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August 2000, Volume 24, Number 8, Pages 945-953
Table of contents    Previous  Article  Next   [PDF]
Paper
Common apolipoprotein A-IV variants are associated with differences in body mass index levels and percentage body fat
M Lefevre, J C Lovejoy, S M DeFelice, J W Keener, G A Bray, D H Ryan, D H Hwang and F L Greenway

Pennington Biomedical Research Center, Baton Rouge, LA, USA

Correspondence to: M Lefevre, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70808-4124, USA. lefevrm@mhs.pbrc.edu

Abstract

OBJECTIVE: To determine the relationship between two common apoA-IV variants (Thr347right arrowSer; Gln360right arrowHis), and body mass index (BMI) and percentage body fat.

DESIGN: Cross-sectional study.

SUBJECTS: Eight-hundred and forty-eight subjects screened for participation in ongoing clinical studies.

MEASUREMENTS: ApoA-IV genotype, body mass index, waist-to-hip ratio and percentage body fat by bioelectric impedance.

RESULTS: Participants had an average age of 41±12 y and an average BMI of 28.2±5.5 kg/m2. Individuals homozygous for the Ser347 allele had higher BMI (32.3±6.6 vs 28.6±5.3 kg/m2; P<0.01) and percentage body fat (36.9±7.8 vs 31.0±9.6%; P<0.05) compared with individuals homozygous for Thr347. In contrast, the presence of at least one copy of the His360 allele was associated with lower BMI (27.2±5.0 vs 28.4±5.6 kg/m2; P<0.05) and percentage body fat (28.6±8.2 vs 30.7±9.1%; P<0.05). The genotype effects persisted after normalization of the data for the potential confounding effects of gender, age and race. When grouped by BMI percentile, the frequency of the Ser347/Ser347 genotype increased while the frequency of the His360 allele decreased with increasing BMI.

CONCLUSIONS: These data suggest a role for apoA-IV in fat storage or mobilization and that genetic variations in the apoA-IV gene may play a role in the development of obesity.

International Journal of Obesity (2000) 24, 945-953

Keywords

apoA-IV; obesity; genetics; gene polymorphisms

Introduction

Human apolipoprotein A-IV (apoA-IV) is a 46 kDa protein synthesized primarily by the intestine with some additional synthesis by the liver.1 Hepatic apoA-IV synthesis can be up regulated by glucocorticoids,2,3,4 while the effects of insulin are conflicting.2,4 Intestinal apoA-IV synthesis is acutely up-regulated by dietary fat5,6,7,8 and may be particularly responsive to presence of dietary fat in the ilium.8 Intestinally synthesized apoA-IV is secreted into the intestinal lymph as a component of triglyceride-rich lipoproteins and in a lipoprotein-unassociated form.5 Upon entering the plasma, apoA-IV rapidly dissociates from triglyceride-rich lipoproteins and appears in both high-density lipoprotein (HDL) and the lipoprotein-unassociated form.9 The relative amount of plasma apoA-IV in HDL has been estimated to be between 15 and 70%10,11 and is influenced by the activities of both lecithin:cholesterol acyltransferase12 and cholesterol ester transfer protein,13 and the hydrolysis of triglyceride-rich lipoproteins by lipoprotein lipase.14

Plasma apoA-IV levels are determined mainly by apoA-IV production rates15 and are responsive to acute changes in dietary fat.5,10,16 Plasma apoA-IV increases between 15 and 30% in response to acute ingestion of moderate fat loads (40-55 g), with the peak in apoA-IV concentration lagging behind the peak in triglyceride concentration by 1-2 h. Plasma apoA-IV levels are also influenced by longer term changes in dietary fat levels. Increases or decreases in the percentage of calories from fat produce parallel changes in apoA-IV levels after one week.17 However, these changes are transitory with apoA-IV levels returning towards baseline levels by 2 weeks.

Despite reasonable knowledge regarding apoA-IV's metabolism, its physiological role remains to be unequivocally established. Several functions for apoA-IV have been proposed, including: (1) activating lecithin:cholesterol acyltransferase;18 (2) modulating the activities of both of lipoprotein lipase19 and cholesterol ester transfer protein;20 and (3) facilitating cholesterol removal from peripheral cells.21,22 Particularly interesting are studies suggesting that apoA-IV inhibits gastric emptying23 and serves as a satiety factor in response to ingestion of dietary fat.24,25,26

Two common apoA-IV variants have been described: Gln360right arrowHis and Thr347right arrowSer.27,28 Both variants influence lipid and lipoprotein metabolism.29,30,31,32,33,34,35,36,37 Given the evidence that apoA-IV may be involved in the inhibition of food intake following consumption of a high-fat meal, we examined the potential effects of these two apoA-IV variants on indices of body weight and body fat content. A preliminary report of these findings has been presented.*

Methods

Study population

The population selected for analysis was derived from a pool of individuals seeking participation in ongoing clinical studies conducted at the Pennington Bio-medical Research Center. The eight studies selected for inclusion were classified as being either: (1) dietary intervention; (2) weight loss; or (3) population survey. The four dietary intervention studies sought to examine the acute or long-term effect of modifying dietary fat on risk factors for coronary heart disease. Participants were screened by telephone prior to presentation to the clinic to ensure they were free of major chronic diseases (including cardiovascular disease, diabetes or other endocrine disorders, cancer within 5 y, and gastrointestinal disease) and had a self-reported body mass index (BMI) of <32 kg/m2 in two studies, <30 kg/m2 in one study, and <28 kg/m2 in the remaining study. The three weight loss studies sought to examine the effects of various pharmacological approaches to reduce weight in obese individ-uals. Participants were screened by telephone prior to presentation to the clinic to be free of major chronic diseases and to have a BMI of >30 kg/m2 in one trial and >27 kg/m2 in the remaining two trials. The population survey consisted of individuals participating in work-site and community health screenings conducted in collaboration with a local hospital. All individuals gave informed consent as a component of the screening process for their individual studies and all individual protocols and consent forms were approved by the Institutional Review Board.

Anthropometric measurements

For the dietary intervention and weight loss trials, body weights were measured to the nearest 0.1 kg and height measured to the nearest 0.1 cm. For the population survey, weight and height were measured to the nearest pound and inch, respectively, and then converted to metric units. In selected participants, percentage body fat was estimated by bioelectric impedance using a RJL Systems BIA-101A and gender-specific equations.38

ApoA-IV genotyping

ApoA-IV was genotyped for both the Ser347 and His360 alleles by PCR amplification followed by restriction enzyme digestion essentially as described by Hixson et al 39 using buffy coat DNA as a template. Genotype information was available on 613 subjects. In instances where no buffy coat was available (235 subjects), apoA-IV was phenotyped for the presence of the His360 allele by isoelectric focusing as described by Menzel et al.29 In all cases, the presence of the A-IV-2 band was assumed to be due to the His360 allele as opposed to the rarer four amino acid deletion variant.39 In 376 samples which were both phenotyped and genotyped for His360 allele, only one sample gave discordant results (phenotype=A-IV 1-2; genotype=Gln360/Gln360).

Statistical analysis

All dependent variables were analyzed by ANOVA with Bonferroni post-hoc adjustments where appropriate. Multiple linear regression was used to estimate regression coefficients associated with each genotype or combination of genotypes and to estimate the percentage of the study population variance accounted for by each genotype or combination of genotypes. Logistic regression was used to determine if each variant apoA-IV genotype frequency differed as a function of defined BMI percentiles estimated from NHANES III data.40

To minimize potential confounding effects of gender and age between genotype groups, the BMI data were adjusted and presented in two additional ways. Using data from NHANES III41 multiple linear regression was used to determine the gender-specific linear and quadratic effects of age on BMI using the published mean BMI values as the dependent variable and the midpoint age and (midpoint age)2 for each of five age ranges as predictor variables. The residuals between the actual and predicted BMI based on the gender-specific regression analysis was added to the gender-specific means to provide an adjusted BMI. The adjusted BMI was subsequently used in the ANOVA to test for differences among genotypes.42 Data for percentage body fat were similarly adjusted. However, since no national reference data were available for percentage body fat, the regression equations were instead developed using the actual data set. Finally, Z-scores for BMI were derived for each individual by taking the difference between the observed BMI and the age-and gender-specific BMI mean from NHANES III and dividing it by the age-and gender-specific standard deviation. The derived value estimates the number of standard deviations an individual is away from the national populationaverage for that specific age and gender group.

Results

Population description

Data from 848 individuals were included in this analysis (Table 1). The majority of the individuals were those seeking participation in one of several controlled dietary intervention trials. While the overall study sample had roughly equal numbers of men and women, more men than women were involved in the dietary intervention studies while more women than men were in the population survey and weight loss studies. Individuals from the dietary intervention studies tended to be younger, were composed of a greater percentage of non-Caucasians (primarily African Americans), and had a lower average BMI. Not unexpectedly, individuals from the weight loss studies had the greatest average BMI.

Analysis of apoA-IV genotype frequencies for both the Ser347 and His360 alleles revealed clear trends across study type which appeared to be related to BMI. There was a clear and significant (P=0.003) increase in the frequency of the homozygous Ser347/ Ser347 genotype as BMI increased across study type. A trend was also noted for a inverse relationship between the frequency of the Ser347/Thr347 genotype and BMI across study type, but this was not significant (P=0.17). The frequency of genotypes with the His360 allele declined across study type as BMI increased, although these differences were also not statistically significant.

Genotype frequency by BMI percentile

The apparent relationship between apoA-IV genotype frequency and BMI levels across study types prompted us to examine apoA-IV genotype frequency directly as a function of BMI. Individuals were classified relative to gender-and age-specific BMI percentiles from NHANES III.40 The frequency of the different genotypes at codon 347 differed significantly as a function of BMI interval (Figure 1A). The frequency of the Ser347/Ser347 genotype increased progressively across increasing BMI percentiles (P<0.05 for trend). In individuals at or above the 75th BMI percentile the frequency of the Ser347/ Ser347 genotype was over five times that of individuals BMI below the 25th percentile. In contrast, the frequency of the Ser347/Thr347 genotype appeared to decline with increasing BMI percentile although this relationship escaped significance (P=0.06).

The frequency of the different genotypes at codon 360 was also significantly related to BMI (Figure 1B) with a significant trend (P<0.05) towards a de-creasing frequency of both the His360/Gln360 and His360/His360 genotypes with increasing BMI percentile. The His360 allele frequency in individuals at or above the 75th BMI percentile was 45% lower than that of individuals below the 25th percentile.

Effect of genotypes on BMI and body fat

Both apoA-IV variants were found to be significantly associated with differences in BMI and percentage body fat. Individuals who were homozygous for the Ser347 allele had significantly and substantially higher mean BMI levels (Table 2). This relationship persisted after gender-specific adjustment of the BMI for the linear and quadratic effects of age. The presence of the Ser347 allele still had a significant effect after analysis of the BMI data in terms of Z-scores (normalized to age-and gender-specific means and standard deviations based on NHANES III data41). The higher BMI in Ser347 homozygous subjects was also associated with higher percentage body fat. Interestingly, those with just one Ser347 allele had significantly lower percentage body fat and tended to have a lower BMI.

In contrast to the effects of the Ser347/Ser347 genotype, the presence of at least one His360 allele was associated with a significantly lower BMI (Table 3). This difference persisted after adjustment of the BMI for age effects or when expressed as a Z-scores. Those individuals who had at least one copy of the His360 allele also had slightly, but significantly lower percentage body fat. When analyzed separately, the four individuals homozygous for the His360 allele had a mean adjusted BMI of 24.0±0.8 kg/m2. While this suggests that those homozygous for the His360 may have the lowest overall average BMI, this did not reach statistical significance when compared with those homozygous for Gln360, presumably because of the small numbers of homozygous His360 individuals.

Variants at neither the 347 nor 360 codon affected body fat distribution as measured by the waist-to-hip ratio (data not shown).

Both allele frequency and average BMI can differ as a function of race thereby confounding the interpretation of the results. Although there were no significant differences in BMI between Caucasians and African Americans (28.3±5.5 and 27.7± 4.5 kg/m2, respectively) in this study population, significant differences in genotype frequency were observed. The frequency of the Ser/Thr (0.205 vs 0.320), Ser/Ser (0.000 vs 0.045), Gln/His (0.050 vs 0.154) and His/His (0.000 vs 0.006) genotypes were all significantly lower in African Americans vs Caucasians. We therefore re-examined the effects of both variants on BMI and percentage body fat in Caucasians only (Tables 4 and 5). With the exception of percentage body fat with His360, the effects of both variants remained statistically significant in this population subset.

Similar subgroup analyses for the effects of the Ser/Ser genotype in African Americans was not possible because of its absence. Analysis of the effects of the His360 allele showed similar trends in African Americans (adjusted BMI: Gln/Gln, 28.0±4.4; His/*, 25.0±4.2; P=0.15), but was not statistically significant. No evidence of a genotype by race interaction was found. Similarly, no evidence of a genotype by gender interaction was found.

Analysis of the haplotypes confirmed32 a strong linkage disequilibrium between the two variants (Table 6). Individuals with Ser/Ser+Gln/Gln had significantly higher adjusted BMI vs all other haplotypes. The association of a lower adjusted BMI with the presence of the His360 allele was independent of the genotype at position 347. In Caucasians, but not the entire study population, an association between a lower adjusted BMI and the presence of one copy of the Ser347 and was also independent of the genotype at position 360.

Finally, using multiple regression analysis, we confirmed the independent effects of each genotype on adjusted BMI (Table 7). The best model was one in which the number of His360 alleles was entered as a single term and the Ser347/Thr347 and Ser347/Ser347 genotypes were each entered separately. The Ser347/ Ser347 term was significant and the His360 term was marginally significant in the total study population while all three terms were significant when only Caucasians were included in the analysis. The estimated average effect of having two Ser347 alleles was to increase adjusted BMI by 2.6-3.3 kg/m2 while the average effect of each His360 allele was to decrease adjusted BMI by 1.1-1.4 kg/m2. In Caucasians, the presence of the Ser347/Thr347 genotype was estimated to lower BMI by about 1.2 kg/m2. In the total population, these apoA-IV variants explained 2.5% of the population variance in adjusted BMI while in Caucasians, 3.8% of the population variance was explained.

Discussion

In this report we show that two separate variants in the apoA-IV gene are associated with differences in both BMI and percentage body fat. The genotype effects on BMI persisted after the data were adjusted for gender-specific age effects, after they were normalized relative to age-and gender-specific means and standard deviations derived from NHANES III population survey data,41 and after the analysis was restricted to a single race to minimize potential confounding effects. Furthermore, the frequency of both apoA-IV genotypes varied as a function of BMI percentile in a manner consistent with apparent effects on BMI.

The most consistent effects were shown for individuals who were homozygous for the Ser347 allele or who had at least one copy of the His360 allele. Individuals with two copies of the Ser347 allele had a BMI that was on average 3 kg/m2 greater than those individuals without either apoA-IV variant. Each copy of the His360 allele was associated with about a 1 kg/m2 reduction in BMI with the effects being at least additive in homozygous individuals. Paradoxically, the presence of a single copy of the Ser347 allele was associated with lower BMI, especially in Caucasians where the effects were of the same magnitude as the His360 allele. The presence of one His360 allele and one Ser347 allele resulted in apparent additive effects with average BMI at least 2 kg/m2 lower than individ-uals without either variant.

Both variants occur within the carboxyl-terminal domain of apoA-IV in a region which shares little sequence homology with a preceding stretch of 14.5 tandem repetitions of a 22 amino acid amphipathic alpha-helical element characteristic of many apolipoproteins.43 The Ser347 mutation occurs at the end of a stretch of 13 amino acids in helical conformation and extends a region of four amino acids in coil conformation by one amino acid.27 The His360 variant resides within an evolutionarily conserved region containing four repeats with a consensus sequence of Glu-Gln-X-Gln.28 The mutation occurs at the X position within the second repeat and interrupts a region of high chain flexibility.28 It has been reported that the His360 variant has more alpha-helical structure, is more hydrophobic and has a greater ability to penetrate phospholipid surfaces.44 The greater ability to associate with HDL has been used to explain the slower fractional catabolic rate observed for the His360 variant.15 Since there are no known functional properties attributed to carboxyl-terminal region of apoA-IV, the significance of the reported conformational changes associated with either variant are difficult to interpret.

An effect of apoA-IV polymorphisms on BMI has been previously reported. In 375 control subjects participating in the European Atherosclerosis Research Study II, the presence of at least one Ser347 allele was associated with a greater BMI and waist-to-hip ratio, while the presence of at least one His360 allele was associated with a lower BMI.45 Additionally Hanis et al 31 in a study of Mexican-Americans reported lower BMI levels in individuals with the His360 allele (determined by IEF) than in those without the allele (26 vs 28 kg/m2), but the difference escaped significance (P=0.07). They further reported on the identification of four rare apoA-IV variants, two of which were found in non-insulin-dependent diabetics with extreme obesity and a third found in an individual with a BMI in the lowest decile. Our data are consistent with these previous findings of an effect of apoA-IV gene variation on BMI.

Apart from their effects on BMI and body fat, there exists ample evidence that these two variants are associated with physiological changes in both lipid and carbohydrate metabolism. The His360 allele has been most extensively studied. The presence of the His360 allele has been associated with higher plasma levels of HDL cholesterol,29,30,36 apoA-I31 and LDL cholesterol,32,33 and lower plasma levels of triglycerides34,36 and Lp(a)32,35 as well as decreased cholesteryl ester transferase activity.46 The Ser347 variation has also been associated with differences in lipids and lipoproteins including lower plasma levels of LDL cholesterol32,37 and apoB32 and a clear trend for increased plasma levels of HDL cholesterol.32 Furthermore, individuals with the His360 allele display an attenuated LDL cholesterol response to changes in dietary saturated fat and cholesterol,47,48 while those with the Ser347 allele display a greater decrease in LDL cholesterol and apoB to changes in diet.49

The physiological effects of these variants are not limited to lipid and lipoprotein metabolism. The His360 allele has also been associated with higher glucose levels50 and higher fasting insulin level in men.33 For glucose, a significant interaction was observed with body weight; glucose levels increased to a greater extent with increasing body weight in individuals with the His360 allele.50 This observation may be related to the substantial increase in relative risk for myocardial infarction in obese type 2 diabetics with the His360 allele over both obese type 2 diabetics without His360 (relative risk=5.1) and lean non-diabetics without His360 (relative risk=7.7).51

Some of the effects of the apoA-IV variants can be plausibly explained by their association with body mass index and body fat. Thus, the higher HDL cholesterol and lower triglycerides could be related to the lower BMI associated with the His360 allele. Indeed, Hanis et al 31 observed that the significant effects of the His360 allele on triglycerides, HDL3 and apoA-I disappeared after adjustment of the data for differences in BMI. However, the reported effects of the His360 allele on glucose and insulin levels are seemingly at odds with the associated lower BMI and underscore the likely complex nature of the interaction between apoA-IV genotype and metabolism.

The mechanism by which these variants influence BMI and body fat remains unknown. A likely mechanism involves apoA-IV's reported function as a satiety factor. Intestinally synthesized apoA-IV is believed to mediate at least part of the anorectic effect associated with lipid infusion in the duodenum.24 Peripheral26 and central25 infusion of apoA-IV in rats resulted in a short-term inhibition of food intake. Part of the inhibition of food intake may be related to the inhibition of gastric emptying produced with central apoA-IV administration.23 It is possible that the His360 and Ser347 alleles may alter the affinity of apoA-IV for putative binding sites within the brain.52 However, it should be pointed out that transgenic mice over-expressing apoA-IV do not show any changes in feeding behavior.53

Alternatively, apoA-IV may influence BMI and body fat through direct effects on dietary fatty acid metabolism. ApoA-IV modulates the activity of lipoprotein lipase,19 which could subsequently affect the sites of clearance and deposition of dietary fat.54 Additionally binding sites for apoA-IV have been identified on both hepatocytes and adipocytes.22,55,56 While the presence of these apoA-IV binding sites have been interpreted as evidence for a role of apoA-IV in reverse cholesterol transport, it is also conceivable that these binding sites may mediate other unidentified effects on cellular fatty acid metabolism.

Finally, the two variants described in this report may in fact not be directly responsible for the observed variation in BMI and body fat, but rather, may be linked to other causative variants either within the apoA-IV gene or in adjacent genes. The apoA-IV gene has been mapped to the long arm of chromosome 11 and lies within a gene cluster which includes apoA-I and apoC-III.57 Regions adjacent to this gene cluster has been linked to percent body fat in the Pima Indians58 and to dietary obesity in mice.59 However, in both studies the peak of the LOD scores did not include the apoA-IV gene.

In summary, two variants in the apoA-IV gene have been significantly associated with variations in both BMI and percentage body fat. Combined, these variants accounted for less than 4% of the population variance in Caucasians. The mechanisms by which these variants potentially influence BMI and percentage body fat remain to be elucidated.

Acknowledgements

The research was supported in part by a grant from Lake Charles Memorial Hospital. The authors wish to thank Ms Bhagya Rajendram for her expert technical assistance.

*Presented in part at the Annual Meeting of the North American Association for the Study of Obesity, Baton Rouge, LA, 12 October, 1995 andpublished in Abstract form in Obes Res 1995;3: 354S.

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Figures

Figure 1 Variant allele frequencies at codon 347 (panel a) and codon 360 (panel b) as a function of BMI percentile estimated from NHANES III data. Alleles associated with homozygous genotypes are indicated by solid bars. Homozygous genotype frequencies are one-half the indicated allele frequencies.

Tables

Table 1 Participant characteristics by study type

Table 2 Effect of apoA-IV(347) genotype on indices of body weight and body fat in entire study population

Table 3 Effect of apo-A-IV (360) genotype on indices of body weight and body fat in entire study population

Table 4 Effect of apoA-IV(347) genotype on indices of body weight and body fat in Caucasians only

Table 5 Effect of apoA-IV(360) genotype on indices of body weight and body fat in Caucasians only

Table 6 Effect of apoA-IV haplotypes on adjusted BMI

Table 7 Multiple regression coefficients between apoA-IV genotype parameter and adjusted BMI

Received 28 June 1999; revised 15 February 2000; accepted 29 February 2000
August 2000, Volume 24, Number 8, Pages 945-953
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