### Institutional review boards for human subjects' protection protocol approval

The project was approved by the ethics committees/Institutional Review Boards for Human Subjects' Protection of the respective institutions. Participants provided written informed consent in accordance with the accepted standards for each respective country.

### Registry

In total, 1,149 index cases presenting with symptomatic pheochromocytoma have been registered through December 2008. The median age-at-diagnosis was 42.3 years (5-83 years). The male/female ratio was 493:656. Of the 1,149 index cases, 91 (7.9%) had malignant disease, 243 (21.1%) had multiple pheochromocytomas, and 25 (2.2%) had a previous HNP presentation. The majority of index cases presented with unilateral adrenal tumors [816 (71.0%)], followed by bilateral adrenal [149 (13.0%)], extra-adrenal abdominal or pelvic [117 (10.2%)], and extra-adrenal thoracic tumor location [22 (1.9%)]. Concomitant presence of adrenal and extra-adrenal tumors was observed in 45 cases (3.9%).

### Germline mutations

The mutation frequency in our registry, considering all patients (syndromic and nonsyndromic) was 30.1% (346 of 1,149). Of these 346 cases, 43 (12.4%) had clinical evidence of NF 1. VHL germline mutations were detected in 120 (34.7%), RET in 80 (23.1%), SDHB in 73 (21.1%), and SDHD in 28 (8.1%) cases. SDHC germline mutations were detected in two cases, one of them, an intragenic mutation, previously published (12). Among mutation carriers, 18 were found to carry a large deletion or rearrangement: 12 involving SDHB, 4 VHL, and 1 each SDHD and SDHC. The SDHC deletion involved exons 5 to 6 of the gene in a 56-year-old male patient with a single extra-adrenal thoracic pheochromocytoma without family history for paraganglial tumors. The two SDHC carriers were excluded form further analysis.

After exclusion of the syndromic cases, in particular all the NF 1 cases, as well as 49 RET and 65 VHL mutation carriers with either personal or family histories of MEN 2 or VHL, respectively, a total of 989 eligible patients were available for further study. The mutation frequency of the remaining cases was 18.9% (187 of 989) with the majority affecting SDHB (73 of 187), followed by VHL (55 of 187), RET (31 of 187), and SDHD (28 of 187). The clinical and demographic features of the eligible cases after exclusion of these patients are represented in Table 1. From the 187 mutation carriers, 9.1% (17 of 187) had a positive family history for paraganglial tumors only.

Table 1.

Demographic and clinical data of the patients included in the analysis

VariableAll patientsMutation positive patients
NtotalAny mutation%SDHB%VHL%RET%SDHD%
Sex
Male 425 97 22.8 39 40.2 32 33.0 12 12.4 14 14.4
Female 564 90 16.0 34 37.8 23 25.6 19 21.1 14 15.6
Age
≤45 521 158 30.3 56 35.4 52 32.9 23 14.6 27 17.1
>45 468 29 6.2 17 58.6 10.3 27.6 3.4
Tumor biology
Benign 901 162 18.0 53 32.7 51 31.5 31 19.1 27 16.7
Malignant 88 25 28.4 20 80.0 16.0 4.0
Tumor number
Single 818 94 11.5 52 55.3 22 23.4 9.6 11 11.7
Multiple 171 93 54.4 21 22.6 33 35.5 22 23.7 17 18.3
Tumor location
Adrenal mono-/bilateral 816 111 13.6 24 21.6 45 40.5 31 27.9 11 9.9
Extra-adrenal abd/pelvic/thoracic 135 56 41.5 45 80.4 7.1 12.5
Previous HNP
Yes 25 22 88.0 27.3 16 72.7
No 964 165 17.1 67 40.6 55 33.3 31 18.8 12 7.3
Family history
Positive 42 17 40.5 14 82.4 17.6
Negative 947 170 18.0 59 34.7 55 32.4 31 18.2 25 14.7
Total 989 187 18.9 73 39.0 55 29.4 31 16.6 28 15.0
VariableAll patientsMutation positive patients
NtotalAny mutation%SDHB%VHL%RET%SDHD%
Sex
Male 425 97 22.8 39 40.2 32 33.0 12 12.4 14 14.4
Female 564 90 16.0 34 37.8 23 25.6 19 21.1 14 15.6
Age
≤45 521 158 30.3 56 35.4 52 32.9 23 14.6 27 17.1
>45 468 29 6.2 17 58.6 10.3 27.6 3.4
Tumor biology
Benign 901 162 18.0 53 32.7 51 31.5 31 19.1 27 16.7
Malignant 88 25 28.4 20 80.0 16.0 4.0
Tumor number
Single 818 94 11.5 52 55.3 22 23.4 9.6 11 11.7
Multiple 171 93 54.4 21 22.6 33 35.5 22 23.7 17 18.3
Tumor location
Adrenal mono-/bilateral 816 111 13.6 24 21.6 45 40.5 31 27.9 11 9.9
Extra-adrenal abd/pelvic/thoracic 135 56 41.5 45 80.4 7.1 12.5
Previous HNP
Yes 25 22 88.0 27.3 16 72.7
No 964 165 17.1 67 40.6 55 33.3 31 18.8 12 7.3
Family history
Positive 42 17 40.5 14 82.4 17.6
Negative 947 170 18.0 59 34.7 55 32.4 31 18.2 25 14.7
Total 989 187 18.9 73 39.0 55 29.4 31 16.6 28 15.0

Abbreviation: Abd, abdominal.

### Demographic and clinical predictors for presence of germline mutations

The optimal age cutoff identified by recursive partitioning analysis was 42 years. To obtain a practical cutoff for clinical use, age was rounded to 40 and 45 years and both were assessed; age 45 years was chosen because it resulted in better identification of mutation-positive cases than age 40 years (data not shown). Seven variables were significantly associated with any mutation in univariable analysis, but only four were significant in multivariable analysis: age of ≤45 years, multiple pheochromocytoma/paraganglioma, extra-adrenal location, and previous HNP. This finding was confirmed by bootstrap analysis (Table 2).

Table 2.

Univariable and multivariable logistic regression analysis results for presence of any germline mutation

VariableUnivariableMultivariable*Bootstrap assessment of risk factors
OR (95% CI)POR (95% CI)P
Gender
Male/female 1.56 (1.13-2.14) 0.007 1.32 (0.90-1.95) 0.16 26.1%
Age (y)
<45/>45 6.59 (4.33-10.02) <0.001 5.37 (3.34-8.62) <0.001 99.8%
Tumor biology
Malignant/benign 1.81 (1.10-2.96) 0.018 1.35 (0.71-2.59) 0.36 15.2%
Tumor number
Multiple/single 9.18 (6.34-13.29) <0.001 8.78 (5.47-14.08) <0.001 100.0%
Tumor location
Other tumors (HNP)
Yes/No 35.51 (10.51-120.0) <0.001 11.95 (3.15-45.32) <0.001 92.4%
Family history
Positive/negative 3.11 (1.64-5.88) <0.001 2.07 (0.94-4.55) 0.07 40.9%
VariableUnivariableMultivariable*Bootstrap assessment of risk factors
OR (95% CI)POR (95% CI)P
Gender
Male/female 1.56 (1.13-2.14) 0.007 1.32 (0.90-1.95) 0.16 26.1%
Age (y)
<45/>45 6.59 (4.33-10.02) <0.001 5.37 (3.34-8.62) <0.001 99.8%
Tumor biology
Malignant/benign 1.81 (1.10-2.96) 0.018 1.35 (0.71-2.59) 0.36 15.2%
Tumor number
Multiple/single 9.18 (6.34-13.29) <0.001 8.78 (5.47-14.08) <0.001 100.0%
Tumor location
Other tumors (HNP)
Yes/No 35.51 (10.51-120.0) <0.001 11.95 (3.15-45.32) <0.001 92.4%
Family history
Positive/negative 3.11 (1.64-5.88) <0.001 2.07 (0.94-4.55) 0.07 40.9%

NOTE: Univariable and multivariable logistic regression analysis was used to determine which of the seven patient characteristics are associated with any mutation. Results are summarized as the odds ratio, 95% confidence interval for the odds ratio, and the corresponding P value. All variables are included in the multivariable model regardless of their level of statistical significance. Bootstrap analysis were done by 1,000 random samples of size 989 were selected with replacement from the original 989 study patients. Stepwise logistic regression analysis was then run on each of the 1,000 samples and the number of times each variable appeared in the final model were tabulated. Variables occurring with >50% frequency are important risk factors.

*C-statistic for this multivariable model is 0.856.

Frequency of occurrence in 1,000 bootstrap analyses.

### Algorithm for genetic testing and costs

Although malignant tumors and family history were not significant in the multivariable logistic regression analysis, they were included in the algorithm for genetic testing because of the importance of mutation screening in such patients for prognostic risk assessment (Table 3; refs. 4, 21). By preselecting the cases with at least one of these six predictors, 342 (34.6%) would be excluded from genetic testing. By doing so, 8 (4.3%) of 187 mutation carriers would be missed (Table 4).

Table 3.

Bootstrap assessment of screening algorithm

VariableOriginal sample1,000 bootstrap samples
MedianMinimumMaximum95% CI
Descriptive information
Patients with 1 or more risk factors (#) 647 647 606 697 621, 677
Patients with 0 risk factors (#) 342 342 292 383 312, 368
Patients with any mutation (#) 187 187 153 222 162, 211
Mutations missed (#) 18 3, 14
Mutations missed in all patients (%) 0.8 0.8 0.1 1.8 0.3, 1.4
Mutations missed in patients with mutations (%) 4.3 4.2 0.5 9.8 1.6, 7.3
Logistic regression model performance
Sensitivity (%) 95.7 95.8 90.2 99.5 92.6, 98.3
Specificity (%) 41.6 41.6 35.8 46.9 38.2, 44.7
Positive predictive value (%) 23.0 − 22.5 32.6 24.1, 31.0
Negative predictive value (%) 97.7 97.7 94.9 99.7 96.0, 99.1
C-statistic 0.853 0.853 0.789 0.898 0.822, 0.882
Cost calculations in U.S. $Cost per patient, approach 1* 3,119 3,119 3,045 3,182 3,078, 3,160 Cost per patient, approach 2 1,876 1,875 1,733 2,038 1,787, 1,971 Reduction in cost for approach 2 relative to 1 (%) −39.8 −39.8 −44.3 −34.5 −42.7, −36.9 VariableOriginal sample1,000 bootstrap samples MedianMinimumMaximum95% CI Descriptive information Patients with 1 or more risk factors (#) 647 647 606 697 621, 677 Patients with 0 risk factors (#) 342 342 292 383 312, 368 Patients with any mutation (#) 187 187 153 222 162, 211 Mutations missed (#) 18 3, 14 Mutations missed in all patients (%) 0.8 0.8 0.1 1.8 0.3, 1.4 Mutations missed in patients with mutations (%) 4.3 4.2 0.5 9.8 1.6, 7.3 Logistic regression model performance Sensitivity (%) 95.7 95.8 90.2 99.5 92.6, 98.3 Specificity (%) 41.6 41.6 35.8 46.9 38.2, 44.7 Positive predictive value (%) 23.0 − 22.5 32.6 24.1, 31.0 Negative predictive value (%) 97.7 97.7 94.9 99.7 96.0, 99.1 C-statistic 0.853 0.853 0.789 0.898 0.822, 0.882 Cost calculations in U.S.$
Cost per patient, approach 1* 3,119 3,119 3,045 3,182 3,078, 3,160
Cost per patient, approach 2 1,876 1,875 1,733 2,038 1,787, 1,971
Reduction in cost for approach 2 relative to 1 (%) −39.8 −39.8 −44.3 −34.5 −42.7, −36.9

NOTE: #, for absolute number; %, for percentages. One thousand samples of size 989 were selected with replacement from the original group of 989 patients. Performance indicators were calculated from each of the 1,000 bootstrap samples and summarized as the median, minimum, maximum, and 95% confidence interval. The confidence interval was estimated as the 2.5th percentile to the 97.5th percentile of the 1,000 samples.

*Test all patients first for SDHB, then for VHL, then for RET, then for SDHD.

Test patients who have at least one of six risk factors in the order specified in the testing algorithm.

Table 4.

Demographic and clinical characteristics of the carriers without risk factors

Case IDSexAge at diagnosisPheochromocytoma locationOther tumorsClinical follow up data after mutation detectionMutation
47 Right adrenal None MTC at age 47 RET c. 1900 T>C
48 Right adrenal None Lost from follow-up RET c. 1900 T>C
52 Left adrenal None MTC at age 52 RET c. 1900 T>A
48 Left adrenal None Lost from follow-up SDHB c. 87_90dupCCAG
51 Right adrenal None No further manifestation at age 67 SDHB c. 423+1 G>A
60 Right adrenal None No further manifestation at age 85 SDHB c. 607 G>A
47 Left adrenal None No further manifestation at age 67 SDHD c. 112 C>T
58 Left adrenal None No further manifestation at age 63 VHL c. 549 A>G
Case IDSexAge at diagnosisPheochromocytoma locationOther tumorsClinical follow up data after mutation detectionMutation
47 Right adrenal None MTC at age 47 RET c. 1900 T>C
48 Right adrenal None Lost from follow-up RET c. 1900 T>C
52 Left adrenal None MTC at age 52 RET c. 1900 T>A
48 Left adrenal None Lost from follow-up SDHB c. 87_90dupCCAG
51 Right adrenal None No further manifestation at age 67 SDHB c. 423+1 G>A
60 Right adrenal None No further manifestation at age 85 SDHB c. 607 G>A
47 Left adrenal None No further manifestation at age 67 SDHD c. 112 C>T
58 Left adrenal None No further manifestation at age 63 VHL c. 549 A>G

NOTE: At time of diagnosis this patient were older than 45 y, without family or personal history for paraganglial tumors, with benign pheochromocytoma of adrenal location. The age of follow-up was the age at last clinical screening.

Further, from data obtained by simple frequency tabulation, we established the order in which the genes should be tested (Fig. 1). This assessment indicated that patients with previous HNP were most likely to have SDHD, followed by SDHB, then RET, and least likely to have VHL mutations. Among patients with no prior HNP and a single extra-adrenal tumor, the order was SDHB as the most likely gene to be involved, followed by VHL, SDHD, and then RET. Among patients with no prior HNP and either multiple pheochromocytoma or a single tumor in a location other than extra-adrenal, the order was VHL as the most likely gene, followed by RET, SDHB, and SDHD as the least likely. As the major consequence, the screening algorithm as deduced is shown in Fig. 1.

Fig. 1.

Genetic testing algorithm for apparently nonsyndromic cases.

Fig. 1.

Genetic testing algorithm for apparently nonsyndromic cases.

Close modal

### Cost analysis

If we chose to screen all pheochromocytoma-susceptibility genes (excluding NF1 and SDHC) for all patients presenting with pheochromocytoma, the average cost is currently ∼\$3,400 per patient tested. Screening all patients in the gene order proposed (approach 1; Table 3), we achieve an 8.0% cost reduction without missing any mutation carrier. If we preselect the cases to be tested and test them in the order proposed (approach 2; Table 3), we can achieve a 39.8% cost reduction relative to approach 1, and a 44.7% cost reduction relative to testing all patients, but we miss eight mutation carriers (0.8% of all patients). The robustness of our results has been confirmed by bootstrap assessment analysis as shown in Table 3.

Our study confirmed that ∼30% of index cases had evidence, clinical or genetic, of one of the six known pheochromocytoma-associated syndromes. This frequency is much lower (18.9%) if clinical or anamnestical evidence of a syndrome (NF 1, VHL, and MEN 2) are excluded.

At the current state of knowledge, consequences of genetic testing play an important role in medical management, such as timely diagnosis of asymptomatic tumor manifestations, accurate genetic counseling and predictive testing, institution of gene-specific clinical surveillance, and/or prophylactic surgery (4, 22). According to the general recommendation for genetic screening in cancer-associated syndromes, all pheochromocytoma patients should be tested (23). However, the availability of genetic counselors and laboratories experienced with this relatively uncommon tumor as well as the costs of genetic analysis of multiple genes suggest the importance of prioritizing these analyses.

The most straightforward manner to help identify heritable pheochromocytoma is to retrieve and analyze the personal medical and family histories of each patient. In our series, from a total of 346 mutation carriers, clinical evidence of a syndrome (NF 1, MEN 2, or VHL) was present in 45% of the cases, which was confirmed by genetic testing. In particular, as previously published, all the cases harboring NF1 germline mutations had clinical evidence of this syndrome, and therefore, routine testing of this gene in pheochromocytoma patients is not recommended (5, 16).

But this type of syndromic recognition is insufficient. Excluding all these clinically obvious syndromic cases, we still have ∼19% (187 of 989) who are germline mutation carriers and only 9% of the 187 had a positive family history for paraganglial tumors. It is this subset of patients without the traditional syndromic “red flags” that is the focus of this study. Currently, this subset would be offered mutation testing of SDHB, SDHD, VHL, and RET. Our data confirmed that SDHC gene mutations are extremely rare in pheochromocytoma patients, and whether testing for SDHC in pheochromocytoma presentations in the absence of PGL should be done at all is currently not known (12, 13). To date, several demographic and clinical features have been associated with the presence of a germline mutation, such as young age at diagnosis, multifocal disease, extra-adrenal location, and malignancy (2, 7, 8, 21, 24). Thus, we took advantage of easily obtainable demographic and clinical features to come up with a robust algorithmic model to help predict who should be genetically tested and which gene to begin testing.

The major focus of our analysis was to obtain a model with the lowest number of missed genetic cases and highest cost reduction. Using the algorithm that would provide for identification of the highest proportion of individuals with mutations with the greatest cost reduction in the molecular diagnostic process, we would achieve a 44% cost reduction compared with testing all genes with the trade-off of missing eight individuals with mutations (0.8% of all 989 cases, 4.3% of 187 mutation carriers; Tables 24; Fig. 1). In contrast, if we used a model that picked up all cases with mutations, the overall cost reduction would only be 8%.

Currently, there are several proposed age cutoff points used to select cases for genetic screening (2527). But, thus far, they have been based on frequency observations in small samples that were not statistically derived or confirmed in multivariable analysis. We have determined age ≤45 years to be an excellent cutoff in this large series of patients, and confirmed this finding in multivariable analysis.

From the eight cases (8 of 989) in whom the mutations were missed (Table 4), none developed further manifestations to date. Of interest is that in two of the three RET mutation carriers, clinical reinvestigation identified the presence of medullary thyroid carcinoma, which confirmed the presence of the MEN 2 syndrome. For one case, follow-up data are missing. These patients benefit immediately from genetic testing.

The costs can be further decreased, if testing for large VHL deletions is excluded. Our four cases with such VHL deletions had also clinical evidence of VHL and were excluded from this specific analysis.

For the SDHB and SDHD genes, however, large deletions or duplications were identified in 13% of the nonsyndromic pheochromocytoma patients with SDHB and SDHD mutations. Therefore, testing for large deletions of these genes should be done, after intraexonic mutations have been excluded.

When considering gene prioritization and cost reduction in this diagnostic process, special consideration is given to country-specific or ancestry-specific founder mutations. For example, 40% of all unselected male breast cancers and 10% of all female breast cancers diagnosed in Iceland are accounted for by a single mutation in BRCA2, 999del5 (28, 29). Similarly, several mutations in the SDHB, SDHD, and VHL genes have been observed to occur in higher frequencies in some restricted geographic areas and most likely represent founder mutations (14, 3034). For patients coming from these areas, genetic testing for the region-specific founder mutation should be offered first in the absence of obvious syndromic features. If negative, then our algorithm should be followed.

Other genetic differences (e.g., variants) among distinct ethnicities and ancestries that might influence a patient's phenotype, and thus our algorithm, are currently not known, and therefore, our algorithm should be considered valid for all patients regardless of ethnicity. As data accumulate in this regard, our algorithm should be reevaluated and updated accordingly.

For purposes of genetic counselling, it should be noted that other mutation types or other as yet not identified pheochromocytoma-predisposing genes might exist (35, 36). In fact, in our series, 25 of 42 index cases with family histories of paraganglial tumors, no germline mutation in the known genes has been identified. Nonetheless, our algorithm can be easily recalculated after new gene or genes are identified.

With exponential advances in genetic and genomic technologies, many heritable disorders are being found to have several to multiple susceptibility genes. Importantly, subsets of these disorders present in a manner seemingly similar to that of sporadic disease. In the ideal world, these individuals should be offered genetic testing for all known susceptibility genes for that particular disease. However, this becomes impractical as health care costs soar. We provide here an algorithm that will help identify who should be offered genetic testing for nonsyndromic pheochromocytoma presentations and subsequently help prioritize which gene to begin testing based on modeled demographic and clinical features.

Study Group Members:

Germany: Alexander, Kappel-Grafenhausen; Allolio, Würzburg; Anlauf, Bremerhaven; Arlt, Würzburg; Arnold, Marburg; Bachmann, Arnsberg; Baumeister, München; Behrend, Freiburg; Bertram, Osnabrück; Beyer, Oberhausen; Beyersdorf, Deggendorf; Bergmann, Erlangen; Bierkamp, Bocholt; Bloch, Lübeck; Boehncke, Frankfurt; Bojunga, Frankfurt; Bosse, Stuttgart; Bootz, Bonn; Borany, Kleeve; Borch, Münster; Bornstein, Dresden; Brauckhoff, Halle; Brietzke, Schwerin; Büchler, Heidelberg; Callies, Hannover; Caspari, Bonn; Cupisty, Düsseldorf; Demtröder, Dortmund; Desiderato, Kappel-Grafenhausen; Dickert, Bodenwöhr; Diefenbach,/Roos, Beselich-Obertiefenbach; Dietz, Essen; Dörr, Erlangen; Donauer, Freiburg; Dost, Giessen; Dralle, Halle; Dupuis, Karlsruhe; Ebbinghaus, Bremen; Egli, Mönchengladbach; Eisenhofer, Dresden; Engert, Bochum; Fade, Homburg; Falk, Karlsruhe; Fassbinder, Fulda; Feldkamp, Bielefeld; Finke, Berlin; Fischer, Heidelberg; Fischer, Trier; Fritze, Rheine; Frilling, Hamburg; Furtwängler, Freiburg; Gal, Garbrecht, München; Hamburg; Gerke, Freiburg; Glien, Bonn; Götte, Bad Hersfeld; Goretzki, Düsseldorf; Graubner, München; Grotz, Essen; Haag, Frankfurt; Hagen, Oberhausen; Hahner, Würzburg; Harsch, Erlangen; Hartung, Düsseldorf; Hauch, Hamburg; Happ, Frankfurt; Hauschild, Lübeck; Hecht, Leipzig; Hector, Hamburg; Hehmann, Stuttgart; Hehr, Regensburg; Heidemann, Augsburg; Heidemann, Stuttgart; Heins, Osnabrück; Hempel, München; Heppner, Köln; Hamburg; Herbach; Karlsruhe; Herlan, Bötzingen; Hetzer, Berlin; Hirsch, Offenburg; Hofmeier, Windsbach; Hofmockel, Würselen; Holbeck, Solingen; Hopt, Freiburg; Host, Münster; Hudelmayer, Pforzheim; Isermann, Heidelberg; Iwen, Lübeck; Jacobs, Osnabrück; Jarzab, Gleiwitz; Jocham, München; Jürgens, Münster; Judy, Giessen; Junginger, Frankfurt; Jurdi, Niederfischbach; Justl, Würzburg; Kabisch, Hamburg; Karaa, Beeskow; Kavelage, Hamburg; Kindler, Würselen; Kisters, Herne; Kirste, Freiburg; Klein-Franke, Göttingen; Klingebiel, Frankfurt; Koch, Leipzig; Köster, Hamburg; Kornely, Duisburg; Krämer, Warstein; Kremens, Essen; Krone, Köln; Krumme, Wiesbaden; Kühn, Freiburg; Kusterer, Mannheim; Lampe, Magdeburg; Lamster, Erfurt; Lederbogen, Essen; Lee, München; Leisner, Kirchheim; Lindemann, Ettlingen; Lohmann, Dresden; Löβner, Magdeburg; Lohöfener, Berlin; Lorenz, Halle; Lückhoff, Landshut; Luft, Berlin; Macazhavy, Düsseldorf; Malyar, Münster; Mann, Essen; Meyer, Erfurt; Meyer, Frankfurt; Mitter, Essen; Mittler, Magdeburg; Möller, Essen; Mönig, Kiel; Nawroth, Heidelberg; Netzer, Köln; Neumann, Berlin; Niebling, Titisee; Niedermeyer, Düsseldorf; Niemeyer, Freiburg; Nies, Osnabrück; Noah, Villingen; Normann, Düsseldorf; Opitz, Schwetzingen; Otto, Freigericht; Pätsch, Hannover; Pasedag, Regensburg; Pavel, Erlangen; Pavenstädt, Münster; Peter, Freiburg; Petzold, Dresden; Pfäffle, Leipzig; Pichl, Nürnberg; Pielken, Oberhausen; Plingen, Düsseldorf; Ploner, Stuttgart; Pohl, Stuttgart; Quante, Düsseldorf; Racasan, Frankfurt; Reichhardt, Leipzig; Reincke, München; Reiβenweber, Bamberg; Reschke, Magdeburg; Reuter, Cuxhaven; Rieg, Freiburg; Riel, Erlangen; Ritter, Münster; Röher, Düsseldorf; Rohrer, Homburg; Roth, Bonn; Rudolph, Risum-Lindholm; Rudolph, Bad Berka; Rückauer, Freiburg; Schäfer, Magdeburg; Schäfer, Pfarrkirchen; Schellong, Erlangen; Scherbaum, Düsseldorf; Schlottmann, Unna; Schmidt, München; Schmid, München; Schmitz, Bonn; Schöninger, München; Schogohl, München; Scholz, Mainz; Schott, Düsseldorf; Schulze, Freiburg; Schumann, Köln; Schumm-Dräger, München; Schweinfest, Emershausen; Seppel, Mönchengladbach; Seufert, Freiburg; Siegler, Frankfurt; Sievers, Bederkesa; Simon, Düsseldorf; Sieverts, Heidelberg; Slisko, München; Sommer, Osnabrück; Speicher, München; Stahl, Lörrach; Stephan, Karlsruhe; Stoll, Offenburg; Tan, Essen; Trupka, Starnberg; Unger, Essen; Vogelsang, München; Wahl, Frankfurt; Walter, Limburg; Weber, Mainz; Weber, Schaffhausen; Weber, Wickede; Wehinger, Kassel; Weisbrod, Hasloch; Wenzel, Essen; Wiegand, München; Wiiwer, Waldeck; Willenberg, Düsseldorf; Wilms, Bonn; Yüce, Essen; Zimmerhackl, Freiburg; Zimmermann, Chemnitz; Zumkeller, Halle; zur Mühlen, Hannover;

Austria: Fauth, Innsbruck

Chile: Bello, Candia

England: MacGregor, London; Bockenhauer, London; Schulte, London;

Finland: Aittomäki, Helsinki; Paronen, Helsinki; Valimäki, Helsinki; Virkamäki, Helsinki;

France: Brunaud, Nancy; Klein, Nancy; Limacher, Straβbourg; Pigny, Lille; Weryha, Nancy;

Greece: Zamboulis, Thessaloniki;

Israel: Regev, Assaf Haroteh; Glase, Jerusalem;

Italy: Falcioni, Piacenza; Riegler, Bozen; Schiavi, Padova, Stanzial, Bozen;

Norway: Heimdal, Oslo, Laurent, Oslo

Romania: Milos, Timisoara

Sweden: Ramme, Stockholm; Wallin, Stockholm

Switzerland: Angst, Aarau; Mohaupt, Bern; Müller, Basel; Pichert, Zürich; Preiswerk, Zürich; Sanssonnens, Bern; Stettler, Bern; Sulzer, Zürich; Unger, Lausanne; Walter, Basel;

Turkey: Huseyn, Ankara

Ukraine: Kollkh, Kiev; Yaremchuk, Kiev;

USA: Uckun-Kitapci, Chapel-Hill

No author had any financial and personal relationships with other people or organizations that could inappropriately influence or bias this work.

We thank all patients who contributed to this work, Aurelia Winter (study assistant) for her technical support, and Mary Buchta, Gani Berisha, and Tobias Blüm (technicians) working at the Department of Nephrology, Section of Preventive Medicine, Albert-Ludwigs University, Freiburg, Germany.

1
DeLellis
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