Sodium-lithium countertransport and probability of hypertension in Caucasians 47 to 89 years old.

The objectives of the present study were to determine whether sodium-lithium countertransport contributes to predicting the probability of having hypertension and to determine whether it does so after other predictor traits have been considered. We used logistic regression to model the relation between sodium-lithium countertransport and the probability of having hypertension, estimated by the prevalence of hypertension among 172 men and 252 women, aged 47-89 years, from the Caucasian population of Rochester, Minn. When sodium-lithium countertransport was the only predictor trait considered, it made a statistically significant contribution to prediction both in men (model chi(2)1df = 20.50, p < 0.001) and in women (model chi(2)1df = 16.69, p < 0.001). For each standard deviation increase in sodium-lithium counter-transport, the expected odds of having hypertension increased 2.25 times in men (95% confidence interval [CI], 1.44-3.51) and 1.77 times in women (95% CI, 1.32-2.37). When sodium-lithium countertransport was not considered, the other traits identified as predictors were age, body mass index, and plasma apolipoprotein CII and CII squared; plasma apolipoprotein AI was an additional predictor in women but not in men. When sodium-lithium countertransport was added to models that included the other predictors, it improved prediction both in men (increase in model chi(2)1df = 12.29, p < 0.001) and in women (increase in model chi(2)1df = 4.86, p < 0.027). Based on these complete models, when the other predictors remained at their mean values, each standard deviation increase in sodium-lithium countertransport increased the expected odds of having hypertension 2.06 times in men (95% CI, 1.31-3.22) and 1.48 times in women (95% CI, 1.04-2.21). These results establish that sodium-lithium countertransport provides information that is helpful in predicting the probability of having hypertension and is not reflected in other identified predictor traits.