The Variations of Insulin-like Growth Factor 1 Measured between Siemens Immulite, DiaSorin Liaison and IDS iSYS

Abstract Introduction/Objective Insulin-like Growth Factor 1 (IGF-1) is a biomarker for the evaluation of growth hormone activity in the hypothalamic-pituitary axis. The current most common methodology for IGF-1 measurement is automated immunometric assays. In this study, we compared the IGF-1 on Siemens Immulite 2000, DiaSorin Liaison XL and IDS iSYS. Methods/Case Report Residual 30-110 serum specimens were randomly selected from routine hospital orders. IGF- 1 was measured on these three platforms and compared with Passing-Bablok regression. Bias was evaluated using the Bland-Altman method. Results (if a Case Study enter NA) Weighted Deming regression analysis showed approximately 80% and 56% positive bias on IDS iSYS and DiaSorin Liaison, compared with Siemens Immulite (iSYS=1.81*Immulite-117.65, r=0.91; Liaison=1.56*Immulite-4.58, r=0.98). There was approximately 8% positive bias on Liaison, compared with iSYS (Liaison=1.08*iSYS+0.56, r=0.99). The Passing-Bablok regression analysis revealed approximately 67% and 54% positive bias (iSYS=1.67*Immulite-75, r=0.91; Liaison=1.54*Immulite-3.44, r=0.91). Approximately 8% positive bias on Liaison was observed, compared with iSYS (Liaison=1.08*iSYS+5.65, r=0.99). The Bland-Altman plot showed the agreement between iSYS and Immulite IGF-1 was on average 129.6±123.3 ng/mL, 98.6±148.8 ng/mL between Liaison and Immulite and 37.0±46.5 ng/mL between Liaison and iSYS. Conclusion Immunoassays rely on the specificity of antibodies. There are wide variations between different immunoassay platforms for IGF-1 measurement. The standardization of IGF-1 assay is lack. It would be a challenge for clinicians to monitor IGF-1 or treat the patients with pituitary disorders, when switching to another platform. The potential impact of the variations in IGF-1 measurement between different immunoassay platforms should be aware.

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Endogenous Insulin-Like Growth Factor II Mediates Growth in Preimplantation Mouse Embryos Through the Insulin-Like Growth Factor I Receptor: A Case Study on the Role of Growth Factors in Mammalian Development

Journal of Animal Science ◽

10.2527/1992.70suppl_242x ◽

1992 ◽

Vol 70 (suppl_2) ◽

pp. 42-50 ◽

Cited By ~ 1

Author(s):

Daniel A. Rappolee

Keyword(s):

Growth Factor ◽

Insulin Like Growth Factor ◽

Mammalian Development ◽

Factor I ◽

Endogenous Insulin ◽

Preimplantation Mouse Embryos ◽

Factor Ii ◽

Preimplantation Mouse

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The Insulin-Like Growth Factor Axis and Plasma Lipid Levels in the Elderly1

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jcem.83.2.4548 ◽

1998 ◽

Vol 83 (2) ◽

pp. 499-502

Author(s):

Gian Paolo Ceda ◽

Elisabetta Dall’Aglio ◽

Andrea Magnacavallo ◽

Nicola Vargas ◽

Vittorio Fontana ◽

...

Keyword(s):

Body Mass Index ◽

Regression Analysis ◽

Growth Factor ◽

Body Mass ◽

Elderly Population ◽

Elderly Subjects ◽

Insulin Like Growth Factor ◽

Lipid Levels ◽

Igf I ◽

Igfbp 3

The activity of the hypothalamic-GH-insulin-like growth factor (IGF) network declines with age. It has recently been shown that increased cardiovascular mortality occurs in adults with GH deficiency. As hypercholesterolemia is common in GH-deficient adults, and because there is experimental evidence that GH may play a role in regulating plasma cholesterol, we decided to investigate the activity of the GH-IGF axis in an elderly population by measuring serum IGF-I, IGF-II, and IGF-binding protein-3 (IGFBP-3) levels and to study their relationship with blood lipid levels. One hundred and thirty-two elderly subjects, 52 men and 80 women, were studied (age range, 60–91 yr). Men had significantly lower levels of IGFBP-3, high density lipoprotein cholesterol (HDL-C) and apoprotein A1 (ApoA1) compared to the women, whereas IGF-I and IGF-II were only slightly lower. Using linear regression analysis, we observed an inverse relationship of age with IGF-I (r = −0.35; P < 0.001), IGF-II (r = 0.40; P < 0.001), IGFBP-3 (r = 0.52; P < 0.001), body mass index, and lipid levels. Univariate regression analysis showed a strong and positive correlation of both IGF-I and IGFBP-3 with HDL-C and ApoA1. Partial correlation analysis, after adjustment for age and body mass index, showed that IGFBP-3 and IGF-II were still significantly and positively related to HDL-C and ApoA1. Furthermore, a strong association was documented among IGF-I, IGF-II, and IGFBP-3. These data demonstrate that even in an elderly population, further aging is accompanied by a progressive decline in circulating IGF-I, IGF-II, and IGFBP-3, suggesting a continuing diminution of the GH-IGF axis throughout aging. Moreover, the strong correlation between HDL-C and an index of GH secretion, such as IGFBP-3, suggests that GH might play an important role in lipid metabolism in healthy elderly subjects.

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Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis

The Lancet ◽

10.1016/s0140-6736(04)16044-3 ◽

2004 ◽

Vol 363 (9418) ◽

pp. 1346-1353 ◽

Cited By ~ 1079

Author(s):

Andrew G Renehan ◽

Marcel Zwahlen ◽

Christoph Minder ◽

Sarah T O'Dwyer ◽

Stephen M Shalet ◽

...

Keyword(s):

Systematic Review ◽

Regression Analysis ◽

Growth Factor ◽

Cancer Risk ◽

Insulin Like Growth Factor ◽

Igf I ◽

Igf Binding ◽

Meta Regression ◽

Meta Regression Analysis ◽

Igf Binding Protein

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Nesidioblastosis and Mixed Hamartoma of the Liver in Beckwith-Wiedemann Syndrome: Case Study Including Analysis of H19 Methylation and Insulin-like Growth Factor 2 Genotyping and Imprinting

Pediatric and Developmental Pathology ◽

10.1007/s10024001-0003-8 ◽

2001 ◽

Vol 4 (4) ◽

pp. 381-390 ◽

Cited By ~ 12

Author(s):

Ryuji Fukuzawa ◽

Akihiro Umezawa ◽

Yukihiko Morikawa ◽

Kyong Chang Kim ◽

Toshiro Nagai ◽

...

Keyword(s):

Growth Factor ◽

Insulin Like Growth Factor

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Peak and Trough Growth Hormone Concentrations Have Different Associations with the Insulin-Like Growth Factor Axis, Body Composition, and Metabolic Parameters1

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jcem.82.7.4036 ◽

1997 ◽

Vol 82 (7) ◽

pp. 2172-2176 ◽

Cited By ~ 1

Author(s):

P. C. Hindmarsh ◽

C. H. D. Fall ◽

P. J. Pringle ◽

C. Osmond ◽

C. G. D. Brook

Keyword(s):

Body Composition ◽

Regression Analysis ◽

Growth Factor ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Binding Protein ◽

Insulin Concentration ◽

Insulin Like Growth Factor ◽

Fasting Insulin ◽

Serum Gh

GH is secreted in a pulsatile fashion, promoting growth and anabolism. The components of the pulsatile signal involved in these diverse effects are unclear. We constructed (20-min sampling interval) and analyzed 24-h serum GH profiles in 45 adult male volunteers, 59.4–69.9 yr old, body mass index (BMI) 21.9–36.5 Kg/m2, using Fourier transformation and a concentration distribution analysis that determines the concentration at or below which the serum GH concentrations in the 24-h profile spend a percentage of the total time. The observed concentrations (OC) below which 95% and 5% of the values in the time series lie [lsb]OC95 (peaks) and OC5 (troughs)] and mean 24-h serum GH concentrations were related to measures of the insulin-like growth factor (IGF) family, parameters of body composition, fasting insulin and cholesterol measures, and GH-binding protein concentrations. Mean 24-h serum GH concentrations ranged between 0.19 and 2.15 mU/L (1μ g/L = 2.6 mU/L). Pulse periodicity was between 180 and 200 min. There was a positive relationship between peak GH levels and serum IGF-1 and IGFBP-3 levels (r = 0.39; P = 0.009 and r = 0.32; P = 0.03, respectively). GH trough levels were unrelated to these measures of the IGF family. In contrast, GH troughs were related inversely to BMI (r = −0.31; P = 0.04) and waist-hip ratio (r = −0.4; P = 0.006). Peak GH levels were not related to these measures. Factors known to influence these measures, fasting insulin concentration, or cortisol secretion did not alter the trough GH relationship in multiple regression analysis. All GH parameters were related inversely to fasting insulin concentration. Although GH parameters were related inversely to cholesterol and low-density lipoprotein-cholesterol, this effect disappeared when age and fasting insulin levels were introduced into the regression. GH-binding protein levels related most strongly to BMI (r = 0.60; P < 0.001), with no effect of any GH parameter observed in multiple regression analysis. These results suggest that the peak values of a GH concentration profile may influence the IGF axis, whereas trough values may influence body composition and metabolic parameters of GH action.

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