혈청 대사체와 뇌졸중 발생위험의 용량반응 분석
Dose-response Relationship between Serum Metabolomics and the Risk of Stroke
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objectives: Except the known risk factors for stroke, few studies have identified novel metabolic markers that could effectively detect stroke at an early stage. In this study, we explored the dose-response relationship between serum metabolites and the incidence of stroke. Methods: We studied 213 adults in the Korean Cancer Prevention Study-II (KCPS-II) biobank and estimated dose-response relationship between serum metabolites and stroke (42 cases and 171 controls). Three serum metabolites (Acetylcholine, HexadecylAcetylGlycerol, and 1-acetyl-2-formyl-sn-glycero-3-phosphocholine) were used in this study. The analysis included (1) exploratory nonlinear analysis, (2) estimation of flexion points and slopes at below and above the points. In the model to estimate risk of incidence of stroke, we controlled for conventional risk factors such as age, sex, systolic blood pressure, type 2 diabetes, triglyceride, and smoking status. Results: The relationship between incidence of stroke and log-transformed 1-acetyl-2-formyl-sn-glycero-3-phosphocholine was non-linear with flexion point around intensity score of 8.8, whereas other metabolites, log-transformed Acetylcholine and HexadecylAcetylGlycerol, showed negative linear patterns. Conclusions: The study suggests that metabolic markers are associated with incidence of stroke, particularly, at or above the flexion point. The study result may contribute to developing a novel system for precise stroke prediction.
Abstract
Objectives: Except the known risk factors for stroke, few studies have identified novel metabolic markers that could effectively detect stroke at an early stage. In this study, we explored the dose-response relationship between serum metabolites and the incidence of stroke. Methods: We studied 213 adults in the Korean Cancer Prevention Study-II (KCPS-II) biobank and estimated dose-response relationship between serum metabolites and stroke (42 cases and 171 controls). Three serum metabolites (Acetylcholine, HexadecylAcetylGlycerol, and 1-acetyl-2-formyl-sn-glycero-3-phosphocholine) were used in this study. The analysis included (1) exploratory nonlinear analysis, (2) estimation of flexion points and slopes at below and above the points. In the model to estimate risk of incidence of stroke, we controlled for conventional risk factors such as age, sex, systolic blood pressure, type 2 diabetes, triglyceride, and smoking status. Results: The relationship between incidence of stroke and log-transformed 1-acetyl-2-formyl-sn-glycero-3-phosphocholine was non-linear with flexion point around intensity score of 8.8, whereas other metabolites, log-transformed Acetylcholine and HexadecylAcetylGlycerol, showed negative linear patterns. Conclusions: The study suggests that metabolic markers are associated with incidence of stroke, particularly, at or above the flexion point. The study result may contribute to developing a novel system for precise stroke prediction.
참고문헌 (20)
- "Fact sheet on cardiovascular diseases"[URL]
- 2012 Annual report on the causes of death statistics, "Statistics Korea", Statistics Korea, ~ (2013)
- Jee SH, "Smoking and atherosclerotic cardiovascular disease in men with low levels of serum cholesterol: the Korea Medical Insurance Corporation Study", JAMA, 282(22), pp.2149~2155 (1999)
- Del Gobbo LC, "Contribution of major lifestyle risk factors for incident heart failure in older adults: the Cardiovascular Health Study", JACC Heart Fail, 3(7), pp.520~528 (2015)
- Ho JE, "Metabolomic profiles of body mass index in the framingham heart study reveal distinct cardiometabolic phenotypes", PLoS One, 11(2), pp.e0148361~ (2016)
- Gieger C, "Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum", PLoS Genet, 4(11), pp.e1000282~ (2008)
- Park JK, "The accuracy of ICD codes for cerebrovascular diseases in medical insurance claims", Korean J Prev Med, 33, pp.76~82 (2000)
- Hastie TJ, "Generalized additive models", Stat Sci, 1(3), pp.297~318 (1986)
- Hastie TJ, "Generalized additive models" (1990), Chapman & Hall
- Venables WN, "Modern applied statistics with S-PLUS" (1999), Springer-Verlag
- Akaike H, "Information theory and an extension of the maximum likelihood principle" (1973), Akademiai Kiado
- "R: Health effects of air pollution and temperature (HEAT)" (2015)[URL]
- Daniels MJ, "Estimating particulate matter-mortality dose–response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities", Am J Epidemiol, 152(5), pp.397~406 (2000)
- Ostro BD, "A search for a threshold in the relationship of air pollution to mortality; a reanalysis of data on London winters", Environ Health Perspect, 58, pp.397~399 (1984)
- Kim SY, "Determining the threshold effect of ozone on daily mortality: an analysis of ozone and mortality in Seoul, Korea, 1995–1999", Environ Res, 94(2), pp.113~119 (2004)
- Calleman CJ, "Linear versus nonlinear models for hemoglobin adduct formation by acrylamide and its metabolite glycidamide:Implications for risk estimation", Cancer Epidemiol Biomarkers Prev, 1(5), pp.361~368 (1992)
- Flöter A, "Threshold extraction in metabolite concentration data", Bioinformatics, 20(10), pp.1491~1494 (2004)
- lank ML, "Antihypertensive activity of an alkyl ether analog of phosphatidylcholine", Biochem Biophys Res Commun, 90(4), pp.1194~1200 (1979)
- Kramer RM, "Hydrolysis of 1-alkyl-2-arachidonoyl-sn-glycero-3-phosphocholine, a common precursor of platelet-activating factor and eicosanoids, by human platelet phospholipase A2", Biochim Biophys Acta, 959(3), pp.269~279 (1988)
- Hill AB, "The environment and disease: association or causation", Proc R Soc Med, 58(5), pp.295~300 (1965)
