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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2008-0621
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The Journal of Clinical Endocrinology & Metabolism Vol. 93, No. 11 4307-4314
Copyright © 2008 by The Endocrine Society

Fat Mass and Apolipoprotein E Genotype Influence Serum Lipoprotein Levels in Early Adulthood, whereas Birth Size Does Not

R. W. J. Leunissen, G. F. Kerkhof, T. Stijnen and A. C. S. Hokken-Koelega

Department of Pediatrics (R.W.J.L., G.F.K., A.C.S.H.-K.), Subdivision of Endocrinology, Erasmus MC/Sophia Children’s Hospital, 3015 GJ Rotterdam, The Netherlands; and Department of Epidemiology and Biostatistics (T.S.), Leids University Medical Centre, 2300 RC Leiden, The Netherlands

Address all correspondence and requests for reprints to: R. W. J. Leunissen, Erasmus MC/Sophia Children’s Hospital, Room number: Sb 2670, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands. E-mail: r.leunissen{at}erasmusmc.nl.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Background/Objectives: An association between an unfavorable lipid profile and low birth weight has been reported, although this association remains controversial. We hypothesized that birth size does not have any influence on serum lipid levels but fat accumulation during childhood has.

Methods: In the PROgramming factors for GRowth And Metabolism study, a cohort of 297 young adults, aged 18–24 yr, the influence of clinical parameters on total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, lipoprotein a, and apolipoprotein (apo) A-1 and apoB was analyzed with multiple regression modeling. In addition, differences in these lipid levels and ApoE genotype prevalence were analyzed in four subgroups: young adults either born small for gestational age with short stature or with catch-up growth, or born appropriate for gestational age with idiopathic short stature or with normal stature (controls).

Results: Birth length SD score (SDS) and birth weight SDS were no significant determinants of the serum lipid levels, whereas gender, ApoE genotype, adult height SDS, adult weight SDS, and fat mass were. Comparison of the subgroups showed that small for gestational age with short stature subjects had a significantly higher apoB than controls. There were no other significant differences in lipid levels or ApoE genotype prevalence among the four subgroups.

Conclusions: ApoE genotype is an important genetic determinant of lipid levels in young adulthood. Furthermore, fat accumulation during childhood significantly determines serum lipid levels, whereas birth size has no significant contribution. For public health practice, this means that parents and their children need to be informed about the risks of fat accumulation during childhood.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
Increased levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDLc), apolipoprotein (apo) B, and lipoprotein a [Lp(a)], together with reduced levels of high-density lipoprotein cholesterol (HDLc) and apoA-I increase the risk for cardiovascular disease (1, 2, 3, 4). In the literature there is an ongoing discussion whether low birth weight has a significant influence on various lipid levels, like TC, LDLc, HDLc, apoA-I, and apoB. Some reports found an association between low birth weight and high TC levels (5, 6, 7, 8), indicating that subjects born with a low birth weight would have an increased risk for cardiovascular disease due to an unfavorable lipid profile. Conversely, systematic reviews showed no associations between serum birth weight and lipid levels (9, 10). Also in subjects born small for gestational age (SGA), normal lipid profiles were found, although a distinction between SGA subjects with or without catch-up growth was seldom made (11, 12, 13, 14, 15, 16).

ApoE is an important regulator of serum lipid levels because it affects hepatic binding and uptake of several lipoproteins (17). The affinity for the LDLc receptor is different for the six ApoE genotypes. ApoE genotype {epsilon}3/{epsilon}3 is most common. Subjects carrying the {epsilon}2 allele have a lower affinity for the LDLc receptor, which results in an up-regulation of the LDLc receptor. This leads to more uptake of lipoproteins and, thus, lower serum lipid levels. In contrast, subjects carrying the {epsilon}4 allele have a higher affinity for the LDLc receptor, which results in higher lipid levels (17, 18). The {epsilon}4 genotype is associated with higher coronary disease risk (18, 19). So far, no study has investigated the influence of the ApoE genotype in subjects born SGA and subjects with idiopathic short stature (ISS).

Recently, we demonstrated in young adults of the PROgramming factors for GRowth And Metabolism study that birth size had no influence on insulin sensitivity in young adulthood, whereas fat accumulation during childhood did (20). Therefore, we hypothesized that prenatal growth, reflected by size at birth, has no influence on serum lipid levels in young adulthood, whereas postnatal growth and genetic factors do. To test our hypothesis, we investigated the influence of birth length, birth weight, adult size, body composition, and ApoE genotype on TC, triglycerides (TGs), HDLc, LDLc, apoA-I, apoB, and Lp(a) in 297 young adults. In addition, we investigated if there were differences in the prevalence of ApoE genotype and serum lipid levels among four clinically relevant subgroups of young adults: born SGA with either short adult height (SGA-S) or normal adult height (SGA-CU), or born appropriate for gestational age (AGA) with either ISS or normal adult height (controls).


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

The PROgramming factors for GRowth And Metabolism study cohort consists of 323 healthy subjects with an age between 18 and 24 yr. They were randomly selected from hospitals in The Netherlands, were they had been registered because of their being small at birth (SGA with a birth length < –2 SD) (21) or showing short stature (after being born SGA or AGA with an adult height < –2 SD) (22). In addition, healthy subjects of different schools were randomly asked to participate as controls. Only those born at 36 wk or more of gestation, born singleton and Caucasian, were invited to participate to exclude a potential influence of prematurity, parity, and ethnicity, respectively. All subjects fulfilled the same inclusion criteria: an uncomplicated neonatal period without signs of severe asphyxia (defined as an APGAR score less than three after 5 min); and without sepsis or long-term complications of respiratory ventilation, such as bronchopulmonary dysplasia. Subjects were excluded if they had been suffering from any serious condition or had been receiving any treatment known to interfere with growth (e.g. GH deficiency, severe chronic illness, emotional deprivation, GH treatment, treatment with glucocorticosteroids, radiotherapy), or if they had endocrine or metabolic disorders, chromosomal defects, syndromes, or serious dysmorphic symptoms suggestive of a yet unknown syndrome. Birth data were taken from records of hospitals, community health services, and general practitioners. The Medical Ethics Committee of Erasmus Medical Centre, Rotterdam, The Netherlands, approved this study. Written informed consent was obtained from all participants.

Of the 323 participants who entered the study, complete data on lipid levels, body composition, and anthropometry were obtained in 297 subjects. Based on the SD scores (SDSs) of birth length and adult height, the subjects were also assigned to one of four subgroups. Normal birth length and adult height were set at an SDS of more than –1 (± 0.1 SDS), to increase the contrast among the subgroups. This resulted in an increased statistical power to identify differences among subgroups. Of the 297 subjects, 208 fulfilled the inclusion criteria for the subgroup analyses:

Born SGA (< –2 SDS) with a SGA-S (< –2 SDS)
Born SGA (< –2 SDS) with catch-up growth resulting in a SGA-CU (> –1 SDS)
Born AGA (birth length > –1 SDS) with growth retardation resulting in a SGA-S (< –2 SDS) (ISS)
Born AGA (birth length > –1 SDS) and a normal adult height (> –1 SDS) (controls)

Measurements

All participants were invited to visit Erasmus Medical Centre in Rotterdam. They had been fasting for 12 h, and had abstained from smoking and alcohol for 16 h. Height was measured to the nearest 0.1 cm by a Harpenden stadiometer and weight to the nearest 0.1 kg by a scale (Servo Balance KA-20-150S, Servo Berkel Prior, Katwijk, The Netherlands). All anthropometric measurements were performed twice, and the mean value was used for analysis.

In all participants, lean body mass (LBM) and fat mass (FM) were measured on one dual-energy x-ray absorptiometry machine (Lunar Prodigy; GE Healthcare, Chalfont St. Giles, UK). Quality assurance was performed daily. The intraassay coefficients of variation for lean tissue and fat tissue were 1.57–4.49% and 0.41–0.88%, respectively (23).

Laboratory methods

Fasting levels of TC, TG, HDLc, apoA-1, apoB, and Lp(a) were measured. LDLc was calculated using the Friedewald formula: LDLc (mmol/liter) = TC – HDLc – 0.45 x TGs.

TC and TG were measured using an automated enzymatic method with the CHOD-PAP reagent kit and with the GPO-PAP reagent kit, respectively (Roche Diagnostics, Mannheim, Germany). HDLc was measured using a homogenous enzymatic colorimetric assay (Roche Diagnostics). apoA-1, apoB, and Lp(a) were determined by rate nephelometry on the Image Immunochemistry System, according to the manufacturer’s instructions (Beckman Coulter, Mijdrecht, The Netherlands). The intraassay variations of measurements of TC, TG, and HDLc were 2.9, 3.3, and 3.9%. Between-run coefficients of variation for apoA-1, apoB, and Lp(a) were 4.2, 2.8, and 6.9% at levels of 0.94, 0.53, and 0.35 g/liter, respectively.

ApoE genotype was analyzed in 246 of the 297 subjects. There were no differences in clinical characteristics between the 246 with ApoE genotyping and the 51 without. The ApoE genotype was determined on DNA samples using a PCR, followed by enzymatic digestion using methods previously described (24).

Statistical analysis

SDSs for birth length, birth weight, adult height, and adult weight were calculated to correct for gestational age, gender, and age (21, 22). Percentage of body fat was calculated as: [body fat (kg)/weight (kg)] x 100%. Due to a skewed distribution, TG, LDLc, apoB, and Lp(a) were log transformed. Multiple linear regression (MR) analysis was performed to determine the association between birth size and the lipid variables, correcting for age, gender, and adult size. First, we entered age, gender, birth length SDS, birth weight SDS, and adult height SDS (model 1). The interaction term birth length SDS adult height SDS, birth length SDS, and adult height SDS were added to all following MR models because the study cohort had been selected on birth length and adult height. This ensured that the effect of these variables was modeled correctly. Second, we added adult weight SDS to the model (model 2). Third, weight SDS was replaced by FM and LBM to specify weight (model 3). The interaction term gender LBM was added to the model to investigate if the effect of LBM on women was different than the effect of LBM on men. Finally, we added ApoE genotype to investigate if this had a significant influence on serum lipid levels (model 4). ApoE genotype was added with ApoE {epsilon}3/{epsilon}3 as a reference group and the other five genotypes as dummy variables. The model assumptions independence, linearity, normality, and homoscedasticity were examined in a histogram of the residuals, and subsequently by plotting the residuals against each predictor and against predicted values.

Differences in the prevalence of ApoE genotype among the subgroups were analyzed with the Fisher’s exact test. ANOVA was used to determine whether there were differences among the subgroups with regard to the group characteristics. Bonferroni correction was used for pair-wise group comparisons. To determine differences among the groups corrected for age, gender, percent body fat, and ApoE genotype, an analysis of covariance model was used, with controls as a reference group, and SGA-S, SGA-CU, and ISS as dummy variables. Statistical package SPSS version 11.0 (SPSS, Inc., Chicago, IL) was used for analysis. Results were regarded statistically significant if P was less than 0.05.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Total study population

The clinical characteristics and lipid levels of the total study population are shown in Table 1Go. The mean (SD) age at measurement was 20.9 yr (1.7). The data on body composition and lipid concentrations presented in Table 1Go are not yet corrected for age, gender, and adult size. Table 2Go shows the prevalence of ApoE genotypes in the total study population.


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  		TABLE 1. Clinical characteristics and lipid concentrations of the total study group, four subgroups, and unclassified group

 

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  		TABLE 2. Prevalence of the six genotypes of ApoE (%) in the total study population, four subgroups, and unclassified group

 
By MR, we evaluated the contribution of birth size to the serum lipid levels in young adulthood. First, a multiple regression was performed with birth length SDS and birth weight SDS after correction for age, gender, and adult height SDS (model 1). Second, adult weight SDS was added (model 2). Subsequently, we replaced weight SDS for FM and LBM (model 3). Finally, ApoE genotype was added (model 4). The results are shown in Table 3Go.


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  		TABLE 3. MR of the lipid variables

 
The first model showed that gender had a significant influence on all serum lipid levels, whereas birth length and birth weight did not. The second model showed that shorter adult height SDS and higher adult weight SDS resulted in a significantly higher TC, TG, LDLc, and apoB. For HDLc the relation was inversed; a higher adult height SDS and a lower adult weight SDS resulted in a significantly higher level of HDLc. For apoA-1 this was not significant. Birth length SDS and birth weight SDS remained not significant.

After replacement of adult weight SDS by FM and LBM, FM was a significant determinant for all lipid variables, except for HDLc and apoA-1 (model 3). The significance of gender disappeared for most lipid variables, but adult height SDS remained a significant determinant of lipid levels after correction for FM and LBM, except for apoA-1. LBM, birth length SDS, and birth weight SDS were not significant determinants for any lipid variable.

Finally, several ApoE genotypes were significant determinants for TC, LDLc, and apoB. Subjects carrying a {epsilon}2 genotype have lower levels, whereas subjects carrying a {epsilon}4 genotype have higher levels. In these models, the explained variance increased by 6, 10, and 7%, respectively. The increase in explained variance established by FM was less, 1, 2, and 2%, respectively. For Lp(a), none of the variables had a significant influence (data not shown). An additional correction for smoking did not change the results in the models of Table 4Go (data not shown).


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  		TABLE 4. Differences in LDLc and apoB levels among subgroups and controls after correction for age, gender, percent body fat, and ApoE genotype

 
Comparison of subgroups

Of the 297 subjects, 208 could be included in one of the four subgroups. Their clinical characteristics and serum lipid levels are shown in Table 1Go. The significant differences in birth size and adult size among the subgroups are due to the selection criteria. SGA-S and ISS subjects had a significantly lower LBM than the other subgroups, when no corrections were made for age, gender, and adult weight. Almost all uncorrected lipid levels were higher in the SGA and ISS groups compared with those of controls. However, levels were not significantly different and within the normal range.

Table 2Go and Fig. 1Go show the prevalence of ApoE genotype in the subgroups. In SGA-S subjects, the prevalence of the {epsilon}3/{epsilon}3 genotype was higher, and the favorable {epsilon}2/{epsilon}3 genotype was lower than in controls, whereas the SGA-CU subjects had a higher prevalence of the unfavorable {epsilon}3/{epsilon}4 genotype than controls, but these differences were not significant.


Figure 1
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  		FIG. 1. The prevalence of ApoE genotype in the subgroups.

 
We also analyzed if there were differences in lipid levels among the subgroups, after correction for age and gender. These analyses showed that SGA-S subjects had significantly higher LDLc and apoB than controls (P = 0.014 and P = 0.006, respectively) (Table 4Go). SGA-CU subjects almost had a significantly higher apoB than controls (P = 0.051). There were no other significant differences in lipid levels among the four subgroups after correction for age and gender. Because of differences in FM and the prevalence of ApoE genotype among the subgroups, an additional correction for percent body fat and ApoE genotype was performed. Only the difference in apoB levels remained significant between the SGA-S subjects and controls (P = 0.034).


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Our study in 297 young adults shows that birth length SDS and birth weight SDS were not significant determinants of serum lipid levels, whereas gender, adult height SDS, adult weight SDS, FM, and ApoE genotype were. ApoE genotype was the most important determinant. There was no difference in ApoE genotype prevalence among the subgroups, although the SGA-S group had a lower prevalence of the favorable {epsilon}2 allele. After correction for age, gender, percent body fat, and ApoE genotype, comparison of the subgroups showed that only apoB was significantly higher in SGA-S subjects than in controls.

Birth weight SDS was not a significant determinant in any of the models. Some studies found a negative correlation between birth weight and cholesterol after correction for adult weight, but this might have occurred because they did not adjust birth weight for gestational age (25, 26, 27). Two reviews concluded that there was no relationship between birth weight and adult lipid profile (9, 10). Birth length SDS was also not a significant determinant in any of the models. In other reports the relationship between birth length and lipid levels remained unclear (28, 29, 30). Our study results showed no significant relation between birth weight as well as birth length and adult serum lipid levels after correction for age, gender, ApoE genotype, and adult size.

All serum lipid levels were higher in young women than men after correction for birth size and adult size, as shown by models 1 and 2. However, after correction for FM, this significant difference between women and men disappeared. This indicates that for serum lipid levels, gender-related body composition is an important factor in young adulthood.

The first model showed that for all lipid variables, except HDLc and apoA-1, a shorter adult height and a higher adult weight resulted in significantly higher serum lipid levels. It is likely that this reflexes FM as subjects with a similar adult weight, but a shorter adult height has a higher FM. The same accounts for HDLc, but inversely.

Several reports have indicated that weight gain during life might result in an unfavorable lipid profile (31, 32). Although it has been suggested that this is due to an increase in FM, none of the studies could substantiate this theory because FM had not been measured. Our third model showed that an increase in FM resulted in higher serum levels of TC, TG, LDLc, and apoB. Only HDLc and apoA-1 were not related to FM. The latter might indicate that HDLc and apoA-1 are not directly influenced by fat or fat accumulation but more by other mechanisms like an impaired lipolysis of TG-rich lipoproteins. This would lead to a decreased transfer of apos and phospholipids from TG-rich lipoproteins to HDLc (33).

Next to FM, other factors determine serum lipid levels, like genetic factors (34, 35). In our total study population, the prevalence of ApoE genotypes was comparable with that of 86,000 disease-free adults (18). ApoE genotype is an important gene in the determination of especially TC, LDLc, and apoB, as shown by the last MR model. Not all ApoE genotypes were significant, but this might be due to the relatively small number of subjects in these subgroups. The explained variance increased from 6–16% to 14–21% when only ApoE genotype was added to the model. This may suggest that ApoE genotype is more important than body composition in determining lipid levels in young adulthood.

Comparison of the subgroups showed a significant difference in LDLc and apoB between SGA-S subjects and controls, after correction for age, gender, and percent body fat. After an additional correction for ApoE genotype, the difference in LDLc disappeared. Various studies investigated lipid profiles in SGA subjects because it is thought that subjects born SGA have an increased risk for an unfavorable lipid profile, due to their low birth size. One study found a less favorable lipid profile in 3-d-old SGA infants (36), but most studies showed no increased lipid levels in children born SGA, although they did not differentiate between SGA children with or without catch-up growth (11, 12, 13, 14, 15). Only Arends et al. (11) investigated various lipid variables in prepubertal SGA-S subjects but did not find any differences compared with age and height-matched controls.

Only one study was performed in young adults. They found significantly lower levels of HDLc and higher levels of TG in a mixed group of SGA-S and SGA-CU subjects (16). Our present study specified the SGA population, and in contrast to our expectations, not SGA-CU but SGA-S subjects had higher apoB in early adulthood, although all lipid levels were still in the normal range. The reason for these higher levels remains unknown, but a reduced amount of LDLc receptors might be an explanation because this leads to increased levels of apoB. In SGA-CU subjects, apoB was borderline significantly higher than controls, but this difference disappeared after an additional correction for percent body fat and ApoE genotype. The reason that SGA-CU subjects did not have a significant difference in lipid levels might be due to the fact that the difference in FM SDS between the SGA-CU and other subgroups is not large enough. Only one study reported lipid levels in ISS children, which were normal (37). Our results in the adult ISS group are in line with that study.

ApoE genotype did not differ among the subgroups, although SGA-S subjects had a lower prevalence for the advantageous {epsilon}2 allele ({epsilon}2/{epsilon}2 and {epsilon}2 /{epsilon}3). Infante-Rivard et al. (38) concluded that the {epsilon}2 allele might protect from intra-uterine growth retardation. Our results showed that SGA-CU subjects had the same prevalence of the {epsilon}2 allele, so we could not confirm their results.

Our study population consisted of a relative high percentage of subjects born SGA and short adults. This created greater contrast in the study population, which contributed to a better statistical model in which relationships between various factors could be detected with more statistical power. In addition, this study population allowed comparison between clinically relevant subgroups. Better insight could be obtained in the differences among the subgroups, with regard to serum lipid levels after correction for age, gender, ApoE genotype, and adult size. Although a larger sample size in our subgroup analyses might reveal smaller significant differences in lipid levels, this sample size revealed significant differences in two lipid variables among the subgroups and controls. Therefore, we state that our negative results in the subgroup analyses are justified.

In conclusion, our study demonstrates that prenatal growth has no influence on serum lipid levels in young adulthood, whereas postnatal growth, specified as fat accumulation during childhood, and genetic factors do. For public health practice, this means that parents of all children, regardless of size at birth, should be aware of the risks of fat accumulation in their children. Therefore, health care workers need to inform parents about the risks of fat accumulation during childhood.


   Acknowledgments
 
We thank Mrs. J. Dunk and Mrs. I. van Slobbe, research nurses, for their technical assistance and support with data collection. We acknowledge Mrs. J. Sluimer for checking the dual-energy x-ray absorptiometry results. We also thank Drs. Y. B. de Rijke and A. W. van Toorenenbergen for analyzing the lipid values.


   Footnotes
 
This work was supported by Netherlands Organisation for Scientific Research (NWO) [A.C.S.H.-K. received the ASPASIA-award (Grant 015 000 088)], and by grants from Revolving Fund 2001 and Vereniging Trustfonds, Erasmus University Rotterdam, and a grant by the Jan Dekkerstichting/Dr. Ludgardine Bouwmanstiching and the Stichting De Drie Lichten, The Netherlands.

Disclosure Statement: The authors have nothing to disclose.

First Published Online August 26, 2008

Abbreviations: AGA, Appropriate for gestational age; apo, apolipoprotein; FM, fat mass; HDLc, high-density lipoprotein cholesterol; ISS, idiopathic short stature; LBM, lean body mass; LDLc, low-density lipoprotein cholesterol; Lp(a), lipoprotein a; MR, multiple linear regression; SDS, SD score; SGA, small for gestational age; SGA-CU, small for gestational age with normal adult height; SGA-S, small for gestational age with short adult height; TC, total cholesterol; TG, triglyceride.

Received March 18, 2008.

Accepted August 18, 2008.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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R. W. J. Leunissen, G. F. Kerkhof, T. Stijnen, and A. Hokken-Koelega
Timing and Tempo of First-Year Rapid Growth in Relation to Cardiovascular and Metabolic Risk Profile in Early Adulthood
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