Low IGF-I and Elevated Testosterone During Puberty in Subjects With Type 1 Diabetes Developing Microalbuminuria in Comparison to Normoalbuminuric Control Subjects: The Oxford Regional Prospective Study

The Oxford Regional Prospective Study of Childhood Diabetes (ORPS) was established in 1986, and the characteristics of the cohort have been previously described (3). Children under 16 years of age were recruited within 3 months of diagnosis of type 1 diabetes and were assessed at the end of the first year from diagnosis and annually thereafter. Assessments consisted of measurements of height, weight, and three consecutive early-morning (first void) urine specimens for the albumin-to-creatinine ratio (ACR). Blood samples were collected for the central measurement of HbA_1c, and annual nonfasting blood samples were stored on each patient. Ethical approval was obtained from the regional ethics committees, with written consent from the parents and assent from the children.

MA was defined as an ACR >3.5 mg/mmol in male subjects and >4.0 mg/mmol in female subjects and <35 mg/mmol in two of three consecutive early-morning urine collections (3). This corresponded to an albumin excretion rate of between 20 and 200 mcg/min, as determined in 304 timed overnight urine samples using linear regression equations.

Of the 494 subjects recruited, 63 had an ACR that fell within our definition of MA on at least one annual assessment. A total of 55 subjects had sufficient blood samples for analysis, and these were designated as case subjects (MA⁺ group). Case subjects were matched for age, sex, and duration of diabetes to control subjects selected from the remaining subjects who, to date, have no evidence of MA (MA⁻ group). For each subject, all available longitudinally collected blood samples were analyzed for the relevant hormones.

BMI standard deviation scores (SDSs) were calculated using data based on the British 1990 Growth Reference and Cole’s LMS method (14).

Details of sample collection and storage were reported previously (4). Albumin was measured centrally by an enzyme-linked immunosorbent assay method (3). The within- and between-assay coefficients of variation (CVs) was 6 and 12%, respectively.

Creatinine was measured using a Jaffe method (Unimate 7; Roche Diagnostic Systems, Basel, Switzerland) on a Cobas Mira (Roche Diagnostic Systems) spectrophotometer. The CV was 2% at 2.2 mmol/l.

HbA_1c was measured centrally, initially by an electrophoretic method (Ciba Corning Diagnostics, Halstead, U.K.), which was replaced by high-performance liquid chromatography (DIAMAT; Bio-Rad, U.K.) in 1992 (15). The relationship between the two methods has been described previously (3). The within-batch CV was 2.2 and 1.3% at a level of 9.8 and 10.1%, respectively. The between-batch CV was 3.5 and 2.2% at 5.6 and 10.1%, respectively. The normal range for the assay is 4.4–6.2%.

Serum free IGF-I levels were determined using ultrafiltration by centrifugation at conditions approaching those in vivo (16), using Amicon YMT 30 membranes and MPS-1 supporting devices (Amicon Division, Beverly, MA). Serum samples were diluted (1 in 11) in Krebs-Ringer bicarbonate buffer (pH 7.4) containing 50 g/l human serum albumin (Behring, Marburg, Germany). Then, 600 μl were applied to the membranes and incubated (30 min at 37°C) and centrifuged (1,500 r/min at 37°C). The assay detection limit was 40 ng/l. The overall within- and between-assay CVs averaged 20%.

Total IGF-I levels were determined by radioimmunoassay (RIA) after acid-acetone extraction using rabbit anti-serum developed by L. Underwood (North Carolina University, Chapel Hill, NC). The assay was standardized against a pool of normal human serum, defined as containing 1.0 units IGF-I/ml, equivalent to 159 ng/ml of a purified preparation of IGF-I (17). The intra- and interassay CVs were 6.2 and 3.5%, respectively.

IGF binding protein-1 (IGFBP-I) was determined by a novel in-house RIA based on a monoclonal antibody (mAb), which recognizes all phosphorylated isoforms of IGFBP-I (mAb 6303; Medix Biochemica, Kainiainen, Finland) (18). Microtiter plates (Nunc, Roskilde, Denmark) were coated overnight at 5° with 4 mg/l anti-mouse IgG (Sigma Aldrich, Copenhagen, Denmark) in 15 mmol/l sodium carbonate, 35 mmol/l sodium hydrogen carbonate, pH 9.6. After washing (50 mmol/l Tris-HCl, pH 8.0, 0.9% [wt/vol] NaCl, 0.5% [vol/ vol] Tween 20, and 0.05% [wt/vol] NaN₃), all wells were blocked with 1% (wt/vol) BSA in 40 mmol/l phosphate buffer with 0.05% (wt/vol) NaN_3, pH 8.0, for 2 h at room temperature. After washing, 100 μl of standard (purified amniotic human IGFBP-I; HyTest, Turku, Finland) or diluted serum (1 in 4), 50 μl of ¹²⁵I-labeled IGFBP-I (∼10.000 cpm), and 50 μl of specific antibody (mAb 6303, 12.5 μg/l) were added to all wells. All reagents were dissolved in 40 mmol/l phosphate buffer containing 0.2% (wt/vol) BSA, 0.9% (wt/vol) NaCl, 0.2% (wt/vol) Tween 20, and 0.05% (wt/vol) NaN₃. The plates were then incubated for 2 days at 5°C, washed three times, and counted in a γ -counter. The working range of the assay was 1–200 μg/l, with ED₅₀ ∼25 μg/l. The lower detection limit was <2.5 μg/l, and the within- and between-assay CVs were <5 and < 16%, respectively. The cross-reactivity of IGF-I, IGF-II, and IGFBP-2, -3, -4, and -5 was <1% (up to 10.000 μg/l).

Samples were analyzed on a Bayer Technicon Immuno-1 fully automated immunoassay analyzer (Bayer, Neubury, U.K.), using a competitive magnetic separation format and an enzymatic end-point detection system. The assay has three “in-house” quality control (QC) pools, two female and one male. The two female pools’ interassay imprecision is: QC low mean 1.7 nmol/l, CV 8.0%, n = 114; and QC medium mean 2.9 nmol/l, CV 4.6%, n = 143.

Samples were analyzed on an Immulite semiautomated immunoassay analyzer (Diagnostic Products, Llanberis, Gwynedd, Wales), using a solid-phase two-site chemiluminescent immunoassay. The assay has three “in-house” internal QC pools. Imprecision is as follows: QC low mean 19.5 nmol/l, CV 6.0%, n = 84; and QC medium mean 38.0 nmol/l, CV 5.7%, n = 84; and QC high mean 86.0 nmol/l, CV 5.7%, n = 84.

All data were normally distributed, except IGFBP-1, testosterone, SHBG, and free androgen index (FAI; FAI = testosterone × 100/SHBG), which were log transformed to allow parametric analyses. Levels of each hormone were compared between groups using an ANOVA model. To adequately consider the effects of puberty, a Cox proportional hazards model was fitted with duration of diabetes as the time variable, MA as the outcome, and pubertal onset as a time-dependent covariate (using age 11 years as a surrogate marker for puberty onset). For this model, data from each subject was summarized by calculating the mean of all measurements. The covariates examined included HbA_1c, free IGF-I, daily insulin dose, and testosterone SDS. Testosterone SDSs were used to allow both sexes to be considered together and were derived from comparison of data against comparable age- and sex-matched nondiabetic subjects using the Growth Analyser Program (Dutch Growth Foundation). To display the longitudinal changes in hormone levels over time, we used a multilevel modeling software (MLwiN version 1.0 beta; Institute of Education, London) (19). This is an extension of multiple regression, using repeated-measures data and analyses within and between individual effects, allowing consideration of individual curves and their summation by predefined groups. SPSS version 10.0 was used for analysis. Data are presented as the mean ± SD or median (interquartile range). A P value <0.05 was considered significant.

There were no significant differences between the groups in demographic characteristics, BMI SDS, and insulin daily dose (Table 1). Of the 55 case subjects, 33 (60%) had transient MA (defined as MA present for 1 year with normoalbuminuria the following year) and 22 (40%) developed persistent MA (defined as MA present for ≥2 consecutive years).

HbA_1c levels were higher in the MA⁺ compared with the MA⁻ group (10.8 ± 1.8 vs. 9.9 ± 1.3%, P < 0.001), and when female subjects were considered separately (10.9 ± 2.0 vs. 9.8 ± 1.8%, P < 0.001) to male subjects (10.6 ± 1.4 vs. 9.7 ± 1.3, P = 0.005).

Total IGF-I levels were lower in the MA⁺ compared with the MA⁻ group (1.2 ± 0.6 vs. 1.4 ± 0.6 mU/l, P = 0.001), and when female subjects (1.2 ± 0.6 vs. 1.4 ± 0.6 mU/l, P = 0.03) were considered separately to male subjects (1.1 ± 0.6 vs. 1.3 ± 0.6 mU/l, P = 0.006).

Free IGF-I levels were lower in the MA⁺ compared with the MA⁻ group (1,678 ± 745 vs. 1,887 ± 906 ng/l, P = 0.004). In female subjects, the difference between the groups was highly significant (1,767 ± 634 vs. 2010 ± 881 ng/ l, P = 0.002) (Fig. 1A and B), but this was not the case in males (1,552 ± 946 vs. 1,668 ± 930 ng/l, P = 0.77). In females with similar HbA_1c levels, free IGF-I levels were consistently lower in those with MA compared with those without (Fig. 1C).

No significant differences in IGFBP-I levels were found

In female subjects, testosterone levels (median [interquartile range]) were higher in the MA⁺ compared with the MA⁻ group (1.3 [0.9–1.6] vs. 1.1 [0.7–1.3] mmol/l, P = 0.04), as was the FAI (2.4 [1.3–4.0] vs. 2.0 [1.0–3.2], P = 0.03) (Fig. 1D and E). At similar HbA_1c levels, FAI was consistently higher in those with MA compared with those without MA (Fig. 1F). In male subjects, no differences were found. No relationship was seen with daily insulin dose and SHBG levels for either sex (nonfasting samples).

Independent of puberty, the probability of having MA was associated with lower free IGF-I levels (P = 0.01, Exp[B] = 0.999, 95% CI 0.998–1.0; i.e., a 10% increase in probability with a reduction in free IGF-I levels by 100 ng/l), with higher testosterone SDS (P < 0.001, Exp[B] = 3.9, 95% CI 2.1–7.3; i.e., by a factor of 3.9 for a unit rise), and with poor glycemic control (P < 0.001, Exp[B] = 3.5, 95% CI 2.1–5.8; i.e., by a factor of 3.5 for a 1% increase in mean HbA_1c levels). When the sexes were considered separately, the findings for female subjects were similar to when both sexes were considered together, but for male subjects, the probability of having MA was associated with poor glycemic control only (P = 0.04).

Of the 35 female case subjects, 11 developed persistent MA, whereas 24 had transient MA. Of the 20 male subjects, 11 developed persistent MA, whereas 9 had transient MA. This sex difference was not significant (χ² = 2.9, P = 0.09). Comparison of hormonal variables are described in Table 2. Urinary ACR plotted against years relative to onset of MA is displayed in Fig. 2. For female subjects only, in the persistent MA group compared with the transient and control groups, free IGF-I levels were lower (1,519 ± 752 vs. 1,874 ± 1,063 vs. 2010 ± 881 ng/l, P = 0.02), whereas HbA_1c levels were higher (11.7 ± 2.6 vs. 10.4 ± 1.9 vs. 9.8 ± 1.8%, P < 0.001). For male subjects only, in the persistent MA group compared with the transient and control groups, total IGF-I levels were lower (1.0 ± 0.4 vs. 1.1 ± 0.6 vs. 1.3 ± 0.6 mU/l, P = 0.007), whereas HbA_1c levels were higher (11.2 ± 2.1 vs. 9.8 ± 1.1 vs. 9.7 ± 1.3%, P = 0.004). No other differences were found.

There is little longitudinal data on the natural history of MA in children with type 1 diabetes. Data from ORPS has shown that the cumulative probability of developing MA after 9 years’ diabetes duration is 40% (3). Follow-up of the cohort shows that MA persists in 40% of these subjects, with persistence being unequivocally related to poor glycemic control. However, the difference in HbA_1c between those with transient MA and control subjects was not significant, and the increasing prevalence of MA during puberty cannot be entirely explained by HbA_1c. Puberty and sex are also independent risk factors for the development of MA (3). Pubertal onset is associated with an acceleration in the urine albumin excretion rate compared with the years during prepuberty, and this may be predictive of MA (20), and female subjects are at a twofold greater risk of MA compared with male subjects (3). This marked sexual dimorphism is also apparent in other areas at puberty, as evidenced by the increased risk in adolescent female subjects for developing retinopathy (7), obesity (5), and attenuated growth (6). These data suggest hormonal changes during puberty may effect the development of MA in susceptible individuals.

In female subjects and, to a lesser extent in male subjects, those with MA had lower total and free IGF-I levels than their control subjects, and this could by implication relate to variation in GH levels. In type 1 diabetes, relative portal insulinopenia (caused by failure to administer insulin directly into the portal vein) results in impaired hepatic generation of total and free IGF-I (9), leading to a lack of negative feedback drive for GH hypersecretion, over and above that seen in normal puberty (10). Thus, circulating GH levels are increased while circulating IGF-I levels remain low, and these changes may be more apparent in female than in male subjects (10). The integrity of GH pathways in tissues other than the liver are thought to remain intact, and both GH hypersecretion and local paracrine IGF-I production have been implicated in the pathophysiology of diabetic nephropathy. In animal models, GH increases renal blood flow (12), renal expression of IGF-I, and renal size (21), with IGF-I expression within the kidney correlating directly with diabetic nephropathy-like changes (22). Selective GH blockade leads to reduced renal expression of IGF-I, renal size, GFR, and urine albumin excretion (22). In humans, GH hypersecretion and local paracrine IGF-I generation have been associated with the development of diabetic nephropathy (11) and retinopathy (23,24) in cross-sectional studies. Acromegaly is associated with increases in GFR and urinary albumin excretion (25), whereas specific GH blockade leads to normalization of these factors (25). However, to date no renal physiology data exist on such intervention in type 1 diabetic subjects.

Our findings of raised androgens in those with MA, particularly in female subjects, confirms findings in previous cross-sectional studies (13). It is probable, but unproven, that the elevated androgens in female subjects derive from the ovary, and that ovarian hyperandrogenism is a principal feature of PCOS. The relationship between ovarian hyperandrogenism and polycystic morphologic changes of the ovary is much debated, but in one study of adolescent girls with type 1 diabetes, up to 50% had ovarian changes characteristic of PCOS, in contrast to 20–30% in the general population (5).

Ovarian hyperandrogenism in PCOS is related to insulin resistance and peripheral hyperinsulinemia (26), although elevated GH and reduced IGF-I levels could also theoretically affect ovarian function (27). The GH-associated increase in insulin resistance during puberty in type 1 diabetes may be the principal cause of the ovarian hyperandrogenism. However, other genetic variation in factors leading to insulin resistance may also be important because insulin resistance can predate the appearance of MA (28), with familial clustering of diabetic nephropathy occurring in association with changes in insulin resistance (29).

We currently have no direct measures of insulin resistance in our case-control series. Insulin doses were similar in case and control subjects, yet HbA_1c was higher in case subjects. This might indicate underinsulinization rather than insulin resistance, and poor control might lead to abnormalities of both the GH-IGF-I axis and MA. However, free IGF-I levels were lower and FAI higher in those with MA, independent of HbA_1c (Fig. 1C and F), suggesting that these hormone changes and any associated increase in GH levels may influence the expression of MA in genetically susceptible individuals as they move through puberty. Thus, background variability in common genetic polymorphisms, such as in the IGF-I gene and the androgen receptor gene, may contribute to MA risk; however, this has yet to be determined. The role of changes in IGF-I levels and FAI in the pathogenesis of MA cannot be further elucidated by these studies. However, the differences in the degree of abnormality seen in those with persistent and transient MA may be predicted by lower HbA_1c (20) and less profound abnormalities of the GH-IGF-I and ovarian axes.

In summary, the development of MA at puberty may reflect not only poor glycemic control but also changes in the GH-IGF-I axis and ovarian function. Changes in pubertal hormonal variables differ in those with MA, particularly in female patients, and these differences may relate to disease progression. Aggressive insulin therapy has been shown to reduce diabetic complication risk (2). However, this may confer increased weight gain and hypoglycemia (2) and predispose to the detrimental effects of peripheral hyperinsulinemia, such as the development of ovarian hyperandrogenism. Increasing the insulin dose to overcome insulin resistance may lead to further weight gain (2) and, in female patients, a cycle of insulin omission to lose weight (30). This pattern of insulin misuse has been linked to the increased risk of diabetic complications in adolescent girls during puberty (31). Our observations suggest alternative therapy directed at underlying mechanisms, such as insulin sensitizers, IGF-I therapy, and anti-androgen therapy, might also be explored in attempts to reduce progression of microvascular complications in high-risk individuals during puberty.

Members of the ORPS Steering Committee are D.B. Dunger, R.N. Dalton, J. Fuller, E.A.M. Gale, H. Keen, M. Murphy, H.A.W. Niel, C.J. Schultz, R.J. Young, and T. Konopelska-Bahu.

Members of the ORPS are R.A.F. Bell and A. Taylor, Horton General Hospital, Banbury, U.K.; A. Mukhtar, B.P. O’Malley, B.R. Silk, and E.H. Smith, Kettering District Hospital, Kettering, U.K.; R.D.M. Scott, King Edward VII Hospital; F.M. Ackland, C.J. Fox, and N.K. Griffin, Northampton General Hospital, Northampton, U.K.; N. Mann, H. Simpson, P. Cove Smith, and M. Pollitzer, Royal Berkshire Hospital, Reading, U.K.; R.S. Brown and A.H. Knight, Stoke Mandeville Hospital, Aylesbury, U.K.; J.M. Cowen and J.C. Pearce, Wexham Park Hospital, Slough, U.K.

This study was supported by the Novo Nordisk Center for Research in Growth and Regeneration, Aarhus University, Denmark (Danish Health Research Council Grant 9700592); the Institute of Experimental Clinical Research, University of Aarhus, Denmark; and the Hørslev Foundation. R.A. is a Novo Nordisk U.K. Research Foundation fellow. The ORPS is funded by the British Diabetic Association.

We acknowledge the Juvenile Diabetes Research Foundation; the study field workers; the laboratory assistance of Angie Watts and Dot Harris; the Barts-Oxford Study field workers; and the pediatricians, physicians, and diabetes nurse specialists in the Oxford region. In addition, We are indebted to Joan Hansen for skilled technical assistance.