types of alcoholic beverages consumed in Spain, which presumably covered all types of alcoholic beverages
usually consumed. For each item, the questionnaire included seven frequency categories, and requested in-
formation on the number of glassesday. For each item, glassesday were calculated by adding the weekend
consumption to the workday consumption. For each alcoholic beverage, mean of daily ethanol consumption
in grams was calculated by multiplying the amount consumed in ml by the percentage of ethanol supplied
by each specific beverage. From the reported alcoholic beverages, alcohol consumption was categorized as a
drinker variable according to the amount of alcohol consumed. Men and women were classified into three
groups according to the population tertiles: No con- sumption alcohol consumption = 0 gday, Moderate
B 10.5 g alcoholday for men, and B 5.5 gday for women, and High consumption \ 10.6 g alcoholday
for men, and \ 5.6 g alcoholday for women. In some analyses, only two categories were considered: non-
drinkers alcohol consumption = 0 and drinkers sub- jects with any amount of alcohol consumed.
Physical activity was estimated by questions about regular leisure-time physical sports aerobics, basket-
ball, bicycling, gymnastics, running, soccer, squash, swimming, tennis, volleyball, and others, as well as the
average number of h per week spent in each activity. According to the type and time [23], subjects were
categorized as Sedentary no physical exercise, Moder- ate one sport less than 3 hweek and High one sport
more than 3 hweek or more than two sports. For regression analyses, physical exercise was also dichoto-
mized into Sedentary versus Active Moderate plus High. In addition to the type and time spent per week
in physical exercise, another question about regular daily walking more than 20 min, with two possible
answers ‘yes’ and ‘no’, was also included.
Marital status, classified as married, single and di- vorced, was dichotomized as being Single living alone
and divorced or Living with a partner. Education was initially coded into five categories: Non-schooling, Pri-
mary school, Secondary school, University-1 short cy- cle and University-2 long cycle, and afterwards
recoded into three levels: Primary, Secondary and Uni- versity short + long cycles.
2
.
4
. Laboratory analyses Plasma total cholesterol and tryglicerides were deter-
mined by a Technicon Chem 1 assay Technicon Instru- ments, Tarrytown, NY, and HDL-C was measured in
the supernatant after precipitation of apolipoprotein B — containing lipoproteins with heparin-manganese
chloride. LDL-C was calculated according to the equa- tion of Friedewald et al. [24] for samples with serum
triglyceride concentrations below 400 mg per deciliter.
2
.
5
. DNA extraction and genotyping Genomic DNA was extracted from white blood cells
by phenol extraction. A fragment of 535 base pairs in the intron 1 of CETP gene was amplified by polymerase
chain reaction PCR. Each amplification was per- formed using 500 ng of genomic DNA in a volume of
50 ml containing 40 pmol of each oligonucleotide U: CACTAGCCCAGAGAGAGGAGTGCC and L: CT-
GAGCCCAGCCGCACACTAAC, 0.2 mM dNTPs, 1.5 mM MgCl
2
, 10 mM Tris pH 8.4 and 0.25 U of Taq polymerase Gibco BRL, Paisley, UK. The PCR con-
ditions were 95°C for 5 min, and subsequently 28 cycles at 95°C for 30 s, 60°C for 30 s, and 72°C for 45 s, and
finally at 72°C for 5 min. The PCR products were subject to restriction enzyme analysis by digestion with
4 units of the restriction endonuclease TaqI for 16 ml of PCR sample at 65°C for 2 h, in the buffer recom-
mended by the manufacturer Pharmacia Inc. Sweden.
3. Statistical analysis
Questionnaires, biochemical and genetic data were key-entered twice and processed to avoid errors in
coding and interpretation. Normal distribution for all continuous variables was checked by graphical methods
and by hypotheses tests. Triglycerides and alcohol in- take were markedly skewed, and these variables were
logarithmically and squared root transformed, respec- tively, to improve normality for statistical testing. To
assess mean differences of lipid and anthropometric variables between genders, Student’s t test for indepen-
dent samples was used after determining the homogene- ity of variances by the Levene statistic. Chi square tests
were conducted to compare differences in percentages. This statistical test was also performed to examine
whether the genotype frequencies were in Hardy – Wein- berg equilibrium [25]. For multiple comparisons of
means between genotypes, one way analysis of variance ANOVA was performed. P values for linear trends
between categories were also calculated using the ANOVA analysis by partition of the between-groups
sums of squares into trend components. Once estab- lished that differences exist among means, to determine
which means differ with correction for multiple com- parisons, Bonferroni test was applied. To test the null
hypotheses of no association between CETP genotypes and HDL-C, controlling by one or more potential
environmental
and genetic
confounders or
effect modifiers, multivariate modeling was used. Analyses of
covariance ANCOVA were conducted as exploratory analysis and to evaluate the statistical significance of
multiple categorical terms or interaction terms into the multivariate model. To estimate the extent, direction
and strength of the association between predictors and
CETP genotypes, multiple linear regression analysis with dummy variables for categorical and interaction
terms was applied. In order to improve statistical power, data from men and women were analyzed to-
gether, including a dummy variable for gender into the regression model. A core model with gender, age, BMI,
CETP TaqIB genotypes, and a set of variables for tobacco smoking and alcohol consumption, was first
identified using forward and backward procedures. The selection criterion for choosing the core model was the
following: a variable or interaction term was retained into the model if the global term was statistically sig-
nificant P B 0.05. Regression diagnostics analysis of residuals, influence of outliers and colinearity were
employed to check the assumptions and to assess the
Table 2 CETP TaqIB polymorphisms in the population by gender
a
Women All
Men n
n n
Genotypes 117 41.3
B1B1 222 43.2
105 45.5 100 43.3
223 43.4 123 43.5
B1B2 69 13.4
26 11.3 B2B2
43 15.2 514
283 100.0 Total
231 100.0
100.0 Allele frequency
B2 Allele 0.329
0.351 0.369
0.329–0.398 0.286–0.372
95 C.I. 0.322–0.380
a
Differences by gender across CETPTaqIB polymorphisms were nonsignificant. Chi square P value = 0.332.
Table 1 Demographic, biochemical and life-style characteristics of the popula-
tion Men n = 231
Women n = 283 Mean SD
P Mean SD
0.102 Age years
35.7 10.4 37.4 10.1
Body mass index B
0.001 23.9 4.2
26.4 4.8 kgm
2
5.3 1.0 Total-C mmoll
4.9 0.9 B
0.001 3.1 0.8
3.5 0.9 LDL-C mmoll
B 0.001
1.1 0.3 HDL-C mmoll
1.4 0.3 B
0.001 Triglycerides
B 0.001
1.3 1.2 1.1 0.7
mmoll B
0.001 127.2 15.9
Systolic blood 117.5 17.7
presure mmHg
0.017 73.8 11.6
70.7 12.4 Diastolic blood
presure mmHg
44.6 0.998
44.6 Current smokers
Past smokers 0.002
25.7 12.9
B 0.001
Alcohol users 88.9
59.9 Physical exercise
36.5 61.7
Sedentary Moderate
32.7 39.6
5.6 0.005
23.9 High
74.3 Daily walkers
71.9 0.872
Education 0.0
Non-schooling 0.0
26.8 34.7
Primary 29.6
Secondary 31.4
University short 19.6
32.3 cycle
University long 9.8
16.1 0.002
cycle Marital status
66.7 52.1
Married 41.8
30.8 Single
2.5 6.3
Divorced 0.016
P value in the comparison between men and women. Student’s test for comparison of means, and Chi square tests for percentages.
accuracy of computations. Once the core model was identified for the total sample, separate models with the
same variables were computed for men and women. Finally, additional life-style variables physical activity,
marital status and educational level were included into the core model defined in the previous stage. A variable
or interaction term was maintained in the final regres- sion model as independent term or as control variable
on the basis of statistical significance P B 0.05 or P B 0.20, respectively. All analyses were done using the
Statistical Package for Social Sciences SPSS, version 8.0 for windows.
4. Results
