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Socioeconomic and race/ethnic differences in daily salivary cortisol profiles: The Multi-Ethnic Study of Atherosclerosis

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It has often been hypothesized that stress and its biological consequences mediate the relationship between low socioeconomic status (SES) or minority status and poor cardiovascular disease outcomes. The objective of this study was to determine if daily cortisol patterns, a biomarker of the stress response, differ by race/ethnicity and socioeconomic status. Data were collected from 935 Black, White and Hispanic adults age 48-90 years old. Salivary cortisol samples were collected six times per day over 3 days: at awakening, 30min later, at 1000h, noon, 1800h and at bedtime. Blacks and Hispanics had lower levels of wake-up cortisol and less steep early declines, while Blacks had flatter and Hispanics steeper late day declines relative to Whites. Similarly the low socioeconomic status group also had lower levels of wake-up cortisol and less steep decline during the early part of the day. These patterns remained after adjustment for health behaviors and psychosocial factors. This study finds an association between salivary cortisol and race/ethnicity and SES in a multi-ethnic study population. Further work is needed to determine the health consequences of these differences.
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Socioeconomic and race/ethnic differences in daily
salivary cortisol profiles: The Multi-Ethnic Study of
Atherosclerosis
Anjum Hajat
a,
*, Ana Diez-Roux
a
, Tracy G. Franklin
a
, Teresa Seeman
b
,
Sandi Shrager
c
, Nalini Ranjit
d
, Cecilia Castro
e
, Karol Watson
f
,
Brisa Sanchez
g
, Clemens Kirschbaum
h
a
University of Michigan, School of Public Health, Department of Epidemiology, 109 Observatory, 3rd Floor Tower, Ann Arbor,
MI 48109-2029, United States
b
David Geffen School of Medicine at UCLA, Department of Medicine, Division of Geriatrics, 10945 Le Conte Avenue, Los Angeles, CA
90095, United States
c
University of Washington, Department of Biostatistics, Collaborative Health Studies Coordinating Center, 6200 NE 74th Street,
Seattle, WA 98115, United States
d
University of Texas Health Sciences Center, School of Public Health, 1200 Herman Pressler Drive, Houston, TX 77030, United States
e
Columbia University, Mailman School of Public Health, Department of Epidemiology, 722 West 168th Street, New York,
NY 10032, United States
f
David Geffen School of Medicine at UCLA, Department of Medicine, Division of Cardiology, 200 ucla Medical Plaza, Los Angeles, CA
90095-1679, United States
g
University of Michigan, School of Public Health, Department of Biostatistics, 1420 Washington Heights, Ann Arbor, MI 48109-2029,
United States
h
Technische Universita¨t Dresden, Department of Psychology, 01062 Dresden, Germany
Received 27 July 2009; received in revised form 19 October 2009; accepted 14 December 2009
Psychoneuroendocrinology (2010) 35, 932—943
KEYWORDS
Salivary cortisol;
Cortisol diurnal pattern;
Race/ethnicity;
Socioeconomic status;
Cortisol awakening
response;
Stress
Summary It has often been hypothesized that stress and its biological consequences mediate
the relationship between low socioeconomic status (SES) or minority status and poor cardiovas-
cular disease outcomes. The objective of this study was to determine if daily cortisol patterns, a
biomarker of the stress response, differ by race/ethnicity and socioeconomic status. Data were
collected from 935 Black, White and Hispanic adults age 48—90 years old. Salivary cortisol samples
were collected six times per day over 3 days: at awakening, 30 min later, at 1000 h, noon, 1800 h
and at bedtime. Blacks and Hispanics had lower levels of wake-up cortisol and less steep early
declines, while Blacks had flatter and Hispanics steeper late day declines relative to Whites.
Similarly the low socioeconomic status group also had lower levels of wake-up cortisol and less
* Corresponding author at: University of Michigan, School of Public Health, Department of Epidemiology, Center for Social Epidemiology and
Population Health, 109 Observatory Street, 3rd Floor Tower, Room 3620w1, Ann Arbor, MI 48109-2029, United States. Tel.: +1 734 615 9228;
fax: +1 734 763 5706.
E-mail address: ahajat@umich.edu (A. Hajat).
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/psyneuen
0306-4530/$ — see front matter #2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.psyneuen.2009.12.009
1. Introduction
It has often been hypothesized that stress and its biological
consequences are one potential mechanism by which low
socioeconomic status (SES) or minority status result in poor
cardiovascular disease outcomes (Brunner, 1997; Baum et al.,
1999; Steptoe and Marmot, 2002). Minorities and the poor are
hypothesized to feel a greater degree of chronic stress
resulting from both an increased exposure to stressful events
and fewer social and material resources with which to com-
bat the effects of chronic stress (Adler et al., 1994; Baum
et al., 1999; Pearlin et al., 2005). These stressors have
biological consequences, such as alterations in the levels
of stress hormones, resulting from activation of the hypotha-
lamic—pituitary—adrenal (HPA) axis and sympathetic nervous
system (Cohen et al., 1995). Although evidence is still sparse,
cortisol, one of the key hormones released in response to
stressors, has been linked to a number of cardiovascular risk
factors. For example, increases in cortisol have been linked
to CVD risk factors such as central adiposity (Bjorntorp, 1997;
Epel et al., 2000), hypertension (Rosmond and Bjorntorp,
2000; Hammer and Stewart, 2006) and inflammation (Pet-
rovsky et al., 1998; Miller et al., 2002), as well as early
indicators of atherosclerosis such as increased intima media
thickness (Eller et al., 2001) and coronary calcification (Mat-
thews et al., 2006).
Cortisol levels rise sharply during the first 30—45 min
immediately after awakening, known as the cortisol awaken-
ing response (CAR), and then decline gradually over the day
(with some oscillations around meal time) reaching their
lowest daily level late in the evening. It is still uncertain
which specific features of the daily cortisol profile may be
most relevant to health outcomes; however, several features
have been examined in relation to SES and race/ethnicity.
Features of the daily cortisol curve examined in the literature
include wake-up levels (Steptoe et al., 2003; Kunz-Ebrecht
et al., 2004; Ranjit et al., 2005a; Wright and Steptoe, 2005;
Cohen et al., 2006a,b; Eller et al., 2006) the CAR (Steptoe
et al., 2003, 2005; Bennett et al., 2004; Kunz-Ebrecht et al.,
2004; Wright and Steptoe, 2005; Cohen et al., 2006b; Eller
et al., 2006; Garcia et al., 2008) and the diurnal cortisol slope
(Ockenfels et al., 1995; Steptoe et al., 2005; Cohen et al.,
2006a,b; DeSantis et al., 2007).
Although there is some evidence of differences in daily
cortisol profiles by race/ethnicity or SES, results have not
always been consistent. For example, some studies found no
association between SES and cortisol levels at wake-up (Kunz-
Ebrecht et al., 2004; Ranjit et al., 2005a; Wright and Steptoe,
2005; Cohen et al., 2006a,b; Eller et al., 2006), while others
found higher wake-up cortisol levels among higher SES groups
(Brandtsta¨dter et al., 1991; Steptoe et al., 2003; Bennett
et al., 2004). The picture for CAR is even less consistent:
some studies find no association (Steptoe et al., 2003, 2005;
Cohen et al., 2006b; Eller et al., 2006; Garcia et al., 2008),
others find a steeper CAR among high SES groups (Bennett
et al., 2004; Ranjit et al., 2005a) and still others a flatter CAR
among high SES groups (Kunz-Ebrecht et al., 2004; Steptoe
et al., 2005; Wright and Steptoe, 2005). As for the diurnal
slope, a few studies found no association between cortisol
and SES (Steptoe et al., 2005; Cohen et al., 2006a). Others
found flatter slopes for low SES groups (Cohen et al., 2006b)
and minorities (DeSantis et al., 2007), while one study found
the unemployed had steeper slopes compared to the
employed (Ockenfels et al., 1995). Establishing whether daily
cortisol profiles are patterned by SES and race/ethnicity
would provide support for the hypothesis that stress may
mediate disparities in cardiovascular disease (and perhaps
other outcomes). In addition, identifying the specific fea-
tures of the daily cortisol curve that are most affected by SES
and race/ethnicity could also provide clues regarding the
features of the curve (and the biological mechanisms) most
relevant to understanding how chronic stress affects health.
Using data from a large and diverse population-based
sample with multiple timed measures of cortisol over 3 days,
we examined the SES and race/ethnic patterning of various
features of the daily cortisol profile. Although several studies
have documented the effect of SES and race/ethnicity on
diurnal cortisol pattern, the current study addresses impor-
tant limitations of prior studies including small sample sizes,
around 250 or less (Ockenfels et al., 1995; Decker, 2000;
Steptoe et al., 2003, 2005; Bennett et al., 2004; Kunz-
Ebrecht et al., 2004; Ranjit et al., 2005a; Wright and Steptoe,
2005; Cohen et al., 2006a; Eller et al., 2006; DeSantis et al.,
2007; Garcia et al., 2008), selected populations and conve-
nience sampling (e.g. only young or old adults, only women)
(Ockenfels et al., 1995; Bennett et al., 2004; Ranjit et al.,
2005a; Wright and Steptoe, 2005; Cohen et al., 2006a; Eller
et al., 2006; DeSantis et al., 2007; Garcia et al., 2008), and
limited numbers of cortisol measures and days of assessment
per subject (Brandtsta¨dter et al., 1991; Decker, 2000; Ben-
nett et al., 2004; Cohen et al., 2006a,b; Li et al., 2007).
2. Methods
The Multi-Ethnic Study of Atherosclerosis (MESA) is a long-
itudinal study, funded by the National Heart Lung and Blood
Institute designed to investigate risk factors for subclinical
cardiovascular diseases and its progression to clinical dis-
ease. At baseline MESA included 6814 men and women aged
44—84 years without clinical cardiovascular disease recruited
from six sites. At each site a probability sample of partici-
pants was selected through a variety of population-based
approaches, including lists of area residents, area residents
enrolled in a union health plan, random digit dialing and lists
from the Centers for Medicare and Medicaid Services for
participants 65 or older (Bild et al., 2002).
An ancillary study to MESA, the MESA Stress study col-
lected detailed measures of stress hormones, including sali-
steep decline during the early part of the day. These patterns remained after adjustment for
health behaviors and psychosocial factors. This study finds an association between salivary cortisol
and race/ethnicity and SES in a multi-ethnic study population. Further work is needed to
determine the health consequences of these differences.
#2009 Elsevier Ltd. All rights reserved.
Socioeconomic and race/ethnic differences in daily salivary cortisol profiles 933
vary cortisol measures, on a subsample of 1002 participants
enrolled at the New York and Los Angeles MESA sites. All
procedures were carried out with the adequate understand-
ing and written consent of the subjects. These data were
collected in conjunction with the third and fourth follow-up
exams of the full MESA cohort between 2004 and 2006.
Participants were enrolled in the order in which they
attended the follow-up exam. Enrollment continued until
approximately 500 participants were enrolled at each site.
This procedure resulted in an approximately random sample
of white, black and Hispanic participants from each site.
Compared to other eligible participants at the two sites, the
MESA Stress study was similar to the parent study, with a few
exceptions. There were fewer persons in the 75—84 years age
range (12.1% compared to 18.2% in the overall MESA study),
slightly more men (47.6% compared to 44.7%) and more
participants with some college education (29.7% compared
to 23.9%).
Each MESA Stress participant was instructed to collect six
saliva samples per day over 3 weekdays, resulting in a max-
imum of 18 samples per person. The first sample was to be
taken immediately after waking (and before getting out of
bed), the second sample 30 min later, the third sample at
around 1000 h, the fourth sample at around 1200 h (or before
lunch if lunch occurred before noon), the fifth sample at
around 1800 h (or before dinner if dinner occurred before 6
pm), and the sixth sample right before bed. Detailed instruc-
tions and training in sample collection were provided to
participants by trained staff.
Saliva samples were collected using Salivette collection
tubes and stored at 20 8C until analysis. Before biochemical
analysis, samples were thawed and centrifuged at 3000 rpm
for 3 min to obtain clear saliva with low viscosity. Salivary
cortisol levels were determined employing a commercially
available chemi-luminescence assay (CLIA) with high sensi-
tivity of 0/16 ng/mL (IBL-Hamburg; Germany). Intra- and
inter-assay coefficients of variation were below eight per-
cent. Cortisol was measured in nmol per liter.
Participants recorded the collection time of salivary sam-
ples on special cards; in addition a container with a time
tracking device (known as track-caps) automatically regis-
tered the time at which cotton swabs were extracted to
collect each sample. Participants were told of this time
tracking device. Prior work has shown that the use of this
device increases compliance with the requested timing of
samples (Kudielka et al., 2003). At the end of each day
participants completed a short daily questionnaire including
their wake-up time on that day and whether they had been
able to collect the first sample immediately after wake-up.
We examined education, income and wealth as SES vari-
ables. Education was defined as the participant’s highest
level of education and was categorized as less than or equal
to high school, some college or greater than or equal to
bachelor’s degree. The wealth measure was derived based on
ownership of the following assets: owning one or more car,
owning a home or paying mortgage on a home, owning land or
owning an investment (such as stocks, bonds, mutual funds,
retirement investments). A 5-point wealth index was cre-
ated, where 1 point was given for ownership of the above
mentioned assets. Families who owned all of these assets
received a score of four and those who owned none received a
score of zero. Total annual family income was obtained
through questionnaire, in 13 categories ranging from less
than $5000 to greater than $100,000. In order to adjust
family income for the number of people living in the house-
hold, a family income of less than $5000 was assigned a family
income of $2500 and greater than $100,000 was assigned an
income of $112,500. For all other income categories the
midpoint of the category was used. Family income was then
divided by 10,000 and categorized into quintiles (zero being
the poorest and four the richest). An income—wealth index
was created by summing the five category per capita income
variable and the 5-point wealth index, yielding an income—
wealth index with a total of 9 points ranging from zero to
eight. Those with an annual per capita family income in the
lowest quintile and no assets received a score of zero and
those with income in the highest quintile and all four assets
received a score of eight. This scored variable was specified
as continuous in regression models. Income and wealth data
collected at exam three were used in this analysis, but any
missing data were imputed from previous waves. Race/eth-
nicity was reported by participants in response to questions
modeled on the year 2000 Census and was categorized into
white, black, and Hispanic.
Previous research has found age and gender to be asso-
ciated with cortisol levels (Clow et al., 2004; Ranjit et al.,
2005b; Cohen et al., 2006a; Hansen et al., 2008). Hence
continuous age and gender were adjusted for in all models.
Other behavioral factors such as smoking, exercise and body
mass index have also been shown to be associated with
cortisol levels (Clow et al., 2004; Ranjit et al., 2005b; Cohen
et al., 2006a; Hansen et al., 2008). In our data, all behavioral
variables were also associated with both race and income—
wealth. Since these behavioral variables could be confoun-
ders and/or mediators of stress effects on cortisol, we
reported estimates before and after adjustment for these
covariates. Smoking was categorized into current, past, or
never. Body mass index (BMI) was calculated as weight in
kilograms divided by height in meters squared and was
modeled as standard categories: normal, overweight (BMI
between 25 and 29.9) and obese (BMI >30). Physical activity
questions were adapted from the Cross-Cultural Activity
Participation Study (Irwin et al., 2000). Higher scores of
intentional exercise, measured in metabolic equivalent
(MET)-minutes/week, indicated higher levels of moderate
and vigorous activities. Intentional exercise was categorized
into approximate quartiles, where the first quartile was
composed of all those who reported no exercise, about
25% of the study population.
Several psychosocial factors were also explored as poten-
tial confounders or mediators, namely hostility, depression,
emotional support and chronic burden. Much previous
research has linked cortisol to these factors (Pope and Smith,
1991; Yehuda et al., 1996; Pruessner et al., 2003; Cohen
et al., 2006b; Sjogren et al., 2006; Ranjit et al., 2009) and our
data also supported their association with SES. Cynical hos-
tility was derived from an 8-item subscale of the full Cook-
Medley Hostility Scale and is a key component of hostility
(Barefoot et al., 1989) that has been linked to salivary
cortisol in earlier studies (Pope and Smith, 1991; Ranjit
et al., 2009). Depression was measured by summing the
20-item Center for Epidemiologic Studies Depression scale
(Radloff, 1977). Emotional social support was derived by
summing a 6-item scale and chronic burden was derived from
934 A. Hajat et al.
Table 1 Selected characteristics of cortisol data collection by demographic characteristics for MESA Stress Study participants.
Percent
distribution
of participants
Percent of
participants
with five or
more samples
on all days
a
Percent of
samples with
reported times
within 15 min of
track-caps times
b
Percent of
days for which
first sample was
taken within
5 min of wake-up
c
First sample
time (median)
Last
sample
time
(median)
Difference
between first
and second
sample time
(median)
Cortisol area
under the
curve
d
(median)
All subjects (n= 935) 100.0% 85.2% 86.0% 78.2% 6:42 22:26 0:34 6270
Site
New York 51.9% 86.0% 86.6% 77.7% 6:49 22:46 0:34 6403
Los Angeles 48.1% 84.4% 85.1% 78.7% 6:36 22:07 0:35 6112
p-Value
e
0.5085 0.0084 0.5146 0.0683 0.0313 0.5319 0.4893
Age
48—54 17.0% 86.8% 87.1% 88.4% 6:33 22:45 0:33 5562
55—64 31.1% 85.2% 86.9% 77.6% 6:32 22:24 0:34 6122
65—74 33.1% 86.7% 86.0% 76.6% 6:47 22:25 0:36 6380
75—89 18.8% 81.3% 82.9% 72.8% 6:53 22:23 0:35 6924
p-Value
e
0.2638 <0.0001 <0.0001 0.0051 0.1615 0.3855 0.0003
Sex
Female 51.6% 84.2% 86.1% 79.2% 6:40 22:25 0:35 5859
Male 48.5% 86.3% 85.7% 77.2% 6:45 22:29 0:34 6689
p-Value
e
0.3699 0.5702 0.2027 0.9091 0.0750 0.0595 0.1030
Race
White 19.6% 85.3% 89.1% 86.2% 6:45 22:44 0:33 7048
Black 27.6% 84.9% 85.2% 73.4% 6:58 22:51 0:35 6300
Hispanic 52.8% 85.4% 85.1% 77.7% 6:32 22:10 0:35 5935
p-Value
e
0.9804 <0.0001 <0.0001 0.6883 0.1798 0.1514 0.0017
Total gross family income
<$25,000 39.5% 83.0% 82.8% 73.4% 6:45 22:05 0:37 6152
$25—49,999 32.6% 88.9% 87.6% 80.2% 6:39 22:37 0:34 6208
3$50,000 27.9% 85.0% 88.5% 83.1% 6:42 22:44 0:33 6543
p-Value
e
0.3694 <0.0001 <0.0001 0.1008 0.0035 0.5271 0.8716
Wealth
0 assets 18.2% 82.9% 83.9% 75.3% 6:48 22:13 0:37 6167
1 assets 25.2% 85.2% 83.0% 78.6% 6:42 22:32 0:34 6157
2 assets 24.2% 86.3% 86.5% 79.3% 6:38 22:21 0:34 6219
3 assets 20.2% 86.8% 89.7% 79.5% 6:42 22:26 0:34 6292
4 assets 12.2% 84.2% 87.3% 77.4% 6:41 22:35 0:34 6696
p-Value
e
0.5391 <0.0001 0.3203 0.0846 0.3911 0.6958 0.6506
Income—wealth index
0—1 points 19.6% 84.2% 81.4% 75.2% 6:42 22:13 0:39 5923
2—3 points 28.9% 84.0% 83.9% 75.8% 6:47 22:07 0:35 6105
Socioeconomic and race/ethnic differences in daily salivary cortisol profiles 935
a 5-item scale regarding difficulties in five separate domains
of life (Bromberger and Matthews, 1996). All four variables
were specified as continuous.
We first examined selected characteristics of sample
collection and cortisol levels by site, age, sex, race/ethnicity
and SES indicators. Due to its skewed distribution cortisol was
log transformed for analysis. Up to 18 measures collected
over the 3 days were included for each person. Exploratory
data analyses including locally estimated scatter plot
smoothing (LOESS) curves were used to examine the shape
of the cortisol profile over the course of the day for the full
sample and stratified by age, gender, race/ethnicity and SES.
LOESS models are a nonparametric regression method which
fit models to localized subsets of data. This allows greater
flexibility because no assumptions about the global form of
the regression surface are needed (Cleveland et al., 1988;
Devlin and Cleveland, 1988).
Based on these descriptive analyses and the shape of the
LOESS plots, and in order to capture the non-linearity of
cortisol over the day, knots were selected to describe a
piecewise linear regression. Two fixed knots, at 30 min after
wake-up and 120 min after wake-up, were used to model
cortisol levels. Inclusion of the second knot (120 min) sub-
stantially improved the fit of the model, especially for the
early part of the day. Results were robust to alternate
specifications of the second knot.
In regression analyses, within-person correlations and
person-to-person variation in slopes were accounted for by
using mixed models and allowing random components for the
person specific intercept and person specific slopes. The
between day variability in our data was small (and the
addition of a random component for day resulted in non-
convergent models), thus we did not model day as a random
effect. Instead day level variability was addressed through
the use of the day variable as a fixed effect and through the
use of robust standard errors. The inclusion of random com-
ponents for all three slopes led to convergence problems so
only the first and third slopes were modeled as random.
Results were invariant regardless of which of the two slopes
were modeled as random. An unstructured covariance matrix
was used to obtain robust standard errors. Models also con-
trolled for day (first, second or third day of data collection)
and wake-up time. Main effects of covariates as well as their
interactions with different pieces of the daily slope were
included to estimate adjusted associations of SES and race/
ethnicity with the shape of the cortisol profile. Since all
cortisol values were log transformed, exponentiated coeffi-
cients from the models were interpreted as percent differ-
ences.
In addition to modeling log cortisol values over time, we
estimated an area under the curve (AUC) measure for each
day where a participant collected at least three cortisol
samples. AUC is a summary measure that represents the
total amount of cortisol measured over the course of the
day and was calculated using the trapezoidal rule; where the
area under the curve was divided into several trapezoids, and
a total AUC was obtained as the sum of the areas of these
individual trapezoids (Yeh and Kwan, 1978). Time, on the x-
axis, was measured in minutes since wake-up. AUC was
calculated for the period between wake-up and 16 h after
wake-up to ensure that each participant contributed the
same number of waking hours to the measure. Cortisol values
Tabl e 1 (Continued )
Percent
distribution
of participants
Percent of
participants
with five or
more samples
on all days
a
Percent of
samples with
reported times
within 15 min of
track-caps times
b
Percent of
days for which
first sample was
taken within
5 min of wake-up
c
First sample
time (median)
Last
sample
time
(median)
Difference
between first
and second
sample time
(median)
Cortisol area
under the
curve
d
(median)
4—6 points 36.3% 87.3% 88.8% 80.9% 6:37 22:34 0:34 6403
7—8 points 15.2% 84.5% 88.3% 79.6% 6:46 22:41 0:34 6682
p-Value
e
0.5346 <0.0001 0.0097 0.0798 0.0542 0.4611 0.3693
Education
2Completed HS/GED 48.0% 82.6% 83.3% 74.8% 6:37 22:06 0:36 6118
Some college — associate degree 28.3% 89.1% 88.1% 79.5% 6:42 22:42 0:34 6119
3Bachelor’s degree 23.6% 86.0% 88.4% 83.4% 6:51 22:50 0:33 6748
p-Value
e
0.1289 <0.0001 <0.0001 0.9702 0.0067 0.8616 0.3164
a
Ninety-seven percent of participants had data for all 3 days.
b
Based on 15,774 samples with both form and cap times.
c
Based on 2774 days with daily questionnaire information on whether first sample was collected at wake-up. Ninety-eight percent of days had this information on the survey to use.
d
The area under the curve (AUC) was calculated using the trapezoidal rule. AUC was calculated for each day and then averaged across all 3 days using time sincewake-up in minutes. Cortisol values
were interpolated at 16 h to ensure all participants contributed thesame amount of time to the AUC measure. Only days with three or more sampleswere used. AUC is measured in units of nmol/L min.
e
p-Values are calculated using chi-square tests and ANOVAs (age, income, and education are trend tests).
936 A. Hajat et al.
for 16 h were linearly interpolated based on adjacent values.
Because it is a summary measure, cortisol values were not log
transformed when calculating the AUC, which is in units of
nmol/L min. Mixed models with random intercepts (to
account for within-person correlations in the three daily
measures) were used to model AUC as a function of SES
and race/ethnicity adjusted for covariates.
All times used in analyses were those registered by the
track-caps device. Since participants were instructed to take
their first sample when they woke up, the time of the first
sample was used as the wake-up time. For the small number
of days for which no first sample was collected, but at least
one of the other samples was, the wake-up time recorded on
the daily questionnaire was used instead (40/2899, 1.4%).
Days missing both the first sample and the reported wake-up
time (5/2899, 0.2%) were excluded.
The 1002 participants enrolled in the MESA Stress Study
yielded a maximum of 3006 participant-days of data collec-
tion. Of these 127 days were excluded because no track-caps
times were available (n= 107 days), no cortisol samples were
collected (n= 15 days), or because there was no first cap time
or reported wake-up time to use for a wake-up time (n=5
days). We excluded 936 samples with no track-cap time,
insufficient sample for assay, or unreliable cortisol value (0
or >100 nmol/L). Lastly we excluded those that reported
taking oral or inhaled steroids (n= 35 persons). This resulted
in a total of 935 participants, 2774 days, and 15,774 samples
for analysis.
3. Results
Table 1 shows selected characteristics of study participants
by site, age, sex, race/ethnicity and SES indicators. The
median age of the participants was 65 years. Approximately
49% of the sample was male, 20% were white, 28% black, and
53% Hispanic. Approximately 85% of participants collected at
least five samples per day for all days on which they collected
samples (97% of participants collected samples on all 3 days).
The percentage of participants with at least five samples per
day was similar across socio-demographic characteristics.
Overall 86% of self-recorded times were within 15 min of
the registered track-cap times a measure of time-recording
accuracy. Younger participants, whites, and participants with
higher SES showed higher percentages of time-recording
accuracy ( p<0.001 for all comparisons).
Overall the first sample was taken within 5 min of wake-up
for 78% of days across participants. Again this measure of
concordance was higher among younger people, whites and
participants with higher SES. The median wake-up time (first
sample) was 0642 h and the median bedtime (last sample) was
2226 h. Older people reported later wake-up times
(p= 0.005), and higher income persons reported later bed-
times ( p= 0.004). A few other SES variables also showed
significant differences in bedtimes. The median time differ-
ence between the first and second sample was 34 min and did
not vary substantially by demographic characteristics. The
AUC for cortisol increased with age ( pfor trend 0.0003) and
was higher in whites than minorities ( p= 0.002). There were
no significant differences by site, gender, income, wealth or
education.
Fig. 1 shows smoothed LOESS curves for cortisol daily
profiles stratified by age, sex, race/ethnicity and income/
wealth. In general cortisol values were higher in older than in
younger participants. This difference was most pronounced
later in the day, suggesting flatter declines as age increased.
Males generally had higher cortisol values than females,
except later in the day, when values were similar or slightly
higher for females. Cortisol levels were higher in whites than
in blacks at wake-up and 30 min after wake-up, but blacks
had a slower decline later in the day resulting in slightly
higher levels than whites before bedtime. Hispanics tended
to have lower cortisol levels than other groups overall.
Persons in the lowest income/wealth category had less pro-
nounced increases after wake-up and less steep declines
later in the day.
Table 2 shows percent differences in different aspects of
the daily cortisol profile associated with race/ethnicity, and
income/wealth. Separate estimates are shown for cortisol at
wake-up and for three different portions of change over the
day: (1) CAR or the morning rise (the increase between wake-
up and 30 min), (2) the decline between 30 and 120 min after
wake-up (henceforth referred to as ‘‘early decline’’) and (3)
the decline between 120 min after wake-up and bedtime
(henceforth referred to as ‘‘late decline’’). All estimates
were obtained simultaneously from a piecewise linear mixed
model and were adjusted for race/ethnicity, income—wealth
index, age, sex, day and wake-up time. Models adjusted for
health behaviors (smoking, exercise and obesity), psychoso-
cial factors (cynical hostility, depression, emotional support
and chronic burden) and both health behaviors and psycho-
social factors are also presented in Table 2. Positive percent
differences in wake-up levels indicate higher cortisol levels.
Positive percent differences in the CAR indicate a more
pronounced or steeper increase and positive percent differ-
ences in the early or late decline indicate a less pronounced
or flatter decline.
In the minimally adjusted model (adjusted for age, sex,
day, wake-up time, race/ethnicity and the income—wealth
index) blacks and Hispanics had significantly lower levels of
cortisol than whites at wake-up (17.2% and 15.7% lower in
blacks and Hispanics, respectively, p20.005 for both com-
parisons). They also had a less pronounced CAR than whites
but these differences were not statistically significant. Both
groups also had less pronounced early declines than whites
(7.1% for blacks p-value 0.068 and 12.0% for Hispanic p-
value <0.001). The late decline was also significantly less
pronounced in blacks than in whites but in contrast Hispanics
had a more pronounced late decline than whites, although
differences in the late decline were very small.
Adjustment for behavioral or psychosocial factors did not
substantially modify point estimates, although reductions
(e.g. in differences at wake-up) were observed. Overall,
the general pattern of lower wake-up, less pronounced
CAR and less steep early decline observed in Blacks and
Hispanics compared to whites persisted after adjustment.
Persons with the lowest levels of income/wealth (score of
zero on the combined income—wealth index) had 18.2% (CI:
3.4, 35.1) lower wake-up levels compared to those with the
highest income/wealth score (score of eight). Lower income/
wealth was also associated with a less pronounced early and
late decline, although differences in the late decline were
small and were not statistically significant. As in the case of
differences by race/ethnicity, point estimates were largely
unchanged after adjustment for behavioral and psychosocial
Socioeconomic and race/ethnic differences in daily salivary cortisol profiles 937
factors, and differences in wake-up values and early decline
remained statistically significant.
Wealth was largely driving the observed associations of
cortisol with the combined income—wealth index (results not
shown, but percent differences are similar to those pre-
sented in Table 2). In fact, in the fully adjusted model the
5-point wealth index showed slightly stronger associations at
wake-up and early decline than did the combined variable
(18.7% less cortisol at wake-up for the least wealthy; 18.7%
less pronounced early decline among the least wealthy). In
contrast, income and education were associated with rela-
tively small non-significant percent changes over the day.
Table 3 shows mean differences in the AUC associated with
race/ethnicity and income/wealth. Hispanics consistently
had a significantly smaller AUC compared to whites
(1261.2 less according to the fully adjusted model, CI:
2074.0, 448.5). Blacks, however, were not significantly
different from whites after controlling for health behaviors
or psychosocial factors. Similarly the income—wealth index
showed no association with AUC, nor did wealth, income or
education (results not shown).
Several sensitivity analyses were conducted. First, alter-
nate ways of creating the income—wealth index were
explored, but the version used here provided the most detail
without losing much power. Including employment status to
the model did not change estimates appreciably, so it was not
included in the final model for the sake of parsimony. In
addition, two participants reported oral contraception (OC)
use, a common confounder in cortisol studies (Kirschbaum
et al., 1995). Adjusting for OC use made little difference to
the estimates; thereforeit was not included in the final model.
Other research has pointed to seasonal changes in cortisol
levels (King et al., 2000); controlling for season in which the
sample was collectedalso made little difference to our results.
In addition, to assess concerns about timing of morning cortisol
samples, findings were similar when analyses were restricted
to the 2185 days wherethe first sample and the reportedwake-
up time were within 5 min of each other. Lastly, results were
robust to alternate specifications of the second knot in the
mixed model. We attempted to place the second knot at 90,
120, and 210 min but 120 min provided the best fit and yielded
a more precise estimate of the early decline compared to
approaches that placed the knot earlier in the day.
4. Discussion
This study found evidence of associations of daily cortisol
profiles with race/ethnicity and SES. Hispanics, blacks and
Figure 1 LOESS curves of cortisol by age, gender, race and income/wealth.
938 A. Hajat et al.
low SES individuals had lower cortisol at wake-up and slower
declines over the day (especially the earlier part of the day).
Differences in declines later in the day were very small, but in
general a less pronounced decline was associated with being
black and having low income/wealth, while a more pro-
nounced decline was associated with being Hispanic. No
significant differences by income/wealth were observed
for AUC cortisol and only one for race/ethnicity: Hispanics
had significantly lower AUC than whites.
Although work in this area has grown exponentially over
the past few years, relatively few studies have investigated
differences by race/ethnicity and SES in features of the
cortisol curve. Studies have varied widely in sample size,
selection criteria, and number of measures collected. A
relatively consistent finding that has emerged is the presence
of a higher evening cortisol levels in Blacks compared to
white. Two studies reported this finding, one in adults and
another in youth (Cohen et al., 2006b; DeSantis et al., 2007).
These studies, however, did not report the decline during
evening hours but rather the level of cortisol taken at evening
measurements.
Studies with numerous repeat daily cortisol measure-
ments have demonstrated similar results to ours (Cohen
et al., 2006b; DeSantis et al., 2007). Although these studies
did not use the same analytic approach as we did, our results
confirmed the presence of a less steep daily decline in Blacks
compared to whites and in low SES compared to high SES
persons in a large population sample with measures over 3
days. However, the differences we observed were more
robust in the earlier rather than in the later decline. This
could be due to the very low levels of cortisol present in the
body during evening hours making differences more difficult
to detect. We further expanded prior work by showing that
the less steep early decline was also observed in Hispanic
compared to whites. Only one other study examined cortisol
in a US Hispanic population and found higher bedtime cortisol
levels among Hispanics compared to whites (prior to adjust-
ing for SES) and flatter cortisol slopes (DeSantis et al., 2007).
Table 2 Percent differences (95% confidence intervals) in log wake-up cortisol, log cortisol awakening response, early and late
decline in log cortisol (nmol/L) associated with race/ethnicity and income—wealth index, controlling for health behaviors and
psychosocial factors.
Race
a
SES
b
Blacks Hispanic Income—wealth index
Percent difference at wake-up
Minimally adjusted model
c
17.2 (30.0, 5.7) 15.7 (28.2, 4.5) 18.2 (3.4, 35.1)
Model controlling for health behaviors
d
12.1 (24.6, 0.9) 10.9 (23.3, 0.2) 17.4 (2.4, 34.6)
Model controlling for psychosocial factors
e
15.7 (28.5, 4.1) 14.3 (26.9, 3.0) 17.1 (1.7, 34.8)
Model controlling for health behaviors
and psychosocial factors
f
10.5 (23.0, 0.7) 9.4 (21.9, 1.8) 15.9 (0.5, 33.7)
Percent difference in cortisol awakening response
Minimally adjusted model
c
13.0 (35.6, 6.3) 15.0 (37.4, 3.8) 0.02 (27.4, 27.4)
Model controlling for health behaviors
d
15.4 (39.5, 4.7) 18.1 (42.8, 2.4) 4.3 (22.9, 33.7)
Model controlling for psychosocial factors
e
12.3 (35.5, 7.5) 14.4 (37.4, 4.9) 2.2 (31.2, 25.7)
Model controlling for both health behaviors
and psychosocial factors
f
15.9 (40.9, 5.0) 18.9 (44.7, 2.4) 2.8 (25.3, 32.4)
Percent difference at early decline
Minimally adjusted model
c
7.1 (0.5, 15.2) 12.0 (4.8, 19.7) 14.1 (26.2, 3.1)
Model controlling for health behaviors
d
6.7 (1.0, 15.0) 10.3 (3.0, 18.3) 15.9 (28.4, 4.6)
Model controlling for psychosocial factors
e
6.3 (1.5,14.6) 11.2 (3.8, 19.2) 11.2 (23.7, 0.01)
Model controlling for both health behaviors
and psychosocial factors
f
6.2 (1.7, 14.7) 10.1 (2.4, 18.3) 12.9 (25.7, 1.5)
Percent difference at late decline
Minimally adjusted model
c
1.7 (0.7, 2.8) 2.1 (3.1, 1.1) 1.1 (2.4, 0.2)
Model controlling for health behaviors
d
1.5 (0.4, 2.6) 2.1 (3.1, 1.0) 0.9 (2.3, 0.4)
Model controlling for psychosocial factors
e
1.9 (0.8, 3.0) 2.0 (3.0, 0.9) 1.2 (2.6, 0.1)
Model controlling for both health behaviors
and psychosocial factors
f
1.6 (0.5, 2.7) 1.9 (3.0, 0.8) 1.0 (2.4, 0.3)
a
Referent group for race is White non-Hispanic.
b
Percent difference for income—wealth index reflects 8-point change from 0 (lowest income—wealth category) to 8 (highest income—
wealth category) on income—wealth scale.
c
Minimally adjusted model controls for race/ethnicity, income—wealth index, age, sex, day, and wake-up time.
d
Model controlling for health behaviors includes smoking, body mass index and physical activity and all factors in minimally adjusted
model.
e
Model controlling for psychosocial factors includes cynical hostility, depression, emotional support and chronic burden and all factors in
minimally adjusted model.
f
Includes covariates in minimally adjusted model, psychosocial factors and health behaviors.
Socioeconomic and race/ethnic differences in daily salivary cortisol profiles 939
We also found persistent differences by race/ethnicity
and SES in wake-up values. Wake-up cortisol levels have been
studied much more widely than features of the diurnal
pattern and have resulted in the emergence of a very mixed
overall picture. Similar to our results, several studies
reported whites had higher cortisol levels at wake-up than
blacks (Bennett et al., 2004; Cohen et al., 2006b; DeSantis
et al., 2007) and high SES participants had higher morning
cortisol levels than low SES participants (Brandtsta¨dter
et al., 1991; Steptoe et al., 2003; Bennett et al., 2004).
However, two studies found the opposite association with SES
(Ockenfels et al., 1995; Garcia et al., 2008), while several
others found no association between wake-up levels of cor-
tisol and SES (Kunz-Ebrecht et al., 2004; Ranjit et al., 2005a;
Wright and Steptoe, 2005; Cohen et al., 2006a,b; Eller et al.,
2006). This study and others with larger, more diverse popu-
lations and denser cortisol sampling during the day consis-
tently found whites and high SES groups had higher levels of
cortisol upon awakening (Brandtsta¨dter et al., 1991; Cohen
et al., 2006b; DeSantis et al., 2007).
The cortisol awakening response is thought by some to be
the most important piece of the diurnal cortisol pattern, thus
it has been the subject of much examination (Clow et al.,
2004). As it relates to SES, results thus far have been mixed.
Among studies that found an association between SES and
CAR, a few found CAR was steeper in low SES populations
(Kunz-Ebrecht et al., 2004; Wright and Steptoe, 2005), while
others found CAR to be steeper among populations in better
economic situations (Bennett et al., 2004; Ranjit et al.,
2005b). Similar to our results, several studies found no
association between SES and CAR (Steptoe et al., 2003;
Cohen et al., 2006b; Eller et al., 2006; Garcia et al.,
2008). We did, however, find some evidence that the CAR
was less pronounced in Blacks and Hispanics compared to
whites. Cohen et al. (2006b) and Bennett et al. (2004) were
the only other studies to look at the association between race
and CAR and found no association. AUC was another measure
of cortisol found in the literature. Unlike other studies that
found low SES individuals had higher AUC (Cohen et al.,
2006a; Li et al., 2007; Garcia et al., 2008), our results showed
little association between current SES measures (income,
education, wealth or combined income—wealth) and AUC.
The association between AUC and race revealed an interest-
ing finding. To our knowledge, ours is the first study to find an
association between Hispanics and AUC, where Hispanics
have less total AUC relative to whites.
A number of stress related mechanisms could explain our
findings of differences in wake-up values, late decline, and
possibly CAR by SES and or race/ethnicity. It has been
hypothesized that in the face of an acute stressor cortisol
levels increase, however, as the stressor becomes chronic and
with the passage of time a below-normal cortisol response is
observed. The lower levels of cortisol at wake-up and during
the CAR for blacks, Hispanics and the poor are consistent with
this chronic stress theory. The higher mid-day cortisol levels
may be a result of stressful daily experiences (Miller et al.,
2007). The activation of the HPA axis during day time hours
and after specific race-related incidents has been observed
by other studies (Steptoe et al., 2003; Miller et al., 2007;
Richman and Jonassaint, 2008) and is consistent with the
observed higher mid-day cortisol levels in our study. As a
summary measure of cortisol, AUC may not be capturing the
intricacies of the diurnal cortisol pattern. Although our
piecewise models found significant differences at wake-up
and early decline, no association was evident with our mea-
sure of AUC.
A number of behavioral and psychosocial factors could
confound and/or mediate SES or race/ethnicity differences
in cortisol. The cross-sectional nature of our analyses did not
allow us to separate confounders from mediators. The inclu-
Table 3 Mean differences and 95% confidence intervals in cortisol measured as area under the curve (nmol/L min) associated with
race/ethnicity and income—wealth index.
Race
a
SES
b
Blacks Hispanics Income—wealth index
Mean
difference
95% confidence
interval
Mean
difference
95% confidence
interval
Mean
difference
95% confidence
interval
Minimally adjusted model
c
797.2 1569.2, 25.1 1261.2 2074.0, 448.5 208.0 1198.7, 782.8
Model controlling for
health behaviors
d
668.4 1503.8, 167.1 1225.3 2085.7, 364.8 223.1 1244.9, 798.6
Model controlling for
psychosocial factors
e
688.2 1461.5, 85.1 1192.0 2009.5, 374.5 181.3 1261.0, 898.5
Model controlling for
both health behaviors and
psychosocial factors
f
548.9 1384.0, 286.2 1117.6 1980.8, 254.4 182.0 1279.3, 915.3
a
Referent group for Blacks and Hispanics is White.
b
Mean differences for income—wealth reflects 8-point change from 0 (lowest income—wealth category) to 8 (highest income—wealth
category) on income—wealth scale.
c
Minimally adjusted model controls for race/ethnicity, income—wealth index, age, sex and wake-up time.
d
Model controlling for health behaviors includes smoking, body mass index and physical activity and all factors in minimally adjusted
model.
e
Model controlling for psychosocial factors includes cynical hostility, depression, emotional support and chronic burden and all factors in
minimally adjusted model.
f
Includes covariates in minimally adjusted model, psychosocial factors and health behaviors.
940 A. Hajat et al.
sion of these variables in our models did not substantially
change the general patterns. These results suggested that
the associations are at least partly independent of these
factors. However measurement error in these covariates
could have affected our results. Additional work with
improved measurement and longitudinal data will be neces-
sary to better understand the behavioral and/or psychosocial
processes mediating SES or race/ethnicity effects on cortisol.
Since the study of cortisol in large populations is relatively
new, there is limited epidemiologic evidence that helps
explain the consequences of cortisol dysregulation. A few
papers have reported an association between cortisol and
indicators of early atherosclerosis such as coronary calcifica-
tion and intima media thickness (Eller et al., 2001; Matthews
et al., 2006). Others have found an association between
cortisol and inflammation (Petrovsky et al., 1998; Miller
et al., 2002), obesity (Bjorntorp, 1997; Epel et al., 2000)
and hypertension (Rosmond and Bjorntorp, 2000; Hammer
and Stewart, 2006), all important risk factors that could lead
to poor cardiovascular disease outcomes. A better under-
standing of the importance of atypical diurnal cortisol pat-
terns will require additional work.
Our study had limitations. First, although we observed
effects during the later part of the day, only two samples
were collected in the evening hours. Second, many studies
have documented non-compliance to the study protocol,
specifically with morning samples, resulting in misleading
cortisol curves in the morning hours (Clow et al., 2004; Kunz-
Ebrecht et al., 2004; Wright and Steptoe, 2005). Although
our study improved upon past research by using track-caps to
ensure better adherence, the accuracy of the wake-up
sample was still uncertain. In addition, although we were
able to collect three days of cortisol samples, there was still
some uncertainty about the stability of cortisol measure-
ments over time. Lastly, our use of the combined income—
wealth index as our main measure of SES was a departure
from other studies. Since we are unable to compare it to
other studies, it is unclear if the results produced will be
replicated in the future. It should be noted, however, that
wealth has been shown to be a better measure of SES among
older retired populations (Keister and Moller, 2000; Pollack
et al., 2007). Since over half of our sample was older than 65
years old and almost 45% indicated their employment status
asretired,wefeeltheuseofwealthwasappropriateinthis
study.
Our study improved upon past studies of salivary cortisol in
its sampling approach (taking six samples over 3 days),
sample size (almost 1000 individuals) and large Hispanic
population. Our study was one of the few to examine a large
Hispanic population and to find significant difference in the
slope of the cortisol curve for Hispanics compared to whites.
In addition the use of track-caps to record the time samples
were taken helped reduce issues with compliance that sev-
eral observational studies have noted. Lastly, our use of
piecewise linear models to analyze cortisol data allowed
flexibility in modeling and yielded more specific results of
which features of the cortisol curve were associated with
race/ethnicity and SES. This approach did raise issues about
the independence of the different pieces of the cortisol
curve; such that the inference about one piece of the curve
may be correlated to the inference about another piece.
However, the correlations between the estimated pieces of
the curve were low to moderate (range 0.02—0.44), mini-
mizing this concern.
In future work on cortisol and race/ethnicity and SES it
would be useful to collect additional evening samples
in order to produce more robust measurements of
cortisol patterns later in the day. Our study was one of
the few to suggest that declines in the later portion of the
day were association with race/ethnicity and SES. In addi-
tion, cortisol measurements in large multi-ethnic study
populations are needed. This would enhance our under-
standing of how diurnal cortisol patterns differ among
different race/ethnic group and could potentially corro-
borate the results we observed. Lastly, repeat cortisol
measures taken over the course of time would help us
understand the stability (or instability) of cortisol in an
individual over time.
Role of the funding sources
MESA was supported by contracts N01-HC-95159 through N01-
HC-95169 from the National Heart, Lung, and Blood Institute
(NHLBI). NHLBI had no further role in the study design; in the
collection, analysis and interpretation of data; in the writing
of the report; and in the decision to submit the paper for
publication. This research was also supported in part by the
Michigan Center for Integrative Approaches to Health Dis-
parities (P60MD002249) funded by the National Center on
Minority Health and Health Disparities.
Conflict of interest
All authors declare they have no conflicts of interest.
Acknowledgements
The authors thank the other investigators, the staff, and the
participants of the MESA study for their valuable contribu-
tions. A full list of participating MESA investigators and
institutions can be found at http://www.mesa-nhlbi.org.
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... Structural racism and discrimination also leads to within racial group differences in chronic stress exposure and cortisol due to income (Samuel et al., 2018;Cohen et al., 2006), educational levels (Ursache et al., 2017), discrimination (Yip et al., 2021;Palmer-Bacon et al., 2020), and the intersectionality of gender (Kudielka et al., 2004). However, race remains a persistent predictor of cortisol even after controlling for education, gender, and income (Cohen et al., 2006;Hajat et al., 2010). Testing methodological variables like hair type will better probe within-group racial differences in racism and discrimination exposure, and more broadly psychosocial and biological stressors. ...
... H1 was supported such that hair shape, based on the Andre Walker (Walker et al., 1997) hair typing scale, impacted cortisol levels. These findings remained significant even when accounting for race, a major contributor to cortisol levels (Hajat et al., 2010;O'Brien et al., 2013) (H2 and H3). The impact of hair type replicated across Black, White, Multiracial, and Latino/Hispanic samples, but with a twist. ...
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The biomarker cortisol assesses the impact of biopsychosocial stressors that activate the stress response system. Hair has emerged as a valid and non-invasive means of gauging cumulative cortisol deposited over month-long periods of time. Established protocols for the extraction of hair cortisol are being validated and refined in humans, yet methodological information about hair characteristics on cortisol remains limited. In addition to external hair exposures (e.g. dye, time spent outside), we examined hair categorization or type (e.g. kinky, straight) by extending a hair typing methodology for scientific use that is currently popular among hair care professionals. We then examined the interaction between hair type and race on cortisol levels with a hair questionnaire. Three studies were pooled to investigate how sample weight, hair type, race, heat exposures, and hair treatments impacted cumulative hair cortisol concentrations. Study 1 consisted of Adult Kenyan Medical Workers (N=44); Study 2 Mexican and Mexican Americans (N=106); and Study 3 American Youth (N=107). We found significantly higher cortisol in 5 mg of hair when compared to larger sample weights, and higher cortisol in those who spent more time outdoors. Cortisol concentrations differed between racial groups and varied by hair type; moreover, there were directional differences in cumulative cortisol from straighter to curlier hair types which depended on racial group. In addition to demonstrating the impact of relatively novel control factors like hair sample weight, outdoor exposure, and hair type, the present study illustrates the importance of disentangling hair type and race to understand variability in cumulative hair cortisol. These influences should be included in future studies that measure hair cortisol.
... Poor SES is associated with increased stress that is evident from variations in diurnal rhythms of cortisol [17]. AAs have a more blunted diurnal cortisol slope than CAs as assayed using salivary samples [30], whereas hair cortisol levels are higher in AAs than CAs [96]. Besides, irregular sleep schedule, which is often found in AAs, is associated with significantly higher levels of salivary cortisol in AAs compared to CAs, in addition to smoking [99]. ...
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Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the United States. Despite increased screening options and state-of-art treatments offered in clinics, racial differences remain in CRC. African Americans (AAs) are disproportionately affected by the disease; the incidence and mortality are higher in AAs than Caucasian Americans (CAs). At the time of diagnosis, AAs more often present with advanced stages and aggressive CRCs, primarily accounting for the racial differences in therapeutic outcomes and mortality. The early incidence of CRC in AAs could be attributed to race-specific gene polymorphisms and lifestyle choices associated with socioeconomic status (SES). Altered melatonin-serotonin signaling, besides the established CRC risk factors (age, diet, obesity, alcoholism, and tobacco use), steered by SES, glucocorticoid, and Vitamin D status in AAs could also account for the early incidence in this racial group. This review focuses on how the lifestyle factors, diet, allelic variants, and altered expression of specific genes could lead to atypical serotonin and melatonin signaling by modulating the synthesis, secretion, and signaling of these pineal hormones in AAs and predisposing them to develop more aggressive CRC earlier than CAs. Crosstalk between gut microbiota and pineal hormones and its impact on CRC pathobiology is addressed from a race-specific perspective. Lastly, the status of melatonin-focused CRC treatments, the need to better understand the perturbed melatonin signaling, and the potential of pineal hormone-directed therapeutic interventions to reduce CRC-associated disparity are discussed.
... The income-wealth index was specified as a 9-point scale (0 being the lowest level of income and no assets and 8 being the highest level of income and all 4 assets). Details about the index are shown in the note of Table 1 and the index was described previously in depth (Hajat et al., 2010). Area-level covariates included population density in a 1-mile residential buffer, street network ratio in a 1-mile residential buffer, and region (from census categories: Northeast, Midwest, South, West). ...
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We employed a longitudinal distributed lag modeling approach to systematically estimate how associations between built environment features and transport walking decayed with the increase of distance from home to built environment destinations. Data came from a cohort recruited from six U.S. cities (follow-up 2000–2010, N = 3913, baseline mean age 60). Built environment features included all walkable destinations, consisting of common and popular destinations for daily life. We also included two subsets frequent social destinations and food stores to examine if the spatial scale effects differed by varying density for different types of built environment destinations. Adjusted results found that increases in transport walking diminished when built environment destinations were farther, although distance thresholds varied across different types of built environment destinations. Higher availability of walking destinations within 2-km and frequent social destinations within 1.6-km were associated with transport walking. Food stores were not associated with transport walking. This new information will help policymakers and urban designers understand at what distances each type of built environment destinations influences transport walking, in turn informing the development of interventions and/or the placement of amenities within neighborhoods to promote transport walking. The findings that spatial scales depend on specific built environment features also highlight the need for methods that can more flexibly estimate associations between outcomes and different built environment features across varying contexts, in order to improve our understanding of the spatial mechanisms involved in said associations.
... A greater morning rise in HR was observed in individuals with a low occupation level compared to those with a high occupation level in the Whitehall study [22]. Low SES has also been associated with a lower morning peak and lower nocturnal decline in salivary, urinary and serum cortisol [22][23][24]. A lower reduction in HR and blood pressure has been reported in African Americans compared to Americans from other ethnic groups [25]. ...
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Thesis
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