SAINBIOSE U1059, Inserm, Saint-Etienne Jean-Monnet University, Clinical Physiology and Exercise, CHU of Saint-Etienne, Saint-Etienne, France
| | - Christophe Corbier
- LASPI EA3059, Saint-Etienne Jean-Monnet University, Roanne Technology University Institute, Roanne, France
| | - Florian Chouchou
- IRISSE EA4075, UFR SHE, University of La Réunion, Le Tampon, France
| | - Jean-Claude Barthélémy
- SAINBIOSE U1059, Inserm, Saint-Etienne Jean-Monnet University, Clinical Physiology and Exercise, CHU of Saint-Etienne, Saint-Etienne, France
| | - Frédéric Roche
- SAINBIOSE U1059, Inserm, Saint-Etienne Jean-Monnet University, Clinical Physiology and Exercise, CHU of Saint-Etienne, Saint-Etienne, France
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Zhang W, Liu B. iSnoDi-MDRF: Identifying snoRNA-Disease Associations Based on Multiple Biological Data by Ranking Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3013-3019. [PMID: 37030816 DOI: 10.1109/tcbb.2023.3258448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Accumulating evidence indicates that the dysregulation of small nucleolar RNAs (snoRNAs) is relevant with diseases. Identifying snoRNA-disease associations by computational methods is desired for biologists, which can save considerable costs and time compared biological experiments. However, it still faces some challenges as followings: (i) Many snoRNAs are detected in recent years, but only a few snoRNAs have been proved to be associated with diseases; (ii) Computational predictors trained with only a few known snoRNA-disease associations fail to accurately identify the snoRNA-disease associations. In this study, we propose a ranking framework, called iSnoDi-MDRF, to identify potential snoRNA-disease associations based on multiple biological data, which has the following highlights: (i) iSnoDi-MDRF integrates ranking framework, which is not only able to identify potential associations between known snoRNAs and diseases, but also can identify diseases associated with new snoRNAs. (ii) Known gene-disease associations are employed to help train a mature model for predicting snoRNA-disease association. Experimental results illustrate that iSnoDi-MDRF is very suitable for identifying potential snoRNA-disease associations. The web server of iSnoDi-MDRF predictor is freely available at http://bliulab.net/iSnoDi-MDRF/.
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Lawrence S, Mueller BR, Kwon P, Robinson-Papp J. Phenotyping autonomic neuropathy using principal component analysis. Auton Neurosci 2023; 245:103056. [PMID: 36525943 PMCID: PMC9899306 DOI: 10.1016/j.autneu.2022.103056] [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: 06/27/2022] [Revised: 09/12/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
To identify autonomic neuropathy (AN) phenotypes, we used principal component analysis on data from participants (N = 209) who underwent standardized autonomic testing including quantitative sudomotor axon reflex testing, and heart rate and blood pressure at rest and during tilt, Valsalva, and standardized deep breathing. The analysis identified seven clusters: 1) normal, 2) hyperadrenergic features without AN, 3) mild AN with hyperadrenergic features, 4) moderate AN, 5) mild AN with hypoadrenergic features, 6) borderline AN with hypoadrenergic features, 7) mild balanced deficits across parasympathetic, sympathetic and sudomotor domains. These findings demonstrate a complex relationship between adrenergic and other aspects of autonomic function.
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Affiliation(s)
- Steven Lawrence
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, USA
| | - Bridget R Mueller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, USA
| | - Patrick Kwon
- Department of Neurology, NYU Grossman School of Medicine, USA
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Cui Q, Che L, Zang H, Yu J, Xu L, Huang Y. Association between preoperative autonomic nervous system function and post-induction hypotension in elderly patients: a protocol for a cohort study. BMJ Open 2023; 13:e067400. [PMID: 36717143 PMCID: PMC9887722 DOI: 10.1136/bmjopen-2022-067400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Post-induction hypotension (PIH), which is prevalent among elderly patients, is associated with adverse perioperative outcomes. As a critical part of blood pressure regulation, baroreflex control is believed to be closely related to intraoperative blood pressure fluctuations. Spontaneous baroreflex sensitivity and heart rate variability measurement can aid evaluation of patients' autonomic function. This study aims to determine the association between preoperative decreased baroreflex function and PIH in elderly patients. METHODS AND ANALYSIS This prospective cohort study will enrol patients who are 65 years old and above, scheduled for elective non-cardiac surgery under general anaesthesia, and American Society of Anesthesiologists physical status I-III (n=180). Baseline assessment will include routine preoperative evaluations as well as symptoms and anamneses associated with baroreflex failure. Preoperative autonomic function monitoring will be performed through 20 min of continuous beat-to-beat heart rate and blood pressure monitoring using LiDCO rapid (Masimo Corporation, USA). The primary outcome will be PIH. Detailed use of anaesthetic agents during induction and maintenance will be documented for adjustment in multivariable analyses. ETHICS AND DISSEMINATION The Research Ethics Committee of Peking Union Medical College Hospital approved the study protocol (I-22PJ008). We aim to publish and disseminate our findings in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT05425147.
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Affiliation(s)
- Quexuan Cui
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Lu Che
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Han Zang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jiawen Yu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Li Xu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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Chen D, Li S, Chen Y. ISTRF: Identification of sucrose transporter using random forest. Front Genet 2022; 13:1012828. [PMID: 36171889 PMCID: PMC9511101 DOI: 10.3389/fgene.2022.1012828] [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: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 12/05/2022] Open
Abstract
Sucrose transporter (SUT) is a type of transmembrane protein that exists widely in plants and plays a significant role in the transportation of sucrose and the specific signal sensing process of sucrose. Therefore, identifying sucrose transporter is significant to the study of seed development and plant flowering and growth. In this study, a random forest-based model named ISTRF was proposed to identify sucrose transporter. First, a database containing 382 SUT proteins and 911 non-SUT proteins was constructed based on the UniProt and PFAM databases. Second, k-separated-bigrams-PSSM was exploited to represent protein sequence. Third, to overcome the influence of imbalance of samples on identification performance, the Borderline-SMOTE algorithm was used to overcome the shortcoming of imbalance training data. Finally, the random forest algorithm was used to train the identification model. It was proved by 10-fold cross-validation results that k-separated-bigrams-PSSM was the most distinguishable feature for identifying sucrose transporters. The Borderline-SMOTE algorithm can improve the performance of the identification model. Furthermore, random forest was superior to other classifiers on almost all indicators. Compared with other identification models, ISTRF has the best general performance and makes great improvements in identifying sucrose transporter proteins.
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Affiliation(s)
- Dong Chen
- College of Electrical and Information Engineering, Qu Zhou University, Quzhou, China
| | - Sai Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yu Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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Bönhof GJ, Herder C, Ziegler D. Diagnostic Tools, Biomarkers, and Treatments in Diabetic polyneuropathy and Cardiovascular Autonomic Neuropathy. Curr Diabetes Rev 2022; 18:e120421192781. [PMID: 33845748 DOI: 10.2174/1573399817666210412123740] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/24/2021] [Accepted: 03/02/2021] [Indexed: 11/22/2022]
Abstract
The various manifestations of diabetic neuropathy, including distal symmetric sensorimotor polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN), are among the most prevalent chronic complications of diabetes. Major clinical complications of diabetic neuropathies, such as neuropathic pain, chronic foot ulcers, and orthostatic hypotension, are associated with considerable morbidity, increased mortality, and diminished quality of life. Despite the substantial individual and socioeconomic burden, the strategies to diagnose and treat diabetic neuropathies remain insufficient. This review provides an overview of the current clinical aspects and recent advances in exploring local and systemic biomarkers of both DSPN and CAN assessed in human studies (such as biomarkers of inflammation and oxidative stress) for better understanding of the underlying pathophysiology and for improving early detection. Current therapeutic options for DSPN are (I) causal treatment, including lifestyle modification, optimal glycemic control, and multifactorial risk intervention, (II) pharmacotherapy derived from pathogenetic concepts, and (III) analgesic treatment against neuropathic pain. Recent advances in each category are discussed, including non-pharmacological approaches, such as electrical stimulation. Finally, the current therapeutic options for cardiovascular autonomic complications are provided. These insights should contribute to a broader understanding of the various manifestations of diabetic neuropathies from both the research and clinical perspectives.
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Affiliation(s)
- Gidon J Bönhof
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
| | - Dan Ziegler
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
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