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Original Article
Dynamic changes in clinical biomarkers of cardiometabolic diseases by changes in exercise behavior, and network comparisons: a community-based prospective cohort study in Korea
JooYong Park, Jaesung Choi, Ji-Eun Kim, Sang-Min Park, Joo-Youn Cho, Daehee Kang, Miyoung Lee, Ji-Yeob Choi
Epidemiol Health. 2023;45:e2023026.   Published online February 16, 2023
DOI: https://doi.org/10.4178/epih.e2023026
  • 3,095 View
  • 72 Download
  • 1 Web of Science
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Lifestyles, including exercise behaviors, change continually over time. This study examined whether the clinical biomarkers (CBs) related to cardiometabolic diseases (CMDs) and their relationships differed with changes in exercise behavior.
METHODS
The Ansan-Ansung cohort study (third to fifth phases; n=2,668) was used in the current study. Regular exercise behavior was investigated using a yes/no questionnaire. Changes in exercise behavior were classified into 4 groups: Y-N, N-Y, Y-Y, and N-N, with “Y” indicating that a participant regularly engaged in exercise at a given time point and “N” indicating that he or she did not. Fourteen CBs related to CMDs were used, and the associations between changes in exercise behavior and relative changes in CBs were examined. CB networks were constructed and topological comparisons were conducted.
RESULTS
Y-N was associated with increases in fasting blood sugar and insulin levels in men, and increased total cholesterol and low-density lipoprotein cholesterol levels in women. Meanwhile, N-Y was inversely associated with body fat percentage, visceral fat percentage, fasting insulin, and triglyceride level. Waist circumference played a central role in most networks. In men, more edges were found in the N-Y and Y-Y groups than in the N-N and Y-N groups, whereas women in the N-Y and Y-Y groups had more edges than those in the N-N and Y-N groups.
CONCLUSIONS
Consistent exercise or starting to engage in regular exercise had favorable effects on CBs related to CMDs, although their network patterns differed between the sexes.
Summary
Korean summary
한국 지역사회기반 코호트 자료를 이용하여, 운동 행태 변화에 따른 심혈관대사질환 관련 임상 생체 지표들의 변화가 남녀에 따라, 변화 행태에 따라 다르게 나타남을 확인하였다. 이런 변화와 차이는 네트워크 분석을 통한 구조적인 차이로도 확인되었다.
Key Message
This study examined that changes in the clinical biomarkers related to cardiometabolic diseases differed with changes in exercise behavior using a community-based prospective cohort study in Korea. Consistent exercise or change into exercise behavior had favorable effects on CB related to CMD, although their network patterns differed between the sexes.
Methods
The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers
Stefano Parodi, Damiano Verda, Francesca Bagnasco, Marco Muselli
Epidemiol Health. 2022;44:e2022088.   Published online October 17, 2022
DOI: https://doi.org/10.4178/epih.e2022088
  • 3,679 View
  • 106 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
The area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the analysed sample. However, its actual usefulness in the clinical context has been questioned, because it does not seem to be directly related to the actual performance of a diagnostic marker in identifying diseased and non-diseased subjects in real clinical settings. This study evaluates the relationship between the AUC and the proportion of correct classifications (global diagnostic accuracy, GDA) in relation to the shape of the corresponding ROC curves.
METHODS
We demonstrate that AUC represents an upward-biased measure of GDA at an optimal accuracy cut-off for balanced groups. The magnitude of bias depends on the shape of the ROC plot and on the proportion of diseased and non-diseased subjects. In proper curves, the bias is independent from the diseased/non-diseased ratio and can be easily estimated and removed. Moreover, a comparison between 2 partial AUCs can be replaced by a more powerful test for the corresponding whole AUCs.
RESULTS
Applications to 3 real datasets are provided: a marker for a hormone deficit in children, 2 tumour markers for malignant mesothelioma, and 2 gene expression profiles in ovarian cancer patients.
CONCLUSIONS
The AUC is a measure of accuracy with potential clinical relevance for the evaluation of disease markers. The clinical meaning of ROC parameters should always be evaluated with an analysis of the shape of the corresponding ROC curve.
Summary
Key Message
The area under a ROC curve is a measure of diagnostic accuracy with potential clinical relevance, whose meaning should always be evaluated analysing the shape of the corresponding curve.

Citations

Citations to this article as recorded by  
  • Interpretable machine learning for predicting chronic kidney disease progression risk
    Jin-Xin Zheng, Xin Li, Jiang Zhu, Shi-Yang Guan, Shun-Xian Zhang, Wei-Ming Wang
    DIGITAL HEALTH.2024;[Epub]     CrossRef
  • Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis
    Yue Wang, Yinyin Cao, Yang Li, Meifen Yuan, Jin Xu, Jian Li
    Frontiers in Molecular Biosciences.2023;[Epub]     CrossRef
  • Escherichia coli triggers α-synuclein pathology in the LRRK2 transgenic mouse model of PD
    Dongxiao Liang, Han Liu, Ruoqi Jin, Renyi Feng, Jiuqi Wang, Chi Qin, Rui Zhang, Yongkang Chen, Jingwen Zhang, Junfang Teng, Beisha Tang, Xuebing Ding, Xuejing Wang
    Gut Microbes.2023;[Epub]     CrossRef
Review
Relationship between Stress and Biomarkers.
Sang Baek Koh
Korean J Epidemiol. 2002;24(2):137-147.
  • 5,709 View
  • 22 Download
AbstractAbstract PDF
Abstract
Stress can induce modifications in the central nervous(CNS), autonomic nervous and neuroendocrine system. Thus, the stress response has long been measured in laboratory experiments by biochemical changes in the hormone systems that are referred to as the sympathetic nervous system(SNS) and pituitary-adrenocortical axes(HPA). These axes react to acute stress or chronic stress. The activation of these two particular pathways result in elevated serum levels of catecholamines, cortisol, ACTH, dopamine, and others hormones. But there is considerable debate about the relevance of traditional laboratory stress findings to real-life situation. The neurobiology of stress is a key step to the understanding of stress-induced changes of immune functions. The immune system operates in communication with brain and endocrine system. Because of this extensive communication, the immune system can influence how we feel and behave. The stress are associated with endocrine and autonomic changes that can inhibit immune system function. The concept of neurocardiology renders plausible the various theoretical constructs of stress as they relate to circulatory vascular disease. Detailed reviews of the anatomic connections between the brain and the heart and of experimental and clinical data on the role of the CNS in cardiac dysfunction can be found elsewhere. In this study, we reviewed that stress was associated with cardiovascular disease mortality through the known cardiovascular risk factors(hypertension, heart rate variability, homocycteine, and clotting system).
Summary
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