The soluble interleukin-2 receptor predicts mortality in older hospitalized men.
BACKGROUND
There is an inverse relationship between the soluble interleukin-2 receptor (sIL-2R) and serum albumin, cholesterol, transferrin, prealbumin, and hemoglobin. Inasmuch as low serum albumin and cholesterol have been associated with excess mortality, we hypothesized that elevated sIL-2R would predict mortality in older adults.


OBJECTIVE
To determine if elevated sIL-2R predicts mortality in patients on a geriatric rehabilitation unit.


METHODS
Prospective cohort.


METHODS
University-affiliated VA medical center.


METHODS
Seventy-two male patients aged greater than 60 years admitted to a geriatric rehabilitation unit. Patients with severe hepatic or renal disease were excluded.


METHODS
We measured serum albumin, prealbumin, cholesterol, transferrin, hemoglobin, body mass index (BMI), C-reactive protein (CRP), and sIL-2R upon admission. Subjects were followed for 1 year.


RESULTS
Low serum albumin, prealbumin, and hemoglobin and high sIL-2R and CRP predicted 1-year mortality on univariate analysis. When these predictors were included as covariates in a Cox regression model, only sIL-2R was a significant independent predictor of mortality (P = .043). Multiple linear regression with the above covariates revealed that only sIL-2R predicted time to death at (P = .003).


CONCLUSIONS
High sIL-2R and CRP and low albumin, prealbumin, and hemoglobin predicted mortality using univariate analysis on a rehabilitation unit. However, with multivariate analysis, sIL-2R was the sole predictor of mortality.
Rosenthal
A J
AJ
Medical Service, McGuire VA Medical Center (181), Richmond, VA 23249, USA.
McMurtry
C T
CT
Sanders
K M
KM
Jacobs
M
M
Thompson
D
D
Adler
R A
RA
D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D002784:Cholesterol / Q000097:blood; D006299:Health Services for the Aged / Q000706:statistics & numerical data*; D006454:Hemoglobins / Q000032:analysis; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data*; D006785:Hospitals, University / Q000706:statistics & numerical data; D006787:Hospitals, Veterans / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D011228:Prealbumin / Q000032:analysis; D011237:Predictive Value of Tests; D015375:Receptors, Interleukin-2 / Q000032:analysis*; D012709:Serum Albumin / Q000378:metabolism; D016019:Survival Analysis; D014168:Transferrin / Q000032:analysis; D014768:Virginia
D015415:Biomarkers; D006454:Hemoglobins; D011228:Prealbumin; D015375:Receptors, Interleukin-2; D012709:Serum Albumin; D014168:Transferrin; D002784:Cholesterol