Job Strain and Type 2 Diabetes Risk: Pooled Analysis of 124,808 Men and Women

Job Strain as a Risk Factor for Type
2 Diabetes: A Pooled Analysis of
124,808 Men and Women
Diabetes Care 2014;37:2268–2275 | DOI: 10.2337/dc13-2936
OBJECTIVE
The status of psychosocial stress at work as a risk factor for type 2 diabetes is
unclear because existing evidence is based on small studies and is subject to
confounding by lifestyle factors, such as obesity and physical inactivity. This col-
laborative study examined whether stress at work, defined as “job strain,” is
associated with incident type 2 diabetes independent of lifestyle factors.
RESEARCH DESIGN AND METHODS
We extracted individual-level data for 124,808 diabetes-free adults from 13 Eu-
ropean cohort studies participating in the IPD-Work Consortium. We measured
job strain with baseline questionnaires. Incident type 2 diabetes at follow-up was
ascertained using national health registers, clinical screening, and self-reports. We
analyzed data for each study using Cox regression and pooled the study-specific
estimates in fixed-effect meta-analyses.
RESULTS
There were 3,703 cases of incident diabetes during a mean follow-up of 10.3 years.
After adjustment for age, sex, and socioeconomic status (SES), the hazard ratio
1 Finnish Institute of Occupational Health, Hel-
sinki, Tampere, and Turku, Finland
2
School of Health Sciences, J ̈onk ̈oping University,
J ̈onk ̈oping, Sweden
3
Institute of Environmental Medicine, Karolinska
Institutet, Stockholm, Sweden
4
Stress Research Institute, Stockholm University,
Stockholm, Sweden
5
Centre for Occupational and Environmental Medi-
cine, Stockholm County Council, Stockholm, Sweden
6
National Research Centre for the Working Envi-
ronment, Copenhagen, Denmark
7
Department of Occupational and Environmen-
tal Medicine, Bispebjerg University Hospital, Co-
penhagen, Denmark
8 Federal Institute for Occupational Safety and
Health (BAuA), Berlin, Germany
9
Institute for Medical Sociology, Medical Faculty,
University of D ̈usseldorf, D ̈usseldorf, Germany
10
Versailles-Saint Quentin University, Versailles,
France
11
Inserm U1018, Centre for Research in Epidemi-
ology and Population Health, Villejuif, France
12 Department of Epidemiology and Public
Health, University College London, London, U.K.
13
Institute of Behavioral Sciences, University of
Helsinki, Helsinki, Finland
14 Department of Health Sciences, Mid Sweden
University, Sundsvall, Sweden
15
Department of Public Health, University of Hel-
sinki, Helsinki, Finland
16 School of Sociology, Social Policy & Social
Work, Queen’s University Belfast, Belfast, U.K.
17
UKCRC Centre of Excellence for Public Health North-
ern Ireland, Queen’s University Belfast, Belfast, U.K.
18 Department of Psychology, Ume ̊a University,
Ume ̊a, Sweden
19 The Danish National Centre for Social Re-
search, Copenhagen, Denmark
20
Department of Public Health and Department
of Psychology, University of Copenhagen, Copen-
hagen, Denmark
21
Department of Psychology, University of Turku,
Turku, Finland
22
Folkh ̈alsan Research Center, Helsinki, Finland
23
Nordic School of Public Health, G ̈oteborg, Sweden
24 Department of Public Health, University of
Turku, Turku, Finland
25
Turku University Hospital, Turku, Finland
26 Occupational and Environmental Medicine,
Uppsala University, Uppsala, Sweden
27
Centre for Cognitive Ageing and Cognitive Epide-
miology, University of Edinburgh, Edinburgh, U.K.
28
School of Community and Social Medicine, Uni-
versity of Bristol, Bristol, U.K.
29 Hjelt Institute, Medical Faculty, University of
Helsinki, Helsinki, Finland
Corresponding authors: Solja T. Nyberg, solja.
[e-mail], and Mika Kivim ̈aki, m.kivimaki@
ucl.ac.uk.
Received 16 December 2013 and accepted 18
April 2014.
This article contains Supplementary Data online
at http://care.diabetesjournals.org/lookup/
suppl/doi:10.2337/dc13-2936/-/DC1.
© 2014 by the American Diabetes Association.
Readers may use this article as long as the work
is properly cited, the use is educational and not
for profit, and the work is not altered.
Solja T. Nyberg, 1 Eleonor I. Fransson, 2,3,4
Katriina Heikkil ̈a, 1 Kirsi Ahola, 1
Lars Alfredsson, 3,5 Jakob B. Bjorner, 6
Marianne Borritz, 7 Hermann Burr, 8
Nico Dragano, 9 Marcel Goldberg, 10,11
Mark Hamer, 12 Markus Jokela, 13
Anders Knutsson, 14 Markku Koskenvuo, 15
Aki Koskinen, 1 Anne Kouvonen, 16,17
Constanze Leineweber, 4 Ida E.H. Madsen, 6
Linda L. Magnusson Hanson, 4
Michael G. Marmot, 12 Martin L. Nielsen, 7
Maria Nordin, 18 Tuula Oksanen, 1
Jan H. Pejtersen, 19 Jaana Pentti, 1
Reiner Rugulies, 6,20 Paula Salo,1,21
Johannes Siegrist, 9 Andrew Steptoe, 12
Sakari Suominen, 22,23,24 T ̈ores Theorell, 4
Ari V ̈a ̈an ̈anen, 1 Jussi Vahtera, 1,24,25
Marianna Virtanen, 1
Peter J.M. Westerholm, 26
Hugo Westerlund, 4 Marie Zins, 10,11
G. David Batty, 12,27 Eric J. Brunner, 12
Jane E. Ferrie, 12,28
Archana Singh-Manoux, 11,12 and
Mika Kivim ̈aki, 1,12,29 for the IPD-Work
Consortium
2268 Diabetes Care Volume 37, August 2014
EPIDEMIOLOGY/HEALTH SERVICES RESEARCH

(HR) for job strain compared with no
job strain was 1.15 (95% CI 1.06–1.25)
with no difference between men and
women (1.19 [1.06–1.34] and 1.13
[1.00–1.28], respectively). In stratified
analyses, job strain was associated
with an increased risk of diabetes
among those with healthy and un-
healthy lifestyle habits. In a multivari-
able model adjusted for age, sex, SES,
and lifestyle habits, the HR was 1.11
(1.00–1.23).
CONCLUSIONS
Findings from a large pan-European
dataset suggest that job strain is a
risk factor for type 2 diabetes in men
and women independent of lifestyle
factors.
Diabetes, a group of diseases of which
type 2 diabetes is the most common, is a
rapidly growing health problem world-
wide (1,2). Type 2 diabetes is a progres-
sive disease in which the advanced
stages are characterized by micro- and
macrovascular complications (e.g., reti-
nopathy, nephropathy, and neuropathy)
and atherosclerosis (3,4). It affects qual-
ity of life and ranks ninth as a cause of
global mortality (1).
Physical inactivity and obesity are the
most important modifiable risk factors
for type 2 diabetes (5,6). Some studies
suggest that exposure to job strain, the
most widely studied form of work stress
(7), is also associated with an increased
risk of type 2 diabetes (8–10). An asso-
ciation between job strain and diabetes
is biologically plausible (11) because
stress response increases secretion
of the fight-or-flight hormone cortisol,
which stimulates glucose production in
the liver and antagonizes the action
of insulin in peripheral tissues (12–
14). However, evidence of a job strain–
diabetes association remains scarce and
inconsistent. Whereas some studies
have shown an association (8–10),
other studies have found no evidence
for job strain as a risk factor for diabetes
(15–17).
A further complication is that lifestyle
risk factors for type 2 diabetes tend to
cluster in those who also report job
strain (18–22). Dissecting out the effects
of job strain from those of an unhealthy
lifestyle is challenging as few studies
are large enough to determine the
association between job strain and
type 2 diabetes in analysis stratified by
lifestyle factors.
To address these limitations, we
pooled results from 13 cohort studies
and conducted an analysis of individual-
participant data on almost 125,000
men and women initially free from di-
abetes. The size of the data and the
number of incident type 2 cases at
follow-up exceed those of previous
reports.
RESEARCH DESIGN AND METHODS
Studies and Participants
Data are drawn from 13 independent
cohort studies from Finland, France,
Denmark, Sweden, and the U.K. All
the studies are part of the Individual-
Participant-Data meta-analysis of Work-
ing populations (IPD-Work) Consortium
(23). Details of the study design and par-
ticipants have been published previously
(Supplementary Data).
We included a total of 131,955 partic-
ipants who were employed at the base-
line assessment, which took place
between 1986 and 2008, depending on
the study. We excluded from the anal-
yses 4,080 (3%) participants with missing
values for sex, age, job strain, or diabe-
tes and 3,067 (2%) with a diagnosis of
diabetes before or at study baseline.
Thus, 124,808 participants were in-
cluded in the analyses.
Each constituent study in the consor-
tium was approved by the relevant local
or national ethics committees, and all
participants gave informed consent
(Supplementary Data).
Measurement of Job Strain
Job strain was measured with questions
from the validated job content ques-
tionnaire and demand control ques-
tionnaire, which were included in the
baseline self-report questionnaire of all
studies (24,25). We have previously
published a detailed description of the
job-strain measure, including its valida-
tion and harmonization, as part of the
consortium (24). In brief, participants
were asked to answer questions about
psychosocial aspects of their job. For
each participant, mean response scores
were calculated for job demand items
(i.e., inquiries about whether the partic-
ipant had to work very hard or had ex-
cessive amounts of work, conflicting
demands, or insufficient time) and job
control items (i.e., inquiries about deci-
sion freedom and learning new things at
work). The agreement between the har-
monized scales used in this study and
the complete versions was mostly
good or very good (k statistic .0.68)
with a few exceptions for which agree-
ment was moderate (k between 0.54
and 0.60) (24).
We defined high job demands as
having a job demand score that was
greater than the study-specific median
score; similarly, we defined low job con-
trol as having a job control score that
was lower than the study-specific me-
dian score. These are the original and
most commonly used categorizations
(26). We defined the exposure as a bi-
nary variable: job strain (high demands
and low control) versus no job strain (all
other combinations) according to the
job strain model (25). As an alternative
conceptualization, we defined job strain
quadrants: high-strain job (high de-
mands and low control), active job
(high demands and high control), pas-
sive job (low demands and low control),
and low-strain job (low demands and
high control). To minimize investigator
bias, we validated the job strain mea-
sure before extracting data on incident
type 2 diabetes, with investigators
masked to outcome information (24).
Ascertainment of Incident Type 2
Diabetes
The outcome was the first record of type 2
diabetes, diagnosed corresponding to
ICD-10 code E11. We collected records
from hospital admissions and discharge
registers and mortality registers with a
mention of diagnosis of type 2 diabetes
in any of the diagnosis codes. Additionally,
in the Finnish datasets (FPS, HeSSup, and
Still Working), participants were also de-
fined as an incident type 2 diabetes case
the first time they appeared in the nation-
wide drug reimbursement register as eli-
gible for type 2 diabetes medication (27).
In the Whitehall II study, type 2 diabetes
was ascertained by 2-h oral glucose toler-
ance test administered every 5 years (11)
using World Health Organization criteria
and complemented by self-reports of di-
abetes diagnosis and medication (28). In
the Gazel study, we only had ICD codes for
mortality data so new nonfatal cases were
based on self-report from annual ques-
tionnaires. The date of incident diabetes
was defined as the date of the first record
care.diabetesjournals.org Nyberg and Associates 2269

during the follow-up in any of the previ-
ously mentioned sources (Supplementary
Table 1).
Prevalent (existing) type 2 diabetes
cases were defined using information
from any of the following: hospital re-
cords (all studies except for Gazel and
Whitehall II), baseline medical assess-
ment (Whitehall II), self-report from
the baseline questionnaire (COPSOQ-II,
FPS, Gazel, HeSSup, IPAW, SLOSH,
Whitehall II, WOLF Norrland [WOLF N],
and WOLF Stockholm [WOLF S]), or drug
reimbursement register in Finland (FPS,
HeSSup, and Still Working). We ex-
cluded participants with a diagnosis of
either type 1 or type 2 diabetes either
before or at the study baseline (ICD-10
codes E10–E11 or ICD-9 and ICD-8 code
250) (Supplementary Table 2).
Covariates
In addition to age and sex, we used data
on socioeconomic status (SES), working
hours, BMI, leisure-time physical activ-
ity, smoking, and alcohol consumption
as covariates (that is, confounders or
mediators). SES was defined based
on occupational title, which was regis-
ter based (in COPSOQ-I, COPSOQ-II,
DWECS, FPS, Gazel, IPAW, PUMA, and
Still Working) or self-reported (in White-
hall II, SLOSH, WOLF N, and WOLF S). In
HeSSup, SES was based on self-reported
highest educational qualification. SES
was categorized into low, intermedi-
ate, high, and other, with participants
who were self-employed or whose job
title was missing included in the last
category.
Working hours were divided into cat-
egories of ,35, 35–40, 41–48, 49–54,
and 55+ hours per week with the cate-
gory 35–40 as the reference. Informa-
tion on working hours was not available
for Still Working, Gazel, and those SLOSH
participants who responded to the ques-
tionnaire in 2006.
All lifestyle covariates were defined
and harmonized across cohorts before
linkage to outcome data. We calculated
BMI using height and weight (weight in
kilograms divided by height in meters
squared), which were measured (in
Whitehall II, WOLF N, and WOLF S) or
self-reported (in COPSOQ-II, DWECS,
Gazel, FPS, HeSSup, IPAW, PUMA, and
SLOSH) (21). BMI data were not avail-
able in COPSOQ-I and Still Working stud-
ies. BMI was categorized according to
the World Health Organization recom-
mendations into ,18.5 kg/m 2 (under-
weight), 18.5–24.9 kg/m 2 (normal
weight), 25–29.9 kg/m 2 (overweight),
30–34.9 kg/m 2 (obese, class I), 35–39.9
kg/m 2 (obese, class II), and $40 kg/m 2
(obese, class III) (29). Participants with
BMI values ,15 or .50 kg/m 2 were ex-
cluded from the analysis including BMI.
We grouped participants into three
categories according to their level of
leisure-time physical activity: sedentary
(physically inactive), highly active (at
least 2.5 h of moderate, or at least 1 h
15 min of vigorous, physical activity per
week), or moderately active (all levels in
between). Information on physical activ-
ity was not available for participants in
COPSOQ-I (18). Tobacco smoking was
self-reported and categorized into
never, ex-, and current smoking (19).
We used responses to questions about
the total number of alcoholic drinks con-
sumed per week to classify participants
as nondrinkers, moderate drinkers (1–
14 drinks per week for women and
1–21 drinks per week for men), high-
to-intermediate drinkers (15–20 drinks
per week for women and 22–27 drinks
per week for men), and heavy drinkers
($21 drinks per week for women and
$28 drinks per week for men) (20). Har-
monized data on alcohol consumption
were not available for participants in
COPSOQ-I or SLOSH.
For additional adjustment for biolog-
ical risk markers (representing potential
mediators), we included self-reported
hypertension or use of antihypertensive
medication (FPS, HeSSup, SLOSH, IPAW,
and COPSOQ-II), self-reported elevated
lipids (HeSSup), or measured systolic
blood pressure, triglycerides, and HDL
cholesterol (Whitehall II, WOLF N, and
WOLF S). Because shift work has been
suggested to elevate the risk of type 2
diabetes (30–32), we also identified
respondents who worked in shifts or
during the night. Participants who re-
ported daytime work only were classi-
fied as nonshift workers, and those
reporting nighttime work (between
6:00 P.M. and 6:00 A.M.) or any form of
shift work were classified as shift work-
ers. Participants with unclear or missing
responses were excluded from this anal-
ysis. In addition, data for shift or night-
time working were not available for
COPSOQ-I, COPSOQ-II, DWECS, Gazel,
IPAW, and PUMA.
Data Analyses
Follow-up time was calculated from base-
line assessment until the first record of
type 2 diabetes, death, or end of follow-
up, whichever came first. Job strain was
modeled as a binary exposure (job strain
vs. no job strain [the reference]) and in
sensitivity analysis as a categorical vari-
able (high strain, active, passive, and
low strain [the reference]). All analyses
were adjusted for sex, age, and SES and
then further adjusted for lifestyle vari-
ables (BMI category, physical activity,
smoking, and alcohol consumption). The
models adjusted for age, sex, SES, and
lifestyle factors were also additionally ad-
justed for biological risk markers. To ad-
dress reverse causation, we excluded the
first 3 years of follow-up. To minimize the
possibility that shift work affected any as-
sociations, we repeated the analyses sep-
arately in participants who reported
working shifts or nights and among those
who did not. Participants with missing
data were excluded from this analysis.
As in previous studies from the IPD-
Work Consortium, we also examined
risk of diabetes in the four groups cre-
ated by combining data on job strain and
each lifestyle risk factor (33). Dichoto-
mized lifestyle risk factors used in these
analyses were current smoking (yes vs.
no), heavy alcohol use ($21 drinks per
week for women and $28 drinks per
week for men vs. other), obesity (BMI
$30 vs. ,30 kg/m 2 ), and physical inac-
tivity (yes vs. no).
Within each study, the association be-
tween job strain and incident type 2
diabetes was analyzed using Cox propor-
tional hazards regression models. The
study-specific effect estimates and their
standard errors were pooled in fixed-
and random-effect meta-analyses, and
heterogeneity in effect sizes was as-
sessed with the I2 statistic (34,35). Due
to low heterogeneity, the fixed- and
random-effect estimates were virtually
identical, and fixed-effect estimates
are reported here. We additionally
pooled data from the studies to con-
struct age-, sex-, and SES-adjusted sur-
vival curves for incident type 2 diabetes
by job strain status (individual-level data
for pooling were not available from
COPSOQ-I, COPSOQ-II, DWECS, IPAW,
PUMA, and SLOSH).
SAS 9.2 was used for all analyses, ex-
cept for the meta-analyses, which were
conducted with Stata MP (version 11).
2270 Job Strain and Incident Type 2 Diabetes Diabetes Care Volume 37, August 2014