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C-reactive Protein Concentration Is Associated With a Higher Risk of Mortality in a Rural Korean Population 원문보기

Journal of preventive medicine and public health = 예방의학회지, v.49 no.5, 2016년, pp.275 - 287  

Lee, Jung Hyun (Graduate School of Public Health, Yonsei University) ,  Yeom, Hyungseon (Department of Preventive Medicine, Yonsei University College of Medicine) ,  Kim, Hyeon Chang (Department of Preventive Medicine, Yonsei University College of Medicine) ,  Suh, Il (Department of Preventive Medicine, Yonsei University College of Medicine) ,  Kim, Mi Kyung (Department of Preventive Medicine, Hanyang University College of Medicine) ,  Shin, Min-Ho (Department of Preventive Medicine, Chonnam National University Medical School) ,  Shin, Dong Hoon (Department of Preventive Medicine, Keimyung University School of Medicine) ,  Koh, Sang-Baek (Department of Preventive Medicine, Yonsei University Wonju College of Medicine) ,  Ahn, Song Vogue (Department of Preventive Medicine, Yonsei University Wonju College of Medicine) ,  Lee, Tae-Yong (Department of Preventive Medicine, Chungnam National University School of Medicine) ,  Ryu, So Yeon (Department of Preventive Medicine, Chosun University Medical School) ,  Song, Jae-Sok (Department of Preventive Medicine and Institute of Catholic Kwandong University College of Medicine) ,  Choe, Hong-Soon (Department of Preventive Medicine and Institute of Catholic Kwandong U) ,  Lee, Young-Hoon ,  Choi, Bo Youl

Abstract AI-Helper 아이콘AI-Helper

Objectives: C-reactive protein (CRP), an inflammatory biomarker, has been widely used as a preclinical marker predictive of morbidity and mortality. Although many studies have reported a positive association between CRP and mortality, uncertainty still remains about this association in various popul...

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제안 방법

  • Another aim of our study was to evaluate the effect of hypertension in modifying the association between blood CRP concentrations and mortality. However, our analysis that was stratified according to the presence of hypertension did not show a significant interaction (p for interaction>0.
  • 05). As adjustment variables, we selected age, rural region, BMI, educational status, alcohol use, smoking status, regular exercise, the presence of disease (hypertension, diabetes, and dyslipidemia), SBP, DBP, and fasting concentrations of blood glucose, total cholesterol, triglycerides, and HDL cholesterol. Adjustment variables were selected based on previous studies [11,13,17].
  • , non-linear) hazard functions [25]. In this method, we selected five CRP concentration values as knots based on CRP concentration percentiles, tested the linear and non-linear associations between knots using a cubic function, and presented the integrated graph smoothly. Since the RCS could be affected by outliers, we excluded values lower than the 1st percentile and greater than the 99th percentile.
  • Information about demographic factors, socioeconomic status, health behaviors, and disease history were collected using a standardized questionnaire. Educational status was used to represent socioeconomic status, and was defined as elementary school or below, middle school, high school, and university or above.
  • An interaction term was created between hypertension status and CRP and was added into the Cox proportional hazard model. Sensitivity analysis was performed by excluding deaths that occurred within two years from the month that each participant first participated in the study in order to rule out deaths caused by unknown underlying diseases. Since blood CRP concentrations were right-skewed, another sensitivity analysis was performed using log-transformed blood CRP concentrations.
  • To verify participants’ vital status, their records in our study were crosschecked with death statistics from the National Statistical Office via the Korea Centers for Disease Control and Prevention using an anonymized form through December 2013.

대상 데이터

  • Next, we excluded 227 participants whose vital status could not be confirmed, as well as 1604 participants whose CRP concentrations were not measured. Finally, we excluded 442 participants due to missing data in some independent variables, as well as two participants who died in the same month that they were enrolled in the study. Thus, a total of 23 233 people (8862 men and 14 371 women) were included in the final analysis.
  • In brief, a total of 28 338 community dwellers (age ≥40 years) were recruited from 11 rural communities in Korea from 2005 to 2011. In eight communities, the study participants were recruited beginning in 2005, and those in the other three communities were recruited beginning in 2006. The follow-up is ongoing, and in this study, we assessed mortality cases based on data from the National Statistical Office.
  • Finally, we excluded 442 participants due to missing data in some independent variables, as well as two participants who died in the same month that they were enrolled in the study. Thus, a total of 23 233 people (8862 men and 14 371 women) were included in the final analysis. All participants provided written informed consent, and the study protocol was approved by the institutional review board of each institution that participated in KoGES_CAVAS.

데이터처리

  • Since blood CRP concentrations were right-skewed, another sensitivity analysis was performed using log-transformed blood CRP concentrations. All statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA), and RCS analysis was carried out using the SAS LGTPHCURV9 macro [26]. The p-values <0.

이론/모형

  • Since the RCS could be affected by outliers, we excluded values lower than the 1st percentile and greater than the 99th percentile. An interaction term was created between hypertension status and CRP and was added into the Cox proportional hazard model. Sensitivity analysis was performed by excluding deaths that occurred within two years from the month that each participant first participated in the study in order to rule out deaths caused by unknown underlying diseases.
  • Laboratory evaluations were performed using blood samples obtained after participants had fasted overnight. Blood concentrations of glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides were measured using the enzyme method (ADVIA 1650 and ADVIA 1800; Siemens Healthineers, Deerfield, IL, USA). Low-density lipoprotein (LDL) cholesterol levels were calculated using the Friedewald formula [24] in individuals with blood triglyceride levels <4.
  • We used the t-test, analysis of variance, the Mann-Whitney test, the Kruskal-Wallis test, and the chi-square test for comparisons. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) between CRP and mortality using the Cox proportional hazard model. The proportional hazard assumption was assessed by including interactions with time as time-dependent covariates in our model, and no obvious violations were found (p for proportional test >0.
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