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1.
Zhonghua Xin Xue Guan Bing Za Zhi ; 46(3): 218-223, 2018 Mar 24.
Artigo em Chinês | MEDLINE | ID: mdl-29562428

RESUMO

Objective: To explore the relationship between overnight urinary sodium to potassium ratio and the risk of cardiovascular disease (CVD). Methods: A subsample of 10 percent of the participants (35-59 years old) from the People's Republic of China-United States Collaborative Study of Cardiovascular and Cardiopulmonary Epidemiology (prospective survey) were used. Three consecutive overnight urine samples were collected in the autumn of 1983-1984 and the spring in 1985-1986, respectively. Urinary sodium and potassium were detected and calculated for 8 hours excretion. The occurrences of cardiovascular events were recorded in 2 years interval from 1987-1988 until December 31, 2005. Participants were divided into first ratio group, second ratio group, and third ratio group based on the tertiles of sodium to potassium ratio. Cox proportional hazard regression model was used to determine the relationship between sodium to potassium ratio and risk of CVD. In addition, participants were divided into 2 subgroups by the median of overnight urinary sodium and potassium, and then combined each other for 4 subgroups including low sodium-low potassium group, low sodium-high potassium group, high sodium-low potassium group, and high sodium-high potassium group, to explore the relationship between different sodium-potassium combinations and the risk of CVD. Results: A total of 954 participants were included in the final analysis, of whom 459 (48.1%) were males. There were 318 cases in the first, second and third ratio group, respectively. There were 347 cases in low sodium-low potassium group and high sodium-high potassium group, and 130 cases in low sodium-high potassium group and high sodium-low potassium group. After a median follow-up of 18.6 (18.3, 19.3) years, cardiovascular events occurred in 81 participants, including 64 stroke and 20 coronary heart disease events. Multivariate analysis showed that comparing with the first ratio group, the hazard ratios (HR) in the second and the third ratio groups were 2.04 (95%CI 1.06-3.95, P=0.034) and 2.07 (95%CI 1.07-4.03, P=0.032), respectively. The CVD risk in low sodium-low potassium group was 24% higher than the low sodium-high potassium group (reference), but this result did not reach statistical significant level (P=0.685). The risks in high sodium-high potassium group (HR=3.32, 95%CI 1.26-8.76,P=0.015) and high sodium-low potassium (HR=3.04, 95%CI 1.05-8.83, P=0.041) group were both significantly increased. Conclusions: Overnight urinary sodium to potassium ratio is positively correlated with the risk of cardiovascular events. High urinary sodium plays a more important role for the increased risk of cardiovascular events than low potassium.


Assuntos
Doenças Cardiovasculares , Potássio , Sódio , Adulto , Doenças Cardiovasculares/epidemiologia , Doenças Cardiovasculares/urina , China , Humanos , Masculino , Pessoa de Meia-Idade , Potássio/urina , Estudos Prospectivos , Fatores de Risco , Sódio/urina , Estados Unidos
2.
Zhonghua Liu Xing Bing Xue Za Zhi ; 37(5): 737-40, 2016 May.
Artigo em Chinês | MEDLINE | ID: mdl-27188374

RESUMO

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.


Assuntos
Modelos Logísticos , Modelos de Riscos Proporcionais , Análise de Sobrevida , Estudos de Casos e Controles , Humanos
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