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American Journal of Clinical Nutrition, Vol. 81, No. 6, 1429-1435, June 2005
© 2005 American Society for Clinical Nutrition

ORIGINAL RESEARCH COMMUNICATION

Interactions between the –514C->T polymorphism of the hepatic lipase gene and lifestyle factors in relation to HDL concentrations among US diabetic men1,2,3

Cuilin Zhang, Ruy Lopez-Ridaura, Eric B Rimm, Nader Rifai, David J Hunter and Frank B Hu

1 From the Departments of Nutrition (CZ, RL-R, EBR, DJH, and FBH) and Epidemiology (EBR and FBH), Harvard School of Public Health, Boston, MA; the Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (RL-R, EBR, DJH, and FBH); and the Department of Laboratory Medicine, Children's Hospital and Harvard Medical School, Boston, MA (NR)

2 Supported by awards from the National Institutes of Health (HL 65582 and HL 35464) and by an American Heart Association Established Investigator Award (to FBH).

3 Address reprint requests and correspondence to C Zhang, Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. E-mail: czhang{at}hsph.harvard.edu.

See corresponding article on page 1429.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
BACKGROUND:: Low plasma HDL-cholesterol concentrations are a hallmark of diabetic dyslipidemia. A common polymorphism (–514C->T) of the hepatic lipase gene (LIPC), which accounts for up to 30% of the variation in hepatic lipase activity, has been associated with low hepatic lipase activity and high HDL-cholesterol concentrations.

OBJECTIVE:: We examined the association between this polymorphism and plasma HDL-cholesterol concentrations and evaluated whether this association was modified by adiposity and dietary fat intake.

DESIGN:: We followed men aged 40–75 y who participated in the Health Professionals Follow-Up Study in 1986. Among 18 159 men who returned blood samples by 1994, 780 had confirmed type 2 diabetes at blood drawing or during follow-up to 1998 and were free of cardiovascular disease at blood drawing.

RESULTS:: After adjustment for age, smoking, alcohol consumption, fasting status, glycated hemoglobin concentration, physical activity, and body mass index, HDL-cholesterol concentrations were significantly higher in men with the C/T or T/T genotype than in those with the C/C genotype (adjusted : 40.9 and 38.8 mg/dL, respectively; P = 0.01). We observed significant LIPC –514 polymorphism x body mass index and LIPC –514 polymorphism x saturated fat intake interactions for HDL-cholesterol concentrations (P = 0.003 for both). The T allele was associated with higher HDL-cholesterol concentrations only in men who were not overweight or who had higher saturated fat intake.

CONCLUSION:: Our study suggests that the effects of –514C->T of the LIPC gene on HDL concentrations were modified by saturated fat intake and obesity.

Key Words: Interactions • –514C->T polymorphism • hepatic lipase gene • dietary fat • body mass index • HDL • type 2 diabetes • Health Professionals Follow-Up Study


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Low plasma HDL-cholesterol concentrations are a hallmark of diabetic dyslipidemia (1). Plasma HDL-cholesterol concentration is modulated by both environmental and genetic factors. Findings from several studies suggest that polymorphisms at the hepatic lipase (HL) (2-4), apolipoprotein AI/CIII/AIV (2), and cholesteryl ester transfer protein (5) genes are major sources of genetically determined variation in plasma HDL-cholesterol concentrations. Notably, allelic variations at the HL gene account for up to 25% of the variability in plasma HDL-cholesterol concentrations (2). HL is a lipolytic enzyme that is synthesized in the hepatocytes, secreted, and bound extracellularly to the endothelial cells in hepatic sinusoids. It plays a key role in remodeling remnant lipoprotein, LDL, and HDL (6-8). HL participates in the metabolism of HDL by converting large triacylglycerol-rich HDL2 into small, dense HDL3, as well as by mediating the unloading of cholesterol from HDL to the plasma membrane in the liver (9-13). In addition, HL facilitates the hepatic uptake of lipoproteins, including HDL, by acting as a ligand that mediates the binding and uptake of lipoproteins through proteoglycans, receptor pathways, or both (9, 14).

The human hepatic lipase gene (LIPC), located on chromosome 15q21, spans >120 kb of DNA and encodes a protein of 449 amino acids (15). Four polymorphisms in the 5-flanking region of the LIPC (G->A at position –250, C->T at –514, T->C at –710, and A->G at –763 with respect to the transcription start site) (4) were observed to be in complete linkage disequilibrium (16). Together they were designated as the (–514)C and T alleles (3, 4, 17). The C->T base pair substitution at position –514 is associated with up to 30% reduction in promoter activity in vitro (6, 18, 19), marked decreased plasma HL activity, and increased concentrations of plasma HDL, HDL2, and large buoyant LDL particles (3, 9, 17, 20).

Several studies also documented potential interactions between the LIPC –514 polymorphism and environmental factors in determining plasma HDL-cholesterol concentrations, although the precise physiologic mechanism is not understood. Dietary fat intake, for example, was shown to significantly modify the association between the LIPC –514C->T polymorphism and HDL-cholesterol concentrations (21, 22). A possible HL polymorphism x body adiposity interaction for plasma HDL-cholesterol concentration was observed (23-26). So far, however, data on the effect of genetic variants on lipid and lipoprotein concentrations and on the heterogeneity of their effect in response to lifestyle factors among diabetics are clearly missing. In the current study, we investigated the relation between the LIPC –514 polymorphism and plasma HDL-cholesterol concentrations and further evaluated whether this association was modified by lifestyle factors among diabetic subjects in the Health Professionals Follow-Up Study (HPFS).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study population
The details of the HPFS have been reported elsewhere (27). Briefly, HPFS is a prospective cohort study of the etiologies of heart disease, cancer, and other major chronic diseases in 51 529 US male health professionals (dentists, veterinarians, pharmacists, optometrists, osteopathic physicians, and podiatrists) who were aged 40–75 y at study baseline in 1986 (27). Lifestyle factors and health outcomes have been obtained by questionnaires every 2 y. Dietary information has been collected every 4 y by food-frequency questionnaire (FFQ). Blood samples were provided between 1993 and 1999 (mostly in 1993 and 1994) by 18 159 study participants, 1000 of whom were diagnosed with definite type 2 diabetes at baseline or during follow-up to 1998 (with most being diagnosed before or ≤2 y after blood collection). The current study included 780 diabetic men who did not report on any of the biennial questionnaires a diagnosis of angina pectoris, myocardial infarction, coronary artery bypass graft surgery, coronary heart disease, or stroke before blood collection.

On the basis of diagnostic criteria proposed by the National Diabetes Data Group (28), a diagnosis of diabetes was established when ≥1 of following criteria was reported on a supplementary questionnaire sent to all men who reported a diagnosis of diabetes on any biennial follow-up questionnaire: 1) ≥1 classic symptoms (excessive thirst, polyuria, weight loss, hunger, or coma) plus a fasting plasma glucose concentration of ≥140 mg/dL or a random plasma glucose concentration of ≥200 mg/dL, or 2) ≥2 elevated plasma glucose concentrations on different occasions (fasting ≥140 mg/dL, random ≥200 mg/dL, or ≥200 mg/dL after ≥2 h of oral-glucose-tolerance testing) in the absence of symptoms, or 3) treatment with hypoglycemic medication (insulin or oral hypoglycemic agents). We used the National Diabetes Data Group criteria to define diabetes because most of our cases were diagnosed before the release of the American Diabetes Association criteria (29). Men with type 1 diabetes were excluded. A validation study in a subsample of the HPFS showed that our supplementary questionnaire is highly reliable in confirming a diagnosis of diabetes (30). Among a random sample of 71 men classified by our criteria as having type 2 diabetes according to the information reported on the supplementary questionnaire, medical records were available for 59. A physician blinded to the information reported on the questionnaire reviewed the records. The diagnosis of type 2 diabetes was confirmed in 57 (97%) of the 59 men.

Ascertainment of diet and lifestyle factors and anthropometric measurements
Average nutrient intake was derived from the semiquantitative FFQ administered in 1994. For each food, a commonly used unit or portion size was specified, and participants were asked how often, on average, they consumed that amount of each food over the previous year. Nutrient densities for total, saturated, monounsaturated, polyunsaturated, and trans unsaturated fat were calculated as energy derived from dietary fat divided by total dietary energy. The dietary questionnaire has been evaluated in detail for reproducibility and validity within the HPFS (31). After adjustment for energy and after deattenuation for within-subject variation, the Pearson correlation between the questionnaire and the average of 2 single-week diet records 6 mo apart was 0.67 for total, 0.75 for saturated, 0.68 for monounsaturated, and 0.37 for polyunsaturated fat. The correlation between the reported alcohol intakes in the FFQ and the average of two 1-wk diet records was 0.86 (32).

Each participant was asked to report his height to the closest inch at baseline and his current weight in pounds at baseline and on each biennial questionnaire (1994 questionnaire in the present analysis). Self-reports of body weight have been shown to be highly correlated with technician-measured weights (r = 0.96) in a parallel cohort of female health professionals (33). We calculated body mass index (BMI) as the ratio of weight (kg) to squared height (m2); the latter was assessed only in 1986.

Participants also provided information biennially on their cigarette smoking, aspirin use, and physical activity. Physical activity [metabolic equivalent (MET)-h/wk] was calculated by using the reported time spent on various activities and weighting each activity by its intensity level. History of high blood pressure was determined from self-reports before the blood collection. Any family history of coronary heart disease was reported in 1986. Alcohol intake was estimated with a dietary questionnaire in 1994. Participants also provided information biennially on whether they were regularly using cholesterol-lowering drug (eg, Questran, Mevacor, or Colestid).

Blood collection
Each interested participant was sent a blood collection kit containing instructions and supplies (ie, blood tubes, tourniquet, gauze, bandages, and needles). The participants made arrangements for blood to be drawn. Approximately 95% of participants in the current study provided blood samples in 1993 and 1994. Blood samples were collected in three 10-mL blood tubes containing liquid EDTA, placed on ice packs stored in styrofoam containers, and returned to our laboratory via overnight courier; >95% of the samples arrived within 24 h. After receipt the chilled blood was centrifuged at 1530 x g for 20 min at 4 °C; aliquotted into plasma, erythrocytes, and buffy coat; and stored in continuously monitored nitrogen freezers at temperatures ≤–130 °C. We requested information on the date and time of the blood drawing and the time elapsed since the preceding meal to identify nonfasting (>8 h) subjects.

Laboratory methods
All lipid profiles were assayed in the laboratory of NR (at The Children's Hospital, Boston, MA), which is certified by the Lipid Standardization Program of the National Heart, Lung, and Blood Institute and Centers for Disease Control and Prevention. All assays except the enzyme-linked immunosorbent assay and radioimmunoassay employed the Hitachi 911 analyzer (Roche Diagnostics, Indianapolis, IN). Concentrations of total cholesterol, triacylglycerols, and HDL cholesterol were analyzed simultaneously by using enzymatic assays with CVs of 1.7%, 1.8%, and 2.5%, respectively. LDL-cholesterol concentrations were determined by using a homogenous direct method (Genzyme, Cambridge, MA), with a CV <3.1%. Apolipoprotein B-100 was measured by using an immunoturbidimetric technique (Roche Diagnostics), with a CV of 4.3%, and lipoprotein(a) was measured by using a latex-enhanced immunoturbidimeteric method (Denka Sieken, Tokyo, Japan), with a CV of 2.6%. Glycated hemoglobin (Hb A1c) measurement was based on turbidimetric immunoinhibition using hemolyzed packed red cells. The intraassay CVs at Hb A1c values of 5.5 and 9.1 were 1.9% and 3.0%, respectively.

DNA was extracted from the buffy coat fraction of the centrifuged blood with the use of the QIAmp blood kit (Qiagen, Chatsworth, CA). All samples were genotyped by using the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA) in 384-well format. The 5'-nuclease assay (TaqMan; Applied Biosystems) was used to distinguish the 2 alleles of the LIPC gene by involving a C->T transition at position –514. The polymerse chain reaction amplification of LIPC was carried out on 5–20 ng DNA by using 1x TaqMan universal polymerase chain reaction master mix (No Amp-erase UNG; Applied Biosystems), 900 nmol/L forward (AGGGCATCTTTGCTTCTTCGT) and reverse (TCAAAGTGTGGTGCAGAAAACC) primers, 200 nmol FAM-labeled probe/L (CCCCCATGTCAAAA), and 200 nmol VIC-labeled probe/L (CACCCCCGTGTCAA) in a 5-µL reaction; the polymorphic base is shown by underlining. Amplification conditions on an AB 9700 dual-plate thermal cycle (Applied Biosystems) were as follows: 1 cycle of 95 °C for 10 min and 50 cycles of 92 °C for 15s and 58 °C for 1 min. TaqMan primers and probes were designed, on the reverse strand, by using PRIMER EXPRESS OLIGO DESIGN software (version 2.0; Applied Biosystems).

Genotype data for the LIPC –514C->T polymorphism were available for 752 (96%) of 780 study subjects in the current study. Genotype frequencies did not deviate significantly from Hardy-Weinberg equilibrium in this population (P > 0.10).

Statistical analysis
Frequency distributions of characteristics of study subjects were examined according to LIPC genotype. Student's t tests and chi-square tests were used for comparisons of means and proportions. Because of the small number of subjects who were homozygous for the less common allele (ie, TT genotype; n = 30) and because HDL concentrations for persons with this genotype and heterozygotes (CT genotype) were not significantly different, persons with CT and TT genotypes were grouped together.

Generalized linear models were used to compare age-adjusted geometric mean concentrations of plasma lipids across the LIPC genotype groups. Three additional models were fitted to adjust further for factors that may influence plasma lipid concentrations: fasting status (>8 h), physical activity (in quartiles), cigarette smoking (never, past, or current smoker), alcohol consumption (nondrinker, 0.1–4.9, 5.0–9.9, and ≥10.0 g/d), BMI (<25.0, 25.0–29.9, or ≥30.0), and Hb A1c (in tertiles). To reduce potential confounding due to lipid-lowering treatment, subjects taking cholesterol-lowering medications (n = 77) were excluded in the secondary analyses. Concentrations of triacylglycerols were computed only in samples from subjects who fasted ≥8 h before the blood draw (n = 417).

Stratified analyses were conducted to examine whether the association between the LPIC –514C->T polymorphism and plasma HDL-cholesterol concentrations were modified by self-reported dietary fat intake (high or low, dichotomized according the median values), overall body adiposity (BMI <25.0, 25.0–29.9, or ≥30), physical activity (in quartiles), or alcohol consumption (nondrinker or drinker). Interactions between LIPC genotype and dietary and lifestyle factors were assessed by using a cross-product term between genotypes and the aforementioned factors. Statistical significance was evaluated with a Wald test for the interaction after adjustment for multiple covariates. All reported P values are two-tailed, and significance was defined as {alpha} = 0.05. All analyses were performed with SAS software (version 8.12; SAS Institute, Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Characteristics of study subjects according to genotypes are presented in Table 1Go. Overall, subjects with the variant allele (CT or TT genotype) and those with CC genotype did not differ significantly in age, smoking status, BMI, alcohol consumption, physical activity, and dietary intake of total fat and major types of fat except polyunsaturated fat. Hb Alc concentrations (7.2% compared with 7.5%; P = 0.03) were slightly but significantly lower among those with the CC genotype than among those with the CT or TT genotype.


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  		TABLE 1 Characteristics of study subjects according to genotypes of the hepatic lipase gene (LIPC) among US diabetic men1

 
Age and multivariate-adjusted mean concentrations of lipid profiles across the LIPC genotypes are shown in Table 2Go. After adjustment for age, plasma HDL-cholesterol concentrations were significantly higher among men with the C/T or T/T genotype than among those with the C/C genotype (P = 0.005). This elevation remained significant after additional adjustment for behavioral factors (model b: P = 0.002) and for Hb A1c and BMI (model c: P = 0.01). Moreover, plasma total-cholesterol concentrations were significantly higher among men with C/T or T/T genotypes (multivariate adjusted P = 0.02). Plasma concentrations of the other lipoprotein lipid profiles did not differ significantly between CC and CT/TT genotype groups. Results did not differ significantly with control for BMI as a continuous variable. In addition, results did not change appreciably after we excluded subjects taking cholesterol-lowering medications. We therefore included these subjects in subsequent analyses.


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  		TABLE 2 Age-and multivariate-adjusted lipid profiles according to genotypes of the hepatic lipase gene (LIPC) among US diabetic men1

 
In stratified analyses (Table 3Go), the association between the LIPC –514C->T polymorphism and plasma HDL-cholesterol concentrations varied according to self-reported intakes of total, saturated, monounsaturated, and trans fats (Table 3Go). A significantly higher HDL-cholesterol concentration in relation to the T allele was confined to men with higher intakes of total, saturated, or monounsaturated fat. For example, among men who consumed higher total fat (≥32%), plasma HDL-cholesterol concentrations were significantly higher in those with the T allele than in those with the C/C genotype (42.0 ± 1.1 and 37.0 ± 0.8 mg/dL, respectively; P = 0.002). This association, however, was not observed among men who consumed less total fat (adjusted P value for interaction = 0.01). When different types of fat were examined, the interaction was significant only for saturated fat intake (P for interaction = 0.003).


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  		TABLE 3 Interaction of dietary fat consumption (% of energy) with the effect of hepatic lipase genotypes for HDL-cholesterol concentrations1

 
The association according to BMI (<25.0, 25.0–29.9, and ≥30.0) is shown in Table 4Go. We observed a strong LIPC –514C->T polymorphism x BMI interaction for HDL-cholesterol concentrations (P = 0.003). Only among lean men (BMI <25) was the T allele associated with significantly elevated plasma HDL-cholesterol concentrations (CC compared with CT/TT: 42.1 and 47.0 mg/dL, respectively; P = 0.02). The association reached marginal significance among those with BMI between 25.0 and 29.9 but was not significant among obese men. In addition, there was a significant inverse association between BMI and plasma HDL-cholesterol concentrations in both men with the CC and the CT/TT genotype (P for linear trend < 0.001 in both groups), although the magnitude of the association is stronger in the latter group.


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  		TABLE 4 Interaction of adiposity with the effect of the hepatic lipase genotypes for HDL-cholesterol concentrations1

 
Finally, considering that altered HL activity has been related to other lifestyle factors, including physical activity and alcohol consumption, we further examined whether these factors modulated the observed association between this polymorphism and plasma HDL-cholesterol concentrations. We observed a marginally significant polymorphism x physical activity interaction for HDL-cholesterol concentrations (P = 0.06). The T allele was associated with significantly higher HDL-cholesterol concentrations (CC compared with CT/TT: 37.9 ± 0.8 and 41.9 ± 1.0 mg/dL, respectively; P = 0.004) among those who were less active (total MET score: <4.6 MET-h/wk), but not among those who were more active. No significant interactions between alcohol consumption and this polymorphism in relation to HDL-cholesterol concentrations were observed (P for interaction = 0.75).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In this study of diabetic men, plasma HDL-cholesterol concentrations were significantly higher among subjects with the T allele (CT or TT genotype) than among those with the CC genotype. This association was modulated by dietary fat intake and overall body adiposity. Significant gene x diet interactions were found for this polymorphism and dietary saturated fat intake in association with plasma HDL-cholesterol concentrations. Higher saturated fat intake enhanced the beneficial effect of the LIPC –514C->T polymorphism on HDL-cholesterol concentrations, whereas obesity abolished the beneficial effects of the genotype on HDL-cholesterol concentrations.

Very few data are available concerning the relation between the LIPC –514C->T polymorphism and plasma HDL-cholesterol concentrations among diabetics. Our findings are generally consistent with a relatively large body of literature documenting elevated plasma HDL-cholesterol concentration in association with the T allele among the general white population (2, 4, 34-37). It has been widely accepted that such an association is likely to be mediated by a reduction in HL activity induced by the –514T allele. In vitro studies reported up to 30% reduction in promoter activity attributed to the C->T base pair substitution at the –514 position (18, 19). The role of HL in the metabolism of HDL is well recognized (8, 9, 13, 38). An increase in HL activity favors increased translocation of cholesteryl ester from HDL to triacylglycerol-rich lipoprotein in exchange for triacylglycerols and the formation of smaller HDL particles in a pathway associated with HDL catabolism (8), which results in lower HDL-cholesterol concentrations.

Investigations of potential complex interactions between the LIPC –514 polymorphism and environmental factors in determining plasma HDL-cholesterol concentrations have recently emerged. To our knowledge, there is only one previously published study in whites (21) concerning the relation of the LIPC –514 polymorphism to HDL-cholesterol concentrations and its interaction with dietary fat intake. In accordance with findings from our study, Ordovas et al (21), in their study of 1020 male and 1110 female whites, observed that dietary fat, especially saturated and monounsaturated fat, modified the effect of the –514(C->T) polymorphism on HDL-cholesterol concentrations and subclass. In contrast to our study, the T allele was associated with significantly higher HDL-cholesterol concentrations only in subjects consuming a diet low in fat (<30% of energy) (P < 0.001). Reasons for the discrepancies between the findings of these 2 studies are not clear, although participants in our study were all diabetic men, and the population in the study of Ordovas et al included both men and women from the general population. Studies designed to explore the physiologic and functional significance of this polymorphism on HL activity and lipoprotein lipid metabolism in the context of increased dietary fat, especially saturated fat intake, are clearly warranted.

Another major finding from the current study is the effect of a strong interaction of this polymorphism with BMI for HDL-cholesterol concentrations. Our results are generally consistent with findings from limited studies in nondiabetics. For instance, St-Pierre et al (25), in a sample of 235 white men free of medical treatment for diabetes, hypertension, or CAD, observed that only lean carriers of the T allele had higher plasma HDL2 concentrations than did lean CC homozygotes. The beneficial effect of the T allele was abolished in the presence of visceral obesity (visceral adipose tissue: >130 cm2). Although the exact biological basis for this finding is unclear, the interaction appeared to be associated with high HL activity and low HDL-cholesterol concentrations that were related to being overweight or obese. Indeed, available data suggest that obesity indicated by visceral obesity, intraabdominal fat (39-43), or overall BMI is strongly associated with high HL activity and low plasma HDL-cholesterol concentrations, mainly low HDL2 concentrations.

Several potential limitations must be considered in interpreting results from the current study. First, the composition of HDL particles was not measured in our study. Both the composition and the concentration of HDL-cholesterol particles are modulated by lecithin:cholesterol acyltransferase and cholesteryl ester transfer protein as well as by HL (44, 45). It has been suggested that the HDL particle size distribution (ie, HDL subfractions HDL2 and HDL3) rather than total HDL cholesterol may be a more sensitive marker for the effect of the –514C->T polymorphism on HDL metabolism (35, 46). We, however, observed a positive association between the LIPC –514C->T polymorphism and HDL metabolism even when using the less sensitive total HDL-cholesterol concentrations. Second, the LIPC –514 polymorphism was observed to be in complete linkage disequilibrium with 3 other polymorphisms (ie, G->A at position –250, T->C at –710, and A->G at –763) in the 5-flanking region of the LIPC (4, 16). Studying this polymorphism alone enabled us to study the 5-flanking region efficiently. However, we cannot exclude the possibility that the observed association with HDL-cholesterol concentrations is due to linkage disequilibrium with other, yet undiscovered variants in this gene or to HDL's interaction with other genes that are important to measurements of plasma HDL-cholesterol concentrations, eg, apolipoprotein AI/CIII/AIV and cholesteryl ester transfer protein loci. Third, findings from recent studies suggested that the LIPC –514 polymorphism may be associated with insulin secretion and the conversion from impaired glucose tolerance to type 2 diabetes (47, 48). If this polymorphism is associated with increased diabetes, it may be overrepresented in our sample. However, the variant allele frequency in the current study (0.20) is in agreement with that reported in previous studies among whites (2, 3, 16, 34, 35, 49, 50). Therefore, results from the current study are not likely to be due to the overrepresentation of the –514 polymorphism in our sample. Finally, whereas the HPFS does not represent a random sample of US diabetic men, the relative socioeconomic homogeneity of this population does tend to reduce the effect of unknown confounders.

In summary, we observed that the LIPC –514C->T polymorphism interacts with saturated fat intake and overall body adiposity in determining plasma HDL-cholesterol concentrations. The LIPC –514C->T polymorphism is common among whites. Even a modest alteration in plasma HDL-cholesterol concentrations associated with the presence of this allele would explain a substantial proportion of variations in HDL-cholesterol concentration. If confirmed, our findings imply that diet and lifestyle modification counseling among diabetics aimed at optimizing plasma HDL-cholesterol concentrations and preventing coronary heart disease may need to consider LIPC genotype. Studies of this kind are important not only for better understanding of biological mechanisms, but also for more effective interventions in high-risk populations.


   ACKNOWLEDGMENTS
 
CZ contributed to concept and design, data analysis, statistical support, and manuscript writing and editing.RL-R contributed to statistical support and manuscript writing and editing. EBR contributed to concept and design, data collection, statistical support, and manuscript writing and editing. NR and DJH contributed to data collection and manuscript writing and editing. FBH contributed to funding, concept and design, data analysis, statistical support, and manuscript writing and editing. None of the authors had any personal or financial conflicts of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication July 12, 2004. Accepted for publication December 22, 2004.




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