Coffee, CYP1A2 Genotype, and Risk of Myocardial Infarction

Abstract

The association between coffee intake and risk of myocardial infarction (MI) remains controversial. Coffee is a major source of caffeine, which is metabolized by the polymorphic cytochrome P450 1A2 (CYP1A2) enzyme. Individuals who are homozygous for the CYP1A2*1A allele are "rapid" caffeine metabolizers, whereas carriers of the variant CYP1A2*1F are "slow" caffeine metabolizers.
To determine whether CYP1A2 genotype modifies the association between coffee consumption and risk of acute nonfatal MI.
Cases (n = 2014) with a first acute nonfatal MI and population-based controls (n = 2014) living in Costa Rica between 1994 and 2004, matched for age, sex, and area of residence, were genotyped by restriction fragment-length polymorphism polymerase chain reaction. A food frequency questionnaire was used to assess the intake of caffeinated coffee.
Relative risk of nonfatal MI associated with coffee intake, calculated using unconditional logistic regression.
Fifty-five percent of cases (n = 1114) and 54% of controls (n = 1082) were carriers of the slow *1F allele. For carriers of the slow *1F allele, the multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of nonfatal MI associated with consuming less than 1, 1, 2 to 3, and 4 or more cups of coffee per day were 1.00 (reference), 0.99 (0.69-1.44), 1.36 (1.01-1.83), and 1.64 (1.14-2.34), respectively. Corresponding ORs (95% CIs) for individuals with the rapid *1A/*1A genotype were 1.00, 0.75 (0.51-1.12), 0.78 (0.56-1.09), and 0.99 (0.66-1.48) (P = .04 for gene x coffee interaction). For individuals younger than the median age of 59 years, the ORs (95% CIs) associated with consuming less than 1, 1, 2 to 3, or 4 or more cups of coffee per day were 1.00, 1.24 (0.71-2.18), 1.67 (1.08-2.60), and 2.33 (1.39-3.89), respectively, among carriers of the *1F allele. The corresponding ORs (95% CIs) for those with the *1A/*1A genotype were 1.00, 0.48 (0.26-0.87), 0.57 (0.35-0.95), and 0.83 (0.46-1.51).
Intake of coffee was associated with an increased risk of nonfatal MI only among individuals with slow caffeine metabolism, suggesting that caffeine plays a role in this association.

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ORIGINAL CONTRIBUTION
Coffee, CYP1A2 Genotype,
and Risk of M yocardial Infarction
Marilyn C. Cornelis, BSc
Ahmed El-Sohemy, PhD
Edmond K. Kabagambe, PhD
Hannia Campos, PhD
E
PIDEMIOLOGIC STUDIES EXAMIN-
ing the association between cof-
fee consumption and risk of
myocardial infarction (MI) have
been inconclusive.
1-14
Coffee is a ma-
jor source of caffeine (1,3,7-trimethyl-
xanthine), which is the most widely
consumed stimulant in the world and
has been implicated in the develop-
ment of cardiovascular diseases such
as acute MI.
15-17
However, coffee con-
tains a number of other chemicals that
have variable effects on the cardiovas-
cular system.
18
Because of the strong
collinearity between caffeine intake and
coffee consumption in many popula-
tions, it is not clear whether caffeine
alone affects the risk of MI or whether
other chemicals found in coffee may be
responsible. Furthermore, the associa-
tion between coffee consumption and
unhealthy lifestyle factors suggests that
previous associations might have been
due to residual confounding.
19
Caffeine is metabolized primarily by
cytochrome P450 1A2 (CYP1A2) in the
liver through an initial N
3
-demethyl-
ation.
20,21
CYP1A2 accounts for approxi-
mately 95% of caffeine metabolism and
demonstrates wide variability in en-
zyme activity between individuals.
21-23
An A→C substitution at position 734
(CYP1A2*1F)intheCYP1A2 gene de-
creases enzyme inducibility as mea-
sured by the ratio of plasma or urinary
caffeine to caffeine metabolites after a
dose of caffeine, resulting in impaired
caffeine metabolism.
24-26
Carriers of the
variant CYP1A2*1F allele are “slow” caf-
feine metabolizers, whereas individu-
als who are homozygous for the
CYP1A2*1A allele are “rapid” caffeine
metabolizers.
24-26
The purpose of this
study was to determine whether
CYP1A2 genotype modifies the asso-
ciation between intake of caffeinated
coffee and risk of nonfatal MI.
METHODS
Study Design and Participants
The catchment area for this study com-
prised 7071 km
2
and 2 057 000 indi-
viduals living in Costa Rica who are self-
described Hispanic Americans.
27
This
area included 36 counties in the Cen-
tral Valley of Costa Rica representing
a full range of socioeconomic levels, as
Author Affiliations: Department of Nutritional Sci-
ences, University of Toronto, Toronto, Ontario (Ms Cor-
nelis and Dr El-Sohemy); Department of Nutrition, Har-
vard School of Public Health, Boston, Mass (Drs
Kabagambe and Campos); and Centro Centroameri-
cano de Poblacion, Universidad de Costa Rica, San Pe-
dro de Montes de Oca, Costa Rica (Dr Campos).
Corresponding Author: Ahmed El-Sohemy, PhD, De-
partment of Nutritional Sciences, University of Toronto,
150 College St, Toronto, Ontario, Canada M5S 3E2
(a.el.sohemy@utoronto.ca).
Context The association between coffee intake and risk of myocardial infarction (MI)
remains controversial. Coffee is a major source of caffeine, which is metabolized by
the polymorphic cytochrome P450 1A2 (CYP1A2) enzyme. Individuals who are ho-
mozygous for the CYP1A2*1A allele are “rapid” caffeine metabolizers, whereas car-
riers of the variant CYP1A2*1F are “slow” caffeine metabolizers.
Objective To determine whether CYP1A2 genotype modifies the association be-
tween coffee consumption and risk of acute nonfatal MI.
Design, Setting, and Participants Cases (n=2014) with a first acute nonfatal MI
and population-based controls (n=2014) living in Costa Rica between 1994 and 2004,
matched for age, sex, and area of residence, were genotyped by restriction fragment–
length polymorphism polymerase chain reaction. A food frequency questionnaire was
used to assess the intake of caffeinated coffee.
Main Outcome Measure Relative risk of nonfatal MI associated with coffee in-
take, calculated using unconditional logistic regression.
Results Fifty-five percent of cases (n=1114) and 54% of controls (n=1082) were car-
riers of the slow *1F allele. For carriers of the slow *1F allele, the multivariate-adjusted
odds ratios (ORs) and 95% confidence intervals (CIs) of nonfatal MI associated with
consuming less than 1, 1, 2 to 3, and 4 or more cups of coffee per day were 1.00 (ref-
erence), 0.99 (0.69-1.44), 1.36 (1.01-1.83), and 1.64 (1.14-2.34), respectively. Corre-
sponding ORs (95% CIs) for individuals with the rapid *1A/*1A genotype were 1.00,
0.75 (0.51-1.12), 0.78 (0.56-1.09), and 0.99 (0.66-1.48) (P=.04 for gene⫻coffee in-
teraction). For individuals younger than the median age of 59 years, the ORs (95% CIs)
associated with consuming less than 1, 1, 2 to 3, or 4 or more cups of coffee per day
were 1.00, 1.24 (0.71-2.18), 1.67 (1.08-2.60), and 2.33 (1.39-3.89), respectively, among
carriers of the *1F allele. The corresponding ORs (95% CIs) for those with the *1A/*1A
genotype were 1.00, 0.48 (0.26-0.87), 0.57 (0.35-0.95), and 0.83 (0.46-1.51).
Conclusion Intake of coffee was associated with an increased risk of nonfatal MI
only among individuals with slow caffeine metabolism, suggesting that caffeine plays
a role in this association.
JAMA. 2006;295:1135-1141 wwww.jama.com
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well as urban, periurban, and rural life-
styles. Medical services in this area were
covered by 6 large hospitals, which are
part of the National Social Security Sys-
tem. Eligible case participants were men
and women who were survivors of a
first acute MI as diagnosed by a cardi-
ologist at any of the 6 recruiting hos-
pitals in the catchment area between
1994 and 2004. To achieve 100% as-
certainment, the hospitals were vis-
ited daily by the study fieldworkers. All
cases were confirmed by 2 indepen-
dent cardiologists according to the
World Health Organization criteria for
MI, which require typical symptoms
plus either elevation in cardiac en-
zyme levels or diagnostic change in
electrocardiogram tracings.
28
Enroll-
ment was carried out while cases were
in the hospital’s step-down unit. Case
participants were ineligible if they died
during hospitalization, were 75 years
or older on the day of their first MI,
were physically or mentally unable to
answer the questionnaire, or had a pre-
vious hospital admission related to car-
diovascular disease.
One control participant for each case,
matched for age (±5 years), sex, and area
of residence (county), was randomly se-
lected using information available at the
National Census and Statistics Bureau
of Costa Rica. Eligible controls were
identified within 1 week of the case se-
lection. On average, it took 27 days to
complete data collection for cases and
31 days for controls. Because of the com-
prehensive social services provided in
Costa Rica, all persons living in the
catchment areas had access to medical
care without regard to income. There-
fore, control participants came from the
source population that gave rise to the
cases and are not likely to have had car-
diovascular disease that was not diag-
nosed because of poor access to medi-
cal care. Controls were ineligible if they
were physically or mentally unable to an-
swer the questionnaires or if they had
had a previous hospital admission re-
lated to MI or other cardiovascular
disease.
Participation for eligible cases and
controls was 98% and 88%, respec-
tively. All participants were visited at
their homes for collection of biologi-
cal specimens and information on diet,
medical history, and anthropometric
measurements. Cases and controls gave
written informed consent and the study
was approved by the ethics commit-
tees of the Harvard School of Public
Health and the University of Costa Rica,
the Office for the Protection from Re-
search Risks at the National Institutes
of Health, and the ethics review com-
mittee at the University of Toronto.
All data were collected by trained
fieldworkers during an interview using
2 questionnaires consisting of closed-
ended questions regarding smoking, so-
ciodemographic characteristics, socio-
economic status, physical activity, diet,
and medical history including use of
medication and personal history of dia-
betes and hypertension. Information on
dietary intake was collected using a 135-
item semiquantitative food frequency
questionnaire (FFQ) specifically devel-
oped and validated to assess dietary in-
take during the past year in the Costa
Rican population.
29
For cases, average
intake represented the year preceding
their MI. Intakes of nutrients were cal-
culated using the US Department of Ag-
riculture food composition data file. In-
cluded in the FFQ were questions related
to the consumption of caffeinated cof-
fee, tea, cola beverages, and chocolate.
The standard portion size for coffee in
the FFQ was fixed as 1 cup equivalent
to 250 mL, based on the habitual por-
tion size for coffee-drinking habits es-
tablished in this population during
methods development.
29
Participants
were asked to specify 1 of 9 categories
of coffee intake: none or less than 1 cup/
mo, 1 to 3 cups/mo, 1 cup/wk, 2 to 4
cups/wk, 5 to 6 cups/wk, 1 cup/d, 2 to
3 cups/d, 4 to 5 cups/d, or 6 cups/d or
more. The correlation coefficient for caf-
feine intake between seven 24-hour re-
calls and the average of 2 FFQ inter-
views was 0.83, and the correlation
coefficient between both FFQs was
0.77.
29
These results indicate high va-
lidity and reliability for the usual in-
take of coffee. Method of coffee prepa-
ration was ascertained for coffee
drinkers. Approximately 90% of coffee
consumed in Costa Rica is filtered. Par-
ticipants were categorized into 4 groups
with reported coffee intakes of less than
1, 1, 2 to 3, or 4 or more 250-mL cups
per day.
CYP1A2 Genotyping
Blood samples were collected in the
morning at the participant’s home after
an overnight fast and were centrifuged
to separate the plasma and leukocytes
for DNA isolation by standard proce-
dures. The CYP1A2*1F (rs762551) poly-
morphism was detected by restriction
fragment–length polymorphism poly-
merase chain reaction as previously
described,
30
without knowledge of case-
control status. The genotype distribu-
tion among controls did not deviate from
Hardy-Weinberg equilibrium accord-
ing to a Pearson ␹
2
test with 1 df.
Statistical Analyses
All data were analyzed using SAS ver-
sion 8.2 (SAS Institute Inc, Cary, NC);
P⬍.05 was considered statistically sig-
nificant. DNA was available from 4369
participants (2113 cases and 2256 con-
trols). A total of 341 participants were
excluded because they were missing
data on confounders (33 cases, 26 con-
trols), could not be genotyped (63 cases,
73 controls), or became unmatched
because of missing data (3 cases, 143
controls), leaving 2014 matched case-
control pairs for the final analysis. In-
dividual nutrient intakes were ad-
justed for total energy as described
elsewhere.
29,31
Because of the matched
design, significant differences in the dis-
tribution of variables between cases
and controls were tested using the
McNemar test (categorical variables)
and either paired t tests or Wilcoxon
signed rank tests (continuous vari-
ables). Categorical and continuous non-
dietary and energy-adjusted dietary
variables were assessed for potential
confounding by measuring their effect
on the model parameter estimates us-
ing the likelihood ratio test. Odds ra-
tios (ORs) and Wald 95% confidence
intervals (95% CIs) were estimated by
conditional logistic regression to de-
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
1136 JAMA, March 8, 2006—Vol 295, No. 10 (Reprinted) ©2006 American Medical Association. All rights reserved.
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termine the effect of coffee intake on
the risk of MI, with the lowest level of
coffee intake (⬍1 cup/d) as the refer-
ence group. Confounders included in
the final models were smoking (never,
past, 1-19 cigarettes/d, and ⱖ20 ciga-
rettes/d); alcohol consumption (never,
past, and tertiles of intake among cur-
rent drinkers); history of diabetes (yes/
no), history of hypertension (yes/no);
quintiles of the continuous variables
waist-hip ratio, physical activity, and in-
come; and energy-adjusted intakes of
sucrose, saturated fat, polyunsatu-
rated fat, and trans fat. We evaluated po-
tential gene⫻coffee interactions by de-
termining the relation between coffee
intake and the risk of MI for each geno-
type using conditional and uncondi-
tional logistic regression (with match-
ing variables in the model) and by
comparing –2 log (likelihood) ratios
from a model with coffee and gene main
effects only and from another that in-
cluded their interaction term. Because
results for conditional and uncondi-
tional regressions were similar, we re-
port only the data from unconditional
analyses to maximize the number of
participants. Results are presented us-
ing a dominant *1F allele model with
*1A/*1F and *1F/*1F genotypes com-
bined, because both groups have a simi-
lar rate of caffeine metabolism.
24-26
We
also investigated whether smoking sta-
tus (nonsmoker, current smoker) or age
(below/above median) modified the re-
lationship between CYP1A2 genotype
and MI associated with coffee intake,
by performing stratified analyses and
evaluating the gene⫻ coffee interac-
tion separately for each subgroup.
RESULTS
Demographic and risk factor charac-
teristics of participants based on case-
control status and coffee intake among
controls are presented in T
ABLE 1. The
proportion of *1F carriers did not dif-
fer between cases and controls or be-
tween different categories of coffee in-
take. T
ABLE 2 shows the risk of MI
associated with coffee intake for all par-
ticipants and by CYP1A2 genotype.
Compared with individuals consum-
ing less than 1 cup/d, the multivariate-
adjusted OR (95% CI) of MI associ-
ated with consuming 4 cups/day or
more was 1.40 (1.05-1.87). When par-
ticipants were stratified by CYP1A2
Table 1. Demographic and Risk Factor Characteristics by Case-Control Status and by Coffee Intake Among Controls
Characteristic
No. (%)
P Value,
Cases vs
Controls
Controls Coffee Intake, No. (%)
Cases
(n = 2014)
Controls
(n = 2014)
⬍1
Cup/d
(n = 269)
1
Cup/d
(n = 338)
2-3
Cups/d
(n = 1133)
ⱖ4
Cups/d
(n = 274)
CYP1A2*1A/*1F ⫹ *1F/*F, No. (%) 1114 (55) 1082 (54) .31 156 (58) 180 (53) 595 (53) 151 (55)
Age, mean (SD), y* 58.4 (11.0) 58.1 (11.3) 56.7 (12.4) 58.4 (11.9) 58.7 (11.1) 56.7 (10.2)
Men, No. (%)* 1488 (74) 1488 (74) 190 (71) 243 (72) 818 (72) 237 (86)
Urban residence, No. (%)* 1482 (74) 1482 (74) 209 (78) 273 (81) 801 (71) 198 (72)
Secondary education or higher, No. (%) 733 (36) 806 (40) .007 148 (55) 163 (48) 392 (35) 103 (38)
Household income, mean (SD), US $/mo 499 (388) 571 (425) ⬍.001 735 (492) 632 (456) 522 (381) 543 (441)
Waist-hip ratio, mean (SD) 0.97 (0.07) 0.95 (0.07) ⬍.001 0.94 (0.08) 0.95 (0.08) 0.95 (0.08) 0.96 (0.06)
Physical activity, mean (SD), METs 1.51 (0.69) 1.57 (0.70) .01 1.46 (0.48) 1.50 (0.68) 1.56 (0.69) 1.76 (0.87)
Medical history, No. (%)
History of hypertension† 779 (39) 596 (30) ⬍.001 78 (29) 113 (33) 341 (30) 64 (23)
History of diabetes† 492 (24) 285 (14) ⬍.001 29 (11) 48 (14) 179 (16) 29 (11)
Current smoking‡ 805 (40) 425 (21) ⬍.001 32 (12) 45 (13) 230 (20) 118 (43)
Current alcohol consumption† 984 (49) 1059 (53) .01 138 (51) 200 (59) 576 (51) 145 (53)
Nutrient intakes, mean (SD)
Total energy, kcal 2714 (946) 2457 (764) ⬍.001 2461 (759) 2340 (674) 2445 (748) 2648 (894)
Carbohydrate, % energy 54.3 (7.6) 55.4 (7.3) ⬍.001 54.9 (7.9) 53.7 (7.5) 55.9 (6.8) 55.8 (8.1)
Protein, % energy 13.2 (2.2) 12.9 (2.1) ⬍.001 12.9 (2.3) 13.2 (2.2) 13.0 (2.0) 12.7 (2.2)
Fat, % energy 32.4 (5.9) 31.9 (5.9) .008 32.6 (6.6) 32.9 (6.4) 31.6 (5.3) 31.3 (6.4)
Saturated fat, % energy 12.4 (3.1) 11.7 (2.9) ⬍.001 11.4 (3.0) 11.8 (2.9) 11.7 (2.8) 12.0 (3.2)
Polyunsaturated fat, % energy 6.9 (2.3) 7.1 (2.3) ⬍.001 7.2 (2.0) 7.3 (2.4) 7.1 (2.4) 6.6 (2.4)
Monounsaturated fat, % energy 11.1 (3.5) 11.2 (4.1) .70 12.2 (5.2) 11.9 (4.8) 10.9 (3.4) 11.0 (4.1)
Trans fat, % energy 1.3 (0.6) 1.3 (0.6) .06 1.2 (0.6) 1.2 (0.6) 1.3 (0.7) 1.3 (0.6)
Cholesterol, mg/1000 kcal 127 (59) 118 (52) ⬍.001 120 (48) 113 (50) 119 (53) 117 (57)
Sucrose, g/d 80.1 (50.8) 75.2 (43.2) ⬍.001 74.3 (40.1) 63.6 (33.0) 73.9 (39.0) 95.8 (62.7)
Fiber, g/1000 kcal 9.5 (2.4) 10.0 (2.5) ⬍.001 10.1 (2.7) 9.9 (2.5) 10.1 (2.4) 9.3 (2.3)
Folate, µg/1000 kcal 170 (46) 175 (47) ⬍.001 183 (50) 174 (46) 177 (46) 162 (43)
Abbreviation: METs, metabolic equivalent tasks.
*Matching variable.
†See “Methods” for definition.
‡One or more cigarettes per day.
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
©2006 American Medical Association. All rights reserved. (Reprinted) JAMA, March 8, 2006—Vol 295, No. 10 1137
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genotype, the increased risk of MI as-
sociated with coffee intake was ob-
served only among carriers of the slow
*1F allele (P= .04 for gene⫻ coffee in-
teraction). In this group, the OR (95%
CI) of MI was 1.64 (1.14-2.34) for 4
cups/d or more, as compared with less
than 1 cup/d. The corresponding OR
(95% CI) among those who were ho-
mozygous for the rapid *1A allele was
0.99 (0.66-1.48). Similar results were
observed when men and women were
examined separately. Compared with
less than 1 cup/d, the ORs (95% CIs)
of MI for 4 cups/d or more among in-
dividuals with the *1A/*1A genotype
were 0.86 (0.53-1.36) for men and 1.43
(0.54-3.72) for women. Correspond-
ing ORs (95% CIs) among carriers of
the *1F allele were 1.54 (1.03-2.32) for
men and 2.83 (1.15-6.99) for women.
Because smoking is associated with
coffee consumption and is also a strong
inducer of CYP1A2,
32
we performed
analyses separately for current smok-
ers and nonsmokers (never, past). Al-
though the gene⫻coffee interaction did
not reach significance in either group,
the modifying effect of CYP1A2 geno-
type on risk of MI associated with cof-
fee consumption was similar for both
smokers and nonsmokers (T
ABLE 3).
It has previously been suggested that
coffee may be associated with an in-
creased risk of MI only among younger
individuals.
7,8
To investigate whether
age modified the interaction between
CYP1A2 and coffee on risk of MI, we
assessed risk separately for partici-
pants above and below the median age
(59 years). A significant gene⫻ coffee
interaction (P =.003) was observed
only among the younger participants
(Table 3). For those individuals who
were carriers of the *1F allele, the ORs
(95% CIs) of MI associated with con-
suming less than 1, 1, 2 to 3, or 4 or
more cups of coffee per day were 1.00
(reference), 1.24 (0.71-2.18), 1.67
(1.08-2.60), and 2.33 (1.39-3.89), re-
spectively. Corresponding ORs (95%
CIs) for those with the *1A/*1A geno-
type were 1.00, 0.48 (0.26-0.87), 0.57
(0.35-0.95), and 0.83 (0.46-1.51). Be-
cause of the observed interaction with
participants younger than the median
age of 59 years, we also analyzed those
younger than 50 years (448 cases, 478
controls), as has been previously done.
33
For carriers of the *1F allele, the ORs
(95% CIs) of MI associated with con-
suming less than 1, 1, 2 to 3, or 4 or
more cups of coffee per day were 1.00,
2.12 (0.86-5.24), 2.43 (1.22-4.82), and
4.07 (1.89-8.74), respectively. Corre-
sponding ORs (95% CIs) for those with
the *1A/*1A genotype were 1.00, 0.39
(0.15-0.97), 0.35 (0.17-0.76), and 0.81
(0.32-2.05) (P⬍.001 for gene⫻ coffee
interaction).
COMMENT
Coffee is a major source of caffeine,
which has multiple physiological ef-
fects that could increase the risk of MI.
17
Numerous studies have examined the
association between coffee consump-
tion and risk of MI, but the findings
have been inconclusive.
1-14
Caffeine is
detoxified primarily through an initial
N
3
-demethylation that is catalyzed
by CYP1A2, an enzyme that displays
wide interindividual variability in
activity.
21-23
We investigated whether
a common genetic polymorphism
(CYP1A2*1F) that results in a “slow”
metabolizer phenotype modifies the as-
sociation between intake of caffein-
ated coffee and risk of nonfatal MI. Our
findings show that coffee consump-
tion increases the risk of MI only among
individuals with a slow metabolizer
genotype.
Meta-analyses examining the rela-
tionship between coffee intake and risk
of coronary heart disease have ob-
served a positive association among
case-control studies but not among pro-
spective cohort studies.
1,2
According to
the most recent meta-analysis,
1
the
pooled case-control data show a 60%
increased risk for drinking 5 cups/d. It
has been suggested that the positive as-
sociations reported in case-control stud-
ies may have resulted from recall bias
or confounding by factors such as
smoking.
19,34
However, because we ob-
served an association between coffee
and risk of MI among carriers of the *1F
allele, and not among those homozy-
gous for the *1A allele, the associa-
tions between coffee and MI are un-
likely due to recall bias or residual
confounding. Moreover, when we
stratified our population by smoking
Table 2. Coffee Intake and Relative Risk of Myocardial Infarction by CYP1A2 Genotype
Coffee Intake,
Cups/d
No. (%)
OR (95% CI)
Cases Controls Model 1 Model 2
Total population n = 2014 n = 2014
⬍1 202 (10) 269 (13) 1.00* 1.00†
1 234 (12) 338 (17) 0.92 (0.71-1.18) 0.91 (0.68-1.21)
2-3 1146 (57) 1133 (56) 1.33 (1.09-1.63) 1.13 (0.89-1.42)
ⱖ4 432 (21) 274 (14) 2.11 (1.66-2.69) 1.40 (1.05-1.87)
*1A/*1A n = 900 n = 932
⬍1 94 (10) 113 (12) 1.00‡ 1.00§
1 117 (13) 158 (17) 0.89 (0.62-1.28) 0.75 (0.51-1.12)
2-3 510 (57) 538 (58) 1.14 (0.85-1.54) 0.78 (0.56-1.09)
ⱖ4 179 (10) 123 (13) 1.76 (1.23-2.52) 0.99 (0.66-1.48)
*1A/*1F ⫹ *1F/*1F n = 1114 n = 1082
⬍1 108 (9) 156 (14) 1.00‡ 1.00§
1 117 (11) 180 (17) 0.91 (0.65-1.28) 0.99 (0.69-1.44)
2-3 636 (57) 595 (55) 1.51 (1.15-1.98) 1.36 (1.01-1.83)
ⱖ4 253 (23) 151 (14) 2.43 (1.77-3.35) 1.64 (1.14-2.34)
Abbreviations: CI, confidence interval; OR, odds ratio.
*Conditional logistic regression model (unadjusted).
†Conditional logistic regression model adjusted for smoking (never, past, 1-19 cigarettes/d, ⱖ20 cigarettes/d); waist-
hip ratio; income; physical activity; history of diabetes; history of hypertension; and intakes of alcohol, total energy,
and energy-adjusted saturated fat, polyunsaturated fat, trans fat, folate, and sucrose (see “Methods”).
‡Unconditional logistic regression model that included matching variables (age, sex, and area of residence).
§Results from unconditional logistic regression that included matching variables and the confounders listed above for
model 2. Gene ⫻ coffee interaction, P = .04.
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
1138 JAMA, March 8, 2006—Vol 295, No. 10 (Reprinted) ©2006 American Medical Association. All rights reserved.
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status, the results were similar for non-
smokers and current smokers (Table 3).
A more likely explanation for the dis-
crepancies between case-control and
prospective cohort studies is that cof-
fee drinking has mainly acute effects,
which would be misclassified in pro-
spective studies with a long follow-up
and no updating of coffee intake.
1-3
In
a study by LaCroix et al,
4
the relative
risk of coronary heart disease for 5 or
more cups per day compared with none
increased from 1.89 when intake was
assessed 10 or more years previously to
2.49 when intake within the past 5 years
was used. Similarly, a strong associa-
tion between coffee consumption and
mortality from coronary heart dis-
ease, reported after 6 years of follow-
up,
5
was weakened by 6 more years of
follow-up.
6
The decreased effect of cof-
fee after longer follow-up could also be
a result of caffeine having a weaker
effect in an older population. Indeed,
the CYP1A2⫻coffee interaction we ob-
served among individuals younger than
the median age suggests that caffeine
has a greater relative effect on younger
individuals. Among the slow metabo-
lizers, the risk associated with drink-
ing 4 cups/d or more compared with
less than 1 cup/d increased from 2-fold
for individuals younger than 59 years
to more than 4-fold for those younger
than 50 years. A similar effect of age was
observed by Palmer et al,
7
who found
a greater risk of MI with caffeinated cof-
fee consumption among women 45
through 59 years of age but not among
women 60 years or older.
The absence of an association be-
tween coffee and risk of MI in some
case-control studies may have been due
to a lower frequency of *1F carriers in
the populations that were examined. In
the present study, the frequency of car-
riers of the *1F allele was 54%, but fre-
quencies have been reported to vary by
population.
35-38
Because cases in the pre-
sent study experienced nonfatal MI, we
cannot exclude the possibility that the
observed interaction may affect sur-
vival after an acute MI.
Although smokers metabolize caf-
feine more rapidly than nonsmokers
Table 3. Coffee Intake and Relative Risk of Myocardial Infarction by CYP1A2 Genotype,
Smoking Status, and Age Category
Coffee Intake, Cups/d
No. (%)
OR (95% CI)
Cases Controls Model 1 Model 2
Smoking Status*
Nonsmokers
*1A/*1A n = 532 n = 745
⬍1 75 (14) 101 (14) 1.00 1.00
1 84 (16) 135 (18) 0.84 (0.56-1.25) 0.73 (0.47-1.14)
2-3 312 (59) 436 (58) 0.95 (0.68-1.33) 0.75 (0.52-1.07)
ⱖ4 61 (11) 73 (10) 1.13 (0.71-1.77) 1.02 (0.62-1.67)
*1A/*1F ⫹ *1F/*1F n = 677 n = 844
⬍1 85 (13) 136 (16) 1.00 1.00
1 97 (14) 158 (18) 0.96 (0.66-1.40) 1.01 (0.67-1.51)
2-3 399 (59) 467 (55) 1.32 (0.97-1.79) 1.27 (0.91-1.76)
ⱖ4 98 (14) 83 (10) 1.93 (1.29-2.88) 1.72 (1.11-2.67)
Smokers
*1A/*1A n = 368 n = 187
⬍1 19 (5) 12 (6) 1.00 1.00
1 33 (9) 23 (12) 0.90 (0.36-2.23) 0.87 (0.30-2.51)
2-3 198 (54) 102 (55) 1.23 (0.57-2.64) 1.11 (0.45-2.76)
ⱖ4 118 (32) 50 (27) 1.59 (0.71-3.54) 1.22 (0.47-3.18)
*1A/*1F ⫹ *1F/*1F n = 437 n = 238
⬍1 25 (6) 20 (8) 1.00 1.00
1 20 (5) 22 (9) 0.65 (0.27-1.54) 0.90 (0.33-2.50)
2-3 237 (54) 128 (54) 1.42 (0.75-2.70) 1.77 (0.83-3.76)
ⱖ4 155 (35) 68 (29) 1.83 (0.94-3.56) 1.79 (0.80-3.98)
Age Category†
Age ⬍59 y‡
*1A/*1A n = 451 n = 446
⬍1 51 (11) 50 (11) 1.00 1.00
1 53 (12) 78 (17) 0.66 (0.39-1.12) 0.48 (0.26-0.87)
2-3 228 (51) 252 (57) 0.89 (0.58-1.37) 0.57 (0.35-0.95)
ⱖ4 119 (26) 66 (15) 1.78 (1.09-2.92) 0.83 (0.46-1.51)
*1A/*1F ⫹ *1F/*1F n = 505 n = 552
⬍1 49 (10) 98 (18) 1.00 1.00
1 46 (9) 89 (16) 1.03 (0.63-1.69) 1.24 (0.71-2.18)
2-3 266 (53) 282 (51) 1.87 (1.28-2.75) 1.67 (1.08-2.60)
ⱖ4 144 (29) 83 (15) 3.47 (2.24-5.37) 2.33 (1.39-3.89)
Age ⱖ59 y
*1A/*1A n = 449 n = 486
⬍1 43 (10) 63 (13) 1.00 1.00
1 64 (14) 80 (16) 1.17 (0.70-1.94) 1.10 (0.63-1.92)
2-3 282 (63) 286 (59) 1.44 (0.94-2.20) 1.06 (0.67-1.69)
ⱖ4 60 (13) 57 (12) 1.56 (0.92-2.66) 1.06 (0.58-1.94)
*1A/*1F ⫹ *1F/*1F n = 609 n = 530
⬍1 59 (10) 58 (11) 1.00 1.00
1 71 (12) 91 (17) 0.78 (0.48-1.25) 0.79 (0.47-1.32)
2-3 370 (61) 313 (59) 1.18 (0.79-1.74) 1.06 (0.69-1.63)
ⱖ4 109 (18) 68 (13) 1.54 (0.95-2.48) 1.09 (0.64-1.86)
Abbreviations: CI, confidence interval; OR, odds ratio.
*Model 1: unconditional logistic regression model adjusted for age, sex, and area of residence. Model 2: unconditional
logistic regression model adjusted for age; sex; area of residence; waist-hip ratio; income; physical activity; history of
diabetes; history of hypertension; and intakes of alcohol, total energy, and energy-adjusted saturated fat, polyun-
saturated fat, trans fat, folate and sucrose (see “Methods”). Nonsmokers were further adjusted for never and past
smoking, and smokers adjusted for cigarettes smoked per day.
†Model 1: unconditional logistic regression model adjusted for age, sex, area of residence. Model 2: unconditional
logistic regression model adjusted for age; sex; area of residence; smoking (never, past, 1-19 cigarettes/d, ⱖ20
cigarettes/d); waist-hip ratio; income; physical activity; history of diabetes; history of hypertension; and intakes of
alcohol, total energy, and energy-adjusted saturated fat, polyunsaturated fat, trans fat, folate, and sucrose (see “Meth-
ods”).
‡P = .003 for gene ⫻ coffee interaction.
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
©2006 American Medical Association. All rights reserved. (Reprinted) JAMA, March 8, 2006—Vol 295, No. 10 1139
by guest on June 1, 2008 www.jama.comDownloaded from

due to the well-known CYP1A2-
inducing effect of smoking,
32
the ex-
tent of CYP1A2 induction among smok-
ers is lower for carriers of the *1F
allele.
25,26
Thus, smokers with the slow
metabolizer genotype may still have an
increased risk of MI with increasing cof-
fee consumption. Indeed, for carriers
of the *1F allele, a similar pattern of risk
associated with coffee was observed
among smokers and nonsmokers
(Table 3).
Among younger individuals who
were rapid caffeine metabolizers, cof-
fee intakes of either 1 cup/d or 2 to 3
cups/d were associated with a lower risk
of MI compared with intakes of less
than 1 cup/d. This finding is consis-
tent with a number of previous re-
ports of J- or U-shaped associations be-
tween coffee and MI,
11-14
suggesting a
protective effect of moderate coffee con-
sumption. It has been proposed that the
higher risk of heart disease among the
group with the lowest intake might be
due to individuals with underlying dis-
eases who are limiting their coffee in-
take.
12,14
However, the absence of an el-
evated risk in the lowest category of
coffee intake among the slow metabo-
lizers in the present study indicates that
this is unlikely.
Coffee contains other chemicals that
may have adverse effects on the cardio-
vascular system.
18
Distinguishing be-
tween the effects of caffeine and those
of these other compounds has been dif-
ficult, given the strong collinearity be-
tween caffeine and coffee intake in many
populations. Diterpenoids that are pre-
sent in the lipid fraction of boiled cof-
fee have been shown to increase levels
of serum cholesterol
39-41
and may in-
crease the risk of MI.
9
However, the lev-
els of diterpenoids are greatly reduced
in filtered coffee.
42
About 10% of coffee
drinkers in the current population did
not report consuming filtered coffee, and
excluding them from our analyses did
not materially alter the results (data not
shown). Besides caffeine, no other ma-
jor compound found in filtered coffee
is known to be detoxified by CYP1A2.
Thus, our findings suggest that caf-
feine is the major component of fil-
tered coffee that increases risk of non-
fatal MI. Although the mechanism by
which caffeine increases risk of MI re-
mains unclear, it is known to block the
A
1
and A
2A
adenosine receptors.
43,44
Adenosine is a potent coronary and sys-
temic vasodilator that may play an im-
portant role in the reactivity of inflam-
matory cells and platelets during periods
of myocardial ischemia.
45,46
Although the CYP1A2*1F polymor-
phism is located in a noncoding re-
gion of the gene, the polymorphism
may result in differential binding of
regulatory proteins to the surround-
ing sequence that may affect CYP1A2
expression levels.
25
The regulatory
mechanisms of intronic polymor-
phisms on transcriptional activity have
been described for several genes.
47
Al-
ternatively, the polymorphism may be
in linkage disequilibrium with other
single nucleotide polymorphisms in-
fluencing CYP1A2 inducibility.
25
Nev-
ertheless, in vivo studies clearly show
marked differences in CYP1A2 activ-
ity between carriers of the different
CYP1A2 alleles.
24-26
In summary, consistent with most
case-control studies, we found that in-
creased coffee intake is associated with
an increased risk of nonfatal MI. The
association between coffee and MI was
found only among individuals with the
slow CYP1A2*1F allele, which im-
pairs caffeine metabolism, suggesting
that caffeine plays a role in the asso-
ciation.
Author Contributions: Dr El-Sohemy had full access
to all of the data in the study and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design: El-Sohemy, Campos.
Acquisition of data: Cornelis, El-Sohemy, Campos.
Analysis and interpretation of data: Cornelis,
El-Sohemy, Kabagambe, Campos.
Drafting of the manuscript: Cornelis, El-Sohemy.
Critical revision of the manuscript for important in-
tellectual content: Cornelis, El-Sohemy, Kabagambe,
Campos.
Statistical analysis: Cornelis, El-Sohemy, Kabagambe,
Campos.
Obtained funding: El-Sohemy, Campos.
Administrative, technical, or material support: Campos.
Study supervision: El-Sohemy, Campos.
Financial Disclosures: None reported.
Funding/Support: This research was supported by
grants from the Canadian Institutes of Health Re-
search (MOP-53147) and the National Institutes of
Health (HL 60692 and HL 071888). Ms Cornelis is a
recipient of a Natural Sciences and Engineering Re-
search Council of Canada postgraduate scholarship.
Dr El-Sohemy holds a Canada Research Chair in Nu-
trigenomics.
Role of the Sponsor: The Canadian Institutes of Health
Research and the National Institutes of Health had no
role in the design and conduct of the study: in the col-
lection, analysis, and interpretation of the data; or the
preparation, review, or approval of the manuscript.
Acknowledgment: We thank Xinia Siles, RD, project
director at the Centro Centroamericano de Pobla-
cion, Universidad de Costa Rica, for directing all the
data collection, and Ana Baylin, MD, DrPh, Depart-
ment of Nutrition, Harvard University School of Pub-
lic Health, for data monitoring and management
throughout the study.
REFERENCES
1. Kawachi I, Colditz GA, Stone CB. Does coffee drink-
ing increase risk of coronary heart disease? results from
a meta-analysis. Br Heart J. 1994;72:269-275.
2. Greenland S. A meta-analysis of coffee, myocar-
dial infarction, and coronary death. Epidemiology.
1993;4:366-374.
3. Myers MG, Basinski A. Coffee and coronary heart
disease. Arch Intern Med. 1992;152:1767-1772.
4. LaCroix AZ, Mead LA, Liang KY, Thomas CB, Pear-
son TA. Coffee consumption and the incidence of coro-
nary heart disease. N Engl J Med. 1986;315:977-982.
5. Tverdal A, Stensvold I, Solvoll K, Foss OP, Lund-
Larsen PG, Bjartveit K. Coffee consumption and death
from coronary heart disease in middle aged Norwe-
gian men and women. BMJ. 1990;300:566-569.
6. Stensvold I, Tverdal A, Jacobsen BK. Cohort study
of coffee intake and death from coronary heart dis-
ease over 12 years. BMJ. 1996;312:544-545.
7. Palmer JR, Rosenberg L, Rao RS, Shapiro S. Cof-
fee consumption and myocardial infarction in women.
Am J Epidemiol. 1995;141:724-731.
8. Rosenberg L, Palmer JR, Kelly JP, Kaufman DW,
Shapiro S. Coffee drinking and nonfatal myocardial
infarction in men under 55 years of age. Am J
Epidemiol. 1988;128:570-578.
9. Hammar N, Anderson T, Alfredsson L, et al. Asso-
ciation of boiled and filtered coffee with incidence of
first nonfatal myocardial infarction: the SHEEP and the
VHEEP study. J Intern Med. 2003;253:653-659.
10. Tavani A, Bertuzzi M, Negri E, Loredana S, La
Vecchia C. Alcohol, smoking, coffee and risk of non-
fatal myocardial infarction in Italy. Eur J Epidemiol.
2001;17:1131-1137.
11. Panagiotakos DB, Pitsavos C, Chrysohoou C, Kok-
kinos P, Toutouzas P, Stefanadis C. The J-shaped effect
of coffee consumption on the risk of developing acute
coronary syndromes: the CARDIO2000 case-control
study. J Nutr. 2003;133:3228-3232.
12. Happonen P, Voutilainen S, Salonen JT. Coffee
drinking is dose-dependently related to the risk of acute
coronary events in middle-aged men. J Nutr. 2004;134:
2381-2386.
13. Woodward M, Tunstall-Pedoe H. Coffee and tea
consumption in the Scottish Heart Health Study fol-
low up: conflicting relations with coronary risk fac-
tors, coronary disease, and all cause mortality. J Epi-
demiol Community Health. 1999;53:481-487.
14. Kleemola P, Jousilahti P, Pietinen P, Vartiainen E,
Tuomilehto J. Coffee consumption and the risk of coro-
nary heart disease and death. Arch Intern Med. 2000;
160:3393-3400.
15. Ranheim T, Halvorsen B. Coffee consumption and
human health—beneficial or detrimental?—
mechanisms for effects of coffee consumption on dif-
ferent risk factors for cardiovascular disease and type
2 diabetes mellitus. Mol Nutr Food Res. 2005;49:274-
284.
16. Lundsberg LS. Caffeine consumption. In: Spiller
GA, ed. Caffeine. Boca Raton, Fla: CRC; 1998:199-
224.
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
1140 JAMA, March 8, 2006—Vol 295, No. 10 (Reprinted) ©2006 American Medical Association. All rights reserved.
by guest on June 1, 2008 www.jama.comDownloaded from

17. Nawrot P, Jordan S, Eastwood J, Rostein J, Hu-
genholtz A, Feeley M. Effects of caffeine on human
health. Food Addit Contam. 2003;20:1-30.
18. Spiller MA. The chemical components of coffee.
In: Spiller GA, ed. Caffeine. Boca Raton, Fla: CRC; 1998:
97-161.
19. Schreiber GB, Robins M, Maffeo CE, Masters MN,
Bond AP, Morganstein D. Confounders contributing
to the reported association of coffee or caffeine with
disease. Prev Med. 1988;17:295-309.
20. Butler MA, Iwasaki M, Guengerich FP, Kadlubar
F. Human cytochrome P450A (P-4501A2), the phen-
acetin O-deethylase, is primarily responsible for the
hepatic 3-demethylation of caffeine and N-oxidation
of carcinogenic arylamines. Proc Natl Acad SciUSA.
1989;86:7696-7700.
21. Gu L, Gonzalez FJ, Kalow W, Tang BK. Biotrans-
formation of caffeine, paraxanthine, theobromine and
theophylline by cDNA-expressed human CYP1A2 and
CYP2E1. Pharmacogenetics. 1992;2:73-77.
22. Kalow W, Tang BK. The use of caffeine for en-
zyme assays: a critical appraisal. Clin Pharmacol Ther.
1993;53:503-514.
23. Rasmussen BB, Brix TH, Kyvik KO, Brosen K. The
interindividual differences in the 3-demethylation of
caffeine alias CYP1A2 is determined by both genetic
and environmental factors. Pharmacogenetics. 2002;
12:473-478.
24. Castorena-Torres F, Mendoza-Cantu A, Bermu-
dez de Leon M, et al. CYP1A2 phenotype and geno-
type in a population from Carboniferous Region of
Coahuila, Mexico. Toxicol Lett. 2005;156:331-339.
25. Sachse C, Brockmoller J, Bauer S, Roots I. Func-
tional significance ofaCtoApolymorphism in intron
1 of the cytochrome P450 1A2 (CYP1A2) gene tested
with caffeine. Br J Clin Pharmacol. 1999;47:445-449.
26. Han X-M, Ou-Yang D-S, Lu P-X, et al. Plasma caf-
feine metabolite ratio (17X/137X) in vivo associated
with G-2964A and C734 polymorphisms of human
CYP1A2. Pharmacogenetics. 2001;11:429-435.
27. Hall C. Costa Rica: A Geographical Interpreta-
tion in Historical Perspective. Boulder, Colo: West-
view Press Inc; 1985. Dellplain Latin American Stud-
ies No. 17.
28. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P,
Arveiler D, Rajakangas A-M, Pajak A. Myocardial in-
farction and coronary deaths in the World Health Or-
ganization MONICA project: registration proce-
dures, event rates, and case-fatality rates in 38
populations from 21 countries in four continents.
Circulation. 1994;90:583-612.
29. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegel-
man D, Campos H. Application of the method of tri-
ads to evaluate the performance of food frequency
questionnaires and biomarkers as indicators of long-
term dietary intake. Am J Epidemiol. 2001;154:1126-
1135.
30. Cornelis MC, El-Sohemy A, Campos H. Genetic
polymorphism of CYP1A2 increases risk of myocar-
dial infarction. J Med Genet. 2004;41:758-762.
31. Willet W. Nutritional Epidemiology. 2nd ed. New
York, NY: Oxford University Press; 1998.
32. Kalow W, Tang BK. Caffeine as a metabolic probe:
exploration of the enzyme-inducing effect of ciga-
rette smoking. Clin Pharmacol Ther. 1991;49:44-48.
33. Willett WC, Stampfer MJ, Manson JE, et al. Cof-
fee consumption and coronary heart disease in
women: a ten-year follow-up. JAMA. 1996;275:458-
462.
34. Schreiber GB, Maffeo CE, Robins M, Masters MN,
Bond AP. Measurement of coffee and caffeine in-
take: implications for epidemiological research. Prev
Med. 1988;17:280-294.
35. Tiwari AK, Deshpande SN, Rao AR, et al. Ge-
netic susceptibility to tardive dyskinesia in chronic
schizophrenia subjects, I: association of CYP1A2 gene
polymorphism. Pharmacogenomics J. 2005;5:60-69.
36. Nakajima M, Yokoi T, Mizutani M, Kinoshita M,
Funayama M, Kamataki T. Genetic polymorphism in
the 5’ flanking region of human CYP1A2 gene: effect
on the CYP1A2 inducibility in humans. J Biochem
(Tokyo). 1999;125:803-808.
37. Hamdy SI, Hiratsuka M, Narahara K, et al. Geno-
typing of four genetic polymorphisms in the CYP1A2
gene in the Egyptian population. Br J Clin Pharmacol.
2003;55:321-324.
38. Goodman MT, Tung K-H, McDuffie K, Wilkens
LR, Donlon T. Association of caffeine intake and
CYP1A2 genotype with ovarian cancer. Nutr Cancer.
2003;46:23-29.
39. Bak AA, Grobbee DE. The effect on serum cho-
lesterol levels of coffee brewed by filtering or boiling.
N Engl J Med. 1989;321:1432-1437.
40. Thelle DS, Heyden S, Fodor J. Coffee and cho-
lesterol in epidemiological and experimental studies.
Atherosclerosis. 1987;67:97-103.
41. Jee SH, He J, Appel LJ, Whelton PK, Suh I, Klag
MJ. Coffee consumption and serum lipids: a meta-
analysis of randomized controlled clinical trials. Am J
Epidemiol. 2001;153:353-362.
42. van Dusseldorp M, Katan MB, van Vliet T,
Demacker PN, Stalenhoef AF. Cholesterol-raising fac-
tor from boiled coffee does not pass a paper filter. Ar-
terioscler Thromb. 1991;11:586-593.
43. Fisone G, Borgkvist A, Usiello A. Caffeine as a psy-
chomotor stimulant: mechanism of action. Cell Mol
Life Sci. 2004;61:857-872.
44. Smits P, Boekema P, De Abreu R, Thein T, van’t
Laar A. Evidence for an antagonism between caf-
feine and adenosine in the human cardiovascular
system. J Cardiovasc Pharmacol. 1987;10:136-143.
45. Shyrock JC, Belardinelli L. Adenosine and aden-
osine receptors in the cardiovascular system: biochem-
istry, physiology, and pharmacology. Am J Cardiol.
1997;79:2-10.
46. Hori M, Kitakaza M. Adenosine, the heart, and
coronary circulation. Hypertension. 1991;18:565-574.
47. Le Hir H, Nott A, Moore MJ. How introns influ-
ence and enhance eukaryotic gene expression. Trends
Biochem Sci. 2003;28:215-220.
COFFEE, CYP1A2 GENOTYPE, AND RISK OF MYOCARDIAL INFARCTION
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