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Original Studies |
Co-operative Research Center for Diagnostic Technologies and School of Life Sciences, Queensland University of Technology (R.B., T.V.-L.), Brisbane, Queensland 4001; School of Biomolecular and Biomedical Science, Griffith University (R.S., D.C.), Nathan, Queensland 4111; Garvan Institute of Medical Research (K.-C.L., K.H.), Darlinghurst, New South Wales 2010; University of Liège, Laboratory of Endocrinology (A.I., M.B., G.H.) and Mater Mothers’ Hospital (H.D.M., F.-Y.C., D.C., A.C., A.P., S.J.), South Brisbane, Queensland 4101, Australia
Address all correspondence and requests for reprints to: Dr. Ross Barnard, Co-operative Research Center for Diagnostic Technologies and School of Life Sciences, Queensland University of Technology, Gardens Point, Brisbane, Queensland 4001, Australia; or Dr. David McIntyre, Mater Mothers’ Hospital, South Brisbane, Queensland 4101, Australia.
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Abstract |
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Introduction |
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The effects of PGH on maternal metabolism, on the placenta, and hence on fetal growth are potentially modulated by the high affinity GH-binding protein (GHBP) (16, 17, 18). In the maternal circulation GHBP concentrations are comparable or higher than the levels in the nonpregnant state, depending on gestational stage (19, 20). However, to date there have been no studies relating the concentration of free PGH to maternal biochemical parameters or to fetal growth. For better understanding of the GH axis during pregnancy it is necessary to have measurements of the gestational profile of free PGH in normal and pathological pregnancies. A comparison of the correlations between free PGH and growth parameters and between total PGH and growth parameters would also provide evidence relevant to the impact of maternal GHBP on fetal growth and, more generally, on the physiological role of GHBP in the control of GH action in vivo.
We have previously described significant changes in GH-binding protein (GHBP) in human pregnancy (19). That study demonstrated a reduction in GHBP with advancing gestation and a positive correlation between GHBP and maternal weight and body mass index (BMI). Another finding of that study was the substantial elevation of GHBP across all stages of gestation in cases of noninsulin-dependent diabetes mellitus (NIDDM), which contrasted with the reduction in GHBP in insulin-dependent diabetes mellitus (IDDM). The existence of divergent GHBP concentrations in these different forms of diabetes prompted the present study to investigate the relationship among changes in glycemia, PGH, and changes in GHBP during pregnancy.
Therefore in the present work we have extended the analysis of specimens from our previous study (19) to include measurements of PGH, free PGH, IGF-I, IGF-II, IGF-binding protein-1 (IGFBP-1), IGFBP-2, and IGFBP-3, and we have related these to maternal characteristics, fetal growth, and glycemia. Finally, the present study was undertaken to determine whether any combination of these biochemical variables could predict birth weight.
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Subjects and Methods |
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Patient characteristics have previously been described in detail (19). The current study used all available samples taken in the third trimester from patients with prepregnancy IDDM (n = 13), NIDDM (n = 6), and IUGR (n = 16) pregnancies. A subset of samples from normal pregnancies (n = 23) was analyzed for comparative purposes. Normal women underwent a 50-g nonfasting glucose challenge test at 28 weeks gestation to exclude gestational diabetes.
In the light of previous data regarding PGH secretion (4, 5, 8), samples taken at 28–30 weeks gestation (K28) or at 36–38 weeks gestation (K36) were analyzed. Ultrasonographic fetal assessments were performed at K28 and K36 to assess fetal growth.
Fetal outcome data collected at delivery included weeks of gestation, gender, birth weight, head circumference, and crown-heel length. The z (SD) scores for birth weight, corrected for gestational age and gender [z = birth weight - mean birth weight/SD (birth weight)] were calculated for each baby to allow comparison of relative fetal growth across a range of gestational ages.
Categories of fetal growth were defined prospectively as follows: IUGR, birth weight less than 10th percentile; normal, birth weight more than 10th and less than 90th percentiles; and macrosomia, birth weight more than 90th percentile. Normative data were derived from a contemporary cohort of 21,221 singleton babies born at the Mater Mothers’ Hospital.
Glycemic control records
In a subset of the patients with IDDM (n = 9) or NIDDM (n = 6), records of glycemic control were available from home monitoring and from clinic visits, all using the ACCUTREND reflectance glucose monitor (Roche Molecular Biochemicals, Mannheim, Germany). For each patient, the mean of self-monitored capillary glucose measurements taken while fasting and 2 h after breakfast was calculated in addition to a mean value for capillary glucose measurements taken at clinic visits. Mean capillary glucose values for each patient (fasting, postprandial, and at the clinic) were calculated using all available measurements between 20 and 30 weeks gestation. These mean values therefore represent overall glycemia around the time of the K28 blood sampling and have been used in the correlation analyses described below to explore the relationship between glycemia and parameters of GH metabolism.
Laboratory methods
All GHBP measurements were performed using the ligand immunofunctional assay for GHBP as reported previously (19, 21). PGH was assayed using the recombinant PGH standards and monoclonal antibodies E8 and 7C12 developed by Hennen’s group (22, 23, 24) in an 125I-labeled sandwich immunoassay assay. In our hands the mean intraassay coefficient of variation was 7.7%, and the interassay coefficient of variation was 9.3%. Free PGH was calculated according to the algorithm and computer program of Barsano and Baumann (25), as applied previously by Cramer et al. (26), using the paired values of GHBP and PGH measured in each serum sample and using the affinity for native human GHBP measured by Barnard et al. (18).
Serum IGF-I and IGF-II were assayed using modifications of published RIA protocols after extraction of serum with acetone and formic acid (27, 28). 125I-Labeled IGF-I and IGF-II tracers were purchased from Amersham Pharmacia Biotech (Aylesbury, UK). Primary incubation with rabbit anti-IGF-I or IGF-II antiserum (GroPep Pty. Ltd., Adelaide, Australia) for 16 h was followed by immunoprecipitation with Sac-cel anti-rabbit antibody and counting. For the IGF-I, interassay CVs at the lower and upper ends of the sensitive range were 14.5% and 17.7%, respectively. For IGF-II, interassay CVs at the lower and upper ranges were 16.4% and 11.7%, respectively.
IGFBP-1, -2, and -3 were assayed using immunoradiometric assay kits supplied by Diagnostics Systems Laboratories, Inc. (Webster, TX). Reported intraassay CVs of these assays were 2.7–5.2% for IGFBP-1, 4.7–8.5% for IGFBP-2, and 1.8–3.9% for IGFBP-3. Interassay CVs for these assays were 3.5–6.0%, 4.5–7.4%, and 0.5–1.9% for IGFBP-1 to –3, respectively.
Statistical analysis
Differences between serum hormone concentrations at K28 and K36 and between groups of patients classified by growth (normal, IUGR, and macrosomic) and diagnosis (normal, IDDM, NIDDM, and IUGR) were analyzed using ANOVA and covariance. Significant differences were further examined using the least significant difference test. Linear correlation analysis was used to explore the relationships between continuous variables, and the r values quoted are Pearson’s correlation coefficients.
Forward stepwise multiple regression analysis was used to develop a biochemical model for the prediction of birth weight using all GH-related variables and to examine the influence of glycemia on PGH and GHBP concentrations. In all cases, F > 1.0 was required for variables to enter the model. For all analyses, statistical significance was accepted at the 5% level on two-tailed testing. All statistical analyses were performed using Statistica for Windows (StatSoft, Tulsa, OK).
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Results |
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. Women with NIDDM
showed significantly greater body mass and BMI. Because of this and
because our previous study (19) showed a positive correlation between
BMI and GHBP levels, an analysis of covariance was performed using
maternal weight and BMI as covariates when examining the outcome
parameters. The results were congruent with those obtained without
analysis of covariance. In the interests of simplicity the uncorrected
means are reported in the tables and figures. As expected, birth weight
z-scores were higher than normal in IDDM pregnancies (P
< 0.01) and lower in IUGR (P < 0.001). The IUGR group
also demonstrated significantly reduced head circumference and
crown-heel length.
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Mean maternal total PGH concentrations increased by 69% from K28 to K36 (P < 0.01). GHBP levels did not change significantly over this period, as noted in our previous study (19), but free PGH rose by 87% (P < 0.01). No significant differences were noted in levels of IGF-I, IGF-II, IGFBP-1, IGFBP-2, or IGFBP-3 at these two gestational ages.
Effects of diagnosis
At both K28 and K36, women recruited because of antenatally
detected IUGR showed lower levels of PGH and free PGH than normal
subjects [free PGH, 24.25 ± 2.62 ng/mL (normal) vs.
8.46 ± 1.16 ng/mL (IUGR) at K28 (P < 0.01);
54.87 ± 8.31 ng/mL (normal) vs. 22.96 ± 5.67
ng/mL (IUGR) at K36 (P < 0.01); Fig. 1]. In addition, IUGR patients showed
higher levels of GHBP at K28 and K36 (Figs. 2 and 3).
Levels of IGF-I and IGF-II were reduced in IUGR (Figs. 4 and 5)
Analysis of the IGFBPs showed no consistent pattern, although IGFBP-3
levels were somewhat lower in IUGR patients at K36 (P
< 0.05).
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Effects of fetal size
The results were also analyzed using the predefined growth categories (IUGR, <10th percentile, n = 16; normal, n = 36; macrosomia, >90th percentile, n = 6), as outlined above. In this analysis, recruitment diagnosis was not considered.
The results found were congruent with those outlined above for the recruitment diagnosis, with reductions in PGH, free PGH, IGF-I, and IGF-II found in IUGR at both gestational ages (P < 0.01). Macrosomia was associated with slightly higher numerical values for most of these parameters, but the differences were not statistically significant. IGFBP-1 at K28 showed a significant negative correlation with birth weight (r = -0.35), but no correlation at K36.
Free vs. total GH
The calculated percentage of total placental GH present in the unbound state varied significantly with diagnosis (by ANOVA, P < 0.0001), but not with gestation. In normal subjects 79 ± 2% (n = 33) of total PGH was unbound compared to 67 ± 4% in IUGR (n = 19; P < 0.01), 65 ± 4% in NIDDM (n = 9; P < 0.01), and 87 ± 2% in IDDM (n = 17; P = 0.06).
Correlation analysis
One of the primary aims of the study was to determine the value of
estimations of maternal GH-related parameters as predictors of fetal
growth. In the correlation analysis (see Tables 2 and 3),
levels of hormonal parameters at K28 and K36 were correlated with birth
weight z-scores, and the interrelationship of the various GH-related
parameters was also explored.
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, PGH, free PGH, and IGF-I measured at K36
correlated positively, whereas GHBP correlated negatively with head
circumference and crown-heel length, suggesting an overall positive
effect of PGH on fetal size. Interestingly, only PGH and free PGH
correlated significantly with ponderal index (birth weight/crown-heel
length3) which is a measure of relative fetal
adiposity. Forward stepwise multiple regression analysis was used to determine whether any combination of GH-related variables was able to predict birth weight z-score. At K28, an r of 0.64 (r2 = 0.41; P < 0.01) was obtained, with IGF-II, IGFBP-1, PGH, GHBP, and IGFBP-3 retained in the model as significant variables. At K36 an r of 0.59 (r2 = 0.35; P < 0.01) was noted, with IGF-I, free PGH, and IGFBP-3 retained in the model. These results suggest that these GH-related variables may account for up to 40% of the observed variance in birth weight.
PGH and free PGH correlated closely (r = 0.98–0.99). PGH correlated negatively with GHBP at both gestational ages. At K28, PGH correlated positively with IGF-II, but not with IGF-I. At K36, significant positive correlations with both IGF-I and IGF-II were seen.
IGFBP-3 showed a significant positive correlation with IGF-I and IGF-II at both gestational ages. IGFBP-3 also correlated significantly with free and total PGH at K28 and K36.
Effects of glycemia
Linear correlation analysis of the effects of maternal glycemia on
GH parameters showed significant effects related to postprandial
glucose levels (Table 4). At K28, there
was a positive correlation between mean postprandial glucose and PGH
(r = 0.57; P = 0.03), free PGH (r = 0.64;
P = 0.01), and IGFBP-3 (r = 0.57;
P = 0.03). GHBP correlated negatively with both
postprandial glucose (r = -0.56; P = 0.03) and
fasting glucose (r = -0.58; P = 0.03) at K28.
Insufficient glucose data were available at K36 for an adequate
correlation analysis. Our previous study (19) showed a positive
correlation between GHBP levels, and maternal prepregnancy weight and
BMI. Because of the possibility that this effect may have been
influencing our conclusions about the relationship between glycemia and
PGH and GHBP levels, multiple regression analysis combining both
measures of maternal size (weight, height, and BMI) and of maternal
glycemia (fasting, postprandial, and clinic-measured glucose) as
determinants of PGH and GHBP was also undertaken at K28. Data for all
of these variables were available for 13 women.
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These multiple regression results suggest that glycemia is related to PGH concentrations by mechanisms independent of maternal weight and BMI. Further, maternal weight and BMI influence both PGH and GHBP levels.
IGFBP-1 did not show significant correlation with indexes of glycemia at K28 or K36, but was significantly lower in NIDDM than in normal subjects at K28 (P < 0.02).
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Discussion |
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Our previous study (19) of the same cohort and another study (29) suggested that there may be a link between the glycemic status of patients and the concentration of GHBP and, hence, the concentration of free GH. In the current work we have also attempted to address this hypothesis.
A highly significant correlation was found between PGH and birth weight and between free PGH with birth weight at both K28 and K36. This is consistent with the finding of Evain-Brion et al. for total PGH (11, 22). Correlations and significance levels were very similar for free and total PGH, reflecting the tight correlation between free and total PGH (r = 0.98–0.99). Note that IGF-I and IGF-II also correlated well with birth weight, consistent with previous studies. IGF-I and IGF-II correlated strongly with each other at both gestation stages, consistent with observations by Ferguson et al. (12). Interestingly, although PGH and free PGH concentrations doubled between K28 and K36, IGF concentrations did not change significantly, suggesting a decrease in IGF responsiveness to GH between these gestational ages. The elevation in GH along with its continuous, rather than pulsatile, secretion are congruent with the well known clinical observation of increased glycemia and insulin resistance during later pregnancy.
IUGR patients showed higher levels of GHBP at K28 and K36 along with reduced PGH concentration. Fisker et al. (30) reported that GH replacement in GH-deficient adults resulted in a reduction in GHBP that was secondary to decreased adiposity. Another recent GH replacement study in GH-deficient adults (31) found a very strong three-way correlation (r > 0.81) among adiposity, serum leptin, and GHBP. Indeed, our earlier study (19) of normal and diabetic pregnancies found a significant correlation between BMI and GHBP. However, the elevated concentration of GHBP in IUGR pregnancy does not appear to result from increased adiposity, because the mean maternal BMI of the IUGR group was not significantly different from that in the normal group.
The present study was also undertaken to determine whether any combination of biochemical variables could predict birth weight. At K28 the combination of IGF-II, IGFBP-1, PGH, GHBP, and IGFBP-3 was the strongest predictor of birth weight. At K36 a combination IGF-I, free PGH, and IGFBP-3 was the best predictor. These results show that maternal GH-related parameters account for up to 40% of the observed variance in birth weight.
IGFBP-1 at K28 showed a significant negative correlation with birth weight (r = -0.35), but not at K36. This is consistent with the observations of Baldwin et al. (32). They reported a correlation coefficient at K20-K24 (r = -0.368) very similar to the one we measured at K28 (r = 0.35) and a weaker but significant negative correlation at K30-K34. In the nonpregnant state IGFBP-1 is thought to regulate the availability of IGF-I. In pregnancy its observed relationship to fetal size suggests a similar role that is independent of PGH status, as IGFBP-I showed no correlation with PGH, but showed a significant negative correlation with birth weight.
At both gestational ages IGFBP-3 correlated significantly with free and total PGH. IGFBP-3 is an index of functionality of the GH/IGF axis in the nonpregnant state (33) with the IGFBP-3 gene under direct control of a GH-responsive promoter (34). Our results suggest that this GH-responsive promoter is likely to have an active regulatory role during pregnancy.
There were significant correlations of PGH and free PGH with IGF-II at both gestational ages, but although the correlation coefficients for IGF-I were similar at K28 and K36, it was significant only at K36. Our finding is consistent with a number of studies (5, 8, 22) that showed decreased maternal serum IGF-I in IUGR, but differs from the results of Witznitzer et al. (35). The latter group did not find a correlation between maternal serum IGF-I and birth weight after week 37, but their study did not include cases of IUGR.
IGFBP-1 did not show a significant correlation with indexes of glycemia at K28 or K36. By contrast, Baldwin et al. (32) found a negative correlation in the third trimester between IGFBP-1 and glucose levels after a 50-g oral glucose tolerance test. Our different result probably reflects methodological differences, because our measurements were taken during fasting and 2 h after breakfast, rather than after an oral glucose load. Although IGFBP-1 did not show significant negative correlation with indexes of glycemia, it was significantly lower in NIDDM patients than in normal subjects at K28 (P < 0.02). The latter result could be consistent with suppression of IGFBP-1 by elevated insulin, as observed in the nonpregnant state (36, 37).
PGH, free PGH, and IGFBP-3 were all strongly correlated with glycemia at K28. This is consistent with the well established antiinsulin and hyperglycemic action of GH (38, 39). Whereas GH correlated positively with glycemia, GHBP correlated negatively. This may be consistent with GHBP normally functioning to inhibit GH metabolic actions in vivo [as it does in vitro (40, 41, 42, 43)]. Limited published data concerning PGH regulation in vitro and in vivo suggested inhibition of PGH secretion at high glucose concentrations and stimulation of PGH during hypoglycemia. Patel et al. (44) described a concentration-dependent inhibition of PGH secretion by glucose in human placental explants and in trophoblast cultures, whereas Bjorkland et al. (45) described a mean 27% increase in PGH during a hyperinsulinemic hypoglycemic clamp (glucose, 2.2 mmol/L) in pregnant IDDM subjects. However, this increase in PGH was detected at glucose levels well below the normal range. It appears to represent a counterregulatory response to hypoglycemia, as seen with pituitary GH in the nonpregnant state. Another group (46) described reduction in PGH concentrations during an oral glucose tolerance test in women with gestational diabetes. This also suggests an inhibitory effect of acute hyperglycemia on PGH secretion.
In light of these studies we might have expected to see a counterregulatory decrease in GH concentrations in response to hyperglycemia. However, we observed a positive correlation between PGH and glycemic status, suggesting that the physiological relationship between long term glycemia and PGH in pregnant patients with diabetes may be different. In our patients glucose was generally somewhat elevated chronically, and no patient demonstrated frank hypoglycemia. In the chronic situation we hypothesize that PGH levels are driving increased glycemia, rather than responding to glycemia in a counterregulatory mechanism.
In general, total PGH and free PGH correlate very closely. This would tend to keep the free or bioavailable GH within tightly set limits for a particular subject, particularly as secretion of PGH, unlike that of pituitary human GH, is not pulsatile (5, 47). Our observations in this and our previous study indicate that large excursions in GHBP concentration only appear to occur in chronic pathological states, such as NIDDM and IUGR, where the levels are elevated and cause a significant reduction in the free fraction of PGH.
In conclusion, maternal free PGH, total PGH, and IGF-I correlate significantly with fetal weight. Free and total PGH correlate strongly with postprandial glycemia at K28. Moreover, maternal GH-related parameters, taken together, account for up to 40% of the observed variance in birth weight. In rodents (48) maternal treatment with IGF-I has been associated with an increase in messenger ribonucleic acid for the placental glucose transporters GLUT1 and GLUT3. As GLUT1 is thought to be responsible for glucose transport from mother to placenta and GLUT3 to be responsible for glucose transport from placenta to fetus, this observation, if congruent in humans, would provide a causal link between maternal GH-related parameters and fetal size.
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Footnotes |
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Received August 2, 1999.
Revised October 21, 1999.
Accepted December 4, 1999.
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