Abstract
Summary
Background and objectives
The purpose of this study was to evaluate the levels and patterns of total and differential leukocyte counts and their prognostic importance in a cohort of people with and without chronic kidney disease (CKD).
Design, setting, participants, & measurements
Among 153 veterans without CKD and 267 with, blood leukocyte count was measured at baseline and then repeatedly over a decade. The patterns of change in leukocyte count between the two groups were compared. In the CKD cohort, the spikes in leukocyte counts were compared to the combined endpoint of ESRD and death.
Results
Patients with CKD had more granulocytes and eosinophils and fewer lymphocytes. Over time, granulocytes increased and lymphocytes decreased in those with and without CKD. In addition, in those with CKD, over time eosinophils fell and monocytes increased. Compared with their non-CKD counterparts, patients with CKD had between 1.5- and 3.0-fold more spikes in leukocyte counts. Independent risk factors for the combined endpoint were associated with spikes in the leukocyte counts of absolute and percent eosinophil count, percent granulocyte, and percent monocyte counts. In a multivariate adjusted joint model, both granulocyte and monocyte spikes were independently associated with ESRD and death (hazard ratio 1.67 and 1.52 respectively, P < 0.05).
Conclusions
Compared with those without CKD, patients with CKD have more eosinophils and granulocytes and fewer lymphocytes. Greater variation in leukocytes is seen. Spikes in granulocyte and monocyte percentages among patients with CKD are of independent prognostic importance.
Introduction
Automatic measurement of complete blood count is commonly performed for the routine evaluation of patients with and without chronic kidney disease (CKD). Patients with CKD, especially in advanced stages of kidney disease, have anemia and much attention has been devoted to its evaluation and management. However, the pattern of total and differential leukocyte count has been poorly studied, yet this evaluation may be of critical importance to diagnose inflammatory illnesses (more granulocytes, left shift), nutritional status (low lymphocytes (1)), and allergic reactions (eosinophilia). For example, the diagnosis of acute allergic interstitial nephritis or atheroembolic disease is often entertained if eosinophilia is discovered. So, to make diagnostic decisions at the bedside, it is important to know whether patients with CKD have different levels and patterns of leukocyte count. Should CKD patients have chronic eosinophilia, a higher threshold would be required before diagnosing an elevated level of eosinophils.
Patients with CKD are also predisposed to inflammatory illnesses such as pneumonia, bacteremia, or urinary tract infections. These inflammatory illnesses may promote the progression of kidney disease. Furthermore, inflammation of the kidney may by itself promote progression to ESRD. Whether such a relationship between inflammatory episodes and ESRD or death exists is unknown.
The purpose of this study was therefore to comparatively evaluate the levels and patterns of leukocyte count in patients with and without CKD at baseline and over time. Another purpose was to comparatively evaluate the bursts in inflammatory activity in patients with and without CKD. Finally, another objective of this study was to describe the relationship between inflammatory and eosinophilic episodes and subsequent development of ESRD or death among those with CKD.
Materials and Methods
Study Cohort
This was a prospective cohort study of patients recruited from the renal clinic and a general medicine clinic of the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana. Patients were excluded for body mass index > 40 kg/m2, acute renal failure, receiving renal replacement therapy, atrial fibrillation, or for a change in their antihypertensive drugs within 2 weeks of study enrollment. Because the cohort was primarily assembled for the evaluation of BP as a cardiovascular risk factor, criteria for excluding subjects were selected to allow for accurate measurement of BP. CKD was defined as the presence of proteinuria on a spot urine specimen when the protein/creatinine ratio was ≥0.22 g/g or estimated GFR was <60 ml/min per 1.73 m2 by the four-component Modification of Diet in Renal Disease formula: 186 × creatinine−0.1.54 × age−0.203 × 0.74 if female and × 1.21 if black (2). A urine protein/creatinine ratio of >0.22 g/g correlates with a urine protein excretion of >300 mg/d, the standard definition of clinical proteinuria (3). Accordingly, we selected this threshold of urine protein/creatinine ratio to reflect CKD.
The Institutional Review Board of Indiana University and the Research and Development Committee of the Veterans Affairs Medical Center approved this study, and all patients gave their written informed consent.
A medical history was obtained and physical examination was performed on each participant. Coronary artery disease was defined as previous evidence of myocardial infarction, coronary artery bypass surgery, or percutaneous coronary intervention after consideration of the individual's history and evaluation of his medical records. Diabetes mellitus was defined as treatment with insulin or oral hypoglycemic agents. Actual medications being taken by the subjects were recorded.
Complete blood count and differential leukocyte count was performed at baseline and as clinically indicated in the hospital laboratory using an autoanalyzer. A urine creatinine assay was performed with a modification of the Jaffe reaction (Boehringer Mannheim catalog number 450019) and a urine protein assay was performed with a turbidometric method using benzethonium chloride read at 550 nm using a Hitachi 911 analyzer (Boehringer Mannheim, Roche Diagnostics Corporation, Indianapolis, IN). A single random urine specimen was used from each participant.
Patients received follow-up care (generally three to four annual visits) by their respective physicians at the Veterans Affairs Medical Center. No interventions occurred as a result of participation in the study. For the purposes of this analysis, we only ascertained the outcomes of ESRD or mortality at follow-up.
Ascertainment of ESRD and Mortality
Each patient's electronic medical record was manually examined for notation of ESRD. Specifically, the diagnosis of ESRD was not based on an International Classification of Diseases code. The diagnosis of ESRD required the initiation of chronic dialysis or its refusal by the patient when the estimated GFR was documented to be <15 ml/min per 1.73 m2. The ascertainment of death was established using the computerized Veterans Affairs (VA) electronic medical record system. The last date of visit to any VA facility was used to determine the last date of follow-up. In patients who were not seen at a VA facility in the prior 6 months, the Social Security death index was checked for mortality.
Statistical Analyses
Comparisons between groups (CKD and non-CKD) in baseline characteristics were made by t tests for continuous variables or χ2 tests for discrete variables. Some evidence demonstrates that coronary restenosis is more common when eosinophilia is present (4). Accordingly, we identified all patients with atherosclerotic disease. Atherosclerotic disease was defined as a history of myocardial infarction, coronary intervention, stroke, or peripheral vascular disease. We then tested the effect of CKD and atherosclerosis and their interaction on eosinophil counts using a two-way ANOVA model.
We next plotted each subject's total and differential leukocyte count to evaluate for any outliers. To evaluate baseline differences and the change over time in total and differential leukocyte counts, we used a random coefficient model. In this model we used group assignment (CKD or non-CKD), time, and interaction of group × time as a fixed term; subject and time were used as random terms. The covariance was unstructured, which allows slopes and intercepts to vary independently of each other (5).
To evaluate bursts of inflammation, eosinophilia, lymphocytopenia, and the like, we first categorized events using certain thresholds. These thresholds were based on what are considered outside of the normal range by our hospital laboratory. Abnormal events were classified as follows: total leukocyte count > 11.5 K/μl, absolute granulocyte count > 8.6 K/μl, absolute eosinophil count > 0.45 K/μl, granulocyte count > 70%, lymphocytes < 18%, monocytes > 11%, eosinophils > 3%, and basophils > 2%. To protect the analysis from extreme values, we coded the following as missing: total leukocyte count < 4.5 or > 50 K/μl, absolute granulocyte count <2.3 or > 50 K/μl, absolute eosinophil count > 5 K/μl, granulocyte count < 50%, lymphocytes > 42%, monocytes < 2%, and eosinophils < 1%. Because patients may have elevated leukocyte counts in consecutive samples during an inflammatory illness, we counted only one event per 2-week period. If the event was separated by ≥2 weeks, it qualified as a new event. The incident rate of events and incident rate ratio for CKD were then calculated using Poisson regression. This rate was further adjusted for age, sex, race, diabetes, use of antihypertensive medication, current smoking, angiotensin converting enzyme inhibitor or angiotensin II receptor blocker use, and the use of aspirin or statins. To further explore whether pre-existing atherosclerotic disease influences this rate, we introduced an indicator variable for atherosclerosis in the above model.
Estimates of mortality were made by the Kaplan–Meier method. Estimates of time to composite endpoint of ESRD or death were made by Cox proportional hazards model. Multivariable adjustments followed the same factors noted above. In addition, two factors were added to the list—CKD stage and log protein/creatinine ratio—because they are important in mediating the composite endpoint.
Finally, a best subset regression was performed by selecting out two variables for the total and differential leukocyte count that predicted the composite outcome. Those variables that resulted in the lowest log likelihood function of the Cox model were deemed best (6).
All analyses were performed using Stata 11.0 (Stata Corporation, College Station, TX). P values are two-sided and significance was set at 0.05.
Results
The baseline characteristics of the sample are shown in Table 1. Patients with CKD had the following characteristics that were statistically different from those without CKD: those with CKD were older; more likely to be male, black, and shorter; more likely to have some history of smoking and a greater prevalence of coronary artery disease (especially coronary artery bypass grafts) and diabetes; more likely to use antihypertensive medications (in nearly all categories except thiazides and nondihydropyridine calcium channel blockers); and more likely to have a greater use of statins and aspirin. Those with CKD also had a higher systolic BP, slower heart rate, and lower cholesterol.
Table 1.
Baseline characteristics of the study participants by CKD
| Clinical Characteristic | Non-CKD | CKD | Total | P |
|---|---|---|---|---|
| n | 153 (36%) | 267 (64%) | 420 (100%) | |
| Age (years) | 58.0 ± 13.2 | 67.0 ± 11.6 | 63.7 ± 13.0 | <0.0001 |
| Gender (male) | 141 (92%) | 258 (96%) | 399 (95%) | 0.04 |
| Race | <0.0001 | |||
| white | 137 (89%) | 177 (66%) | 314 (74%) | |
| black | 17 (11%) | 91 (34%) | 108 (26%) | |
| Weight (kg) | 95.3 ± 24.6 | 93.0 ± 21.4 | 93.8 ± 22.6 | 0.3 |
| Height (in) | 69.8 ± 3.0 | 69.1 ± 3.0 | 69.3 ± 3.1 | 0.01 |
| Body mass index (kg/m2) | 30.2 ± 7.1 | 30.1 ± 6.1 | 30.1 ± 6.5 | 0.8 |
| History of smoking | <0.0001 | |||
| never | 43 (28%) | 43 (16%) | 86 (20%) | |
| past | 61 (40%) | 157 (59%) | 218 (52%) | |
| current | 49 (32%) | 66 (25%) | 115 (27%) | |
| Diabetes mellitus | 29 (19%) | 100 (37%) | 129 (31%) | <0.0001 |
| Prevalence of | ||||
| myocardial infarction | 23 (15%) | 57 (21%) | 80 (19%) | 0.1 |
| coronary artery bypass graft | 10 (6%) | 40 (15%) | 50 (12%) | <0.01 |
| percutaneous coronary intervention | 11 (7%) | 35 (13%) | 46 (11%) | 0.06 |
| coronary artery disease | 29 (19%) | 79 (29%) | 108 (26%) | 0.02 |
| Stroke | 7 (5%) | 26 (10%) | 33 (8%) | 0.06 |
| peripheral vascular disease | 6 (4%) | 49 (18%) | 55 (13%) | <0.0001 |
| any atherosclerotic diseasea | 38 (25%) | 114 (43%) | 152 (36%) | <0.0001 |
| BP medication use | 102 (66%) | 250 (93%) | 352 (83%) | <0.0001 |
| BP medications (number) | 1.4 ± 1.4 | 2.8 ± 1.5 | 2.3 ± 1.6 | <0.0001 |
| thiazide diuretic use | 37 (24%) | 70 (26%) | 107 (25%) | 0.6 |
| loop diuretic use | 17 (11%) | 121 (45%) | 138 (33%) | <0.0001 |
| dihydropyridine use | 16 (10%) | 96 (36%) | 112 (27%) | <0.0001 |
| nondihydropyridine calcium channel blocker use | 14 (9%) | 24 (9%) | 38 (9%) | 1 |
| β-blocker use | 39 (25%) | 125 (47%) | 164 (39%) | <0.0001 |
| α-blocker use | 23 (15%) | 81 (30%) | 104 (25%) | <0.0001 |
| centrally acting agent use | 5 (3%) | 27 (10%) | 32 (8%) | <0.01 |
| vasodilator use | 1 (1%) | 12 (4%) | 13 (3%) | 0.03 |
| ACEI use | 55 (36%) | 140 (52%) | 195 (46%) | <0.0001 |
| ARB use | 8 (5%) | 48 (18%) | 56 (13%) | <0.0001 |
| statin use | 46 (30%) | 158 (59%) | 204 (48%) | <0.0001 |
| ASA use | 52 (34%) | 144 (54%) | 196 (46%) | <0.0001 |
| ACEI and/or ARB use | 62 (40%) | 176 (66%) | 238 (56%) | <0.0001 |
| Clinic systolic BP (mmHg) | 138.1 ± 19.1 | 148.1 ± 22.2 | 144.4 ± 21.6 | <0.0001 |
| Clinic diastolic BP (mmHg) | 82.9 ± 11.4 | 81.9 ± 12.6 | 82.2 ± 12.2 | 0.4 |
| Clinic heart rate (bpm) | 74.5 ± 12.2 | 70.2 ± 12.3 | 71.8 ± 12.5 | <0.001 |
| Ambulatory systolic BP (mmHg) | 127.8 ± 12.6 | 135.1 ± 17.0 | 132.5 ± 15.9 | <0.0001 |
| Ambulatory diastolic BP (mmHg) | 74.3 ± 8.3 | 73.8 ± 11.0 | 74.0 ± 10.1 | 0.7 |
| Ambulatory heart rate (bpm) | 73.9 ± 11.3 | 69.9 ± 11.1 | 71.3 ± 11.3 | <0.01 |
| Estimated GFR (ml/min per 1.73 m2) | 81.5 ± 15.0 | 36.1 ± 16.6 | 51.9 ± 27.0 | <0.0001 |
| CKD stage | ||||
| 1 | 2 (1%) | |||
| 2 | 9 (3%) | |||
| 3A | 71 (26%) | |||
| 3B | 67 (25%) | |||
| 4 | 102 (38%) | |||
| 5 | 17 (6%) | |||
| Urine protein/creatinine ratio (g/g) (median, IQR) | 0.09 (0.06, 0.11) | 0.30 (0.08, 1.64) | 0.13 (0.08, 0.74) | <0.0001 |
| Albumin (g/dl) | 3.8 ± 0.4 | 3.8 ± 0.4 | 3.8 ± 0.4 | 0.6 |
| Cholesterol (mg/dl) | 192.6 ± 39.4 | 181.9 ± 36.8 | 185.5 ± 38.0 | <0.01 |
ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ASA, acetylsalicylic acid.
Includes myocardial infarction, stroke, coronary artery bypass graft, percutaneous transluminal coronary angioplasty, peripheral vascular disease.
Baseline complete blood count with differential leukocyte count is shown in Table 2. Those with CKD had lower hemoglobin, more eosinophils, fewer lymphocytes, and fewer platelets. In a two-way ANOVA model, there was no effect of pre-existing atherosclerosis on absolute or relative eosinophil counts; however, the CKD effect remained. The interaction effect between CKD and atherosclerosis on eosinophil count was NS.
Table 2.
Baseline complete blood count of the study participants by CKD
| Clinical Characteristic | Non-CKD | CKD | Total | P CKD |
|---|---|---|---|---|
| Hemoglobin (g/dl) | 14.5 ± 1.5 | 13.1 ± 1.8 | 13.5 ± 1.8 | <0.0001 |
| Leukocytes (K/μl) | 7.5 ± 2.2 | 7.1 ± 2.1 | 7.2 ± 2.2 | 0.09 |
| Absolute granulocyte count | 4979 ± 1987 | 4668 ± 1871 | 4764 ± 1910 | 0.2 |
| Absolute eosinophil count | 194 ± 138 | 225 ± 160 | 216 ± 154 | 0.09 |
| Granulocytes (%) | 62.4 ± 10.1 | 63.7 ± 9.1 | 63.3 ± 9.4 | 0.2 |
| Lymphocytes (%) | 27.2 ± 8.8 | 24.8 ± 8.4 | 25.5 ± 8.6 | 0.01 |
| Monocytes (%) | 7.4 ± 3.1 | 7.7 ± 3.0 | 7.6 ± 3.0 | 0.3 |
| Eosinophils (%) | 2.6 ± 1.7 | 3.2 ± 2.1 | 3.0 ± 2.0 | <0.01 |
| Basophils (%) | 1.1 ± 2.7 | 1.0 ± 2.3 | 1.0 ± 2.4 | 0.5 |
| Platelets (K/μl) | 246.2 ± 69.2 | 230.9 ± 66.7 | 236.1 ± 67.8 | 0.03 |
A follow-up of nearly 10 years from inception of the cohort allowed for the calculation of the intercepts and slopes of total and differential leukocyte count. Table 3 shows the modeled results from the random coefficient model. Total leukocyte count was not higher in the CKD group. No time-dependent change was seen in leukocyte count in either group. Although absolute granulocyte counts were not different between groups, percent granulocytes were approximately 2.3% higher in the CKD group. No time-dependent change in absolute granulocyte count was seen in either group. A time-dependent increase of 0.39% per year in percent granulocytes was seen in the non-CKD group, a change that was not statistically different in the CKD group. Differences were seen between groups in baseline level of lymphocytes. Percent lymphocytes were 3.1% lower in the CKD group. A time-dependent decrease of approximately 0.43% per year was seen in the non-CKD group; this time-dependent decrease was not statistically different in the CKD group. No differences in baseline monocytes were noted between groups. However, only the CKD group experienced a statistically significant increase in percent monocytes over time. Percent eosinophils were approximately 0.6% higher in those with CKD, accounting for approximately 34 cells/μl. A decrease in percent eosinophils and total eosinophils was noted only for the CKD group. No time-dependent differences were seen between groups. Percent basophils were not influenced by CKD state or over time.
Table 3.
Baseline and change in leukocyte count of the study participants by CKD
| Clinical Characteristic | Non-CKD Intercept | No CKD Slope/yr | CKD Intercept | CKD Slope/yr | Difference in Intercept (CKD − no CKD) | P for Difference in Intercept | Difference in Slope (CKD − no CKD) | P for Difference in Slopes |
|---|---|---|---|---|---|---|---|---|
| Leukocytes (K/μl) | 7.7 ± 0.25 | 0.05 ± 0.05 | 7.6 ± 0.16 | 0.04 ± 0.03 | −0.1 (−0.7, 0.5) | >0.1 | −0.01 (−0.13, 0.10) | >0.1 |
| Absolute granulocyte count | 5226 ± 199 | 2 ± 38 | 4947 ± 120 | 70 ± 26a | −279 (−735, 177) | >0.1 | 68 (−23, 159) | >0.1 |
| Absolute eosinophil count | 192 ± 14 | −1 ± 2 | 225 ± 9 | −6 ± 1a | 34 (2, 65) | 0.04 | −5 (−10, 0) | 0.06 |
| Granulocytes (%) | 63.0 ± 0.87 | 0.39 ± 0.19a | 65.3 ± 0.54 | 0.52 ± 0.13a | 2.3 (0.3, 4.3) | 0.02 | 0.13 (−0.32, 0.58) | >0.1 |
| Lymphocytes (%) | 26.1 ± 0.75 | −0.43 ± 0.16a | 23.0 ± 0.47 | −0.55 ± 0.11a | −3.1 (−4.8, −1.3) | <0.001 | −0.12 (−0.50, 0.26) | >0.1 |
| Monocytes (%) | 7.9 ± 0.21 | 0.03 ± 0.04 | 8.0 ± 0.13 | 0.07 ± 0.02a | 0.1 (−0.4, 0.6) | >0.1 | 0.03 (−0.05, 0.12) | >0.1 |
| Eosinophils (%) | 2.6 ± 0.19 | −0.01 ± 0.03 | 3.2 ± 0.12 | −0.07 ± 0.02a | 0.6 (0.2, 1.0) | <0.01 | −0.06 (−0.14, 0.01) | 0.1 |
| Basophils (%) | 1.6 ± 0.28 | −0.08 ± 0.05 | 1.6 ± 0.16 | −0.04 ± 0.03 | −0.0 (−0.7, 0.6) | >0.1 | 0.04 (−0.06, 0.15) | >0.1 |
± denotes standard error, number in parenthesis reflect 95% confidence intervals of the mean.
Slope is significantly different from zero.
Follow-up over 10 years allowed observations on surges and dips in total and differential leukocyte counts. These increments or reductions were modeled using Poisson regression, and the annual incidence rates of “leukocyte spike events” from this model are presented in Table 4. The perturbations were 2 to 3 times as often observed in those with CKD, even after adjusting for covariates. Adjustment for prior atherosclerosis did not remove the statistical significance of CKD on leukocyte spike events.
Table 4.
Incidence rate of leukocyte spike events by presence or absence of CKD
| Event | Annual Incidence Rate |
|||||
|---|---|---|---|---|---|---|
| Non-CKD (95% CI) | CKD (95% CI) | Unadjusted IRR (95% CI) | P for IRR | Adjusted IRR (95% CI) | P for IRR | |
| Total leukocyte count increase > 11.5 K/μl | 1.09 (0.93, 1.26) | 1.95 (1.78, 2.12) | 1.78 (1.50, 2.11) | <0.001 | 1.98 (1.63, 2.41) | <0.001 |
| Absolute granulocyte count increase > 8.6 K/μl | 0.81 (0.68, 0.95) | 1.43 (1.28, 1.58) | 1.76 (1.44, 2.14) | <0.001 | 2.08 (1.66, 2.62) | <0.001 |
| Absolute eosinophil count increase > 0.45 K/μl | 0.56 (0.45, 0.68) | 1.80 (1.63, 1.96) | 3.19 (2.55, 4.00) | <0.001 | 2.29 (1.79, 2.94) | <0.001 |
| Granulocyte (%) increase > 70% | 2.25 (2.02, 2.48) | 6.03 (5.73, 6.34) | 2.68 (2.39, 3.00) | <0.001 | 2.60 (2.29, 2.96) | <0.001 |
| Lymphocyte (%) decrease < 18% | 2.90 (2.64, 3.16) | 8.44 (8.08, 8.80) | 2.91 (2.64, 3.22) | <0.001 | 2.82 (2.53, 3.15) | <0.001 |
| Monocyte (%) increase > 11% | 2.09 (1.86, 2.31) | 5.86 (5.56, 6.16) | 2.81 (2.50, 3.16) | <0.001 | 2.68 (2.35, 3.06) | <0.001 |
| Eosinophil (%) increase > 3% | 1.14 (0.98, 1.31) | 3.12 (2.90, 3.34) | 2.73 (2.32, 3.20) | <0.001 | 2.08 (1.74, 2.49) | <0.001 |
| Basophil (%) increase > 2% | 0.54 (0.43, 0.65) | 1.31 (1.17, 1.45) | 2.43 (1.92, 3.08) | <0.001 | 2.07 (1.58, 2.71) | <0.001 |
Multivariate adjusted for age, gender, race, diabetes, smoking, ACEI or ARB use, statin use, and BP medication use. IRR, incidence rate ratio.
Among those with CKD, we observed 92 deaths and 63 ESRD events. A composite event rate of 155 over 1441.6 years yielded 10.8 events per 100 patient-years. Table 5 shows that compared with people with CKD without these events, multivariate adjustments were associated with increased hazards of the composite endpoint that varied from 1.5- to 2.5-fold.
Table 5.
HR for combined endpoint of all-cause mortality and ESRD among people with CKD
| Clinical Characteristic | Unadjusted |
Adjusted |
||||
|---|---|---|---|---|---|---|
| HR | 95% CI | P | HR | 95% CI | P | |
| Total leukocyte count increase > 11.5 K/μl | 1.63 | (1.16, 2.28) | <0.01 | 1.15 | (0.80, 1.67) | >0.1 |
| Absolute granulocyte count increase > 8.6 K/μl | 1.7 | (1.20, 2.40) | <0.01 | 1.18 | (0.82, 1.71) | >0.1 |
| Absolute eosinophil count increase > 0.45 K/μl | 2.16 | (1.54, 3.03) | <0.001 | 1.49 | (1.02, 2.17) | 0.04 |
| Granulocyte (%) increase > 70% | 2.99 | (2.01, 4.44) | <0.001 | 1.75 | (1.14, 2.68) | 0.01 |
| Lymphocyte (%) decrease < 18% | 1.52 | (1.05, 2.21) | 0.03 | 0.91 | (0.61, 1.36) | >0.1 |
| Monocyte (%) increase > 11% | 2.93 | (2.01, 4.27) | <0.001 | 1.76 | (1.17, 2.64) | <0.01 |
| Eosinophil (%) increase > 3% | 1.94 | (1.41, 2.67) | <0.001 | 1.58 | (1.13, 2.22) | <0.01 |
| Basophil (%) increase > 2% | 1.38 | (0.98, 1.96) | 0.07 | 1.42 | (0.98, 2.06) | 0.06 |
Multivariate adjusted for age, gender, race, diabetes, smoking, ACEI or ARB use, statin use, BP medication use, baseline CKD stage, and baseline log urine protein/creatinine. HR, hazard ratio; CI, confidence interval.
Finally, we created a model by selecting the best two leukocyte markers that predicted the composite endpoint by trying all permutations and combinations. In a fully adjusted model (with covariates reported in footnote of Table 4), the two best markers were a spike in granulocyte count to >70% (hazard ratio [HR] 1.67, 95% confidence interval [CI] 1.09 to 2.57, P = 0.020) and a spike in monocyte count to >11% (HR 1.52, 95% CI 1.08 to 2.14, P = 0.016).
Discussion
Our study demonstrates that the baseline leukocyte count differs between patients with and without CKD. The change over time in leukocyte counts is significant for people without CKD for granulocytes and lymphocytes: granulocytes increase and lymphocytes decrease. As in people without CKD, the change over time in leukocyte count is also significant for people with CKD for granulocytes and lymphocytes. Furthermore, in these patients over time, eosinophils decrease and monocytes increase. Patients with CKD have more spikes in leukocyte counts than their non-CKD counterparts. These spikes are between 1.5- and 3.0-fold higher in those with CKD. Independent risk factors for the combined endpoint of all-cause mortality and ESRD included spikes in absolute and percent eosinophil count and percent granulocyte and percent monocyte counts. Granulocyte and monocyte spikes were independently associated with ESRD and death in this population. Thus, inflammatory events among patients with CKD are of independent prognostic importance.
Baseline Level of Leukocyte Counts and Its Change Over Time
A pediatric study reported low levels of lymphocyte counts in children with CKD, including those on dialysis (7). Subpopulations of CD8+ T cells, B cells, and natural killer cells were all reduced.
Over time, we noted declining levels of lymphocytes in CKD and non-CKD groups. Age-related decreases in lymphocytes have been previously described (8). The most dramatic decrease occurs between birth and young adulthood (8). Subsequently, it stays stable for 2 decades. But after the age of approximately 40 years, lymphocyte counts decrease again; this decrease is a manifestation of immunosenescence (9). However, the Baltimore National Institutes of Health Cooperative Study reported that among 105 elderly men followed for 16 years, no overall change in lymphocyte counts was seen (10). However, lymphocyte numbers decreased 3 years before death; the deaths were primarily cardiovascular in nature. One study has reported that among the elderly, low lymphocyte levels are associated with elevated IL-6 and C-reactive protein levels, low levels of erythropoietin, and anemia (11). Patients with CKD are often inflamed, and low levels of lymphocytes are therefore not surprising. In fact, a progressive decrease in renal function is associated with activation and selective loss of T cells and CD4+ cells and a marked increase in CD8+ cells (12).
Prior studies have reported that the association of eosinophilia with CKD is presumably due to an increased turnover rate of eosinophils (13,14). These studies were small and cross-sectional in nature. We confirmed the presence of increased eosinophil counts in patients with CKD. Whereas those without CKD averaged 2.6% eosinophils, those with CKD had eosinophils counts averaging 3.2% at baseline. The upper bound of the 95% CI for eosinophils was 3.4%. Accordingly, only when eosinophils exceed this threshold should “eosinophilia” be considered. Because the presence of eosinophilia evokes the differential diagnosis of acute interstitial nephritis, out data suggest the use of a more liberal threshold of >4% when diagnosing eosinophilia. Alternatively, it may be prudent to evaluate prior eosinophil counts to evaluate change in an individual patient.
Incidence Rate of Leukocyte Spike Events
We are unaware of prior studies that have related leukocyte spikes with subsequent ESRD and mortality outcomes in patients with CKD. Spikes in leukocytes of all lineages studied except lymphocytes and dips in lymphocyte levels were substantially more common among those with CKD. This was in fact a pronounced feature of the clinical course characterizing those with CKD. For example, it should be noted that although total leukocyte counts were similar between groups, over time the CKD group had more spikes in total leukocyte counts. Thus, the CKD group was inherently more unstable and prone to inflammatory and allergic events.
Prognostic Importance of Leukocyte Spike Events
We found that inflammatory events were associated with an increased risk of ESRD and death. These inflammatory events could be confined to the kidney, which may accelerate the decline in renal function. In fact, Kelly and Dominguez have recently reported prominent clusters of inflammatory cells, particularly macrophages, in renal biopsy specimens from patients with diabetic nephropathy (15). Peritubular macrophages and granulocytes were inversely correlated with estimated GFR at the time of biopsy. However, clinically it is perhaps more likely that these inflammatory events may have included inflammatory illnesses such as pneumonia, bacteremia, and urinary tract infections. Such illnesses are associated with volume depletion and nephrotoxin exposure and perhaps induce an inflammatory response in the kidney, factors that may have caused an accelerated time to ESRD and/or death. Others have reported an increased risk of mortality with neutrophil count among hemodialysis patients (16).
Spikes in eosinophil counts were also associated with ESRD/death outcomes. Why this should be so is not clear. However, several possibilities exist, including subclinical atheroembolic events or allergic responses to environmental or drug exposures. Another intriguing possibility is that of deficiency or resistance to cortisol. George W. Thorn had described the relationship between stress and hypercortisolemia and eosinopenia. In those with corticosteroid deficiency, eosinophilia may be seen. Patients with CKD may have resistance to cortisol and eosinophilia. In a recent cross-sectional study, eosinophilia was associated with albuminuria (17).
We did not discover an independent relationship between dips in lymphocyte counts and subsequent ESRD/death. This is contrast to Kovesdy et al., who have reported among CKD veterans not on dialysis an inverse relationship between lymphocyte count (in baseline and time-varying models) and all-cause mortality (18). Similarly, low lymphocyte counts are associated with mortality among hemodialysis patients (16,19). Differences in covariates and sample size may account for this discrepancy.
Limitations
Our study was limited to veterans, thus it had few women. These studies need to be repeated in women to make it more generalizable. However, the follow-up over a decade and careful collection of leukocyte counts over a 10-year period provide reasonable confidence in drawing an association between inflammatory events and the risk of ESRD and/or death. Whether the risk is causal cannot be ascertained by an observational study. It remains to be seen whether blocking inflammatory events can reduce the progression to ESRD.
In conclusion, this study demonstrates that recurrent inflammatory events are associated with adverse renal and mortal outcomes. It also demonstrates that inflammatory events characterized by increases in granulocytes and monocytes are independently associated with ESRD and mortality. Such adverse outcomes were also associated with bursts of eosinophilia. The mechanisms linking these bursts of inflammation and eosinophilia to adverse outcomes need to be elucidated using appropriate animal and human models.
Disclosures
None.
Footnotes
Published online ahead of print. Publication date available at www.cjasn.org.
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