FASTING LEVELS OF GROWTH HORMONE AS A PREDICTIVE MARKER OF CARDIOVASCULAR RISK

Abstract

Subject matter of the present invention is a method for predicting the cardiovascular risk or the total mortality risk in a subject comprising: determining the fasting level of growth hormone (hGH), and/or its isoforms in a bodily fluid obtained from said subject; and correlating said fasting level of growth hormone (hGH), and/or its isoforms with a cardiovascular risk or the total mortality risk, wherein an enhanced level is predictive for an enhanced risk.

Claims

1-21. (canceled)

22. A sample comprising: a complex of at least one binder, and bodily fluid obtained from said subject at fasting level wherein said binder is at least one monospecific binder, having a binding affinity of 10.sup.8M or less to hGH and its isoforms and wherein said binder(s) is either specific for one of the secreted hGH isomers, or for more than one or is specific for and binds to all secreted isomers of hGH selected from the group comprising isomer 1, isomer 2, isomer 3 and isomer 4 (SEQ ID. NO: 1 to 4) wherein said sample was prepared by binding the fasting level of human growth hormone (hGH), and/or its isoforms in said bodily fluid to said binder using an ultrasensitive hGH assay, wherein the ultrasensitive hGH assay has an analytical assay sensitivity of less than 100 pg/ml; and wherein the fasting level of hGH and/or its isoforms in said bodily fluid is above 340 pg/ml or below 60 pg/ml, and wherein said subject is male.

23. The sample of claim 22 wherein said monospecific binder is a monoclonal antibody.

24. (canceled)

25. The sample of claim 22 wherein the binder in said assay binds all secreted isomers of hGH selected from the group consisting of isomer 1, isomer 2, isomer 3 and isomer 4 (SEQ ID NO: 1 to 4) in said bodily fluid.

26. The sample of claim 22 wherein said sample includes different bodily fluids from the same male subject.

27. The sample of claim 22 wherein the bodily fluid is blood or plasma or serum.

28. The sample of claim 22, wherein the subject is over the age of 57, has diabetes mellitus, is current smoking or a combination thereof.

29. The sample of claim 22, wherein said sample includes an additional biomarker selected from the group consisting of: pro-Neurotensin (PNT) 1-117 and fragments thereof having at least a length of five amino acids, C-reactive protein (CRP), pro-brain natriuretic peptide 1-108 (pro-BNP) 1-108, Pro-BNP, BNP, Pro Atrial Natriuretic Peptide 1-98 (proANP-N-terminal fragment), Pro-ANP, Adrenomedullin, pro-Adrenomedullin (proADM) 24-71, ProADM 127-164, pro-Atrial Natriuretic Peptide (pro-ANP) and fragments thereof having at least a length of five amino acids, ST-2, GDF15, Galectin-3 and Copeptin.

30. The sample of claim 22, wherein said subject has a disease or has had an event selected from the group consisting of atherosclerosis, high blood pressure, heart failure, and myocardial infarction.

31. The sample of claim 22 wherein the ultrasensitive hGH assay comprises at least two monospecific binders.

32. A sample comprising: a complex of at least one binder, and bodily fluid obtained from said subject at fasting level; wherein said binder is a monospecific binder having a binding affinity of 10.sup.8M or less to hGH and its isoforms and wherein said at least one monospecific binder is specific for one of the secreted hGH isomers, for more than one of the secreted hGH isomers, or is specific for and binds to all secreted isomers of hGH selected from the group consisting of isomer 1, isomer 2, isomer 3 and isomer 4 (SEQ ID NO: 1 to 4), wherein said sample was prepared by binding the fasting level of human growth hormone (hGH), and/or its isoforms in said bodily fluid to said binder using an ultrasensitive hGH assay, wherein the ultrasensitive hGH assay has an analytical assay sensitivity of less than 100 pg/ml; and wherein the fasting level of hGH and/or its isoforms is from 340 pg/ml to 60 pg/ml, and wherein said subject is male.

33. A sample comprising: a complex of at least one binder, and bodily fluid obtained from said subject at fasting level; wherein said binder is a monospecific binder having a binding affinity of 10.sup.8M or less to hGH and its isoforms and wherein said at least one monospecific binder is specific for one of the secreted hGH isomers, for more than one of the secreted hGH isomers, or is specific for and binds to all secreted isomers of hGH selected from the group consisting of isomer 1, isomer 2, isomer 3 and isomer 4 (SEQ ID NO: 1 to 4), and wherein the fasting level of hGH and/or its isoforms is above 340 pg/ml or below 60 pg/ml, and wherein said subject is male.

Description

EXAMPLES

Example 1, us-hGH Assay

[0088] Chemicals

[0089] If not stated otherwise, chemicals were obtained at p.a. grade from Merck (Darmstadt, Germany).

[0090] Antigen

[0091] For immunization and for calibration we used recombinant Human Growth Hormone (NIBSC code 98/574, National Institute for Biological Standards and Control, Herfordshire, UK)

[0092] Development of Antibodies

[0093] Mouse monoclonal antibodies against hGH were developed by UNICUS (Karlsburg, Germany).

[0094] The Antibodies were Generated According to the Following Method:

[0095] A BALB/c mouse were immunized with 100 g hGH at day 0 and 14 (emulsified in 100 al complete Freund's adjuvant) and 50 g at day 21 and 28 (in 100 al incomplete Freund's adjuvant). Three days before the fusion experiment was performed, the animal received 50 pg of the conjugate dissolved in 100 l saline, given as one intraperitonal and one intra venous injection.

[0096] Splenocytes from the immunized mouse and cells of the myeloma cell line SP2/0 were fused with 1 ml 50% polyethylene glycol for 30 s at 37 C. After washing, the cells were seeded in 96-well cell culture plates. Hybrid clones were selected by growing in HAT medium [RPMI 1640 culture medium supplemented with 20% fetal calf serum and HAT-supplement]. After two weeks the HAT medium is replaced with HT Medium for three passages followed by returning to the normal cell culture medium.

[0097] The cell culture supernatants were primary screened for antigen specific IgG antibodies three weeks after fusion. The positive tested microcultures were transferred into 24-well plates for propagation. After retesting the selected cultures were cloned and recloned using the limiting-dilution technique and the isotypes were determined. {60}, {61}.

[0098] Monoclonal Antibody Production

[0099] We selected 5 antibodies for further investigations.

[0100] Antibodies were produced via standard antibody production methods (Marx et al., Monoclonal Antibody Production (1997), ATLA 25, 121) and purified via Protein A-chromatography. The antibody purities were >95% based on SDS gel electrophoresis analysis.

[0101] Labelling and Coating of Antibodies.

[0102] All antibodies were labelled with acridinium ester according the following procedure: Labelled compound (tracer): 100 g (100 l) antibody (1 mg/ml in PBS, pH 7.4), was mixed with 10 l Acridinium NHS-ester (1 mg/ml in acetonitrile, InVent GmbH, Germany) (EP 0353971) and incubated for 20 min at room temperature. Labelled antibody was purified by gel-filtration HPLC on Bio-Sil SEC 400-5 (Bio-Rad Laboratories, Inc., USA) The purified labelled antibody was diluted in (300 mmol/l potassiumphosphate, 100 mmol/l NaCl, 10 mmol/l Na-EDTA, 5 g/l bovine serum albumin, pH 7.0). The final concentration was approx. 800.000 relative light units (RLU) of labelled compound (approx. 20 ng labeled antibody) per 200 l. Acridiniumester chemiluminescence was measured by using an AutoLumat LB 953 (Berthold Technologies GmbH & Co. KG).

[0103] Solid Phase Antibody (Coated Antibody):

[0104] Solid phase: Polystyrene tubes (Greiner Bio-One International AG, Austria) were coated (18 h at room temperature) with antibody (1.5 g antibody/0.3 ml 100 mmol/l NaCl, 50 mmol/l Tris/HCl, pH 7.8). After blocking with 5% bovine serum albumin, the tubes were washed with PBS, pH 7.4 and vacuum dried.

[0105] hGH Immunoassay:

[0106] 50 l of sample (or calibrator) was pipetted into coated tubes, after adding labeled antibody (200 ul), the tubes were incubated for 2 h at 18-25 C. Unbound tracer was removed by washing 5 times (each 1 ml) with washing solution (20 mmol/l PBS, pH 7.4, 0.1% Triton X-100). Tube-bound labelled antibody was measured by using the LB 953. Using a fixed concentration 1 ng/ml of hGH. The signal (RLU at 1 ng hGH/ml) to noise (RLU without us-hGH) ratio of different antibody combinations is given in Table 1. All antibodies were able to generate a sandwich complex with any other antibody. The antibody pair with strongest signal to noise ratio (best sensitivity) was subsequently used to perform the us-hGH-immunoassay: hGH G12 antibody was used as coated tube antibody and hGH H4 antibody was used as labelled antibody.

TABLE-US-00002 TABLE 1 Results of noise to ratio determinations between different pairs of hGH antibodies. hGH antibody Labelled Solid phase antibody antibody H2 H8 G12 H4 D7 H2 9.665 11.005 9.259 10.102 H8 8.512 7.833 8.446 6.384 G12 10.112 9.846 10.905 7.751 H4 11.213 8.675 12.225 6.843 D7 2.488 2.761 3.954 2.713

[0107] Calibration:

[0108] The assay was calibrated, using dilutions of recombinant hGH (WHO International Standard, NIBSC code 98/574), diluted in 20 mM K2PO4, 6 mM EDTA, 0.5% BSA, 50 uM Amastatin, 100 uM Leupeptin, pH 8.0. (FIG. 1)

[0109] Assay Specifications

[0110] The analytical assay sensitivity (mean relative light units of 20 determinations of hGH free sample plus 2 S.D.) was 2 pg/ml of hGH and the functional assay sensitivity (see above) was 8.5 pg/ml. Recovery and dilution was >85% intra measurement range of 5-10.000 pg/ml hGH. The coefficient of correlation of N=997 samples between the us-hGH assay and a hGH assay specific for recombinant hGH (22 KD) was r=0.98 and a r of 0.95 was found for an assay recognizing preferentially hGH isoforms naturally produced by the pituitary {20}. These dataindicating the suitability of all hGH isoform measurements within the present invention.

Example 2 Population Study

[0111] Subjects and Methods

[0112] Study Population

[0113] The Malmi diet and cancer study (MDC) is a population-based, prospective epidemiologic cohort of 28 449 individuals examined between 1991 and 1996 {18}. From this cohort a random sample, examined between November 1991 and February 1994 (n=6103) were included in the MDC cardiovascular cohort (MDC-CC), with the primary aim to study the epidemiology of carotid artery disease {19}. When excluding individuals lacking values from fasting plasma samples and thus missing data on prevalence of diabetes mellitus or fasting values of HDL-C, LDL-C or hGH, 4453 persons remained and comprise the primary study cohort.

[0114] All participants provided written consent and the study was approved by the ethical committee at Lund University, Lund, Sweden.

[0115] Clinical Examination and Assays

[0116] Participants underwent a medical history, physical examination and laboratory assessment. Blood pressure was measured with a mercury-column sphygmomanometer after 10 minutes rest in the supine position. Waist circumference was measured at the level of the umbilicus. Current cigarette smoking was defined as any use within the last year, and was surveyed through a self-administered questionnaire. The same questionnaire in combination with a diary was used to record prevalence of anti-hypertensive medication at baseline. Diabetes Mellitus was defined as either self-report of a physician diagnosis, use of diabetes medication or fasting whole blood glucose higher than 6.0 mmol/L (109 mg/dL).

[0117] All samples of plasma and whole-blood were obtained after overnight fasting and samples were drawn between 7:30 am and 9:00 am after 15 minutes of rest in the supine position. Levels of HDL-C and total cholesterol were measured according to standard procedures at the Department of Clinical Chemistry, University Hospital of Malmi. The levels of LDL-C were calculated according to the Friedewald formula.

[0118] hGH levels were measured in stored fasting plasma samples, which had been frozen to 80 C. immediately at the MDC-CC baseline examination. The measurement was made with an ultra-sensitivity chemiluminescence immunoassay (see example 1 us-hGH assay). The detection limit was 2 pg/ml, functional assay sensitivity was 8.5 pg/ml. Measurement of hGH is expressed in ng/ml (1 ng/ml=2.6 mU/L).

TABLE-US-00003 TABLE 2 Clinical characteristics of the study population. Male Female Number (% of whole cohort) 1873 (42.1) 2580 (57.9) Age, mean (SD), y 57.9 (6.0) 57.6 (5.9) Systolic blood pressure, mean 144 (19) 141 (19) (SD), mmHg Body Mass Index, Mean 26.2 (3.5) 25.5 (4.2) (SD), kg/m2 Antihypertensive therapy, 345 (18.4) 402 (15.6) No. (%) Diabetes Mellitus, No. (%) 216 (11.5) 166 (6.4) LDL-C, mean (SD), mmol/L 4.11 (0.90) 4.20 (1.04) HDL-C, mean (SD), mmol/L 1.22 (0.30) 1.51 (0.37) Current smokers, No. (%) 512 (27.3) 661 (25.6) Growth Hormone, median 0.11 (0.06-0.34) 1.22 (0.39-3.15)* (IQR), ng/mL *Significant difference vs males (p < .001). P for difference in other variables not calcuated. Abbreviations: BMI, Body Mass Index; LDL-C, Low-density lipoprotein cholesterol; HDL, High-density lipoprotein cholesterol

[0119] Distribution of hGH in Males and Females

TABLE-US-00004 TABLE 3 frequence distribution of hGH in males and females Range of hGH-values in quartiles for all-cause and CVD mortality. Q1 Q2 Q3 Q4 Males Minimum hGH 0.02 0.06 0.12 0.34 Maximum hGH 0.05 0.11 0.33 23.94 Minimum Ln hGH 3.91 2.81 2.12 1.10 Maximum Ln hGH 3.00 2.21 1.11 3.18 Females Minimum hGH 0.01 0.40 1.22 3.15 Maximum hGH 0.39 1.21 3.14 40.60 Minimum Ln hGH 4.61 0.92 0.20 1.15 Maximum Ln hGH 0.94 0.19 1.14 3.70

[0120] The lowest observed hGH concentrations were 0.02 ng/ml in males and 0.01 ng/ml in females, indicating the suitability of the used us-hGH test (analytical sensitivity: 0.002 ng/ml (2 pg/ml), functional assay sensitivity 0.0085 ng/ml (8.5 pg/ml)) for detecting fasting hGH in all subjects. Cut off values for CVD-mortality in males may be above 0.34 ng/ml for high risk subjects and below 0.06 ng/ml for low risk.

[0121] Clinical End Points

[0122] We examined 4 primary outcomes: Coronary artery disease (CAD), stroke, cardiovascular mortality and total mortality. The endpoints were retrieved through record linkage of the personal identification number of each Swedish individual and the Swedish Hospital Discharge Register (SHDR), the Swedish Cause of Death Register (SCDR), the Stroke in Malmi Register and the Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART). The use of these registries for classification of outcomes has been previously validated and the sensitivity for detecting events such as myocardial infarction has been shown to exceed 90% {21}, {22}, {23}. CAD was defined as fatal or nonfatal myocardial infarction, death due to ischemic heart disease, percutaneous coronary intervention (PCI) or coronary artery by-pass grafting (CABG), whichever came first, on the basis of International Classification of Diseases, 9.sup.th and 10.sup.th revisions (ICD-9 and ICD-10) codes 410 and 121 respectively in the SHDR or SCDR, codes 412 and 414 (ICD-9) or 122, 123 and 125 (ICD-10) of the SCDR. Stroke was defined as fatal or nonfatal stroke on the basis of codes 430, 431, 434 and 436 (ICD-9) or 160, 161, 163 and 164 (ICD-10). Cardiovascular mortality was defined on the basis of (ICD-9) codes 390-459 or (ICD-10) codes 100-199 in the SCDR. Follow up for outcomes extended to Jun. 30, 2009.

[0123] Statistical Analyses

[0124] Fasting values of hGH exhibited a right-skewed distribution and were transformed into the natural logarithm before any analysis. To determine if hGH displayed any correlation with traditional cardiovascular risk factors {24}, cross sectional analyses were performed at baseline using linear regression models with hGH as the dependent variable and age, gender, current smoking, anti-hypertensive medication, diabetes mellitus and the standardized values for: systolic blood pressure, BMI, HDL-C and LDL-C as independent variables entered either separately (crude) or simultaneously (multivariate adjusted).

[0125] Multivariable Cox proportional hazard models were performed to examine the association between hGH and incidence of cardiovascular events and mortality. In the analyses of CAD and stroke, we excluded individuals who had a history of CAD (n=94) or stroke (n=35) before baseline in respective analysis. All models were adjusted for age, sex, systolic blood pressure, use of antihypertensive medication, current smoking, diabetes mellitus, BMI and levels of LDL-C and HDL-C. We confirmed that the proportionality of hazards assumption was met using Schoenfeld residuals (i.e. a test of non-zero slope in a generalized linear regression of the scaled Schoenfeld residuals on time). Hazard ratios (HR) for hGH were expressed per 1-SD increment of natural logarithm of hGH. In order to establish if there were any significant gender differences a sex interaction test was performed.

[0126] To obtain Kaplan-Meier curves, the cohort was split into gender-specific quartiles.

[0127] Table 3 shows the cut off concentrations of the different quartiles in males and females. Cut off concentrations may be adapted if hGH assays are used which recognize different sets of one or more hGH isoforms.

[0128] To evaluate possible different results by using different assays, recognizing different sets of hGH isoforms, we compared the assay (antibodies generated against recombinant hGH (isoform1) described in example 1 with an assay preferentially recognizing isoforms 2-4 (Pit-hGH assay, {29}. hGH-results from 997 healthy subjects were correlated, the coefficient of correlation was 0.95 (r=0.95), indicating almost identical results using assays binding to different hGH isoforms.

[0129] To evaluate if hGH could be used to reclassify risk we followed methods previously described to calculate a net reclassification improvement (NRI) {25}. We used multivariable risk scores with the parameters mentioned above to estimate the 10-year risk of developing a cardiovascular event and classify the participants in the groups: less than 5%, 5% to less than 10%, 10% to less than 20%, 20% or greater. With the addition of values of hGH, participants could then be reclassified into different groups. We assessed the reclassification of subjects and calculated a NRI as a measurement of the successfulness of the model. Model discrimination was assessed by calculating the C statistics (Harrell's), which represents the area under the receiver operating characteristics (ROC) curve, for the different models {25}.

[0130] All analyses except the NRI and C-statistics were performed using SPSS statistical software (version 20.0.0, SPSS inc, Chicago, Ill.). NRI and C-statistics analyses were made with Stata software version 11 (StataCorp, College Station, Tex.). A 2-sided P value of less than 0.05 was considered statistically significant.

[0131] Results

[0132] Cross-Sectional Analysis

[0133] Baseline characteristics of the cohort are shown in Table 2. Women, as expected, had significantly higher fasting values of hGH than men (Table 3). In the crude regression models, all variables were significant determinants of hGH-levels in both genders with the exception of age in women (Table 4). In the adjusted models, age, current smoking and HDL-C had a significant positive correlation with hGH, while LDL-C and BMI exhibited significant negative correlation in both genders (Table 4). Systolic blood pressure had a significant negative correlation in females, but not in males. In women diabetes mellitus had a strong negative correlation with hGH in the simple analysis, but was borderline non-significant in the adjusted one. In contrast, the correlation between hGH and diabetes mellitus in males was positive and significant in both models. The multiple regression models had a R.sup.2 value of 9.5% for males and 11.8% for females. When analyzing the whole cohort and adjusting for gender the R.sup.2 value was 39%. Addition of the hGH-associated variables waist, insulin (natural logarithm) and body fat percentage rendered a R.sup.2 value of 10.4% for males and 12.6% for females. Crude regression models including these extra variables all exhibited significant negative correlation with fasting levels of hGH.

[0134] Growth Hormone and the Risk of CAD

[0135] Out of 4358 (1799 males, 2559 females) individuals without a history of previous CAD, 401 (249 males, 152 females) experienced a CAD-event during a median follow-up time of 16.1 years (IQR, 15.4-16.7). Each SD increase of baseline hGH was associated with a multivariate-adjusted HR of 1.14 (P=0.008) in the total cohort (Table5). The HR was similar in separate analyses of men (HR, 1.17; p=0.01) but not significantly elevated in women. There was no significant sex interaction in the outcome of CAD (p=0.37). The top versus the bottom quartile of hGH was associated with a HR of 1.33 (95% CI, 0.99-1.78; p=0.05) in the total analysis, 1.46 (95% CI, 1.01-2.09; p=0.04) in males and non significant in females (p=0.91), (Table 6).

[0136] Growth Hormone and Risk of Stroke (See Table 5 and 6)

[0137] 4417 individuals (1849 males, 2568 females) in the cohort had no previous history of stroke. During a median follow-up time of 16.2 years (IQR, 15.5-16.7), 265 persons (152 males, 123 females) suffered a stroke. Each SD increase of fasting hGH at baseline was associated with a multivariate-adjusted HR of 1.19 in the whole cohort, whereas the gender-specific analyses generated a HR of 1.18 in males and was non-significant in females. There was no significant sex interaction in the outcome (p=0.81). The top versus the bottom quartile of hGH was associated with a HR of 1.47 (95% CI, 1.02-2.11; p=0.04) in the total analysis, 1.48 (95% CI, 0.91-2.42; p=0.11) in males and 1.46 (95% CI, 0.83-2.58; p=0.19) in females.

[0138] Growth Hormone and Total Mortality (See Table 5 and 6)

[0139] Out of 4452 individuals (1872 males, 2580 females) in the cohort, 698 (383 males, 315 females) were deceased after a median follow-up time of 16.2 years (IQR, 15.6-16.7). Each SD increase of fasting hGH at baseline was related to a multivariate-adjusted HR of 1.21 in the whole cohort, corresponding HR for males was 1.26 and non-significant for females. An analysis of sex interaction showed significant difference (p=0.01) between the genders in the outcome. The top versus the bottom quartile of hGH was associated with a HR of 1.46 (95% CI, 1.17-1.84; p<0.001) in the total analysis, 2.05 (95% CI, 1.47-2.86; p<0.001) in males and non significant in females (p=0.96).

[0140] Growth Hormone and Cardiovascular Mortality (Table 5 and 6)

[0141] In the cohort 4452 individuals were available for analysis at baseline and during the follow-up time which was identical to the one in total mortality, 215 (126 males, 89 females) of these were deceased with a cardiovascular event as the primary cause of death. Each SD increase of fasting hGH at baseline was associated with a multivariate-adjusted HR of 1.51 in the total cohort. The gender specific HRs were 1.41 for males and 1.38 for females. There was no significant sex interaction in the outcome (p=0.81). The top versus the bottom quartile of hGH was associated with a HR of 2.68 (95% CI, 1.70-4.23; p<0.001) in the total analysis, 3.41 (95% CI, 1.76-6.61; p<0.001) in males and 1.94 (95% CI, 0.98-3.81; p=0.06) in females. FIG. 2 illustrates the time-dependent development of CVD mortality events in male.

[0142] Growth Hormone is a Short Term Predictor of CVD-Mortality.

[0143] The time dependent development of CVD mortality is illustrated in FIG. 2.

[0144] Surprisingly, the predictive discrimination between low risk and high risk individuals was stronger if the observation period was shortened (Table 7).

[0145] hGH indicates is extraordinary strong in short term risk prediction of CVD mortality in males. The relative risk of events (quartile 4 vs. quartile 1 at baseline) is >15 for a 2.5 year risk prediction, 9.1 for a 5 year risk prediction, 7.9 for a 10 year risk prediction and 4.2 for a 15 year risk prediction of CVD mortality in males. These data indicate the strong short term prediction power of hGH and the suitability of serial measurements of hGH.

TABLE-US-00005 TABLE 7 hGH quartiles for prediction of CVD-mortality in males, variation of observation period. Table 7 illustrates the short term risk of CVD mortality in males. The shorter the observation period, the stronger the relative risk difference between lowest and highest hGH quartiles: Baseline hGH Baseline hGH <0.06 ng/ml >0.34 ng/ml Relative risk (lowest (highest quartile 4/ quartile) quartile) quartile 1 2.5 year follow 0% CVD 1.2% CVD >15 up mortality mortality 5 year follow 0.35% CVD 3.2% CVD 9.1 up mortality mortality 10 year follow 0.9% CVD 7.1% CVD 7.9 up mortality mortality 15 year follow 2.5% CVD 10.6% CVD 4.2 up mortality mortality

[0146] Discrimination and Risk Reclassification

[0147] NRI-analyses were non-significant for CAD and stroke although a trend of improvement could be seen in the total CAD and stroke analysis. In the outcome of total mortality, NRI was significant for males (4.5%; p=0.005), but non-significant in females. Improvement mostly stemmed from down-classification of nonevents (8.6%). In cardiovascular mortality NRI was 11.1% (p<0.001) in all subjects and 10.2% (p=0.05) in the female cohort, the improvement was especially seen in up-classification of incident events (14.4% of events upgraded in total sample and 16.9% in women).

[0148] The addition of hGH to the basic model improved all C-statistic for CAD, stroke, CVD mortality and total mortality. An improvement from 70.5% to 71.4% was seen in the males for total mortality. The largest improvement was seen in CVD-mortality with the c-statistics increasing from 78.7% to 79.8% in the whole cohort, 75.7% to 77.1% in males and 80.4% to 81.2% in females.

TABLE-US-00006 TABLE 4 Results from multiple linear regression models examining correlations between fasting values of growth hormone and traditional cardiovascular risk markers. Male Female B B Risk marker Coefficient 95% CI P Coefficient 95% CI P Age 0.02 0.01 to 0.03 <.001 0.02 0.01 to 0.02 <.001 Systolic blood pressure 0.03 0.02 to 0.08 .19 0.04 0.08 to 0.00 .05* Antihypertensive medication 0.12 0.00 to 0.24 .05 0.07 0.03 to 0.18 .18 BMI 0.07 0.12 to 0.02 .004 0.25 0.29 to 0.21 <.001 Current smoking 0.30 0.20 to 0.40 <.001 0.19 0.10 to 0.27 <.001 LDL-C 0.08 0.12 to 0.04 <.001 0.10 0.14 to 0.06 <.001 HDL-C 0.20 0.15 to 0.25 <.001 0.08 0.05 to 0.12 <.001 Diabetes Mellitus 0.22 0.08 to 0.36 .002 0.15 0.30 to 0.01 .06

[0149] The B coefficients are expressed as the increment of standardized values of the natural logarithm of hGH per 1 increment of standardized values (or prescence of dichotomized risk marker) of the risk marker in question. NB age is not standardized. BMI (weight in kilograms divided by height in meters squared), systolic blood pressure, and fasting values of HDL and LDL are standardized. Prevalence of Diabetes mellitus, current smoking and use of antihypertensive medication are dichotomous variables. Abbreviations: BMI, Body Mass Index; LDL-C, Low-density lipoprotein cholesterol; HDL, High-density lipoprotein cholesterol

TABLE-US-00007 TABLE 5 Multivariate adjusted Cox proportional hazards model for baseline fasting value of hGH vs. incidence of CAD, stroke, all-cause mortality and cardiovascular mortality. Event Subgroup n/events HR 95% CI P CAD All 4358/401 1.14 1.01-1.29 .04 Males 1799/249 1.17 1.04-1.33 .01 Females 2559/152 1.02 0.86-1.21 .81 Stroke All 4417/265 1.19 1.02-1.39 .02 Males 1849/152 1.18 1.01-1.39 .04 Females 2568/123 1.14 0.94-1.39 .18 Total mortality All 4452/698 1.21 1.11-1.33 <.001 Males 1872/383 1.26 1.14-1.39 <.001 Females 2580/315 1.04 0.92-1.17 .53 CVD mortality All 4452/215 1.51 1.28-1.78 <.001 Males 1872/126 1.41 1.20-1.66 <.001 Females 2580/89 1.38 1.08-1.76 .009

[0150] Hazard ratios (HR) (95% CI) are expressed per 1 SD increment of the natural logarithm of hGH. Variables adjusted for in the analysis: age, systolic blood pressure, use of antihypertensive medication, BMI (weight in kilograms divided by height in meters squared), prevalence of diabetes mellitus, current smoking and fasting values of HDL and LDL. In addition adjusted for sex in the gender combined analyses.

[0151] Quartile Analysis

TABLE-US-00008 TABLE 6 Cox regression hGH. Quartiles. Q1 Q2 Q3 Q4 Trend n/ 95% 95% 95% 95% Event events M/F HR P HR CI P HR CI P HR CI P HR CI P CAD 4358/ 1799/ 1 .21 1.097 0.83- .52 1.057 0.78- .72 1.331 0.99- .05 1.088 0.99- .08 401 2559 (ref) 1.46 1.43 1.78 1.20 1799/ M 1 .02 0.923 0.64- .67 0.938 0.63- .75 1.457 1.01- .04 1.145 1.02- .03 249 (ref) 1.33 1.39 2.09 1.29 2559/ F 1 .45 1.374 0.88- .16 1.241 0.78- .37 1.032 0.62- .91 1.002 0.86- .98 152 (ref) 2.14 1.98 1.73 1.17 Stroke 4417/ 1849/ 1 .22 1.181 0.82- .37 1.279 0.88- .19 1.466 1.02- .04 1.129 1.01- .04 265 2568 (ref) 1.70 1.85 2.11 1.27 1849/ M 1 .19 1.077 0.66- .77 .938 0.55- .82 1.482 0.91- .11 1.130 0.97- .13 142 (ref) 1.77 1.61 2.42 1.32 2568/ F 1 .21 1.294 0.76- .34 1.736 1.04- .04 1.460 0.83- .19 1.154 0.97- .10 123 (ref) 2.21 2.91 2.58 1.37 Total 4452/ 1872/ 1 <.001 .964 0.76- .76 1.342 1.07- .01 1.463 1.17- <.001 1.165 1.09- <.001 mortality 698 2580 (ref) 1.22 1.69 1.84 1.25 1872/ M 1 <.001 1.220 0.86- .27 1.756 1.25- .001 2.049 1.47- <.001 1.283 1.16- <.001 383 (ref) 1.73 2.48 2.86 1.42 2580/ F 1 .24 0.785 0.57- .15 1.086 0.80- .60 0.992 0.71- .96 1.032 0.93- .56 315 (ref) 1.09 1.48 1.38 1.15 CVD 4452/ 1872/ 1 <.001 1.391 0.85- .19 2.346 1.48- <.001 2.677 1.70- <.001 1.393 1.22- <.001 mortality 215 2580 (ref) 2.27 3.72 4.23 1.59 1872/ M 1 <.001 1.764 0.87- .12 2.702 1.36- .005 3.410 1.76- <.001 1.454 1.22- <.001 126 (ref) 3.57 5.37 6.61 1.73 2580/ F 1 .03 1.022 0.51- .95 2.050 1.09- .03 1.936 0.98- .06 1.310 1.06- .01 89 (ref) 2.06 3.86 3.81 1.61 Q1-Q4 represents the different quartiles, where Q1 is the 25% of the cohort with the lowest hGH-value. HR in Q1-Q4 is expressed vs. the HR in Q1 (reference) which is set to 1.00. Quartiles are gender-specific, ie divided into quartiles separately for men and women, which makes male/female ratio the same in all cohorts, but the cutoff-values different in men and women (see also Table 3).

[0152] Trend is a standard cox proportional hazards analysis where the HR is expressed per increment of quartile. The cut off concentrations for the different quartiles is given in Table 3.

BRIEF DESCRIPTION OF THE DRAWINGS

[0153] FIG. 1: shows a typical us-hGH assay dose/signal curve.

[0154] FIG. 2: Kaplan Meier Analysis of hGH quartiles (see Table 3) of CVD mortality prediction in males. Y-axis: One minus Survival Functions (% event/100) X-axis: Follow-up period (years) form baseline to emigration or death or last follow-up date (2009 Jun. 30).

LITERATURE

[0155] 1. Moller N, Jorgensen J O L. Effects of Growth Hormone on Glucose, Lipid, and Protein Metabolism in Human Subjects. Endocrine reviews. 2009; 30(2):152-77. [0156] 2. Rosen T, Bengtsson B A. Premature mortality due to cardiovascular disease in hypopituitarism. Lancet. 1990; 336(8710):285-8. Epub 1990/08/04. [0157] 3. Svensson J, Bengtsson B A, Rosen T, Oden A, Johannsson G. Malignant disease and cardiovascular morbidity in hypopituitary adults with or without growth hormone replacement therapy. The Journal of clinical endocrinology and metabolism. 2004; 89(7):3306-12. Epub 2004/07/09. [0158] 4. Bulow B, Hagmar L, Mikoczy Z, Nordstrom C H, Erfurth E M. Increased cerebrovascular mortality in patients with hypopituitarism. Clinical endocrinology. 1997; 46(1):75-81. Epub 1997/01/01. [0159] 5. Tomlinson J W, Holden N, Hills R K, Wheatley K, Clayton R N, Bates A S, et al. Association between premature mortality and hypopituitarism. West Midlands Prospective Hypopituitary Study Group. Lancet. 2001; 357(9254):425-31. Epub 2001/03/29. [0160] 6. Abs R, Feldt-Rasmussen U, Mattsson A F, Monson J P, Bengtsson B A, Goth M I, et al. Determinants of cardiovascular risk in 2589 hypopituitary GH-deficient adultsa KIMS database analysis. European journal of endocrinology/European Federation of Endocrine Societies. 2006; 155(1):79-90. Epub 2006/06/24. [0161] 7. de Boer H, Blok G J, Voerman H J, Phillips M, Schouten J A. Serum lipid levels in growth hormone-deficient men. Metabolism: clinical and experimental. 1994; 43(2):199-203. Epub 1994/02/01. [0162] 8. Bulow B, Hagmar L, Eskilsson J, Erfurth E M. Hypopituitary females have a high incidence of cardiovascular morbidity and an increased prevalence of cardiovascular risk factors. The Journal of clinical endocrinology and metabolism. 2000; 85(2):574-84. Epub 2000/02/26. [0163] 9. Sesmilo G, Biller B M, Llevadot J, Hayden D, Hanson G, Rifai N, et al. Effects of growth hormone administration on inflammatory and other cardiovascular risk markers in men with growth hormone deficiency. A randomized, controlled clinical trial. Annals of internal medicine. 2000; 133(2):111-22. Epub 2000/07/15. [0164] 10. Beauregard C, Utz A L, Schaub A E, Nachtigall L, Biller B M, Miller K K, et al. Growth hormone decreases visceral fat and improves cardiovascular risk markers in women with hypopituitarism: a randomized, placebo-controlled study. The Journal of clinical endocrinology and metabolism. 2008; 93(6):2063-71. Epub 2008/04/03. [0165] 11. Maison P, Griffin S, Nicoue-Beglah M, Haddad N, Balkau B, Chanson P. Impact of growth hormone (GH) treatment on cardiovascular risk factors in GH-deficient adults: a Metaanalysis of Blinded, Randomized, Placebo-Controlled Trials. The Journal of clinical endocrinology and metabolism. 2004; 89(5):2192-9. Epub 2004/05/06. [0166] 12. Orme S M, McNally R J, Cartwright R A, Belchetz P E. Mortality and cancer incidence in acromegaly: a retrospective cohort study. United Kingdom Acromegaly Study Group. The Journal of clinical endocrinology and metabolism. 1998; 83(8):2730-4. Epub 1998/08/26. [0167] 13. Melmed S, Casanueva F F, Klibanski A, Bronstein M D, Chanson P, Lamberts S W, et al. A consensus on the diagnosis and treatment of acromegaly complications. Pituitary. 2012. Epub 2012/08/21. [0168] 14. Colao A, Marzullo P, Di Somma C, Lombardi G. Growth hormone and the heart. Clinical endocrinology. 2001; 54(2):137-54. Epub 2001/02/24. [0169] 15. Maison P, Tropeano A I, Macquin-Mavier I, Giustina A, Chanson P. Impact of somatostatin analogs on the heart in acromegaly: a metaanalysis. The Journal of clinical endocrinology and metabolism. 2007; 92(5):1743-7. Epub 2007/02/22. [0170] 16. Takala J, Ruokonen E, Webster N R, Nielsen M S, Zandstra D F, Vundelinckx G, et al. Increased mortality associated with growth hormone treatment in critically ill adults. The New England journal of medicine. 1999; 341(11):785-92. Epub 1999/09/09. [0171] 17. Maison P, Balkau B, Simon D, Chanson P, Rosselin G, Eschwege E. Growth hormone as a risk for premature mortality in healthy subjects: data from the Paris prospective study. BMJ (Clinical research ed). 1998; 316(7138):1132-3. Epub 1998/04/29. [0172] 18. Berglund G, Elmstahl S, Janzon L, Larsson S A. The Malmo Diet and Cancer Study. Design and feasibility. Journal of internal medicine. 1993; 233(1):45-51. Epub 1993/01/01. [0173] 19. Rosvall M, Janzon L, Berglund G, Engstrom G, Hedblad B. Incidence of stroke is related to carotid IMT even in the absence of plaque. Atherosclerosis. 2005; 179(2):325-31. Epub 2005/03/22. [0174] 20. Bidlingmaier M, Suhr J, Ernst A, Wu Z, Keller A, Strasburger C J, et al. High-sensitivity chemiluminescence immunoassays for detection of growth hormone doping in sports. Clinical chemistry. 2009; 55(3):445-53. Epub 2009/01/27. [0175] 21. Jerntorp P, Berglund G. Stroke registry in Malmo, Sweden. Stroke. 1992; 23(3):357-61. [0176] 22. Hammar N, Alfredsson L, Rosen M, Spetz C L, Kahan T, Ysberg A S. A national record linkage to study acute myocardial infarction incidence and case fatality in Sweden. International Journal of Epidemiology. 2001; 30(suppl 1):S30. [0177] 23. Ludvigsson J, Andersson E, Ekbom A, Feychting M, Kim J-L, Reuterwall C, et al. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011; 11(1):450. [0178] 24. D'Agostino R B, Sr., Vasan R S, Pencina M J, Wolf P A, Cobain M, Massaro J M, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008; 117(6):743-53. Epub 2008/01/24. [0179] 25. Pencina M J, D'Agostino R B, Sr., D'Agostino R B, Jr., Vasan R S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine. 2008; 27(2):157-72; discussion 207-12. Epub 2007/06/15. [0180] 26. Pencina M J, D'Agostino R B. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statistics in medicine. 2004; 23(13):2109-23. [0181] 27. Engstrom B E, Karlsson F A, Wide L. Marked gender differences in ambulatory morning growth hormone values in young adults. Clinical chemistry. 1998; 44(6 Pt 1):1289-95. Epub 1998/06/13. [0182] 28. Eden Engstrom B, Karlsson F A, Naessen T, Gillberg P, Wide L. Ambulatory morning growth hormone concentrations increase in men and decrease in women with age. Scandinavian journal of clinical and laboratory investigation. 2002; 62(1):25-31. Epub 2002/05/11. [0183] 29. Pincus S M, Gevers E F, Robinson I C, van den Berg G, Roelfsema F, Hartman M L, et al. Females secrete growth hormone with more process irregularity than males in both humans and rats. The American journal of physiology. 1996; 270(1 Pt 1):E107-15. Epub 1996/01/01. [0184] 30. Hindmarsh P C, Dennison E, Pincus S M, Cooper C, Fall C H, Matthews D R, et al. A sexually dimorphic pattern of growth hormone secretion in the elderly. The Journal of clinical endocrinology and metabolism. 1999; 84(8):2679-85. Epub 1999/08/12. [0185] 31. Veldhuis J D, Roelfsema F, Keenan D M, Pincus S. Gender, age, body mass index, and IGF-I individually and jointly determine distinct GH dynamics: analyses in one hundred healthy adults. The Journal of clinical endocrinology and metabolism. 2011; 96(1):115-21. Epub 2010/10/12. [0186] 32. Saini S, Hindmarsh P C, Matthews D R, Pringle P J, Jones J, Preece M A, et al. Reproducibility of 24-hour serum growth hormone profiles in man. Clinical endocrinology. 1991; 34(6):455-62. Epub 1991/06/01. [0187] 33. Surya S, Symons K, Rothman E, Barkan A L. Complex rhythmicity of growth hormone secretion in humans. Pituitary. 2006; 9(2):121-5. Epub 2006/07/18. [0188] 34. Jaffe C A, Ocampo-Lim B, Guo W, Krueger K, Sugahara I, DeMott-Friberg R, et al. Regulatory mechanisms of growth hormone secretion are sexually dimorphic. The Journal of clinical investigation. 1998; 102(1):153-64. Epub 1998/07/03. [0189] 35. Faje A T, Barkan A L. Basal, but not pulsatile, growth hormone secretion determines the ambient circulating levels of insulin-like growth factor-I. The Journal of clinical endocrinology and metabolism. 2010; 95(5):2486-91. Epub 2010/03/02. [0190] 36. Ho K Y, Weissberger A J. Characterization of 24-hour growth hormone secretion in acromegaly: implications for diagnosis and therapy. Clinical endocrinology. 1994; 41(1):75-83. Epub 1994/07/01. [0191] 37. van der Klaauw A A, Pereira A M, van Thiel S W, Frolich M, Iranmanesh A, Veldhuis J D, et al. Attenuated pulse size, disorderly growth hormone and prolactin secretion with preserved nyctohemeral rhythm distinguish irradiated from surgically treated acromegaly patients. Clinical endocrinology. 2007; 66(4):489-98. [0192] 38. Roelfsema F, Biermasz N R, Veldhuis J D. Pulsatile, nyctohemeral and entropic characteristics of GH secretion in adult GH-deficient patients: selectively decreased pulsatile release and increased secretory disorderliness with preservation of diurnal timing and gender distinctions. Clinical endocrinology. 2002; 56(1):79-87. Epub 2002/02/19. [0193] 39. Bartke A. Can growth hormone (GH) accelerate aging? Evidence from GH-transgenic mice. Neuroendocrinology. 2003; 78(4):2.sup.10-6. Epub 2003/10/30. [0194] 40. Coschigano K T, Holland A N, Riders M E, List E O, Flyvbjerg A, Kopchick J J. Deletion, but not antagonism, of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin-like growth factor I levels and increased life span. Endocrinology. 2003; 144(9):3799-810. Epub 2003/08/23. [0195] 41. Brown-Borg H M, Borg K E, Meliska C J, Bartke A. Dwarf mice and the ageing process.

[0196] Nature. 1996; 384(6604):33. Epub 1996/11/07. [0197] 42. Masternak M M, Bartke A. Growth hormone, inflammation and aging. Pathobiology of aging & age related diseases. 2012; 2. Epub 2012/09/07. [0198] 43. Guevara-Aguirre J, Balasubramanian P, Guevara-Aguirre M, Wei M, Madia F, Cheng C W, et al. Growth hormone receptor deficiency is associated with a major reduction in pro-aging signaling, cancer, and diabetes in humans. Science translational medicine. 2011; 3(70):70ra13. Epub 2011/02/18. [0199] 44. Menezes Oliveira J L, Marques-Santos C, Barreto-Filho J A, Ximenes Filho R, de Oliveira Britto A V, Oliveira Souza A H, et al. Lack of evidence of premature atherosclerosis in untreated severe isolated growth hormone (GH) deficiency due to a GH-releasing hormone receptor mutation. The Journal of clinical endocrinology and metabolism. 2006; 91(6):2093-9. Epub 2006/03/09. [0200] 45. Aguiar-Oliveira M H, Oliveira F T, Pereira R M, Oliveira C R, Blackford A, Valenca E H, et al. Longevity in untreated congenital growth hormone deficiency due to a homozygous mutation in the GHRH receptor gene. The Journal of clinical endocrinology and metabolism. 2010; 95(2):714-21. Epub 2009/12/08. [0201] 46. Besson A, Salemi S, Gallati S, Jenal A, Horn R, Mullis P S, et al. Reduced longevity in untreated patients with isolated growth hormone deficiency. The Journal of clinical endocrinology and metabolism. 2003; 88(8):3664-7. Epub 2003/08/14. [0202] 47. Rudling M, Norstedt G, Olivecrona H, Reihner E, Gustafsson J A, Angelin B.

[0203] Importance of growth hormone for the induction of hepatic low density lipoprotein receptors. Proceedings of the National Academy of Sciences of the United States of America. 1992; 89(15):6983-7. Epub 1992/08/01. [0204] 48. Rudling M, Olivecrona H, Eggertsen G, Angelin B. Regulation of rat hepatic low density lipoprotein receptors. In vivo stimulation by growth hormone is not mediated by insulin-like growth factor I. The Journal of clinical investigation. 1996; 97(2):292-9. Epub 1996/01/15. [0205] 49. Vahl N, Klausen I, Christiansen J S, Jorgensen J O. Growth hormone (GH) status is an independent determinant of serum levels of cholesterol and triglycerides in healthy adults. Clinical endocrinology. 1999; 51(3):309-16. Epub 1999/09/01. [0206] 50. Clasey J L, Weltman A, Patrie J, Weltman J Y, Pezzoli S, Bouchard C, et al. Abdominal visceral fat and fasting insulin are important predictors of 24-hour GH release independent of age, gender, and other physiological factors. The Journal of clinical endocrinology and metabolism. 2001; 86(8):3845-52. Epub 2001/08/15. [0207] 51. Miller K K, Biller B M, Lipman J G, Bradwin G, Rifai N, Klibanski A. Truncal adiposity, relative growth hormone deficiency, and cardiovascular risk. The Journal of clinical endocrinology and metabolism. 2005; 90(2):768-74. Epub 2004/12/02. [0208] 52. Bredella M A, Lin E, Brick D J, Gerweck A V, Harrington L M, Torriani M, et al. Effects of GH in women with abdominal adiposity: a 6-month randomized, double-blind, placebo-controlled trial. European journal of endocrinology/European Federation of Endocrine Societies. 2012; 166(4):601-11. Epub 2012/01/26. [0209] 53. Sandberg H, Roman L, Zavodnick J, Kupers N. The effect of smoking on serum somatotropin, immunoreactive insulin and blood glucose levels of young adult males. The Journal of pharmacology and experimental therapeutics. 1973; 184(3):787-91. Epub 1973/03/01. [0210] 54. Wilkins J N, Carlson H E, Van Vunakis H, Hill M A, Gritz E, Jarvik M E. Nicotine from cigarette smoking increases circulating levels of cortisol, growth hormone, and prolactin in male chronic smokers. Psychopharmacology. 1982; 78(4):305-8. Epub 1982/01/01. [0211] 55. Veldhuis J D. Aging and hormones of the hypothalamo-pituitary axis: gonadotropic axis in men and somatotropic axes in men and women. Ageing research reviews. 2008; 7(3):189-208. Epub 2008/03/18. [0212] 56. Bramnert M, Segerlantz M, Laurila E, Daugaard J R, Manhem P, Groop L. Growth hormone replacement therapy induces insulin resistance by activating the glucose-fatty acid cycle. The Journal of clinical endocrinology and metabolism. 2003; 88(4):1455-63. Epub 2003/04/08. [0213] 57. Norrelund H, Nielsen S, Christiansen J, Jorgensen J, Moller N. Modulation of basal glucose metabolism and insulin sensitivity by growth hormone and free fatty acids during short-term fasting. European Journal of Endocrinology. 2004; 150(6):779-87. [0214] 58. Ho K Y, Evans W S, Blizzard R M, Veldhuis J D, Merriam G R, Samojlik E, et al. Effects of sex and age on the 24-hour profile of growth hormone secretion in man: importance of endogenous estradiol concentrations. The Journal of clinical endocrinology and metabolism. 1987; 64(1):51-8. Epub 1987/01/01. [0215] 59. Lorenz et al., Functional Antibodies Targeting IsaA of Staphylococcus aureus Augment Host Immune Response and Open New Perspectives for Antibacterial Therapy; Antimicrob Agents Chemother. 2011 January; 55(1): 165-173. [0216] 60. Lane, R. D. A short-duration polyethylene glycol fusiontechnique for increasing production of monoclonal antibody-secreting hybridomas, J. Immunol. Meth. 81: 223-228; (1985) [0217] 61. Ziegler, B. et al. Glutamate decarboxylase (GAD) is not detectable on the surface of rat islet cells examined by cytofluorometry and complement-dependent antibody-mediated cytotoxicity of monoclonal GAD antibodies, Horm. Metab. Res. 28: 11-15, (1996)