Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes

被引:1
|
作者
Bertrand, Ambre [1 ]
Lewis, Andrew [2 ]
Camps, Julia [1 ]
Grau, Vicente [3 ]
Rodriguez, Blanca [1 ]
机构
[1] Univ Oxford, Dept Comp Sci, Computat Cardiovasc Sci Grp, Oxford OX1 3QD, England
[2] Univ Oxford, Radcliffe Dept Med, Div Cardiovasc Med, Oxford OX3 9DU, England
[3] Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford OX3 7DQ, England
基金
英国工程与自然科学研究理事会; 欧盟地平线“2020”;
关键词
Diabetes mellitus (type 2); Cardiovascular diseases; Electrocardiography; Magnetic resonance imaging; Cross-sectional studies; UK Biobank; LEFT-VENTRICULAR MASS; HEART-FAILURE; SYSTEMIC HYPERTENSION; QT PROLONGATION; GEOMETRY; CARDIOMYOPATHY; HYPERTROPHY; GUIDELINES; MANAGEMENT; DIAGNOSIS;
D O I
10.1186/s12933-024-02465-y
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundType 2 diabetes mellitus (T2DM) is a major risk factor for heart failure with preserved ejection fraction and cardiac arrhythmias. Precursors of these complications, such as diabetic cardiomyopathy, remain incompletely understood and underdiagnosed. Detection of early signs of cardiac deterioration in T2DM patients is critical for prevention. Our goal is to quantify T2DM-driven abnormalities in ECG and cardiac imaging biomarkers leading to cardiovascular disease. MethodsWe quantified ECG and cardiac magnetic resonance imaging biomarkers in two matched cohorts of 1781 UK Biobank participants, with and without T2DM, and no diagnosed cardiovascular disease at the time of assessment. We performed a pair-matched cross-sectional study to compare cardiac biomarkers in both cohorts, and examined the association between T2DM and these biomarkers. We built multivariate multiple linear regression models sequentially adjusted for socio-demographic, lifestyle, and clinical covariates. ResultsParticipants with T2DM had a higher resting heart rate (66 vs. 61 beats per minute, p < 0.001), longer QTc interval (424 vs. 420ms, p < 0.001), reduced T wave amplitude (0.33 vs. 0.37mV, p < 0.001), lower stroke volume (72 vs. 78ml, p < 0.001) and thicker left ventricular wall (6.1 vs. 5.9mm, p < 0.001) despite a decreased Sokolow-Lyon index (19.1 vs. 20.2mm, p < 0.001). T2DM was independently associated with higher heart rate (beta = 3.11, 95% CI = [2.11,4.10], p < 0.001), lower stroke volume (beta = -4.11, 95% CI = [-6.03, -2.19], p < 0.001) and higher left ventricular wall thickness (beta = 0.133, 95% CI = [0.081,0.186], p < 0.001). Trends were consistent in subgroups of different sex, age and body mass index. Fewer significant differences were observed in participants of non-white ethnic background. QRS duration and Sokolow-Lyon index showed a positive association with the development of cardiovascular disease in cohorts with and without T2DM, respectively. A higher left ventricular mass and wall thickness were associated with cardiovascular outcomes in both groups. ConclusionT2DM prior to cardiovascular disease was linked with a higher heart rate, QTc prolongation, T wave amplitude reduction, as well as lower stroke volume and increased left ventricular wall thickness. Increased QRS duration and left ventricular wall thickness and mass were most strongly associated with future cardiovascular disease. Although subclinical, these changes may indicate the presence of autonomic dysfunction and diabetic cardiomyopathy.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes (vol 23, 371, 2024)
    Bertrand, Ambre
    Lewis, Andrew
    Camps, Julia
    Grau, Vicente
    Rodriguez, Blanca
    CARDIOVASCULAR DIABETOLOGY, 2025, 24 (01)
  • [2] Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings
    Xu, Zhenzhong
    Chen, Xu
    Yang, Linchao
    Xu, Jiangtao
    Zhou, Shenghan
    ELECTRONIC RESEARCH ARCHIVE, 2024, 32 (06): : 4074 - 4095
  • [3] Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer
    Shao, Wei
    Cheng, Jun
    Sun, Liang
    Han, Zhi
    Feng, Qianjin
    Zhang, Daoqiang
    Huang, Kun
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II, 2018, 11071 : 648 - 656
  • [4] A joint multi-modal learning method for early-stage knee osteoarthritis disease classification
    Liu, Liangliang
    Chang, Jing
    Zhang, Pei
    Ma, Qingzhi
    Zhang, Hui
    Sun, Tong
    Qiao, Hongbo
    HELIYON, 2023, 9 (04)
  • [5] Multi-modal deep-learning model for real-time prediction of recurrence in early-stage esophageal cancer: A multi-modal approach
    Jung, H. A.
    Lee, D.
    Park, B.
    Lee, K.
    Lee, H. Y.
    Kim, T. J.
    Jeon, Y. J.
    Lee, J.
    Cho, J. H.
    Kim, H. K.
    Choi, Y. S.
    Park, S.
    Sun, J-M.
    Lee, S-H.
    Ahn, J. S.
    Ahn, M-J.
    ANNALS OF ONCOLOGY, 2024, 35 : S883 - S883
  • [6] LIPID OVERFLOW IS AN EARLY-STAGE PREDICTOR OF TYPE 2 DIABETES
    Kahn, H. S.
    Cheng, Y. J.
    Thompson, T. J.
    Imperatore, G.
    Gregg, E. W.
    AMERICAN JOURNAL OF EPIDEMIOLOGY, 2009, 169 : S1 - S1
  • [7] Application of multi-modal therapy concepts in the early stage follicular lymphoma
    Witzens-Harig, M.
    Herfarth, K.
    DEUTSCHE MEDIZINISCHE WOCHENSCHRIFT, 2009, 134 (39) : 1953 - 1955
  • [8] Skin Autofluorescence is Associated with Early-stage Atherosclerosis in Patients with Type 1 Diabetes
    Osawa, Saeko
    Katakami, Naoto
    Kuroda, Akio
    Takahara, Mitsuyoshi
    Sakamoto, Fumie
    Kawamori, Dan
    Matsuoka, Takaaki
    Matsuhisa, Munehide
    Shimomura, Iichiro
    JOURNAL OF ATHEROSCLEROSIS AND THROMBOSIS, 2017, 24 (03) : 312 - 326
  • [9] Personalizing Early-Stage Type 1 Diabetes in Children
    Limbert, Catarina
    von dem Berge, Thekla
    Danne, Thomas
    DIABETES CARE, 2023, 46 (10) : 1747 - 1749
  • [10] Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients
    Eiichiro Kanda
    Atsushi Suzuki
    Masaki Makino
    Hiroo Tsubota
    Satomi Kanemata
    Koichi Shirakawa
    Toshitaka Yajima
    Scientific Reports, 12