© 1997 American Heart Association, Inc.
Articles |
Genetic Relationship Between Measures of HDL Phenotypes and Insulin Concentrations
From the Department of Genetics, Southwest Foundation for Biomedical Research, and the Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center (S.M.H.), San Antonio, Texas.
Correspondence to David L. Rainwater, PhD, Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 760549, San Antonio, TX 78245-0549. E-mail david{at}darwin.sfbr.org
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Abstract |
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Abstract We used data from the San Antonio Family Heart Study to determine the HDL correlates of the insulin resistance syndrome (IRS), as reflected by insulin concentrations in nondiabetic subjects. We measured insulin concentrations both in the fasting state and 2 hours after a glucose challenge (2-hour insulin) and we assessed seven aspects of HDL phenotype, including size and concentration of both lipid and protein components. Measurements were obtained from 1202 nondiabetic members of 42 families. Initial quantitative genetic analyses revealed that a substantial portion of phenotypic variation in the nine variables was due to genes (heritabilities, h2 , ranged from 0.32 to 0.47). We then conducted a series of bivariate genetic analyses, which indicated that there were significant additive genetic correlations (ie, pleiotropy) between the two measures of insulin and five of seven HDL measures tested, including concentrations of HDL cholesterol (fasting insulin only) and triglyceride, and HDL size distributions of apoAI, apoAII, and cholesterol; concentrations of apoAI and apoAII were not genetically related to either insulin measure. Increased insulin levels were associated with relatively smaller HDL phenotypes, and considering a similar association with small, dense LDLs, this finding suggests a common effect of insulin resistance on particle size distributions for these lipoproteins. Thus, these results suggest the existence of genes that pleiotropically influence variation in both HDLs and insulin levels and therefore contribute to the clustering of proatherogenic traits in the IRS.
Key Words: HDL • gradient gel electrophoresis • diabetes • insulin
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Introduction |
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Syndrome X,1 or the IRS,2 is associated with a variety of metabolic abnormalities, including hypertension, impaired glucose tolerance, and dyslipidemia, particularly hypertriglyceridemia and low levels of HDLs. Although a number of components of the IRS remain controversial, such as inclusion of hypertension,3 the strong association between insulin resistance and hyperinsulinemia is well established. The familial clustering of these traits suggests defects in one or more genes may contribute to IRS, although the precise nature of such genes and their actions are not well understood.
Many studies have shown hyperinsulinemia to be associated with high triglyceride and low HDL cholesterol levels,2 4 5 6 7 8 9 10 11 12 mainly HDL2.8 11 Although insulin levels are not associated with the absolute concentrations of LDL cholesterol,13 hyperinsulinemia is associated with a relative increase in small, dense LDL particles (LDL subclass B),8 14 15 16 17 which in turn are associated with an increased risk of cardiovascular disease independent of LDL cholesterol concentrations.18 19 20 Furthermore, hyperinsulinemia is associated with an increase in apo B and a decrease in apoAI concentrations.11 This atherogenic lipoprotein profile is also associated with insulin resistance.21 22 23 It is noteworthy that several of these studies reporting significant relationships between insulin and lipoprotein phenotypes were prospective.2 11 24 For example, baseline insulin concentrations predicted the development of hypertriglyceridemia, high apoB, and low apoAI.11 In addition, elevated insulin concentrations predicted subsequent lowering of the HDL cholesterol/apoAI ratio. Thus, hyperinsulinemia is associated prospectively with proatherogenic changes in HDL composition, as well as changes in absolute concentrations.
There is ample evidence that genes influence variation in many of the traits associated with IRS, including insulin concentration,25 26 insulin resistance,27 28 and HDL phenotypes.29 30 31 In a previous study, we found that genes account for a significant proportion of the variability in fasting and 2-hour insulin concentrations.32 Also in the study, genes accounted for one third to one half of variation in seven measures of HDL concentrations (ie, h2 ranged from 0.34 to 0.46 for apoAI, apoAII, apoE, lipoprotein AI, and cholesterol in HDL, HDL1+2, and HDL3)32 and for a similar proportion of variance in HDL size distributions of apoAI, apoAII, and cholesterol.33 For at least some of these traits (eg, postchallenge insulin25 26 and fasting concentrations of HDL-C34 and apoAI35 ), segregation analyses have revealed the existence of individual genes with relatively large effects. Recently, we have demonstrated that significant amounts of variation in fasting insulin and HDL cholesterol are controlled by shared genes (ie, pleiotropy), suggesting they may be responsible in part for the clustering of proatherogenic traits in IRS.36 In this report, we extend these studies by considering the relationship(s) between insulin and a broad selection of HDL phenotypes that include measures of HDL size and concentration.
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Methods |
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Study Participants and Samples
The San Antonio Family Heart Study is a study of risk factors for cardiovascular disease in Mexican-American families.32 All first-, second-, and third-degree relatives of randomly ascertained probands were invited to participate. At a clinic visit, blood samples were obtained after an overnight fast and again 2 hours after administration of a 75-g glucose load (Orangedex, Custom Laboratories). Plasma samples were prepared by low-speed centrifugation and stored in plastic tubing segments37 or in freezer vials at -80°C. Weight, height, and circumferences of waist and hip also were measured during the clinic visit; body mass index was calculated as weight divided by height squared (kg/m2), and waist-hip ratio was calculated as waist circumference divided by hip circumference. Procedures were approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio, and all subjects gave written informed consent.
Biochemical Measurements
Plasma glucose (mmol/L) was measured by using an Abbott
V/P Analyzer, and insulin (pmol/L) was measured by using a
commercial radioimmunoassay kit (Diagnostic). Diabetic
subjects were defined, using World Health Organization criteria, as
individuals satisfying at least one of the following conditions:
fasting glucose concentrations 
7.8 mmol/L (140 mg/dL), glucose
concentrations 11.1 mmol/L (200 mg/dL) 2 hours after
administration of a 75-g glucose load, or currently taking medications
for diabetes. Cholesterol and TG concentrations were
assayed enzymatically in frozen plasma samples using a Gilford SBA-300
clinical chemistry analyzer with commercial reagents supplied
by Boehringer-Mannheim Diagnostics and Stanbio,
respectively. Interassay coefficients of variation for control
products in these assays were 2.1% for cholesterol and
6.2% for TG.
HDL Measurements
Concentrations of HDL-TG and HDL-C were measured as above in
plasma samples after precipitation of ß-lipoproteins by use of
dextran sulfate.38 The interassay coefficients of
variation for control products in these assays were 6.2% for HDL-C
and 10.8% for HDL-TG. Apolipoprotein concentrations were determined by
a commercial laboratory (Medical Research Laboratories, Highland
Heights, Ky). ApoAI concentrations were determined by
nephelometry,39 40 and apoAII concentrations were
determined using competitive immunoassays.41 The
interassay coefficients of variation for control products in these
assays were 3.5% for apoAI and 4.4% for apoAII.
Size distributions of HDL particles were determined by electrophoresis of plasma in nondenaturing 3% to 31% polyacrylamide gradient gels, which were made in the laboratory as described.42 After electrophoretic separation, the proteins were transferred to nitrocellulose paper and detected by binding with sheep anti-apoAI or sheep anti-apoAII (both from Boehringer-Mannheim) as previously described.43 44 These antibodies were in turn bound by donkey anti-sheep IgG (Chemicon International, Inc), which was radioiodinated by using the chloramine T method,45 and distributions were measured by densitometry of autoradiograms using an LKB Ultroscan laser densitometer with GSXL software (Pharmacia). The distribution of cholesteryl esters among HDLs was detected by use of staining with Sudan black B and densitometry as described.46 47 HDL absorbance profiles were decomposed by a curve-fitting procedure as suggested;48 we developed in house a program to automatically fit curves representing the HDL subclasses.44 However, to summarize the data for statistical analyses, we summed all absorbances for HDL2 and HDL1 and divided by the total HDL absorbance to generate a variable that expressed the proportion of each analyte occurring on HDL particles larger than HDL3 (ie, larger than 8.8 nm diameter); these variables are termed large HDL-C, large HDL-apoAI, and large HDL-apoAII.
Statistical Genetic Analyses
A total of 1431 individuals were enrolled in the San
Antonio Family Heart Study, but we excluded from analyses 212
diabetic subjects and 17 nondiabetic subjects who were taking antilipid
medications. Thus, there were data on most or all phenotypes
for 1202 nondiabetic individuals in 42 families. The pedigrees were
multigenerational and contained a rich diversity of pairwise
relationships for genetic analyses, including 2024 pairs of
first-degree relatives, 2438 pairs of second-degree relatives, 3525
pairs of third-degree relatives, 3082 pairs of fourth-degree relatives,
and 1141 pairs of fifth-degree relatives. To improve assumptions of
normality, insulin concentrations were transformed to their natural
logarithms before analyses. The transformed variables gave
distributions not significantly different from normal (see the
Figure
). The other measures were analyzed
without transformation. We used the PEDSYS package of
programs49 to manage the phenotype data
and pedigree information.
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The statistical genetic analyses were based on the principle of partitioning the total phenotypic variance of a trait into the variance due to the effects of genes and residual (environmental) factors. Under this framework, the heritability (h2) may be defined as that proportion of the total phenotypic variance that is attributable to the additive effects of genes.50 Maximum-likelihood methods were used to estimate simultaneously the effects of age, age2, and sex and the residual heritability in each phenotype.
We tested the general hypothesis that a common set of genes influences
both insulin concentrations and HDL phenotypes by obtaining the
genetic and environmental correlations between each pair of traits from
the genetic and environmental variance-covariance matrices. The
variance-covariance matrices were obtained by modeling the
joint distributions of the phenotypes as a function of their
population means, the covariates and their regression coefficients, the
additive genetic values, and random environmental deviations, based on
the kinship coefficients among individuals.36 The
genetic and environmental correlations obtained from these matrices
represent estimates of the effects of shared genes, or
pleiotropy, and of shared environmental factors, respectively, on the
phenotypic covariance for each pair of traits. The significance
of the correlations was determined by comparing the likelihood of an
unrestricted model in which the correlation was estimated with the
likelihood of a submodel in which the correlation was fixed at zero.
The fraction of the total genetic variance between insulin and
HDL phenotypes that is explained by the additive effects of
genes in common is obtained by squaring the genetic correlation,
G; similarly, the fraction of the total
environmental (nongenetic) covariance between two traits is
obtained by squaring the environmental correlation,
E. All statistical genetic analyses
were conducted by using our modified version of the pedigree
analysis program, PAP V3.0.34 36 51
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Results |
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Study Population
There were 1202 nondiabetic individuals who were not taking antilipid medications and who had data available for these analyses. Table 1
gives some of
the characteristics of this group of Mexican Americans. Approximately
64% of the participants were women, and the average age was 36.4
years. The population tended to be obese, with mean body mass index of
28.6 kg/m2. As expected, the sexes differed in
several measures of diabetes and lipoprotein phenotypes.
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Mean levels of insulin and each HDL phenotype are shown in
Table 2 according to age and sex. In
women, but not men, the HDL concentration measures tended to increase
with age. Levels of 2-hour insulin and HDL concentrations (except for
apoAII) were greater in women than in men, and HDL particle sizes
tended to be correspondingly larger in women. Insulin values,
particularly 2-hour insulin, were higher in females and increased with
age in both sexes.
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Univariate Quantitative Genetic Analyses of
Insulin and HDL Phenotype Measures
We performed quantitative genetic analyses for each
of the measures of insulin and HDL. As suggested in Table 2
, we found
significant effects of age and sex for each trait. Also
presented in Table 2 are heritabilities of the insulin and HDL
measures. In general, the heritabilities were high, ranging from 32%
for 2-hour insulin concentrations to 47% for HDL-C, and all were
significantly greater than zero.
Bivariate Quantitative Genetic Analyses of Measures of HDL
Phenotypes and Insulin Concentrations
Bivariate quantitative genetic analyses were used to
estimate the genetic and environmental correlations of insulin
concentrations with measures of HDL. The genetic, environmental, and
total phenotypic correlations of fasting and 2-hour insulin with each
measure of HDL concentration or size are given in Table 3. The genetic correlations between
fasting insulin and two measures of HDL concentration (HDL-C and
HDL-TG) were high (G=-0.334 and 0.299,
respectively) and significant (P=.002 and P=.015,
respectively), indicating that genes in common explained 9% to 11% of
the genetic variance in these pairs of traits. Furthermore,
genes in common explained 16% to 26% of genetic variance
between fasting insulin and the various measures of HDL size
phenotype (each correlation significant at P<.001).
Similarly, genes in common explained 9% to 24% of genetic
variance between 2-hour insulin and the different measures of
HDL size (each correlation significant at P<.02). However,
among the HDL concentration measures, only HDL-TG had a significant
genetic correlation with 2-hour insulin
(G=.408; P=.004), explaining
approximately 16% of the genetic variance in these two traits.
In no case was the environmental or nongenetic correlation,
E, as strong as the corresponding genetic
correlation, suggesting that the phenotypic correlations between
insulin and HDL were primarily due to the pleiotropic actions of shared
genes.
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Discussion |
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We selected insulin concentration as a measure to represent insulin resistance; several studies have shown that serum insulin concentrations in nondiabetic individuals are strongly associated with measures of insulin resistance, in both the fasting and postchallenge states.52 53 54 Clinical abnormalities associated with IRS, including diabetes, obesity, and dyslipidemia, may reflect a common metabolic environment.1 2 55 We have explored this issue by investigating whether shared genes may explain the clustering of one category of IRS-related traits, dyslipidemia, with insulin concentrations. In a previous study, we demonstrated significant genetic correlations between insulin concentrations and one measure of HDL phenotype, HDL-C.36 In the present study, we have attempted to extend the earlier report by evaluating several different measures of HDL phenotype. These HDL phenotypes probably represent different aspects of HDL metabolism. Several lines of evidence suggest the existence of different (independent) sets of genes that influence some of these different HDL phenotypes. For example, measures of apoAI concentration and particle size distribution are substantially independent of each other,44 although they each are under strong genetic control.32 33 44 Also, we have conducted principal components analyses on the genetic correlations among seven different measures of HDL phenotype33 56 and find evidence for at least three independent gene sets explaining covariance among the traits (A.G. Comuzzie and D.L. Rainwater, 1997, unpublished observations). Thus, the various HDL phenotype measures contain information about different sets of genes that influence different aspects of HDL metabolism, only some of which may be responsible for the clustering of HDL among IRS-related traits.
We found modest but significant genetic correlations for several measures of HDL with insulin concentrations. These genetic correlations, which represent the additive effects of genes, suggest that one or more genes exert pleiotropic effects on both HDL and insulin variation. HDL size variables were the strongest correlates of insulin among the HDL measures, explaining 3% to 7% of total phenotypic covariance but 11% to 26% of the genetic variance. The negative correlations suggest that increasing resistance to insulin is associated with relatively smaller HDL particles. These data extend previous reports of significant effects of insulin and diabetes on HDL size phenotypes.44 57 58 Together with a similarly negative correlation with LDL particle size,8 14 15 16 17 58 these data suggest the possibility that in fact a general lipoprotein size phenotype, reflected in measures of LDL and HDL particle size distributions, is associated with IRS. If so, then we predict that shared genes are responsible for the correlated variation in LDL and HDL size phenotypes because both are strongly genetic traits. The primary source of this relationship could be increased free fatty acid transport in IRS, which leads to hypertriglyceridemia and corresponding modifications of lipoprotein composition and metabolism.59 In any event, we suggest that IRS (and diabetes) is associated with a small, dense lipoprotein phenotype, at least with respect to the two major classes of cholesterol-containing lipoproteins.
HDL-C concentrations were also negatively correlated with insulin. Coupled with the fact that the two major proteins of HDLs, apoAI and apoAII, were not at all correlated with insulin, the negative correlation with the major HDL lipid component, HDL-C, may also reflect the association of smaller HDL particles with insulin resistance. Further studies will be necessary to determine whether these correlations reflect a single common metabolic pathway or several. HDL-TG also was significantly correlated with insulin. Because the correlation was positive, it is unlikely that HDL-TG is reporting directly on particle size. However, HDL-TG is positively correlated with total TG (r2=.34 in this study), and the correlation of insulin with HDL-TG could simply reflect variation in total plasma TG, a lipid measure also known to be associated with IRS.5 12 22 Alternatively, HDL-TG concentrations could indicate important compositional variation in HDL particles that in turn influences their metabolism.59 These are not exclusive hypotheses, and the present data do not distinguish them.
CETP facilitates the coordinated exchange of triglycerides and cholesteryl esters among HDLs and ß-lipoproteins. The decrease of HDL-C and increase of HDL-TG with increases of insulin suggest an alteration of CETP activity. Insulin appears to influence CETP function, although the reported effects are not consistent across studies.60 61 This likely is due in part to the difficulties of extrapolating in vitro CETP activity or mass assay measurements to in vivo function across individuals. Nevertheless, the present data are consistent with an opposite effect of insulin resistance on TG and cholesterol in HDL, possibly mediated by CETP.
A number of environmental factors can potentially influence HDL and/or insulin phenotypes. However, the significant genetic correlations we report here should, by definition, be unaffected by such factors. When we excluded 71 subjects who were taking medications that might influence lipid measures (postmenopausal women undergoing estrogen replacement therapy and subjects taking medications for hypertension) from the analyses, we found virtually identical genetic correlations as were found for the entire group (B.D.M., 1997, unpublished observations). Similarly, alcohol, which has a significant effect on HDL but not insulin measures in this population,32 did not appreciably alter the genetic or environmental correlations when included as a covariate (B.D.M., 1997, unpublished observations).
In previous studies of this same population, we have detected the
existence of a single locus with relatively large effects that
influences variation in serum levels of 2-hour
insulin26 and another that influences plasma
levels of HDL-C.34 Both loci were detected by
using complex segregation analysis, although their chromosomal
locations have yet to be identified. The major locus for 2-hour insulin
concentrations accounts for 31% of the total phenotypic variation and
also has a modest effect (P=.02) on fasting insulin
concentrations.26 36 The major locus for HDL-C,
detected after adjustment for triglyceride and apoAI
concentrations, explains 55% of the residual variation in men and 17%
in women.34 Because these loci exert major
effects on trait variation, we speculated that one (or both) of them
might be one of the pleiotropic genes responsible for the relationships
between insulin and HDL found in this study. Accordingly, we used
bivariate segregation analyses36 to
determine whether the major locus for 2-hour insulin influences any of
the HDL measures and whether the major locus for HDL-C influences the
insulin measures. We found that neither major locus is one of the genes
responsible for the genetic correlations between insulin and HDL shown
in Table 3, and therefore, we conclude that neither is a significant
contributor to the IRS.36 62
Taken together, the results of this study lead to several important conclusions regarding insulin and HDL phenotypes: (1) The clustering of these phenotypes associated with IRS is explained in part by genes that exert pleiotropic effects; (2) like LDL particle size, average HDL particle size is also correlated with insulin resistance; and (3) the previously detected major loci for 2-hour insulin and HDL-C concentrations are not among the genes responsible for the aggregation of proatherogenic traits associated with IRS.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This work was supported by NIH grant HL-45522. The authors thank the following for excellent technical assistance during the study: Tom Dyer, Allen Ford, Pat Moore, Wendy Shelledy, and Jane VandeBerg.
Received March 31, 1997; accepted August 4, 1997.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Reaven GM. Banting Lecture 1988: role of insulin resistance in human disease. Diabetes. 1988;37:1595–1607.[Abstract]
2. Haffner SM, Valdez RA, Hazuda HP, Mitchell BD, Morales PA, Stern MP. Prospective analysis of the insulin-resistance syndrome (Syndrome X). Diabetes. 1992;41:715–722.[Abstract]
3. Haffner SM. Editorial: insulin and blood pressure: fact or fantasy? J Clin Endocrinol Metab. 1993;76:541–543.[Medline] [Order article via Infotrieve]
4.
Wing RR, Bunker CH, Kuller LH, Mathews KA. Insulin,
body mass index, and cardiovascular risk factors in
premenopausal women. Arteriosclerosis. 1989;9:479–484.
5.
Orchard TJ, Becker DJ, Bates M, Kuller LH, Drash AL.
Plasma insulin and lipoprotein concentrations: an atherogenic
association? Am J Epidemiol. 1983;118:326–337.
6.
Laakso M, Pyörälä K, Voutilainen E,
Marniemi J. Plasma insulin and serum lipids and lipoproteins in
middle-aged non–insulin-dependent diabetic and non-diabetic subjects.
Am J Epidemiol. 1987;125:611–621.
7.
Modan M, Halkin H, Lusky A, Segal P, Fuchs Z, Chetrit
A. Hyperinsulinemia is characterized by jointly
disturbed plasma VLDL, LDL, and HDL levels: a population-based study.
Arteriosclerosis. 1988;8:227–236.
8.
Hunt SC, Wu LL, Hopkins PN, Stults BM, Kiuda H,
Ramirez ME, Lalouel J-M, Williams RR. Apolipoprotein, low density
lipoprotein subfraction, and insulin associations with familial
combined hyperlipidemia: study of Utah patients with
familial dyslipidemic hypertension.
Arteriosclerosis. 1989;9:335–344.
9. Zavaroni I, Bonora E, Pagliara M, Dall'Aglio E, Luchetti L, Buonanno G, Bonati PA, Bergonzani M, Gnudi L, Passeri M, Reaven GM. Risk factors for coronary artery disease in healthy persons with hyperinsulinemia and normal glucose tolerance. N Engl J Med. 1989;320:702–706.[Abstract]
10.
Laws A, King AC, Haskell WL, Reaven GM. Relation of
fasting plasma insulin concentration to high density lipoprotein
cholesterol and triglyceride concentrations in
men. Arterioscler Thromb. 1991;11:1636–1642.
11.
Mykkänen L, Kuusisto J, Haffner SM,
Pyörälä K, Laakso M.
Hyperinsulinemia predicts multiple atherogenic
changes in lipoproteins in elderly subjects. Arterioscler
Thromb. 1994;14:518–526.
12.
Godsland IF, Crook D, Walton C, Wynn V, Oliver MF.
Influence of insulin resistance, secretion, and clearance on serum
cholesterol, triglycerides, lipoprotein
cholesterol, and blood pressure in healthy men.
Arterioscler Thromb. 1992;12:1030–1035.
13.
Karhapaa P, Voutilainen E, Kovanen PT, Laakso M.
Insulin resistance in familial and nonfamilial
hypercholesterolemia. Arterioscler
Thromb. 1993;13:41–47.
14. Barakat HA, Carpenter JW, McLendon VD, Khazanie P, Leggett N, Heath J, Marks R. Influence of obesity, impaired glucose tolerance, and NIDDM on LDL structure and composition: possible link between hyperinsulinemia and atherosclerosis. Diabetes. 1990;39:1527–1533.[Abstract]
15.
Selby JV, Austin MA, Newman B, Zhang D, Quesenberry CP
Jr, Mayer EJ, Krauss RM. LDL subclass phenotypes and the
insulin resistance syndrome in women. Circulation. 1993;88:381–387.
16.
Haffner SM, Mykkänen L, Valdez RA, Paidi M, Stern
MP, Howard BV. LDL size and subclass pattern in a biethnic population.
Arterioscler Thromb. 1993;13:1623–1630.
17. Reaven GM, Chen Y-DI, Jeppesen J, Maheux P, Krauss RM. Insulin resistance and hyperinsulinemia in individuals with small, dense, low density lipoprotein particles. J Clin Invest. 1993;92:141–146.
18. Crouse JR, Parks JS, Schey HM, Kahl FR. Studies of low density lipoprotein molecular weight in human beings with coronary artery disease. J Lipid Res. 1985;26:566–574.[Abstract]
19.
Austin MA, Breslow JL, Hennekens CH, Buring JE, Willett
WC, Krauss RM. Low-density lipoprotein subclass patterns and risk of
myocardial infarction. JAMA. 1988;260:1917–1921.
20.
Campos H, Genest JJ Jr, Blijlevens E, McNamara JR,
Jenner JL, Ordovas JM, Wilson PWF, Schaefer EJ. Low density lipoprotein
particle size and coronary artery disease. Arterioscler
Thromb. 1992;12:187–195.
21. Mykkänen L, Haffner SM, Rönnemaa T, Bergman R, Leino A, Laakso M. Is there a sex difference in the association of plasma insulin level and insulin sensitivity with serum lipids and lipoproteins? Metabolism. 1994;43:523–528.[Medline] [Order article via Infotrieve]
22. Garg A, Helderman JH, Koffler M, Ayuso R, Rosenstock J, Raskin P. Relationship between lipoprotein levels and in vivo insulin action in normal young white men. Metabolism. 1988;37:982–987.[Medline] [Order article via Infotrieve]
23.
Laakso M, Sarlund H, Mykkanen L. Insulin resistance is
associated with lipid and lipoprotein abnormalities in subjects with
varying degrees of glucose tolerance.
Arteriosclerosis. 1990;10:223–231.
24.
Mitchell BD, Haffner SM, Hazuda HP, Valdez R, Stern MP.
The relation between serum insulin levels and 8-year changes in lipid,
lipoprotein, and blood pressure levels. Am J Epidemiol. 1992;136:12–22.
25. Schumacher MC, Hasstedt SJ, Hunt SC, Williams RR, Elbein SC. Major gene effect for insulin levels in familial NIDDM pedigrees. Diabetes. 1992;41:416–423.[Abstract]
26. Mitchell BD, Kammerer CM, Hixson JE, Atwood LD, Hackleman S, Blangero J, Haffner SM, Stern MP, MacCluer JW. Evidence for a major gene affecting postchallenge insulin levels in Mexican-Americans. Diabetes. 1995;44:284–289.[Abstract]
27. Lillioja S, Mott DM, Zawadski JK, Young AA, Abbott WGH, Knowler WC, Bennett PH, Moll P, Bogardus C. In vivo insulin action is familial characteristic in nondiabetic Pima Indians. Diabetes. 1987;36:1329–1335.[Abstract]
28. Martin BC, Warram JH, Rosner B, Rich SS, Soeldner JS, Krolewski AS. Familial clustering of insulin sensitivity. Diabetes. 1992;41:850–854.[Abstract]
29.
Heller DA, de Faire U, Pedersen NL, Dahlén G,
McClearn GE. Genetic and environmental influences on serum lipid levels
in twins. N Engl J Med. 1993;328:1150–1156.
30. Rao DC, Laskarzewski PM, Morrison JA, Khoury P, Kelly K, Wette R, Russell J, Glueck CJ. The Cincinnati Lipid Research Clinic Family Study: cultural and biological determinants of lipids and lipoprotein concentrations. Am J Hum Genet. 1982;34:888–903.[Medline] [Order article via Infotrieve]
31.
Austin MA, King M-C, Bawol RD, Hulley SB, Friedman GD.
Risk factors for coronary heart disease in adult female twins:
genetic heritability and shared environmental influences. Am
J Epidemiol. 1987;125:308–318.
32.
Mitchell BD, Kammerer CM, Blangero J, Mahaney MC,
Rainwater DL, Dyke B, Hixson JE, Henkel RD, Sharp MR, Comuzzie AG,
VandeBerg JL, Stern MP, MacCluer JW. Genetic and environmental
contribution to cardiovascular risk factors in Mexican
Americans: the San Antonio Family Heart Study. Circulation. 1996;94:2159–2170.
33.
Comuzzie AG, Rainwater DL, Blangero J, Mahaney MC,
VandeBerg JL, MacCluer JW. Shared and unique genetic effects among
seven HDL phenotypes. Arterioscler Thromb Vasc Biol. 1997;17:859–864.
34.
Mahaney MC, Blangero J, Rainwater DL, Comuzzie AG,
VandeBerg JL, Stern MP, MacCluer JW, Hixson JE. A major locus
influencing plasma high-density lipoprotein cholesterol
levels in the San Antonio Family Heart Study: segregation and linkage
analyses. Arterioscler Thromb Vasc Biol. 1995;15:1730–1739.
35.
Blangero J, Williams-Blangero S, Mahaney MC, Comuzzie
AG, Hixson JE, Samollow PB, Sharp RM, Stern MP, MacCluer JW. Effects of
a major gene for apolipoprotein A-I concentration are thyroid hormone
dependent in Mexican Americans. Arterioscler Thromb Vasc
Biol. 1996;16:1177–1183.
36.
Mitchell BD, Kammerer CM, Mahaney MC, Blangero J,
Comuzzie AG, Atwood LD, Haffner SM, Stern MP, MacCluer JW. Genetic
analysis of the IRS: pleiotropic effects of genes influencing
insulin levels on lipoprotein and obesity measures. Arterioscler
Thromb Vasc Biol. 1996;16:281–288.
37. Cheng M-L, Woodford SC, Hilburn JL, VandeBerg JL. A novel system for storage of sera frozen in small aliquots. J Biochem Biophys Methods. 1986;13:47–51.[Medline] [Order article via Infotrieve]
38.
Warnick GR, Benderson J, Albers JJ. Dextran
sulfate-Mg2+ precipitation procedure for
quantitation of high-density-lipoprotein cholesterol.
Clin Chem. 1982;28:1379–1388.
39. Hogle DM, Smith RS, Curtiss LK. Quantitation of plasma apolipoprotein A-I using two monoclonal antibodies in an enzyme-linked immunosorbent assay. J Lipid Res. 1988;29:1221–1229.[Abstract]
40.
Smith SJ, Cooper GR, Henderson LO, Hannon WH,
Apolipoprotein Standardization Collaborating Group. An international
collaborative study on standardization of apolipoproteins A-I and B, I:
evaluation of a lyophilized candidate reference and calibration
material. Clin Chem. 1987;33:2240–2249.
41.
Stein EA, DiPersio L, Pesce AJ, Kashyap M, Kao J-T,
Srivastava L, McNerney C. Enzyme-linked immunoabsorbant assay of
apolipoprotein AII in plasma, with use of a monoclonal antibody.
Clin Chem. 1986;32:967–971.
42. Rainwater DL, Andres DW, Ford AL, Lowe WF, Blanche PJ, Krauss RM. Production of polyacrylamide gradient gels for the electrophoretic resolution of lipoproteins. J Lipid Res. 1992;33:1876–1881.[Abstract]
43. Rainwater DL, Kammerer CM, Cheng M-L, Sparks ML, VandeBerg JL. Distribution of specific apolipoproteins detected by immunoblotting of baboon lipoproteins resolved by polyacrylamide gradient gel electrophoresis. Biochem Genet. 1992;30:143–158.[Medline] [Order article via Infotrieve]
44. Rainwater DL, Blangero J, Moore PH Jr, Shelledy WR, Dyer TD. Genetic control of apolipoprotein A-I distribution among HDL subclasses. Atherosclerosis. 1995;118:307–317.[Medline] [Order article via Infotrieve]
45. Greenwood FC, Hunter WM, Glover JS. The preparation of 131I-labelled human growth hormone of high specific radioactivity. Biochem J. 1963;89:114–123.[Medline] [Order article via Infotrieve]
46. Cheng M-L, Kammerer CM, Lowe WF, Dyke B, VandeBerg JL. Method for quantitating cholesterol in subfractions of serum lipoproteins separated by gradient gel electrophoresis. Biochem Genet. 1988;26:657–681.[Medline] [Order article via Infotrieve]
47.
Rainwater DL, Blangero J, Hixson JE, Birnbaum S, Mott
GE, VandeBerg JL. A DNA polymorphism for LCAT is associated with
altered LCAT activity and high-density lipoprotein size distributions
in baboons. Arterioscler Thromb. 1992;12:682–690.
48. Verdery RB, Benham DF, Baldwin HL, Goldberg AP, Nichols AV. Measurement of normative HDL subfraction cholesterol levels by Gaussian summation analysis of gradient gels. J Lipid Res. 1989;30:1085–1095.[Abstract]
49. Dyke B. PEDSYS: A Pedigree Data Management System. San Antonio, Tex: Population Genetics Laboratory, Southwest Foundation for Biomedical Research; 1994.
50. Falconer DS. Introduction to Quantitative Genetics. London, UK: Longman; 1981.
51. Hasstedt SJ. Pedigree Analysis Package, version 3.0. Salt Lake City, Utah: University of Utah, Department of Human Genetics; 1989.
52. Tobey TA, Greenfield M, Kraemer F, Reaven GM. Relationship between insulin resistance, insulin secretion, very low density lipoprotein kinetics, and plasma triglyceride levels in normotriglyceridemic man. Metabolism. 1981;30:165–171.[Medline] [Order article via Infotrieve]
53. Hollenbeck CB, Chen N, Chen Y-DI, Reaven GM. Relationship between plasma insulin response to oral glucose and insulin-stimulated glucose utilization in normal subjects. Diabetes. 1984;33:460–463.[Abstract]
54.
Laakso M. How good a marker is insulin level for
insulin resistance? Am J Epidemiol. 1993;137:959–965.
55. DeFronzo RA, Ferrannini E. Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14:173–194.[Abstract]
56. Comuzzie AG, Rainwater DL, Blangero J, Mahaney MC, VandeBerg JL, MacCluer JW. Multivariate genetic analysis of high density lipoprotein measures reveals a common major gene effect. Am J Phys Anthropol. 1996;22(suppl):89. Abstract.
57. Williams PT, Haskell WL, Vranizan KM, Krauss RM. The associations of high-density lipoprotein subclasses with insulin and glucose levels, physical activity, resting heart rate, and regional adiposity in men with coronary artery disease: the Stanford Coronary Risk Intervention Project Baseline Survey. Metabolism. 1995;44:106–114.[Medline] [Order article via Infotrieve]
58.
Singh ATK, Rainwater DL, Haffner SM, VandeBerg JL,
Shelledy WR, Moore PH Jr, Dyer TD. Effect of diabetes on lipoprotein
size. Arterioscler Thromb Vasc Biol. 1995;15:1805–1811.
59. Ginsberg HN. Diabetic dyslipidemia: basic mechanisms underlying the common hypertriglyceridemia and low HDL cholesterol levels. Diabetes. 1996;45(suppl 3):S27–S30.
60. Lagrost L. Regulation of cholesteryl ester transfer protein (CETP) activity: review of in vitro and in vivo studies. Biochim Biophys Acta. 1994;1215:209–236.[Medline] [Order article via Infotrieve]
61. Barakat HA, Vadlamudi S, MacLean P, MacDonald K, Pories WJ. Lipoprotein metabolism in non–insulin-dependent diabetes mellitus. J Nutr Biochem. 1996;7:586–598
62. Mahaney MC, Mitchell BD, Rainwater DL, Comuzzie AG, Blangero J, MacCluer JW. Major locus pleiotropic effects on quantitative variation in HDL-cholestrol and fasting insulin levels in the San Antonio Family Heart Study. Cirulation. 1997;96(suppl I):I-335–I-336.
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