Apolipoprotein A-IV Predicts Progression of Chronic Kidney Disease: The Mild to Moderate Kidney Disease Study
Eva Boes*,
Danilo Fliser,
Eberhard Ritz,
Paul König,
Karl Lhotta,
Johannes F.E. Mann||,
Gerhard A. Müller¶,
Ulrich Neyer#,
Werner Riegel**,
Peter Riegler,
Florian Kronenberg* for the MMKD Study Group
* Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Austria; Department of Internal Medicine, Hannover Medical School, Hannover, Germany; Department of Internal Medicine, Division of Nephrology, Ruperto-Carola-University, Heidelberg, Germany; Innsbruck University Hospital, Department of Clinical Nephrology, Innsbruck, Austria; || München General Hospitals, Department of Nephrology, LMU, Munich, Germany; ¶ Department of Nephrology and Rheumatology, Georg-August-University, Göttingen, Germany; # Feldkirch Hospital, Department of Nephrology, Feldkirch, Austria; ** Medizinische Universitätskliniken des Saarlandes, Innere Medizin IV, Homburg/Saar, Germany; and Bozen Hospital, Division of Nephrology and Hemodialysis, Bozen, Italy
Address correspondence to: Dr. Florian Kronenberg, Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Schöpfstrasse 41, A-6020 Innsbruck, Austria. Phone: +43-512-507-3490; Fax: +43-512-507-2680 or 9804; E-mail: florian.kronenberg{at}i-med.ac.at
Received for publication July 15, 2005.
Accepted for publication November 22, 2005.
It has not been established firmly whether dyslipidemia contributesindependently to the progression of kidney disease. Lipid andlipoprotein parameters, including levels of total, HDL, andLDL cholesterol; triglycerides; lipoprotein(a); apolipoproteinA-IV; and the apolipoprotein E and A-IV polymorphisms, wereassessed in 177 patients who had mostly mild to moderate renalinsufficiency and were followed prospectively for up to 7 yr.Progression of kidney disease was defined as doubling of baselineserum creatinine and/or terminal renal failure necessitatingrenal replacement therapy. In univariate analysis, patientswho reached a progression end point (n = 65) were significantlyolder and had higher serum creatinine and proteinuria as wellas lower GFR and hemoglobin levels. In addition, baseline apolipoproteinA-IV and triglyceride concentrations were higher and HDL cholesterollevels were lower. Multivariate Cox regression analysis revealedthat baseline GFR (hazard ratio 0.714; 95% confidence interval[CI] 0.627 to 0.814 for an increment of 10 ml/min per 1.73 m2;P < 0.0001) and serum apolipoprotein A-IV concentrations(hazard ratio 1.062; 95% CI 1.018 to 1.108 for an incrementof 1 mg/dl; P = 0.006) were significant predictors of diseaseprogression. Patients with apolipoprotein A-IV levels abovethe median had a significantly faster progression (P < 0.0001),and their mean follow-up time to a progression end point was53.7 mo (95% CI 47.6 to 59.8) as compared with 70.0 mo (95%CI 64.6 to 75.4) in patients with apolipoprotein A-IV levelsbelow the median. For the apolipoprotein E polymorphism, onlythe genotype 2/4 was associated with an increased risk for progression.In summary, this prospective study in patients with nondiabeticprimary kidney disease demonstrated that apolipoprotein A-IVconcentration is a novel independent predictor of progression.
Without treatment, many kidney diseases tend to progress torenal failure. However, the rate of progression shows considerableinterindividual variability and is dependent on multiple factors.Moorhead et al. (1) proposed in the early 1980s that abnormalitiesin lipoprotein metabolism cause glomerular and tubulointerstitialdamage, promoting progression of chronic kidney disease (CKD).Experimental studies supported this concept that lipids contributeto progressive renal damage (2) by causing macrophage activationand infiltration in the kidney with resultant tubulointerstitialand endothelial cell injury. However, the extent, if any, towhich dyslipidemia contributes to progression of renal diseasein humans and the possible mechanisms by which this may occurhave remained unclear (3). Data from the population-based AtherosclerosisRisk in Communities (ARIC) study support a role of altered lipidmetabolism in developing renal dysfunction (4). Only a few prospectivestudies in patients with primary nondiabetic kidney diseasehave addressed this question (5–12), however, and theresults have remained ambiguous. To establish and characterizea relationship between dyslipidemia and renal risk, long-termprospective follow-up studies with a reliable measurement ofkidney function at baseline and well-defined renal functionend points are required (13). The aim of this prospective 7-yrfollow-up study in a cohort of patients without diabetes andwith mostly mild to moderate primary kidney disease, therefore,was to evaluate the predictive value of lipoproteins for theprogression of kidney disease.
Patients and Baseline Investigations
We examined at baseline 227 white patients who were aged between18 and 65 yr and had nondiabetic CKD and various degrees ofrenal impairment. These patients were recruited from eight nephrologydepartments in Germany, Austria, and South Tyrol as describedearlier (14,15). The study was approved by the institutionalEthics Committees, and all patients gave written informed consent.They had stable renal function for at least 3 mo before entryinto the study. Exclusion criteria were treatment with immunosuppressiveagents, fish oil, or erythropoietin; serum creatinine >6mg/dl; diabetes of any type; malignancy; liver, thyroid, orinfectious disease; nephrotic syndrome (defined as proteinuria>3.5 g/1.73 m2 per d); organ transplantation; allergy toionic contrast media; and pregnancy. According to the NationalKidney Foundation’s classification of CKD, the 227 patientsof our study showed the following stages of CKD: GFR 90 ml/minper 1.73 m2 (stage 1) in 72 (31.7%) patients, GFR 60 to 89 ml/minper 1.73 m2 (stage 2) in 49 (21.6%) patients, GFR 30 to 59 ml/minper 1.73 m2 (stage 3) in 63 (27.8%) patients, GFR 15 to 29 ml/minper 1.73 m2 (stage 4) in 27 (11.9%) patients, and GFR <15ml/min per 1.73 m2 (stage 5) in 16 (7.0%) patients. The primarycause of kidney disease was glomerulonephritis in 97 (biopsy-confirmedin 90) patients, adult polycystic kidney disease in 37 patients,interstitial nephritis in 24 patients, other types of kidneydisease in 43 patients, and unknown in 26 patients.
For avoiding interobserver differences, all patients were recruitedby one physician who visited all participating centers. Patienthistory, including smoking habits and antihypertensive treatmentat baseline, was recorded by interview and confirmed by checkingpatient records. This was complemented by clinical examination,including assessment of body mass index (BMI) and BP. Hypertensionwas defined as BP >140/90 mmHg and/or the use of antihypertensivemedication. We also calculated pulse pressure as the differencebetween systolic and diastolic BP. Antihypertensive medication(if present) was withheld on the day of the study to minimizeinterference with measurements of the GFR. Antihypertensivedrugs were taken by 179 (79%) patients: Diuretics (n = 83; 37%),angiotensin-converting enzyme inhibitors (n = 123; 54%), calciumchannel blockers (n = 78; 34%), receptor blockers (n = 67;30%), and -1 receptor blockers (n = 35; 16%).
Prospective Follow-Up
After the baseline investigation, patients were followed prospectivelyuntil the primary study end point or the end of the observationperiod was reached. The primary end point was defined as doublingof baseline serum creatinine and/or terminal renal failure necessitatingrenal replacement therapy.
A total of 177 (78%) patients from the baseline cohort couldbe assessed during the follow-up. Patients who were lost tofollow-up (n = 50) had significantly better renal function thanpatients not lost for follow-up, i.e., a higher mean GFR (91± 44 versus 64 ± 39 ml/min per 1.73 m2; P <0.01). According to the National Kidney Foundation’s classification,in the 177 patients with follow-up, we found GFR 90 ml/min per1.73 m2 (stage 1) in 43 (24.3%) patients, GFR 60 to 89 ml/minper 1.73 m2 (stage 2) in 40 (22.6%) patients, GFR 30 to 59 ml/minper 1.73 m2 (stage 3) in 57 (32.2%) patients, GFR 15 to 29 ml/minper 1.73 m2 (stage 4) in 24 (13.6%) patients, and GFR <15ml/min per 1.73 m2 (stage 5) in 13 (7.3%) patients. However,both groups did not differ significantly with respect to ageand gender. Patients who were lost to follow-up had moved awayor were not referred by their private physicians for follow-upvisits in the renal units.
Laboratory Measurements
Blood samples for measurement of routine chemistry, high-sensitivityC-reactive protein (hsCRP), and lipid parameters were takenafter an overnight fast of at least 12 h. The samples were immediatelycentrifuged at 1500 x g and 4°C for 10 min, and the supernatantswere stored in aliquots at –80°C until further use.
Serum apolipoprotein A-IV (apoA-IV) concentrations were determinedwith an ELISA (16). Lp(a) quantification and apo(a) phenotypingwere performed as described in detail (14) with a double-antibodyELISA and by SDS-agarose gel electrophoresis, respectively.Measurements of serum albumin, total and HDL cholesterol, triglycerides,and hsCRP were performed using routine laboratory tests. LDLcholesterol was calculated according to the Friedewald formula.In addition, GFR was assessed in all patients using the iothalamateclearance technique as described in detail elsewhere (17). Genotypingof the apolipoprotein E (apoE) and apoA-IV (T347S and Q360Hcorresponding to rs675 and rs5110, respectively) was performedwith 5' nuclease allelic discrimination (Taqman) assays. Genotypingswere performed within the Genotyping Unit of the Gene DiscoveryCore Facility at the Innsbruck Medical University.
Statistical Analyses
Statistical analysis was performed with SPSS for Windows 12.01(SPSS, Inc., Chicago, IL). Univariate comparisons of continuousvariables between various groups were performed using an unpairedt test or the nonparametric Wilcoxon rank sum test in case ofnonnormally distributed variables. Dichotomized variables werecompared using Pearson 2 test. Differences were considered assignificant at P < 0.05. Data are presented as mean ±SD or as median and interquartile range for skewed variables.Univariate correlation analysis was performed by Spearman correlationanalysis. Kaplan-Meier time-to-event curves were generated forpatients with serum apoA-IV concentrations above and below themedian. Multivariable adjusted risk estimates for progressionend points were calculated using a Cox proportional hazardsregression analysis (short Cox regression). A forward likelihoodratio procedure was used to identify variables that were associatedwith progression end points over time. Verification of the resultswas done by a backward likelihood ratio procedure.
Univariate Association of Lipoprotein Parameters and Progression of CKD
Baseline clinical characteristics and laboratory data of thepatients with follow-up are reported in the first data columnof Table 1. The median follow-up after completion of the baselineinvestigation was 53 mo (3 to 84). During the follow-up, 65patients had progressed to a renal end point: 36 patients haddoubled their serum creatinine concentration, and 29 patientshad reached terminal renal failure necessitating renal replacementtherapy. Table 1 further summarizes data of patients with andwithout progression during the follow-up period. Patients whohad reached a progression end point were significantly olderand had higher baseline serum creatinine levels and proteinexcretion rates as well as lower GFR and hemoglobin levels.In addition, they had higher triglyceride and apoA-IV concentrationsand lower HDL cholesterol levels. Lp(a) was slightly but notsignificantly higher in patients who reached a progression endpoint, but the frequency of low molecular weight (LMW) apo(a)phenotypes was similar in the two patient groups. There wereno differences for surrogate parameters of nutritional (BMIand serum albumin) and inflammatory status (hsCRP). Analogousresults were obtained using univariate Cox regression analysis:GFR and apoA-IV had the strongest influence on progression-freesurvival (Table 2). When we constructed Kaplan-Meier curvesof the progression-free survival comparing patients with supramedianand inframedian (25.9 mg/dl) serum apoA-IV concentrations (Figure 1),patients with the apoA-IV levels above the median had aworse prognosis and significantly faster progression to theend point compared with those with apoA-IV levels below themedian (log-rank test, P < 0.0001): The mean follow-up timeto a progression end point was 53.7 mo (95% confidence interval[CI] 47.6 to 59.8) compared with 70.0 mo (95% CI 64.6 to 75.4)in the two groups of patients. There was no difference in thefrequency of the two apoA-IV polymorphisms T347S and Q360H betweenprogressors and nonprogressors of kidney disease (Table 3).
Table 1. Baseline clinical and laboratory data of 177 patients with completed follow-up with further stratification of those with and without progression of kidney disease during the follow-up perioda
Table 2. The association of different variables with progression of kidney disease during the observation period using univariate and multiple Cox proportional hazards regression modelsa
Figure 1. Kaplan-Meier curves of renal end points in patients with infra- and supramedian plasma apolipoprotein A-IV (apoA-IV) concentrations. In patients with supramedian plasma apoA-IV concentrations (i.e., >25.9 mg/dl), progression was significantly faster (log-rank test, P < 0.0001). Numbers near the survival curves represent the number of patients at risk with apoA-IV levels below and above the median at 0, 12, 24, 36, 48, 60, and 72 mo.
Table 3. Influence of the apoA-IV and the apoE polymorphism on the progression of kidney disease during the follow-up period in patients with mild and moderate kidney diseasea
For evaluation of the significance of apoA-IV as predictor forthe progression of kidney disease, the receiver operating characteristic(ROC) analysis was performed for apoA-IV in comparison withGFR (Figure 2). The area under the curve (AUC) was only slightlylarger for GFR (AUC = 0.842; 95% CI 0.780 to 0.904; P < 0.001)than for apoA-IV (AUC = 0.792; 95% CI 0.726 to 0.859; P <0.001).
Figure 2. Receiver operating characteristic (ROC) curve of GFR and apoA-IV levels with progression of kidney disease as status variable. The area under the curve (AUC) is only slightly larger for GFR than for apoA-IV (AUC 0.842 and 0.792, respectively).
Multivariate Association of Lipoprotein Parameters and Progression of CKD
To identify lipoprotein parameters that are associated withprogression over time after adjustment for other variables,we performed a multiple Cox regression analysis. Besides ageand gender, we added to the model variables that showed P <0.2 in the univariate Cox regression analysis (Table 2). Onlybaseline GFR (hazard ratio [HR] 0.714; 95% CI 0.627 to 0.814;P < 0.0001) and serum apoA-IV concentrations (HR 1.062; 95%CI 1.018 to 1.108; P = 0.006) were significantly associatedwith progression during the follow-up period. HDL cholesteroland triglycerides did not enter into the model as independentfactors irrespective of whether we added these variables asa continuous or categorical variable defined by the median ofeach variable. ApoA-IV remained in the model (HR 1.045; 95%CI 1.001 to 1.092; P = 0.046), even when we adjusted for asymmetricdimethylarginine (ADMA), which was shown recently as a significantpredictor for progression of kidney disease (18). The type ofrenal disease as well as the use of antihypertensive medicationsand especially angiotensin-converting enzyme inhibitors or calciumchannel blockers had no influence on the progression of kidneydisease when adjustment for baseline GFR was performed (datanot shown).
ApoE Polymorphism and Progression of CKD Table 3 shows the distribution of apoE genotypes in renal patientswith and without progression to a renal end point during thefollow-up period. There were no major differences between thetwo groups with the exception of apoE genotype 2/4. Of the sixpatients who showed an 2/4 genotype, five progressed to theend point. When formally tested against the combined numberof other genotype carriers, this difference in frequency reachedsignificance (P = 0.027). When we offered a variable describingthe 2/4 carrier status as an additional covariate to the Coxregression model, those with that genotype had a significantlyhigher probability for a progression of kidney disease (HR 2.68;95% CI 1.06 to 6.75; P = 0.037). In this analysis, the estimatesfor GFR and apoA-IV remained almost identical as in the modelabove.
Lipid and Lipoprotein Profile and Progression of Kidney Disease
There is little doubt that kidney disease is associated withabnormalities in lipoprotein metabolism. As reviewed in Table 4,however, it has remained controversial whether dyslipidemiaplays an independent role in the progression of primary CKD.The basic pathophysiologic concept is derived from the hypothesisthat dyslipidemia, among other factors, causes glomerular injurythat leads to glomerulosclerosis (19). It has been suggestedthat glomerulosclerosis and atherosclerosis share common pathophysiologicpathways (20). However, little is known about the exact mechanism(21).
Table 4. Summary of prospective studies in patients with nondiabetic kidney disease examining the relationship of dyslipidemia and the progression of kidney diseasea
This prospective study had a follow-up time of adequate durationand used exact measurements of GFR, and primary end points werereached by more than one third of the participants. No significantassociation was found between baseline classical lipid or lipoproteinparameters and the progression of renal insufficiency. Theseresults are consistent with another prospective study in 138patients with moderate renal insufficiency (8). Locatelli etal. (5) also found no correlation between the baseline lipidprofile and the end point of ESRD in 311 patients who did nothave diabetes or nephrosis and had moderate renal insufficiencyand were followed for 2 yr. Conversely, Samuelsson et al. (7)described in 73 patients with mild to severe renal insufficiencytotal and LDL cholesterol as well as apoB to be significantlyassociated with a rapid decline in renal function but not triglyceridesor HDL cholesterol. The Modification of Diet in Renal Disease(MDRD) Study demonstrated lower HDL cholesterol levels as anindependent predictor of a decline in GFR in patients with kidneydisease; however, triglyceride levels were not measured (6).In our study, lower HDL cholesterol showed only a borderlineassociation with progression of disease, which was no longersignificant after adjustment for GFR in the multiple Cox regressionanalysis. Syrjanen et al. (11) found hypertriglyceridemia asan independent risk factor for progression of IgA nephropathy.
These nonuniform data illustrate that, so far, it has remaineduncertain which lipoproteins are the best predictors for a progressionof kidney disease (Table 4). The finding of low HDL cholesterollevels as predictors of progression is in line with studiesperformed in the general population (4,22). The ARIC study reportedthat higher triglyceride levels and lower HDL cholesterol wereassociated with a higher relative risk for worsening renal functionover 3 yr (4). The prospective, randomized Helsinki Heart Studyinvestigated patients who had dyslipidemia and non-HDL cholesterol>200 mg/dl and were at baseline free of renal disease andfound lower HDL cholesterol and an elevated LDL/HDL cholesterolratio to be independent predictors of an increase of serum creatinineduring study follow-up; LDL cholesterol and triglycerides werenot independent predictors. However, these variables explainedonly a very small fraction (at best 1%) of the variance in creatinineincrease (22). Such small contribution can be detected onlyin large studies. Further explanations for differences to ourand other studies in patients with kidney disease might be thatstudies in the general population investigated individuals withoutprimary kidney disease at baseline or with mild impairment ofkidney function at most. Another explanation might be the definitionof the progression end point, which was only a minor increasein serum creatinine in some of these studies (4).
Even if the evidence for an association between lipid parametersand the progression of renal disease in humans is not consistent,several studies investigated the effect of lipid lowering onthe progression of renal insufficiency. In a number of animalmodels, lipogenic diets worsen whereas cholesterol-loweringmedications ameliorate renal injury (23–27). Fried etal. (28) performed a meta-analysis that examined the role oflipid-lowering therapy on renal function in humans. The datawere derived from lipid-lowering trials that commonly performedpost hoc analysis of subgroups with renal insufficiency forrenal end points that were not predefined. They suggested thatlipid-lowering therapy may slow progression, but this conclusionwas derived from a few trials with small numbers and generallyshort follow-up times (28). In case these agents indeed showa progression-retarding effect, it remains to be proved whetherthis retardation derives from lipid-lowering or from pleiotropiceffects of statins.
It is also interesting to note that Lp(a) concentrations werenot predictive for the progression of kidney disease, althoughin vivo experiments pointed to a progression-advancing effect.Atherogenic lipoproteins such as LDL cholesterol or Lp(a), especiallywhen oxidized, induce the formation of oxygen radicals in arteries,in glomeruli, and in juxtaglomerular cells, which results inan inhibition of nitric oxide–mediated vasodilation (29),stimulation of renin release, and modulation of mesangial cellgrowth and apoptosis (30–33). However, Lp(a) concentrationsin our and two other studies (8,34) were not related to theprogression of kidney disease.
ApoA-IV
This study was the first to investigate the association betweena novel lipid parameter: ApoA-IV concentration and progressionof renal insufficiency. We found that higher baseline plasmaapoA-IV concentrations were one of the best predictors for theprogression of kidney disease apart from baseline GFR. Thisassociation was independent of other lipoprotein parameters;of ADMA, which was shown recently to be a highly significantpredictor of kidney disease progression (18); and of proteinuria,BP, and nutritional and inflammatory status. However, this doesnot necessarily suggest that apoA-IV per se is pathogenic. Itis conceivable that apoA-IV reflects catabolism of this apolipoproteinin the kidney not entirely reflected by the changes in GFR.It is known that ESRD affects apoA-IV serum concentrations.We and others reported a pronounced increase of apoA-IV concentrationsin hemodialysis patients (35–38). We could even demonstratethat in patients with kidney disease, a significant increasein apoA-IV concentrations is found even when GFR is still withinthe normal range (15). It therefore seems that apoA-IV is anearly marker of renal impairment. This is supported by our recentfindings of apoA-IV immunoreactivity in kidney tubular cells,suggesting a direct role of the human kidney in apoA-IV metabolism(39). A granular staining pattern in those cells probably representslysosomes degrading apoA-IV. It is interesting that a high correlationwas found between apoA-IV and GFR (r = –0.62) (15). Whenwe compare GFR and apoA-IV in the univariate Cox regressionmodel, both parameters predict similarly the progression-freesurvival (2 = 43 versus 34; Table 2). Nevertheless, apoA-IVstill significantly contributes to the prediction of diseaseprogression after adjustment for GFR, which supports the additionalvalue of apoA-IV as a kidney function parameter.
The association of higher apoA-IV levels with progression ofkidney disease is unexpected, given the physiologic functionsof apoA-IV. According to in vitro studies, apoA-IV participatesin several steps of the reverse cholesterol transport pathway,which removes cholesterol from peripheral cells and directsit to liver and steroidogenic organs for metabolism (40–42).Furthermore, other studies documented antioxidative propertiesof apoA-IV (43). One therefore might have anticipated that theincreased apoA-IV levels in impaired kidney function would haveresulted in improved removal of cholesterol from mesangial cellsand, together with the antioxidative properties, slower progression.The observation of an opposite effect would be compatible withthe assumption that apoA-IV is not fully functionally activeor that high apoA-IV levels reflect an aspect of renal impairmentthat is not parallel to GFR.
ApoE Polymorphism
Up to now, numerous case-control and cross-sectional studiesinvestigated the apoE polymorphism in patients with kidney diseasecompared with control subjects with contrasting results. Onlya few of them were designed to investigate an association betweenthe apoE polymorphism and the progression of renal insufficiencyby a follow-up observation (44–46). Araki et al. (45)found in Japanese patients with type 2 diabetes that the 31patients who developed or who showed a progression of nephropathyduring an average observation period of 4.4 yr were more often2 carriers. The apo 4 allele was found to be an independentprotective factor for the progression to ESRD in a retrospectivefollow-up study in Japanese patients with type 2 diabetes (44).Results from the prospective ARIC study in the general populationwithout severe renal dysfunction at baseline recently showedthat 2 moderately increased and 4 decreased risk for renal diseaseprogression (46). In contrast to these studies, we could notsee a negative effect of the 2 allele or a protective effectof the 4 allele. When we used the allele-counting method, weobserved that progressors showed a very similar frequency ofthe 2 allele (10.8 versus 9.1%) and a higher rather than a lowerfrequency of the 4 allele (16.9 versus 10.8%), which did notreach significance, however. It is not clear whether this findingcan be explained by differences in study design, patient selection,or definition of kidney disease progression. The above-mentionedprospective studies either investigated patients with type 2diabetes considering albuminuria or proteinuria as progressionend point (45) or focused on patients at the population levelwithout kidney disease at baseline and an end point mostly definedby an increase in creatinine of at least 0.4 mg/dl (46). Ourstudy, however, included patients with primary kidney diseaseat study entry. Progression was defined as doubling of baselineserum creatinine or necessity of renal replacement therapy duringfollow-up. The only noticeable observation concerning the apoEpolymorphism in our study was that five of the six carriersof apoE genotype 2/4 experienced a progression of the disease.However, we are aware of the low frequency of this genotypeand of the need that the finding be confirmed by future largestudies.
Limitations of the Study
One of the limitations of our study is that we do not have sequentialGFR measurements by iohexol technique during the follow-up period.We believed, however, that it was more important to have anexact measurement of GFR at baseline because a simple measurementof creatinine or a GFR calculation by a formula would have beenimprecise. Doubling of serum creatinine is an accepted specificend point (47).
The applied exclusion criteria in our study resulted in a selectedgroup of patients. Therefore, the association of increased apoA-IVand renal disease progression may not be applicable to othertypes of CKD, such as diabetic nephropathy or nephrotic formsof kidney diseases. This selection, however, might reduce confoundingand increase the power of our study. Furthermore, whether thesefindings observed in white individuals can be extrapolated toother ethnic groups has to be investigated in further studies.Finally, the sample size of our study might have been too smallto investigate the influence of various subgroups of glomerulonephritisor tubulointerstitial renal diseases and the influence of variousantihypertensive medications on the progression of kidney diseases.
In our prospective study of patients with nondiabetic primarykidney disease, we found no significant and independent associationbetween the classical lipid parameters and the progression ofkidney disease to an end point. We identified, however, apoA-IVas a novel predictor for progression of kidney disease. Ourstudy failed to confirm an association between apoE polymorphismand progression of CKD that had been described in previous studies.
Acknowledgments
Parts of this work were supported by the Genomics of Lipid-associatedDisorders–GOLD of the Austrian Genome Research ProgrammeGEN-AU and by grants from the Austrian Nationalbank (Project5553 and 9331), the Austrian Heart Fund, and the Universityof Innsbruck (Project M30) to F.K.
Footnotes
Published online ahead of print. Publication date availableat www.jasn.org.
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Top
Abstract
Introduction
2/
90 ml/min per 1.73 m2 (stage 1) in 72 (31.7%) patients, GFR 60 to 89 ml/min per 1.73 m2 (stage 2) in 49 (21.6%) patients, GFR 30 to 59 ml/min per 1.73 m2 (stage 3) in 63 (27.8%) patients, GFR 15 to 29 ml/min per 1.73 m2 (stage 4) in 27 (11.9%) patients, and GFR <15 ml/min per 1.73 m2 (stage 5) in 16 (7.0%) patients. The primary cause of kidney disease was glomerulonephritis in 97 (biopsy-confirmed in 90) patients, adult polycystic kidney disease in 37 patients, interstitial nephritis in 24 patients, other types of kidney disease in 43 patients, and unknown in 26 patients. 
receptor blockers (n = 67; 30%), and 
-1 receptor blockers (n = 35; 16%). 
2 test. Differences were considered as significant at P < 0.05. Data are presented as mean ± SD or as median and interquartile range for skewed variables. Univariate correlation analysis was performed by Spearman correlation analysis. Kaplan-Meier time-to-event curves were generated for patients with serum apoA-IV concentrations above and below the median. Multivariable adjusted risk estimates for progression end points were calculated using a Cox proportional hazards regression analysis (short Cox regression). A forward likelihood ratio procedure was used to identify variables that were associated with progression end points over time. Verification of the results was done by a backward likelihood ratio procedure. 
