Risk Analysis, Vol. 26, No. 1, 2006

DOI: 10.1111/j.1539-6924.2006.00722.x

Relation Between Benchmark Dose and
No-Observed-Adverse-Effect Level in Clinical Research:
Effects of Daily Alcohol Intake on Blood Pressure
in Japanese Salesmen
Miwako Dakeishi,1 Katsuyuki Murata,1 ∗ Akiko Tamura,1 and Toyoto Iwata1

The benchmark dose (BMD) is defined as the dose that corresponds to a specific change inan adverse response compared to the response in unexposed subjects, and the lower 95%
confidence limit is termed the benchmark dose level (BMDL). In this study, the thresholdof daily ethanol intake affecting blood pressure was calculated by both the BMD approachand multiple logistic regression analysis to clarify the relation between the BMDL and noobserved-adverse-effect level (NOAEL). Systolic and diastolic blood pressures (SBP and
DBP) and daily ethanol intake were explored in 1,100 Japanese salesmen. The SBP and DBP
were positively related to daily ethanol intake (p < 0.001) when adjusting for possible confounders such as age, body mass index, and smoking status. The adjusted risk for hypertension(SBP ≥ 140 mmHg or DBP ≥ 90 mmHg) increased significantly when daily ethanol intake
exceeded 60 g/day, and the categorical dose of interest was 60.1–90 g/day. The BMDL and
BMD of ethanol intake for increased SBP and DBP were estimated to be approximately 60
and 75 g/day, respectively. These findings suggest that the BMDL and BMD correspond to
the NOAEL and lowest-observed-adverse-effect level, respectively, if the sample number ofclinical data is large enough to confirm the dose-response association.
KEY WORDS: Benchmark dose calculation; blood pressure; clinical research; daily ethanol intake;no-observed-adverse-effect level

1. INTRODUCTION

lations with specific dysfunction), termed the lowestobserved-adverse-effect level (LOAEL).(1,2) With respect to the estimation of such thresholds, the moredesirable method has been described to use the highest reported dose or exposure level for which no toxicity was observed, i.e., the no-observed-adverse-effectlevel (NOAEL). As an alternative to the NOAEL or
LOAEL approach, however, benchmark dose (BMD)
calculations have been applied in recent studies on environmental or occupational health(2−10) because the
NOAEL has many shortcomings, e.g., not adequatelyreflecting the shape of the dose response and not appropriately accounting for study size.(11) Nonetheless,since there is little practical experience in the BMD

For risk management of a hazardous substancein the environment, it is necessary to identify thediscernible threshold of the substance affecting human health. Previously, the threshold was obtained
from the lowest level producing the adverse effect(e.g., the lowest average value among exposed popu1 Department of Environmental Health Sciences, Akita University

School of Medicine, Akita, Japan.

∗ Address correspondence to Katsuyuki Murata, Department ofEnvironmental Health Sciences, Akita University School of
Medicine, 1-1-1 Hondo, Akita 010-8543, Japan; tel: +81-18-8846085; fax: +81-18-836-2608; winestem@med.akita-u.ac.jp.

115

C 2006 Society for Risk Analysis
0272-4332/06/0100-0115$22.00/1 Dakeishi et al.

116
method (especially the hybrid method),(12) many epidemiologists may arouse concern in applying the
BMD method to clinical research: What is the relation between the values calculated from the BMD
and NOAEL/LOAEL approaches? Or, did the BMD
really correspond to the NOAEL or LOAEL?
The BMD is defined as the dose that corresponds
to a specific change in an adverse response as compared to the response in unexposed subjects.(12) It isdetermined by modeling a dose-response curve in theregion of the dose-response relationship where biologically observable data are available. To take uncertainty of the data into consideration, the dose of interest is the lower 95% confidence limit (i.e., BMDL)
on the BMD. On the other hand, the calculation of a
NOAEL generally utilizes data that are categorized
into distinct dose groups, and categorization of subjects into dose groups is an arbitrary process,(11) while
the BMD method does not rely on categorizationsof either the response or the exposure. Such categorizations therefore may be problematic in most epidemiological studies. For instance, the risk for causing
liver damage steadily increased when ethanol intake
exceeded 30 g/day in the Dionysos study with categorized data,(13,14) but the BMDL of ethanol intake
for the aminotransferase abnormality was estimatedto be approximately 50 g/day in Japanese salesmenof our study with the BMD method.(9) The thresholds
may have differed according to race, sorts of alcoholic
beverage, or endpoints used.
The present article explores the relation between
the BMDL and NOAEL (or LOAEL) in applying the
BMD method to clinical research. As an example, we
use data from a study carried out to investigate theeffects of alcohol consumption on blood pressure inworkers. Since subgroups who consumed an empirical range of ethanol (i.e., 0, 1–30, 31–60, 61–90, or>90 g/day) have been utilized in the preceding alcohol research,(13,14) we employ this categorization, together with the BMD method formulated by BudtzJørgensen et al.(6)2. MATERIALS AND METHODS
2.1. Study Subjects
A self-reported questionnaire with detailed explanations of the study purpose was distributed toapproximately 3,400 salesmen at motor vehicle dealerships in Akita, northeast Japan in 2002, and 1,244of them consented to our proposal and returned theform to an occupational health nurse of a health insurance union (participation rate, 37%). Also, 144of the respondents were excluded according to thefollowing criteria: subjects must have undergone themandatory health checkup, conducted under the Industrial Safety and Health Law in Japan; they must
not suffer from ischemic heart disease, chronic renal
failure, alcoholic dependency diagnosed by the DSMIV,(15) liver cirrhosis, or liver cancer, or have been
taking antihypertensive medication; also, the questionnaire had perfect data regarding drinking habit,
etc. The study population, accordingly, consisted of
1,100 healthy men (respondents) aged 18–29 years(33.9%), 30–39 years (23.1%), 40–49 years (24.6%),
50–59 years (16.9%), and 60–68 years (1.5%) andof 1,726 anonymous salesmen (i.e., nonrespondents)
who underwent the above health checkup. The study
protocol was approved by the ethical review committee of the Akita University School of Medicine.
2.2. Data Collection
The weekly amount of each type of alcoholic beverage consumed was asked as described previously;(9)e.g., “How many 180-mL cups (or 1,800-mL bottles)of sake do you usually drink in a week?” and “Howmany 350-mL cans (or, 500-mL cans or 633-mL bottles) of beer do you usually drink in a week?” Typesof alcoholic beverages listed were sake, beer, shochu(Japanese distilled alcoholic beverage primarily madefrom wheat or sweet potatoes), whisky, wine, and others (e.g., plum wine, brandy, gin, and vodka). A totalof 100% ethanol equivalent dose (g/day) was calculated for each subject on the assumption that sake,beer, shochu, whisky, and wine contain 15%, 5%, 20(or 25)%, 40%, and 12% of ethanol, respectively. Approximately 5% of 883 drinkers, who consumed 100%
ethanol equivalent dose of more than 800 g/week,
were directly asked about recent alcohol consumption by the occupational health nurse 5 months after
the questionnaire collection in order to validate thereported alcohol consumption.
Data of systolic and diastolic blood pressure(SBP and DBP), together with serum total cholesterol (T-CH), HDL-cholesterol (HDL-CH), triglycerides (TG), and body mass index (BMI), were obtained from each record of the health checkup. Bloodpressure was measured twice by a trained nurse with amercury sphygmomanometer, 2 minutes after sitting
in a chair with their backs supported and their arms
bared and supported at heart level. The average ofApplication of BMD Approach to Clinical Research

117

two measurements was used in the analysis. SBP and
DBP were defined as the first and the fifth Korotkoff
sounds. Subjects were asked not to smoke or to consume coffee or tea for 12 hours before the healthcheckup. Serum T-CH, HDL-CH, and TG were determined calorimetrically with enzymatic methods by
the Akita Prefectural Center of Health Care (reference intervals were 130 to 220 mg/dL for T-CH, 41–
70 mg/dL for HDL-CH, and 42–150 mg/dL for TG).
Job stress and smoking habit were also examined in
the questionnaire.
2.3. Statistical Methods
The comparisons in the data collected between
the respondents and nonrespondents and between
the respondents with and without hypertension weredone by unpaired t test or Mann-Whitney U test (orχ 2 test for comparison of proportions). Hypertension was conveniently defined as SBP ≥ 140 mmHg
or DBP ≥ 90 mmHg. The difference between daily
ethanol intakes answered in the questionnaire andinterview was analyzed by the paired-sample t test;also, the relationship between daily ethanol consumption and HDL-CH was examined by the Spearman
rank correlation coefficient (rs ). The differences in
SBP and DBP among categorized groups were compared using the two-way analysis of variance. The relations of daily ethanol intake and six possible confounders (age, BMI, log-transformed T-CH and TG,
job stress, and smoking status) to SBP and DBP in
the respondents were tested by multiple regression
analysis. Multiple logistic regression analysis was used
to calculate the odds ratio of daily ethanol intake
to hypertension after controlling for the above confounders. The response to the question on job stress(i.e., “How stressed are you in your work life?”) wasscored as “not at all” = 1, “slightly” = 2, “moderately” = 3, “very” = 4, and “extremely” = 5; also, theresponse to smoking status was scored as “nonsmoker
or ex-smoker” = 0 and “smoker” = 1.
The BMD is defined as the daily ethanol dose that
results in an increased probability of abnormal bloodpressure by a benchmark response (BMR), i.e., from
P0 to P0 + BMR at the BMD, where the P0 and BMR
represent an abnormal probability of blood pressurein the nondrinking population and an excess risk in thedrinking population, respectively (Fig. 1).(2,6,11) Our
statistical dose-effect model is decided on the basisof power functions [g(d) = dK ], for the dependenceof blood pressure on daily ethanol intake (d), age,

Fig. 1. Dose-response relationships between daily ethanol intake
and systolic blood pressure in 1,100 Japanese salesmen for BMD
calculation after adjusting for six confounders. The P0 , benchmark
response (BMR), and BMD indicate an abnormal probability ofblood pressure in the nondrinkers, an excess risk above P0 in thedrinkers, and benchmark dose, respectively (i.e., P0 = 0.05 andBMR = 0.05).

BMI, log-transformed T-CH and TG, job stress, andsmoking status:
μ(d) = β0 + βg(d) + β1 (age) + β2 (BMI)+ β3 [log(T-CH)] + β4 [log(TG)]+ β5 (job stress) + β6 (smoking status),
where μ(d) is the expected response of the subject.
The power parameter K has been restricted to values equal to 1 or over, thus allowing the dose-effectcurve to be nonlinear. This BMD method is a hybrid
approach and needs an abnormal response level tointerpret continuous data of the endpoint. The cutoff
value (C) is defined so that the risk in an unexposed subject is P0 , and it depends on the above confounders.(6) The cutoff value in subjects with all confounder values equal to 0 can be calculated with useof the following equation: P0 = 1 − [(C − β 0 )/σ ],where  and σ indicate the normal cumulative distribution function and the standard deviation (SD) of
blood pressure in the nondrinking population, respectively. In this study, as six confounders were included,
the cutoff value for an average subject was computedby using the normalized value (i.e., [X i − X mean ]/SD) for each confounder (X i ). Then, the BMD wasgiven by BMD = g −1 {[−1 (1 − P0 )−−1 (1 − P0 −BMR)]σ /β}, and the BMDL was calculated as theDakeishi et al.

118

1,100 Respondents

Age (years)
Body mass index (kg/m2 )Total cholesterol (mg/dL)
Triglycerides (mg/dL)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
WHO-ISH classification(35)Normal blood pressure (%)
High-normal blood pressure (%)
Hypertension (%)

1,726 Nonrespondents

Mean (SD)

Range

Mean (SD)Range

37.3 (11.4)
23.2 (3.4)
195∗
93∗
119 (15)73 (12)

18–68
14.5–43.8
106–317
15–1252
78–182
40–11835.7 (11.1)
23.1 (3.3)
190∗
88∗
118 (14)73 (12)18–66
15.9–40.8
109–371
19–1442
71–19040–118

80.1
6.7
13.2

Table I. Basal Characteristics ofJapanese Salesmen

76.0
11.4
12.6

∗ Median value.

lower 95% confidence limit of the BMD.(6) We used
P0 values of 0.05 and 0.1 and BMR values of 0.02, 0.05,
and 0.1. All analyses, with two-sided p values, were
performed by using the Statistical Package for the
Biosciences (SPBS V9.51) with the BMD program.(16)
3. RESULTS
The basal characteristics in the 1,100 respondents
and 1,726 nonrespondents are shown in Table I. There
was only a significant difference in age between thetwo groups. In 45 respondents who consumed 100%
ethanol equivalent dose of more than 800 g/week, thedaily ethanol intake answered in the questionnaire did
not differ from that in the interview (p > 0.2; data notshown). Also, the daily ethanol intake in the respondents was significantly correlated with the HDL-CH(rs = 0.155; p < 0.001). Proportions of mainly beerand shochu consumers among the respondents were
48.6% and 19.5%, respectively.

Without Hypertension

Age (years)
Body mass index (kg/m2 )Total cholesterol (mg/dL)
HDL-cholesterol (mg/dL)
Triglycerides (mg/dL)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Job stress (score)
Smokers (%)
Drinkers (%)
Daily ethanol intake (g/day)∗ Geometric mean.

In all respondents, daily ethanol intake, BMI,
T-CH, TG, and the proportion of drinkers were significantly higher in the hypertensives than in the normotensives (Table II), while the smoking status was
significantly lower in the hypertensives. Also, the SBP
and DBP tended to be elevated according to aging andincreased daily ethanol intake (Table III). The daily
ethanol intake was significantly related to SBP and
DBP (Table IV) when adjusting for confounders suchas age, BMI, and smoking status. The risk for hypertension (i.e., SBP ≥ 140 mmHg or DBP ≥ 90 mmHg)
increased significantly when daily ethanol intake exceeded 60 g/day (Table V); that is, the NOAEL ofdaily ethanol intake was thought to be 60 g/day, andthe LOAEL was estimated to lie somewhere between
60.1 and 90.0 g/day.
When the K-power and linear dose-responsemodels were applied (Table VI), the BMDLs of daily
ethanol intake, adjusted by six confounders and set
at the P0 of 0.05 and the BMR of 0.05, were 60 g/day

With Hypertension

Mean

SD

Mean

SD

p-Value

36.3
22.8
191∗
57
93∗
115
71
2.2
70.9
79.2
30

11.1
3.2
1.20
14
1.91
11
9
1.0

43.8
25.2
204∗
57
124∗
143
92
2.3
53.8
87.6
48

10.8
3.9
1.19
14
1.87
13
9
1.0

<0.0001<0.0001<0.0001
0.8937<0.0001<0.0001<0.0001
0.3892<0.0001
0.0175<0.0001

34

30

Table II. Differences in Main
Characteristics Between 955
Respondents Without Hypertension and
145 Respondents with Hypertension inJapan

Application of BMD Approach to Clinical Research

119

Table III. Systolic and Diastolic Blood Pressure (SBP and DBP) in Categorized Groups of Japanese Salesmen by Age and Daily Ethanol
Intake (Number, Mean, and Standard Deviation in Parentheses)
Under 29 Years

30–39 Years

40–49 Years

Above 50 Years

Ethanol Intake (g/day)

N

SBP

DBP

N

SBP

DBP

N

SBP

DBP

N

SBP

DBP

Nondrinkers
0.1–30.0
30.1–60.0
60.1–90.0
>90.0

111
180
47
19
16

113 (13)114 (13)116 (11)114 (12)121 (13)66 (11)67 (9)
69 (10)68 (12)75 (12)

41
114
57
19
23

122 (15)117 (12)118 (11)123 (15)127 (14)76 (12)72 (10)75 (9)
72 (12)80 (13)

35
93
71
42
30

115 (12)119 (14)123 (16)122 (15)128 (14)72 (9)
77 (11)80 (12)79 (10)83 (11)

30
62
58
30
22

115 (15)122 (16)125 (16)130 (22)127 (17)69 (9)
76 (10)80 (10)80 (14)78 (11)

Note: Two-way analysis of variance, p < 0.0001.

for SBP and 58 g/day for DBP. In the same table, theBMDs and BMDLs calculated from data without any
confounders were somewhat smaller than those after
adjusting for six confounders.
4. DISCUSSION
There are a large number of reports on the effects of chronic ethanol intake on blood pressure, andethanol has been established as an exacerbating factor of hypertension, preceding coronary heart diseaseor stroke.(17) At the same time, several issues to besolved in such studies also have been raised, e.g., Was
the estimation of daily ethanol intake appropriate?
For that reason, we should first consider our results
before discussing application of the BMD method toclinical research.
4.1. Effects of Daily Ethanol Intake
on Blood Pressure
In this study, we observed significant doseresponse relationships between the daily ethanol
intake and both SBP and DBP in the Japanese salesTable IV. Relations of Daily Ethanol Intake and Confounders to
Systolic and Diastolic Blood Pressures in 1,100 Japanese
Salesmen (Regression Coefficient and p-Value in Parentheses)

Daily ethanol intake
Age
Body mass index
Log[T-CH]
Log[TG]Job stressSmoking status

Systolic
Blood Pressure

Diastolic
Blood Pressure

0.0677 (<0.0001)
0.2066 (<0.0001)
1.1383 (<0.0001)
2.0409 (0.7329)
2.8566 (0.0885)
0.5888 (0.1351)−3.2609 (0.0003)

0.0560 (<0.0001)
0.2836 (<0.0001)
0.8228 (<0.0001)
3.0948 (0.5032)
2.1431 (0.0983)
0.1731 (0.5696)−2.1675 (0.0020)

Note: Results of multiple regression analysis.

men (Tables III and IV). As shown in Fig. 1 andTable III, the linear model appears to be more appropriate than a J-shaped or U-shaped model.(18,19) Also,
other Japanese studies reported independent linear
associations of alcohol with blood pressure.(20,21) Thismay have been due to the fact that Japanese people, including our subjects, generally prefer beer andshochu to wine, while some German studies haveshown a J-shaped association between alcohol andblood pressure.(17,22)
The multiple logistic regression analysis using categorical data showed that the increased ethanol intake was significantly linked with hypertension, andit indicated that the odds ratio for hypertension was
statistically significant when daily ethanol intake exceeded 60 g/day (Table V). Nonetheless, this lies
within the upper limit of moderate alcohol consumption ranging from 24 to 80 g/day of 100% ethanol;(17)it is considerably higher than other thresholds of alcohol causing hypertension, which have been reported
Table V. Relations of Daily Ethanol Intake and Confounders toHypertension (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg)
in 1,100 Salesmen

Odds Ratio
Daily ethanol intake (g/day)
Nondrinkers
1.000 (reference)
0.1–30.0
1.252
30.1–60.0
1.885
60.1–90.0
2.877
>90.0
4.109
Age (years)
1.058
Body mass index (kg/m2 )1.257
Log[T-CH (mg/dL)]
0.423
Log[TG (mg/dL)]
1.319
Job stress (score)
1.040
Smoking status
0.530

95%
Confidence Limit

0.667, 2.347
0.975, 3.647
1.377, 6.010
1.962, 8.606
1.037, 1.079
1.181, 1.338
0.023, 7.669
0.612, 2.839
0.866, 1.250
0.353, 0.796

Note: Results of multiple logistic regression analysis.

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120

Table VI. BMDs and BMDLs of Daily Ethanol Intake (g/day) for Systolic and Diastolic Blood Pressures (SBP and DBP) in 1,100 Salesmen
Linear Model (K = 1)

K-Power Model
BMR = 0.02
BMD

BMDL

BMR = 0.05
BMD

After adjusting for six confounders
P0 = 0.05a
SBP
36
28
78
DBP
34
27
74
P0 = 0.10a
SBP
23
18
53
DBP
22
17
50
Crude data (not including any confounders)
P0 = 0.05a
SBP
28
22
59
DBP
23
19
49
P0 = 0.10a
SBP
17
14
40
DBP
15
12
33

BMR = 0.10

BMR = 0.02

BMR = 0.05

BMR = 0.10

BMDL

BMD

BMDL

BMD

BMDL

BMD

BMDL

BMD

BMDL

60
58

129
123

100
97

36
34

28
27

77
74

60
58

129
123

100
97

41
39

94
90

72
70

23
22

17
17

52
50

40
39

93
90

72
70

48
41

98
83

80
69

27
23

22
19

58
49

47
41

98
83

79
69

32
28

71
60

58
50

17
15

14
12

39
33

32
28

71
60

57
50

a P and BMR indicate an abnormal probability of blood pressure in the nondrinkers and an excess risk in the drinkers, respectively.
0
Note: Cutoff values calculated from the BMD models, after adjusting for six confounders (when confounder was not used in parentheses),
were 140 (140) mmHg for SBP and 90 (89) mmHg when P0 = 0.05; and 135 (134) mmHg for SBP and 86 (85) mmHg for DBP whenP0 = 0.10.

to be 30 g/day in Brazilian and North American populations(18,23) and less than 23 g/day in southwesternJapan.(24) Four possible explanations for this disagreement are as follows:
1. The thresholds may be different between thegeneral population and workers because of
the healthy worker’s effect.
2. Mean age in the study population may havediffered among those researches, even if eachstudy adjusted for age. Since blood pressure,
as well as the prevalence of hypertension, increases with aging (Table III), the elderly population may be more susceptible to alcohol
than the middle-aged population.
3. The regional difference in alcohol/aldehyde
dehydrogenase (ADH/ALDH) genotypes
may have affected the outcomes because there
exists a highly prevalent polymorphism in the
low Km ALDH (ALDH2) gene in Japanese ascompared with the Caucasoid;(25) also, the distribution of such genotypes is greatly different
within Japan.
4. The study designs, i.e., a cross-sectional andcohort studies, were different.
Concerning the self-reported questionnaire used
for the exposure assessment, the salesmen of ourstudy were asked about the number of cans (orbottles) of each alcoholic beverage per week concretely,(9) although a questionnaire utilized in many
health examination centers of Japan included a cognitively difficult task to summarize drinking behavior,(17) e.g., subjects have to calculate the equivalentdose of sake per day if they drink beer or whisky.(26)
A significant association was observed between thedaily ethanol intake and HDL-CH in the 1,100 salesmen; by showing the above correlation, qualitative
evidence is provided that alcohol use is being measured with at least some degree of validity.(17,27) Also,
the daily ethanol intake was confirmed in 45 salesmen who drank too much by interview, to avoid inaccuracy due to underreporting of alcohol consumption.(17,18,28) In addition, there were positively linear
relationships between weekly ethanol intake and liver
enzyme activities in the same salesmen.(9) Thus, ourquestionnaire seemed to provide an appropriate estimate of daily ethanol intake.
In this study, there may have been possible limitations, e.g., sampling, selection, or confounding biases.
However, proportions of 31,800,000 male employees
in Japan in 2001 were 23.4% at 18–29 years of age,
23.7% at 30–39 years, 21.3% at 40–49 years, 23.1% at
50–59 years, and 8.4% at 60–69 years,(29) and weresimilar to those of our study population regarding

Application of BMD Approach to Clinical Research121

age distribution. Also, since annual alcohol consumption per household in Akita has been reported to be
more than that in other Japanese prefectures,(30) thestatistical power may have been potent because of awide range of alcohol intake. The participation rate
was not so high, but the characteristics did not differ
between the respondents and nonrespondents in thisstudy population (Table I). In addition, possible confounders such as age, BMI, job stress, and smoking
status, except for physical activity and sodium intake,were considered in the process of data analysis. Therefore, it is suggested that our findings were not heavily
influenced by such biases.
This study indicated that smoking status affectedblood pressure levels negatively (Table IV). Some epidemiologic studies also have reported that SBP and
DBP were lower in smokers than in nonsmokers.(31)This would be regarded as a paradox because nicotine
has potent sympathomimetic effects, affecting bloodpressure levels and heart rate.(32) On the other hand,
blood pressure was not affected by T-CH, TG, or job
stress in the regression analysis of this study. The associations between job stress and hypertension remain
disputable.(33) Further research with other new scales
is necessary to reconfirm such effects of smoking status and job stress on blood pressure.

and smoking status, as well as alcohol (Table IV), andthe regression coefficients (β) of daily ethanol intake
to SBP and DBP, when no confounders were included,
were approximately 10 times larger than those whensix confounders were employed. This would be due tothe fact that there were a large number of nondrinking salesmen at ages of less than 30 years (Table III),
indicating that the exposure effect was reduced after
confounder adjustment. On the other hand, BudtzJørgensen et al. have described that a higher BMDL
is obtained if a strong predictor of the response thatis not related to the exposure is excluded from the setof independent variables.(6) In either case, important
confounders should be considered in epidemiologicalresearch.
In the BMD method, the abnormal response level
to interpret continuous data of the endpoint can be
specified by defining either a cutoff value (C) or theunexposed risk (P0 ), and these values depend on eachother. When we used the P0 of 0.05, the cutoff values
calculated by our method, i.e., 140 mmHg for SBP and
90 mmHg for DBP (Table VI), were consistent withthe upper limits of the “high-normal” or “prehypertension” classification by the WHO-ISH(34) or JNC
VII.(35) Thus, this approach seems to be preferred interms of statistical efficiency and statistical validity ascompared to transformation of data into dichotomous
format.(12,36) Also, it is crucial to confirm the difference between the cutoff value computed and the clinical standard value (e.g., the upper normal limit ofan endpoint) in deciding which dose-response model
to use for a clinical data set because such cutoff values could be changed by different confounder values
used,(6) especially mean age in each study.
The BMR refers to a specific risk increase above
background risk in BMD calculations for continuous
outcomes.(2) In the quantal-response setting, BMR
values of 0.1, 0.05, or possibly 0.01 are generally chosen, though the EPA water quality criteria guidelines
recommend using either BMR = 0.05 or 0.1.(37) Also,
any need for conservatism could be accommodated
in the choice for the BMR.(11) When P0 = 0.05, thechoice of BMR = 0.05 results in a BMD that represents a doubling of the proportion of the populationthat falls into the adverse effect region. Considering
the consistency of thresholds computed in this study,
the selected BMR value of 0.05 (and the P0 of 0.05)
appears to be more compatible with results of the multiple logistic regression analysis than the BMR of 0.02
or 0.1 (Table VI).
In epidemiological studies, the odds ratio and relative risk are frequently used,(18,21,38) but such values

4.2. Application of BMD Approachto Clinical Research
In the BMD approach that was set at the P0 of
0.05 and BMR of 0.05, the BMDL of daily ethanol
intake for increased blood pressure was perfectly accordant with the NOAEL for hypertension diagnosed
by SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, while
two approaches were mathematically independent.
In addition, the calculated BMD was between 60.1
and 90.0 g/day in the groups categorized (Table V).
Despite the uncertainties of the data used, therefore,
these findings suggest that the BMDL and BMD correspond to the NOAEL and LOAEL, respectively. On
the other hand, it is known that the BMD approach reflects sample size more appropriately than a NOAEL,
and smaller studies tend to result in smaller BMDLs,
whereas the opposite is true for NOAELs.(11,12) If thesample number of clinical data be smaller, the true
threshold would be placed somewhere between the
BMDL and BMD calculated from the research.
All BMDs and BMDLs calculated from data
without any confounders were smaller than those calculated from data with six confounders (Table VI).
Blood pressure was shown to depend on age, BMI,

122
depend on the interval or range used arbitrarily. For
this reason, any changable threshold could be estimated from dose groups categorized. The BMD approach, contrarily, provides a promising method for
calculating a threshold (point of departure) of thedose-response relation of risk factors in the study
population given.(2,8,11) In addition, the BMDs and
BMDLs derived from a study on the effects of leadon δ-aminolevulinic acid levels in plasma, blood, andurine of lead-exposed workers have been shown to bebiologically valid.(8) Although any BMD approaches
should not be applied if there is no significant relationship between an exposure and outcome, the implication of this article is that the BMD method usedin our study is very much available for researches inclinical medicine, as well as in environmental and occupational health and toxicology.
ACKNOWLEDGMENTS
We thank Drs. Esben Budtz-Jørgensen andPhilippe Grandjean for their valuable advice on BMD
calculations. This research was supported in part by
the grants-in-aids for scientific research from the
Nissan Science Foundation and from the Japan Ministry of Education, Culture, Sports and Technology.
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