Intermediate-term outcome after PSMA-PET guided high-dose radiotherapy of recurrent high-risk prostate cancer patients

Sebastian Zschaeck
Peter Wust
Marcus Beck
Waldemar Wlodarczyk
David Kaul
Julian Rogasch
Volker Budach
Christian Furth
Pirus Ghadjar
Intermediate-term outcome after PSMA-PET guided high-dose radiotherapy of recurrent high-risk prostate cancer patients
10.1186/s13014-017-0877-xR E S E A R C H Open Access
Background: By the use of PSMA positron emission tomography (PET) detection of prostate cancer lesions with a high sensitivity and specificity combined with a favorable lesion to background contrast is feasible. Therefore, PSMA-PET is increasingly used for planning of radiotherapy treatment; however, any data on intermediate-term outcome is missing so far.Methods: Patients with high-risk or very high risk prostate cancer, referred for salvage radiotherapy (SRT, n = 22) between 2013 and 2015, underwent PSMA-PET prior to therapy. Irradiation was planned on PET data with boost to macroscopic tumors/metastases. Treatment related toxicity was measured using Common Terminology Criteria for Adverse Events (CTCAE, v4.0). Result: Findings in PSMA-PET led to treatment modifications in 77% of SRT patients compared to available CT information. One patient did not receive irradiation due to disseminated disease, the other patients received increased boost doses to macroscopic disease and/or inclusion of additional target volumes. Toxicity was low as only 2 patients reported toxicities > grade 1. With a Median follow-up time of 29 in patients that were not lost to follow-up, prolonged PSA responses below baseline were observed in the majority of patients (14 of 20). In hormone-naïve SRT patients (n = 11), radiotherapy led to prolonged PSA decrease in 8/11 patients, however with 3 of these 8 patients receiving repeated PSMA based irradiation of novel lesions during follow-up.Conclusion: PSMA-PET guided planning of radiotherapy led to change of treatment in the majority of patients. Treatment related toxicity was well tolerated and promising results regarding intermediate-term PSA decrease were observed.Trial registration: No trial registration was performed due to retrospective evaluation.

Background

Both radiotherapy and radical prostatectomy are primary treatment modalities for localized prostate cancer. Cancer specific mortalities after these treatments is around 1% after 10 years for low and intermediate risk patients [1]. However, patient outcome is much less favorable for highrisk or very high-risk prostate cancer as these patients have a 10 year prostate specific mortality between 10 and 20% following definitive radiotherapy or surgery [2,3]. This is probably due to the existence of unknown micrometastatic disease prior to local treatment in a certain proportion of patients.

In case of biochemical failure (BF) after radical prostatectomy early salvage radiotherapy is the recommended treatment [4]. Postoperative treatment decision is commonly based on serum prostate specific antigen (PSA) values. One drawback of this approach is missing spatial information of recurrence, which would be highly relevant for the planning of a localized treatment like radiotherapy. Increased radiation dose was demonstrated to improve biochemical recurrence free survival after primary radiotherapy [5,6] and was suggested to improve relapse-free survival in meta-analyses of retrospective data from salvage radiotherapy (SRT) patients [7,8], a hypothesis which is currently evaluated by the phase 3 SAKK 09/10 trial [9]. However, dose intensification led to increased toxicity in primary treatment [5,6] and was shown to negatively impact patients quality of life regarding urinary symptoms after SRT [9]. As a result, accurate detection of individual spread of disease or recurrence patterns would be highly warranted. Moreover, in selected oligometastatic patients radical treatment of identified metastatic lesions could further improve outcome.

Uncertainties about optimal treatment exist in a high risk post-operative setting with either high persisting PSA values after surgery or rapidly increasing PSA levels. If these patients benefit from SRT is not proven to date. In case of pre-SRT PSA values higher than 2 ng/ml the 4-year progression-free probability after SRT was only 19% whereas it is around 52% for patients with PSA values ≤2 ng/ml [10].

Recently there is a rapid increasing interest in positron emission tomography (PET) imaging using the tracer prostate specific membrane antigen (PSMA). PSMA is highly overexpressed at the surface of prostate cancer cells [11] and PSMA-PET was shown to be both, highly sensitive and highly specific, even in cases of low PSA values (<1 ng/mL) [12][13][14][15]. Thus, PSMA-PET may be especially relevant for radiotherapy treatment planning in a high-risk salvage setting. Due to high sensitivity and specificity of PSMA-PET even very small nodal metastases (<8 mm) are reliably detectable [16]. Additionally, due to the whole body approach even previously unexpected (distant) metastases can be localized. Thus, improvements in imaging may lead to an improvement in patient's outcome. Therefore, we report on patients that received PSMA PET for high-risk SRT, i.e. patients with doubtful benefit of local standard treatment and focus und intermediate-term PSA control in these patients.

Patients and methods

Patient characteristics

Patients presenting at the department of radiation oncology for salvage radiotherapy after prostatectomy were stratified for their individual risk. High-risk patients as defined as pre-therapeutic PSA values above 20 ng/ml, Gleason score 8 or higher, persisting PSA values after radical prostatectomy (> 0.6 ng/ml), PSA values before SRT above 2 ng/ml or metastatic regional lymphnodes (diagnosed during prostatectomy), were referred for a pre-therapeutic PSMA PET/CT between 2013 and December 2015. If irradiation was still indicated after PSMA PET/CT imaging, patients were irradiated based on data of PSMA PET/CT. Originally 22 patients were scheduled for salvage radiotherapy (SRT) of the prostate bed. Median Age at the time of treatment was 65 years. Further details on the patient characteristics can be found in Table 1.

PET imaging

Images were acquired on a PET/CT device (Gemini TF 16 Astonish, Philips Medical Systems, Cleveland, OH, USA). Patients were examined in supine position from base of scull to the proximal femora 62 ± 35 min after intravenous injection of 113 ± 13.3 MBq of [ 68 Ga]PSMA-HBED-CC (PSMA-11) (PSMA). In all patients, a diagnostic, contrastenhanced CT was acquired and used for attenuation correction. For one patient, no information about injection time and injected dose was available, two patients received radiotherapy planning based on 18 F-Cholin PET, and subsequently underwent PSMA-PET imaging.

The findings in PET imaging were categorized as follows: Local recurrence (T) included recurrence within the prostate bed and seminal vessels. Lymph node metastases within the regional drainage of the prostate was classified as N or as M1a if outside the regional drainage, further metastases (which were in all cases bone metastases) were scored as M1b.

Radiotherapy treatment and clinical follow-up

Intensity-modulated radiotherapy (IMRT) was performed in all patients undergoing radiotherapy after PSMA-PET imaging. Almost all patients were treated with helical tomotherapy (Accuray, USA), two patients were treated with the Novalis™ radiosurgery system (Varian, USA; Brainlab, Heimstetten, Germany) and 2 patients received irradiation by linear accelerators (Varian, USA) using a volumetric modulated arc approach. Radiotherapy prescription was as follows: Usually single fraction doses of 1.8 Gy were prescribed to a total dose of 66.6 Gy to prostate bed, including the base of seminal vesicles and the complete seminal vesicle in case of pT3b tumors. In the histologically described high-risk regions (with positive surgical margins or extracapsular spread) a simultaneous integrated boost (SIB) was applied to a cumulative total dose of 70.3 Gy. In case of PSMA-PET evidence of macroscopic local recurrence the SIB dose was increased to a maximal dose of 74 to 77.7 Gy. The lymphatic drainage was not irradiated unless if PET imaging revealed pelvic lymphnode metastasis. If the latter one was evident the lymphatic drainage was irradiated to a total dose of 54.0 Gy and macroscopic lymphnodes received a SIB to 66 Gy. Evident bony metastases were irradiated to a total dose of 42-66 Gy, depending on location and size. Androgen deprivation therapy (ADT) was prescribed according to the preference of the treating urologist.

Acute toxicities were scored at least every two weeks during treatment and at the end of therapy by the treating physician according to common terminology criteria for adverse events (CTCAE), version 4.0. Information about PSA values, toxicities and ADT was collected from the treating urologist and by patient interviews. PSA values were usually measured every 3 months following radiotherapy by the urologist, who was responsible for decision-making about initiation or continuation of ADT.

Statistical calculations and software

Results

Modification of treatment according to findings in PSMA PET/CT

In high-risk patients PSMA-PET led to treatment modifications in 77% patients referred for SRT. One patient presented with disseminated metastases and SRT was therefore omitted. Figure 1 depicts PSMA-PET based treatment modifications.

Toxicity

Radiotherapy could be delivered as planned in all patients and was well tolerated (Table 2). Only 2 of 21 treated patients developed acute toxicities greater grade 1 according to CTCAE v4.0 (one case of grade 2 noninfective cystitis and one case of grade 2 diarrhea). During follow-up no late toxicities greater grade 1 were observed.  Figure 2 depicts the percentual PSA change between pre-SRT and last follow-up in all patients after SRT.

Out of 9 SRT patients with prior/concomitant ADT, 5 patients started ADT concomitantly to radiotherapy while 4 patients already presented a history of, sometimes longterm, ADT medication (average: 20 months, range: 3-58 months). Some of these patients received SRT with already rising PSA values during ADT (i.e. beginning of hormone refractory situation), which is probably the reason why some patients already presented increasing PSA values during intermediate-term follow-up (Fig. 2). 6 of the patients with ADT had substantial intermediate-term PSA responses. In three patients ADT could be or had to be omitted during follow-up.

Patients were grouped regarding clinical risk factors (Median initial PSA value and PSA value at the time of SRT, Gleason score up to 7 or higher, Median PSA Nadir value, postoperative T stage >3a and postoperative N+) and regarding PET findings (isolated local recurrence, no evidence of PSMA tracer uptake, any distant metastases or only lymphnode metastases N+ and/or Ma+). Median response rates for these different groups are summarized in Table 3. Differences in response were only observed in patients with pN+ and patients receiving salvage radiotherapy without any lesion in PSMA-PET (Median PSA response: −9% for PSMA negative patients versus −79% in case of irradiated PSMA lesions and +34% in case of pN+ versus −79% for pN0).

Discussion

So far the impact of PSMA PET/CT on treatment outcome after radiotherapy is unknown. Here we report the first study with clinical meaningful follow-up longer than two years. We found first evidence that inclusion of PSMA PET/CT positive lesions for radiotherapy planning leads to favorable PSA responses in the majority of high-risk and very high-risk patients. Additionally, no high-grade acute Currently data on PSMA-PET guided therapy is sparse and only a few studies assessed the impact of PSMA-PET on radiotherapy planning: According to previous retrospective studies PSMA-PET altered treatment decisions in 26% to 33% [17,18] of primary treated patients and 42% to 61% of SRT patients [18,19]. One study investigated treatment changes for a very heterogeneous group of patients, with the majority being SRT patients (67%), an reported an overall treatment adaptation rate of 46% [20]. Another recent publication which only analyzed SRT patients with PSA values below 1.0 ng/ml detected PSMA positive lesions in 54%, which potentially altered radiotherapy treatment of these patients [21]. Our data confirm the observations that radiotherapy is often altered in SRT after PSMA-PET imaging. An important difference is the rate of 77% in SRT which is higher than the average rate of the cited publications. This is probably due to a more restrictive use of PSMA-PET imaging in patients described here. Typically PSMA-PET was only applied in very high risk situations, in which the solely use of local therapies could be of doubtful benefit for patients.

The use of PSMA-PET is rapidly increasing in many countries, including Germany and Australia. This led to a recent expression of opinion from several experts in the field that the use of PSMA-PET has reached almost plaguelike proportions [22]. National guidelines usually only propose PSMA-PET in case of recurrent disease as an optional method (German S3 guideline) or don't even mention PSMA-PET (NCCN prostate cancer guidelines version 2.2017) [23,24]. Due to the novel nature of PSMA-PET any long-term data, that include relevant endpoints like overall survival and prostate cancer specific survival, are missing so far. To our best knowledge, there is only limited data with short follow up, e.g. a mean follow-up of 8 months was recently published [19]. Our data has up to now the longest follow-up time and reveals interesting aspects in high-risk patients. Especially the finding that 8 of 11 ADT naïve patients receiving SRT have long-lasting PSA responses >2 years seems encouraging for the future use of PSMA based radiotherapy. Furthermore, in absence of this opportunity these very high risk patients would probably have been referred for ADT in the vast majority of cases. Androgen deprivation commonly fails after 2-3 years in case of macroscopic disease [25,26]. Even in lower-risk patients with average PSA levels of 1.02 at the time of ADT initiation 13% of patients showed clinical progression within an average follow-up time of 2 years in a larger retrospective analysis [27]. Based on the relatively low case number it seems difficult to draw conlcusions on sub-groups that potentially benefit or do not benefit from PSMA-PET based radiotherapy. In our study patients with high-risk factors but without any evidence of PSMA lesions and patients with histological proven lyhmnode metastases (pN+) seemed to benefit less. However these findings should be validated in larger patient cohorts.

For the here described high-risk constellation SRT candidates clinical evidence and guidelines are sparse. Based on a retrospective analysis from Trock and colleagues common criteria to determine if patients benefit from SRT are: SRT within 2 years of BCR and no persisting PSA levels after surgery [28], however another study proved positive effects of SRT even with longer time intervals, at least for T3 tumors [29]. While the study from Trock did not find a correlation between pre SRT PSA values and outcome after radiotherapy, although patients with pre-SRT PSA values up to 57 ng/ml were included, other studies found a positive effect of SRT mainly in patients with PSA values below 1 ng/ml [30]. The multi-istitutional analysis from Tendulkar and colleagues supports the notion that optimal SRT should be delivered as early as possible with low PSA values [31]. The ideal treatment for patients with higher PSA values or a longer time interval between surgery and intervention is therefore unclear.

In case of oligometastastatic prostate cancer radical local therapy including radiotherapy seem to be associated with favourable patient outcome, although any prospective data on this important issue is lacking [32,33].

Conclusions

Our data, although limited by its retrospective nature and small sample size, support the role of radiotherapy in Due to the low number of isolated regional recurrences (n = 2) these patients were combined with M1a patients for analyses combination with state of the art PSMA-PET imaging for individually tailored treatment. The low rate of toxicities and the high rate of durable PSA response are encouraging and merit further prospective evaluation. We are well aware that due to restrictions of imaging resolution, PET based radiotherapy mostly only hits the tip of the iceberg in patients presenting a tumor stage where microscopic spread beyond PET lesions seem to be the rule and not the exception. Therefore we decided against the use of classical criteria for biochemical recurrence, since probably almost all patients described here will relapse. Instead the relative increase/decrease of PSA levels compared to the level at initiation of radiotherapy was applied. As PSA levels are not only highly relevant for the psychological wellbeing of prostate cancer patients, but also for decision making regarding ADT or therapy intensification, an intermediate-term gain of PSA levels below baseline seems to be an appropriate endpoint in this setting. In this regard our findings indicate the safety and effectiveness of PSMA based radiotherapy in the described high-risk patients, having in mind that prospective data for optimal treatment for these patients is currently missing. However these patients should be informed that the radiotherapeutic approach is probably only able to delay ADT as during follow-up arising of novel lesions seems to be frequent, which however often can be temporary treated locally, too (3 of 11 cases received PSMA-PET based irradiation during follow-up).

Fig. 1
1Treatment modifications after PSMA-PET in patients referred for salvage radiotherapy Follow-up One patient was lost to follow-up, median follow up time in the remaining irradiated 20 patients was 29 months (range: 12-49). Median PSA values at the time of last follow-up were 0.15 ng/ml (range: 0-8.45). 11 of these patients did not receive androgen deprivation therapy (ADT) prior or concomitant to SRT. During follow-up only one of these patients started ADT; however, three patients received PSMA-PET based irradiation of de novo lesions during follow-up. In this group of ADT naive patients the mean PSA value after an average follow-up time of 26 months (12-34) was 1.4 ng/ml (range: 0-5) with 8 out of 11 patients still presenting PSA values lower than prior to SRT.

ADT: Androgen deprivation therapy; BF: Biochemical failure; CT: Computed tomography; CTCAE: Common terminology criteria for adverse events; Gy: Gray; IMRT: Intensity-modulated radiotherapy; PET: Positron emission tomography; PSA: Prostate specific antigen; PSMA: Prostate specific membrane antigen; SIB: Simultaneous integrated boost; SRT: Salvage radiotherapy

Table 1
1Patient characteristicsSalvage Radiotherapy (SRT) n = 22

PSA before surgery (ng/ml)
18.9 (3.14-115) Avg. (range)

Postoperative Gleason score
8 (6-10) Avg. (range)

PSA before SRT
6.1 (0.2-34.5) Avg. (range)

Months between surgery and SRT
29 (2-201) Avg. (range)

Concomitant androgen deprivation
10 of 22

Postoperative
T stage

T 2
7

T 3
14

T 4
1

Surgical margin status

R 0
10

R 1
11

R 2
1

Postoperative
PSA values

PSA < 0.1 ng/ml
23

PSA > 0.1 ng/ml
9

Table 2
2Acute toxicities in irradiated patientsToxicity
Grade 0
Grade 1
Grade 2
Salvage Radiotherapy (SRT) n = 21

Proctitis
17
4
0

Diarrhea
14
6
1

Cystitis
13
7
1

Polakisuria
15
6
0

Fatigue
11
10
0

Other
19
2
0

Table 3
3Risk factors and their association with intermediate-term PSA responseRisk factor
-
+
Gleason score > 8
−18
−81

Median Initial PSA value
−76
−94

Median PSA Nadir
−78
−85

Median PSA before irradiation
−78
−86

Postoperative T stage >3a
−78
−85

Postoperative N+
−79
+34

PSMA-PET findings

No detectable lesions
−79
−9

Isolated local recurrence
−89
−42

Distant metastases
−76
−89

Lymphnode metastases (N+/M1a) a
−76
−99

a

AcknowledgmentsNot applicable.FundingThe study did not receive any specific funding.Availability of data and materialsThe dataset supporting the conclusions of this article is included within the article and is available from the corresponding author on reasonable request.Authors' contributions SZ performed the analysis and drafted the manuscript and designed the figs. PW, WW, JR, CF and VB were responsible for imaging, treatment and treatment planning of patients. MB provided data for the evaluation. DK, PW and PG provided ideas and help with the manuscript. PW and PG provided ideas for the analysis, supervised the analysis and interpretation of data and reviewed the manuscript. All authors read and approved the final manuscript.Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Evaluation of the data was approved by the Institutional Ethics Committee (EA4/168/16).Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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Associations of total and abdominal adiposity with risk marker patterns in children at high-risk for cardiovascular disease

Lawrence De Koning
Erica Denhoff
Mark D Kellogg
Sarah D De Ferranti
Associations of total and abdominal adiposity with risk marker patterns in children at high-risk for cardiovascular disease
10.1186/s40608-015-0043-7R E S E A R C H A R T I C L E Open Access
Background: While body mass index percentiles (BMI%) are commonly used to assess childhood cardiovascular risk, waist circumference percentiles (WC%) are not commonly used nor universally accepted. We tested whether BMI% or WC% should be used to identify risk factor patterns in children at high-risk for developing cardiovascular disease (CVD). A total of 107 children (8-19 years) with cardiovascular risk factors or a family history of CVD were studied. Tobacco exposure, screen-time, blood pressure and anthropometric measures were made, as well as serum risk markers. Principal component analysis (PCA) was used to identify patterns explaining risk factor variance. Multiple linear regression was used to test for associations between risk factor patterns, BMI% and WC%.Results: An adverse lipid pattern (low HDL, high triglycerides and LDL), a pro-inflammatory pattern (high ICAM and TNFαR2), a high blood pressure pattern (high SBP and DBP) and a high Lp(a) pattern were identified. Higher BMI% and WC% were associated with significantly higher levels of the lipid pattern (p < 0.05). BMI% explained 20% of variance in this pattern, whereas WC% explained 22%. When both BMI% and WC% were used together, neither BMI% nor WC% were significantly associated with the lipid pattern. However, BMI% was significantly associated with lower levels of the pro-inflammatory pattern, and WC% was associated higher levels of the pro-inflammatory pattern -together explaining 12% of variance. Conclusion: In children at high-risk for CVD, BMI% or WC% explained similar variance in an adverse lipid pattern; however, the combination of BMI% and WC% explained greater variance in a pro-inflammatory pattern than either alone. Both WC% and BMI% should both be used in anthropometric assessments of high-risk children.

Background

Adiposity (overweight and obesity) is a powerful risk factor for cardiovascular disease (CVD) [1], and is recognized in recent obesity screening and treatment guidelines for both adults [2] and children [3,4] Adipocytes secrete fatty acids, express renin-angiotensin system components (e.g. angiotensin converting enzymes), and release inflammatory factors which advance atherosclerosis by respectively promoting hyperlipidemia and insulin resistance, high blood pressure, and vascular inflammation [5]. In particular, visceral adipocytes are extremely metabolically active, and are potent sources of these compounds -especially inflammatory factors, which are recognized as both risk factors and common pathologic features of CVD [6].

Measures of abdominal adiposity such as waist circumference (WC) indirectly assess visceral adipose tissue, and are associated with an increased risk of cardiovascular events [7]. Despite their sometimes high correlation with BMI, using them with along with BMI often improves the predictive power of models [8,9]. The importance of abdominal obesity assessment was recognized in 2008 by the College of Family Physicians of Canada, which endorsed the routine measurement of WC in adults [10]. In children, WC is assessed as a percentile for age and sex; however the role of WC percentiles in pediatric cardiometabolic risk assessment is unknown.

The objective of this study was to compare the crosssectional associations of BMI and WC percentiles with patterns of cardiovascular risk markersincluding inflammatory factors -in a population of children referred to a preventative cardiology clinic.

Methods

This study was approved by the institutional review board of Boston Children's Hospital, and all research conducted was consistent with declaration of Helsinki. Informed consent was obtained from parents or guardians of all participants.

A total of 150 participants aged 8-18 years of age were initially recruited from the Preventive Cardiology Clinic of Boston Children's Hospital, Boston, Massachusetts. Medical and family history was collected by review of the clinic chart, electronic medical record and interview of a parent and patient. Family history of premature cardiovascular disease was defined as heart attack, stroke, angioplasty, stenting, coronary artery bypass grafting (CABG), peripheral arterial disease (PAD), sudden death occurring < 55 years in male and <65 years in female relatives (parents, aunts, uncles, grandparents), in a manner consistent with the National Cholesterol Education Program (NCEP) [11] and the American Academy of Pediatrics (AAP) [3,12]. All demographic information was collected by parental self-report, including exposure to tobacco smoke in the home, hours per day of television watching and computer gaming (screen time), racial background (Black, White, Other [includes Asian, Hispanic, Pacific Islander, Native American], and family history of cardiovascular disease (parent, grandparent, aunt, uncle or sibling).

Anthropometric measures

We used the body mass index (BMI) as a measure of overall adiposity, and WC as a measure of abdominal adiposity. Height and weight were measured using a standing scale and stadiometer, and WC was measured at the level of the superior iliac crest using a tape measure. The average of two WC measures was recorded. BMI was calculated as weight in kg divided by height in meters squared. BMI percentile was determined using Centers for Disease Control (CDC) growth charts [13]. Overall obesity was defined as a BMI ≥ 95% percentile of BMI for age and sex [14,15], and abdominal obesity was defined as a WC ≥ 90 th percentile for age and sex [16].

Systolic (SBP) and diastolic (DBP) blood pressure were measured initially using an oscillometric cuff (Dinamap). The average of 2-3 recordings of SBP and DBP were obtained using appropriately sized cuffs using standard techniques, and converted into percentiles [17]. Elevated blood pressures (SBP > 140 mmHG, DBP > 90 mmHg) were rechecked by auscultation by experienced clinicians [17]. High blood pressure was defined as a SBP or DBP ≥ 95 th percentile [17].

Laboratory measurement of risk markers

A serum sample was obtained from 107 participants after a 12-hour fast, and the following tests were performed: total cholesterol (TC), high density lipoprotein cholesterol (HDL), triglycerides (TG), low-density lipoprotein cholesterol (LDL; calculated), soluble tumor necrosis factor-alpha receptor 2 (TNF-αR2), P-selectin, intracellular adhesion molecule-1 (ICAM-1), and C-reactive protein using a high sensitivity assay (hs-CRP). Soluble tumor necrosis factor-alpha receptor 2 (TNF-αR2) is the more stable receptor of TNF-α, − which is an early inflammatory stimulator of endothelium [18]. P-selectin and ICAM-1 are both endothelial cell adhesion molecules for monocytes and lymphocytes, and C-reactive protein is a pentameric inflammatory marker that binds to bacterial cell wall components [18].

Lipids were measured enzymatically with a Hitachi 911 analyzer using reagents and calibrators from Roche Diagnostics (Indianapolis, IN, USA). The Friedewald equation was used to calculate LDL (TC -(TG/5 + HDL)) if triglycerides were less than 400 mg/dL. A direct method was used to measure LDL if triglycerides were > 400 mg/dL. Very low-density lipoprotein (VLDL) was estimated as the difference between TC and all other major lipoprotein fractions (TC -(LDL + HDL)). National Heart Lung and Blood Institute guidelines (NHLBI) were used to identify individuals with high TC (≥200 mg/dL), high LDL cholesterol (≥130 mg/dL) and low HDL cholesterol (<40 mg/dL) [19].

Lipoprotein(a) was measured with a turbidimetric assay on the Hitachi 911 analyzer (Roche Diagnostics) using reagents and calibrators from Denka Seiken (Niigata, Japan). This is the only commercial assay not affected by Kringle type 2 repeats [20]. The concentration of C-reactive protein was determined using a highsensitivity (hs-CRP) immunoturbidimetric assay on the Hitachi 911 analyzer (Roche Diagnostics), using reagents and calibrators from Denka Seiken (Niigata, Japan). Soluble tumor necrosis factor-alpha receptor 2 (TNF-αR2), P-selectin, and intracellular adhesion molecule-1 (ICAM-1) were measured using enzyme-linked immunosorbent assays (ELISAs) from R & D Systems (Minneapolis, MN, USA).

All samples were assayed in duplicate. Samples with disparate results (coefficient of variance [CV] for duplicates > 10%) were re-assayed a third time. Day to day and within run coefficients of variance (CV) for all assays were <10%. The CVs for lipid tests were less than 2%.

Statistical analysis

Continuous sex and race-specific (Black, White, Other) WC percentiles were interpolated from National Health and Nutrition Examination Survey (NHANES; 1999-2008) data [21] using 5-knot cubic spline regression (R; rms package). To reduce bias associated with extrapolating beyond the data, participant WC percentile above the 90 th percentile or below the 10 th percentile were rounded to 95% and 5%, respectively. The mean and standard deviation (SD) were calculated for all continuous variables. Risk markers were log-transformed prior to statistical tests and regression modeling to normalize skewed distributions. Unpaired Student's T tests and chi squared tests were used to compare the means and proportions of each variable according to overall and abdominal obesity.

Principal component analysis (PCA) with varimax rotation was used to identify the first four independent patterns of risk markers (Proc Factor) that would explain the majority of variance in risk marker data. A continuous score was derived for each participant representing how closely their risk markers levels conformed to each pattern (Proc Score). Scores were then divided into quintiles for ease of interpretation.

Linear regression was used to assess the relationships between BMI percentile, WC percentile and quintiles of risk marker scores (Proc Reg). Partial correlation coefficients were calculated to determine the independent explanatory power of BMI and WC percentile on each score. All regression models were adjusted for exposure to cigarette smoke (Yes vs No), family history of cardiovascular disease (Yes vs No), screen time (hours), and race (Black, White vs Other). Analyses were performed in SAS ver 9.3 (Cary, NC, USA) and R version 2.15.1.

Results

One hundred and seven participants, primarily non-Hispanic whites (82%), with an average age of 13.3 years were enrolled in the study (Table 1). Participants were at generally high risk due to high TC (58%) or hypertension (14%), as one would expect among participants recruited from a Preventive Cardiology clinic, and spent on average 2.7 hours per day watching television and playing computer games. Over half the participants had a family history of coronary heart disease (51%). All participants had hs-CRP concentrations below 1 mg/Lwhich denotes low cardiovascular risk in adults [22].

Forty three percent of participants were classified as obese, whereas 33% were abdominally obese. The correlation of BMI percentile and WC percentile was 0.88. Compared to participants who were not obese, those who were obese were significantly less likely to be white and more likely to be exposed to tobacco smoke, have higher WC percentiles, triglycerides, VLDL cholesterol, hs-CRP, and ICAM-1 levels and lower LDL and HDL cholesterol. Compared to participants who were not abdominally obese, those who were abdominally obese were significantly younger, more likely to be from another racial group besides Black and White, had significantly higher BMI, triglycerides, hs-CRP, ICAM-1, TNF-αR2 but lower HDL cholesterol ( Table 1).

Extraction of the first four factors using PCA identified a common clustering of risk markers suggestive of (1) an adverse lipid pattern (25% of variance explained) dominated by strong positive correlations (0.93) with triglycerides and VLDL, but a negative correlation with HDL (−0.74), (2) a pro-inflammatory pattern (14% of variance explained) having high correlations (>0.84) with ICAM-1 and TNF-αR2, (3) a high blood pressure pattern (12% of variance explained) having positive (>0.66) correlations with SBP and DBP and (4) an elevated Lp (a) pattern (10% of variance explained), having a high positive correlation with Lp(a) (0.75) ( Table 2).

Both BMI and WC percentiles were significantly associated with higher scores for the lipid pattern (Table 3). That is, a greater degree of overall or abdominal adiposity was associated with higher scores representing a constellation of lipid abnormalities including higher TG and VLDL, but lower HDL. Each metric explained between 20 and 22% of variance in this pattern. Neither metric was significantly associated with any of the other patterns. However when BMI and WC percentiles were included in the same model, both were significantly associated with the inflammatory patternbut in opposite directions. In this analysis, an increase in BMI percentile was associated with a decrease in the inflammatory pattern score, whereas an increase in WC percentile was associated with an increase in the inflammatory factor score.

Discussion

In a pediatric population at high-risk for developing cardiovascular disease, both overall and abdominal adiposity as measured by BMI and WC percentiles were similarly associated with an adverse pattern of lipid markers including high TG, high VLDL cholesterol, and low HDL cholesterol. However when considered together, increasing WC percentile was associated with higher levels of a pro-inflammatory pattern whereas increasing BMI percentile was associated with lower levelstogether explaining greater variance in this pattern than either alone.

Anthropometric assessment of cardiovascular disease risk is now routine clinical practice, mainly because adiposity is a major risk factor for conditions that lead to cardiovascular diseaseincluding abnormal lipids, high blood pressure, and type 2 diabetes [5]. Increasing attention is now being paid to abdominal adiposity, characterized by an increased WC or waist to hip ratio, because it provides information about risk for cardiovascular disease above and beyond that provided by BMI [23]. This is particularly the case when systemic inflammation is of interest, because abdominal adiposity is more closely related to inflammation than is general adiposity [5]. Like other risk markers, inflammatory factors may be modifiable through weight loss via a prudent diet and physical activity [24]. Furthermore, the American College of Cardiology, the American Heart Association and the Canadian Cardiovascular Society recommend measuring inflammation by high-sensitivity C-reactive protein (hs-CRP) to identify patients who could obtain particular benefit from statin therapy [5,25].

As with adults, children are routinely assessed using anthropometric measures. This is done to identify individuals with abnormal growth due to malnutrition, endocrine disorders, or those at risk for premature cardiovascular disease and mortality [3]. Childhood overweight and obesity, defined respectively by the Centers for Disease Control as a BMI ≥ the 85 th and 95 th percentile for age and sex [14], is a growing problem around the world. In developed countries, the prevalence of overweight and obesity ranges from 5% in Turkey to 33% in the United States [26]. Childhood adiposity also tends to track throughout the life-course, increasing the risk that obese children will become obese adults [27]. While assessment of overall adiposity using BMI percentile is a well-accepted approach in cardiovascular risk assessment in children, measurement
% DBP > 95 th percentile (n) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Caption: All continuous variables are presented as mean (standard deviation [sd]). All categorical variables and percentiles are presented as % (n).^variables were log-transformed prior to testing for differences. *p value for comparisons < 0.05. Other racial groups were defined as Asian, Hispanic, Pacific Islander, and Native American.

of abdominal adiposity has not yet become an established practice. One impediment has been the lack of age, sex and ethnic specific percentiles for measures of abdominal adiposity. Only recently have these become available through large representative cross-sectional surveys [21,28]. We compared the associations of BMI and WC percentile with risk marker patterns in a population of children referred to a pediatric cardiology clinic. In addition to more standard CVD risk factors, we also measured four inflammatory markers (TNF-αR2, P-selectin, ICAM-1, and hs-CRP) that have been associated either with excess adiposity and atherosclerosis in animal and autopsy studies, pre-clinical vascular testing and as predictors of adult cardiovascular disease events [18]. We used PCA to identify major patterns of risk markers, greatly reducing the number of individual statistical tests required, and simplifying the interpretation of results.

The first four patterns of risk markers roughly corresponded to an abnormal lipid pattern, an abnormal inflammatory marker pattern, an abnormal blood pressure pattern and an abnormal Lp(a) pattern. The lipid pattern accounted for the most variance in risk markers, and was consistent with a pattern reflecting obesity related dyslipidemia. The inflammatory pattern was characterized by increased levels of all markers except hs-CRP. We are unclear why abnormal hs-CRP levels were not associated with this pattern, however it could be due to the low level of variation in hs-CRP in this relatively young population. We found that BMI and WC percentiles were similarly associated with the lipid pattern, which is consistent with others findings in adults suggesting that both abdominal and general adiposity are associated with abnormal lipids. Neither BMI nor WC percentile was significantly associated with inflammatory, BP, or Lp(a) patterns. A different scenario emerged when both measures were used simultaneously. When BMI and WC percentiles were included in the same model, neither was significantly associated with the lipid patternindicating that using WC percentile in addition to BMI percentile adds no significant advantage in predicting lipid abnormalities than using either alone. However WC and BMI percentile were significantly associated with the inflammatory pattern, but in opposite directions. In this model, BMI percentile adjusted for WC percentile can be interpreted as anti-or non-inflammatory lean mass; whereas WC percentile adjusted for BMI percentile is interpreted as pro-inflammatory abdominal adipose tissue. The separation and independent consideration of these measures greatly improves the prediction of abnormal levels of inflammatory factors.

Our study supports the simultaneous use of both BMI and WC percentiles in cardiovascular risk assessment in  children. Measurement of abdominal adiposity is increasingly recommended for adults, and we anticipate this to be the case for children in the future. WC is easy to obtain and as a percentile was at least as good as BMI in predicting lipid abnormalities in our study. Used in conjunction with BMI percentile, WC percentile provides clearer information on underlying cardiovascular risk due to inflammation. However, the clinical utility of using WC percentile in addition to BMI percentile needs to be further assessed in prospective studies using hard endpoints (e.g. incident type 2 diabetes, cardiovascular disease) that occur after the initial risk assessment.

Our study has several strengths. First, it reports levels of inflammatory markers in a group of children at highrisk for future early CVD. Inflammatory markers are not routinely measured in children, and there is little data in the literature on what is 'normal' [29]. However, it is relatively clear that inflammation is a key component of cardiovascular risk in adults, and is likely to be in children as well. Second, we used PCA to identify major risk marker patterns rather than performing individual analyses on each one. This substantially reduced the number of statistical tests we had to perform, and simplified the interpretation of the results. Third, our study is one of the first to contrast the predictive power of BMI vs WC percentiles on risk markers in children.

Our study has some limitations. First, the crosssectional design of our study means that temporal relationships cannot be definitively established. However there is strong evidence that adipose tissue is a causal factor in the appearance of cardiovascular risk factors. Second, we did not measure dietary intake or physical activity, which means that associations between adiposity and risk marker patterns may be overestimated. Third, waist circumference percentiles were derived from aggregate percentile data that was roundedwhich could lead to error in estimates. Fourth, we studied a small number of mostly non-hispanic whites, which limited both our statistical power and generalizability.

Conclusions

In a sample of children at high-risk for developing cardiovascular disease, BMI percentile and WC percentile explained similar variance in a pattern of abnormal lipids, however WC percentile used in conjunction with BMI percentile explained the most variance in a pattern of elevated inflammatory factors. This suggests that both WC percentile and BMI percentile should be used for risk assessments of high-risk children.

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Table 1
1Participant characteristicsOverall
Non-obese
(BMI < 95%)

Obese
(BMI ≥ 95%)

Non-abdominally obese
(WC < 90%)

Abdominally obese
(WC ≥ 90%)

% (n)
100 (107)
57 (61)
43 (46)
67 (72)
33 (35)

Age, y (sd)
13.3 (2.7)
13.5 (2.9)
13.0 (2.5)
13.7 (3.0)
12.4 (2.2)*

Race/ethnicity

% White (n)
82 (88)
90 (55)
72 (33)*
85 (61)
77 (27)

% Black (n)
5.6 (6)
3.3 (2)
8.7 (4)
5.6 (4)
5.7 (2)

% Other racial group (n)
12.1 (13)
6.6 (4)
19.6 (9)*
9.7 (7)
17.1 (6)*

% Exposed to tobacco smoke (n)
19 (20)
8 (5)
33 (15)*
13 (9)
31 (11)*

% Family history of CHD (n)
51 (55)
51 (31)
52 (24)
53 (38)
49 (17)

Screen time, hours^(sd)
2.7 (2.3)
2.4 (1.7)
3.1 (2.9)
2.8 (2.5)
2.5 (1.9)

BMI percentile (n)
80 (23)
68 (24)
97 (1)*
72 (24)
97 (2)*

BMI, kg/m 2 (sd)
25 (6)
21 (3)
30 (5)*
22 (4)
30 (6)*

WC percentile (n)
87 (16)
54 (23)
91 (6)*
58 (23)
95 (0)*

WC, cm (sd)
71 (26)
77 (11)
99 (14)*
80 (13)
100 (15)*

Total cholesterol, mg/dL^(sd)
214 (50)
220 (52)
205 (47)
218 (52)
205 (47)

% High, ≥200 mg/dL (n)
58 (62)
59 (36)
57 (26)
58 (42)
57 (20)

LDL cholesterol, mg/dL^(sd)
137 (50)
145 (50)
127 (49)*
143 (50.0)
126 (48)

% High, ≥ 130 mg/dL (n)
46.7 (50)
47.5 (29)
45.7 (29)
50.0 (36)
40.0 (14)

HDL cholesterol, mg/dL^(sd)
53 (14)
57 (12)
48 (15)*
54 (13)
50 (17)

% Low, < 50 mg/dL (n)
19 (18)
7 (4)
33 (15)*
13 (9)
29 (10)*

Triglycerides, mg/dL^(sd)
118 (75)
93 (57)
152 (83)*
105 (69)
147 (80)*

% High, ≥ 130 mg/dL (n)
34 (36)
16 (10)
57 (26)*
26 (19)
49 (17)*

VLDL cholesterol, mg/dL^(sd)
24 (15)
19 (11)
30 (17)
21 (14)
29 (17)

Lp(a)^, mmol/L (sd)
39 (43)
39 (43)
39 (43)
40 (45)
37 (39)

hs-CRP, mg/L^(sd)
0.1 (0.2)
0.1 (0.1)
0.2 (0.2)*
0.1 (0.2)
0.2 (0.2)*

ICAM-1, ng/L^(sd)
295 (86)
282 (95)
312 (71)*
278 (90)
331 (65)*

P-selectin, ng/L^(sd)
136 (46)
129 (46)
145 (46)
131 (46)
146 (46)

TNF alpha, pg/L^(sd)
2140 (590)
2041 (501)
2271 (675)
2001 (499)
2425 (665)*

SBP, mmHg (sd)
112 (11)
111 (10)
114 (13)
112 (11)
113 (13)

% SBP > 95 th percentile (n)
14 (15)
12 (7)
17 (8)
13 (9)
17 (6)

DBP, mmHg (sd)
63 (7)
62 (8)
64 (6)
63 (8)
63 (6)

Table 2
2Varimax-rotated factor patternsFactor 1:
"Adverse
lipids
pattern"

Factor 2:
"Pro-
inflammatory
pattern"

Factor 3:
"High BP
pattern"

Factor 4:
"High Lp(a)
pattern"

HDL
−0.74
0.02
−0.07
−0.06

LDL
−0.11
0.04
−0.19
0.43

Triglycerides
0.93
0.02
0.11
−0.18

VLDL
0.93
0.00
0.08
−0.17

Lp(a)
0.10
0.01
0.01
0.75

hs-CRP
0.37
0.33
0.47
0.11

ICAM
0.01
0.86
−0.19
−0.04

P-selectin
0.23
0.18
−0.18
−0.51

TNF
−0.02
0.84
0.21
−0.04

Systolic BP
0.06
−0.01
0.66
−0.50

Diastolic BP
0.08
−0.04
0.78
0.03

% variance
explained

25%
14%
12%
10%

Caption: Correlations greater than 0.6 are indicated in bold.

Table 3
3Association of BMI and WC percentiles with quintiles of factor scoresCaption: Beta coefficients are evaluated as change in quintile rank corresponding to a 5% increase in BMI or WC percentiles. Standard errors are presented in parentheses. All models are adjusted for exposure to cigarette smoke, family history of cardiovascular disease, hours of screen-time, white race, black race and other race. pR 2partial R 2 , representing the independent explanatory power of each predictor.Lipid pattern
Inflammatory pattern
BP pattern
Lp(a) pattern

Beta
P for
trend

pR 2
Beta
P for
trend

pR 2 Beta
P for
trend

pR 2
Beta
P for trend pR 2

BMI %
0.12 (0.03) <0.01 20% 0.01 (0.03) 0.72
5%
0.07 (0.03) 0.14
8%
0.00 (0.03) 0.98
8%

WC %
0.11 (0.03) < 0.01 22% 0.05 (0.03) 0.09
8%
0.06 (0.03) 0.06
7%
0.01 (0.03) 0.84
8%

BMI % adjusted
for WC %

0.89 (0.06) 0.14
2%
−0.14 (0.07) 0.03
5%
0.09 (0.07) 0.21
2%
−0.02 (0.07) 0.76
0%

WC % adjusted
for BMI %

0.04 (0.05) 0.45
1%
0.16 (0.06) 0.03
8%
−0.01 (0.06) 0.84
0%
0.02 (0.06) 0.71
0%

Total explanatory power
using both metrics

3%
12%
2%
0%

Explanatory power gained
by using both metrics

None
4-7%
None
None

AcknowledgementWe thank Jill Cotter BA, DO for her assistance with this study.Competing interestThe authors declare that they have no competing interests.Authors' contributionsLdK performed the analysis and wrote the manuscript. ED implemented and managed the study. MK performed laboratory measurement of risk markers, and SdF obtained funding for the original study and edited the manuscript. All authors read and approved the final manuscript.
Waist circumference; WC%: Waist circumference percentile; BMI: Body mass index; BMI%: Body mass index percentile; CVD: Cardiovascular disease. W C Abbreviations, Abbreviations WC: Waist circumference; WC%: Waist circumference percentile; BMI: Body mass index; BMI%: Body mass index percentile; CVD: Cardiovascular disease;

Systolic blood pressure; DBP: Diastolic blood pressure; LDL: Low density lipoprotein cholesterol; HDL: High density lipoprotein cholesterol; VLDL: Very low density lipoprotein cholesterol. TC: Total cholesterol. SBP. TG: TriglyceridesSBP: Systolic blood pressure; DBP: Diastolic blood pressure; LDL: Low density lipoprotein cholesterol; HDL: High density lipoprotein cholesterol; VLDL: Very low density lipoprotein cholesterol; TC: Total cholesterol; TG: Triglycerides;

Lipoprotein a; hs-CRP: High sensitivity C-reactive protein; ICAM-1: Intracellular adhesion molecule one; TNFα-R2: Tumor necrosis factor alpha receptor two. Lp, Lp(a): Lipoprotein a; hs-CRP: High sensitivity C-reactive protein; ICAM-1: Intracellular adhesion molecule one; TNFα-R2: Tumor necrosis factor alpha receptor two;

PCA: Principal component analysis; CABG: Coronary artery bypass grafting. PCA: Principal component analysis; CABG: Coronary artery bypass grafting;

PAD: Peripheral artery disease; NCEP: National cholesterol education program. PAD: Peripheral artery disease; NCEP: National cholesterol education program;

AAP: American association of pediatrics; CDC: Centers for disease control. AAP: American association of pediatrics; CDC: Centers for disease control;

NHLBI: National heart lung and blood institute; ELISA: Enzyme linked immunosorbent assay; CV: Coefficient of variance. SD: Standard deviationNHLBI: National heart lung and blood institute; ELISA: Enzyme linked immunosorbent assay; CV: Coefficient of variance; SD: Standard deviation.

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