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R E S E A R C H A R T I C L E Open Access
Thyroid function and risk of type 2
diabetes: a population-based prospective
cohort study
Layal Chaker
1,2,3
, Symen Ligthart
3
, Tim I. M. Korevaar
1,2,3
, Albert Hofman
3,4
, Oscar H. Franco
3
,
Robin P. Peeters
1,2,3*†
and Abbas Dehghan
3†
Abstract
Background: The association of thyroid function with risk of type 2 diabetes remains elusive. We aimed to
investigate the association of thyroid function with incident diabetes and progression from prediabetes to
diabetes in a population-based prospective cohort study.
Methods: We included 8452 participants (mean age 65 years) with thyroid function measurement, defined by
thyroid-stimulating hormone (TSH) and free thyroxine (FT4), and longitudinal assessment of diabetes incidence.
Cox-models were used to investigate the association of TSH and FT4 with diabetes and progression from
prediabetes to diabetes. Multivariable models were adjusted for age, sex, high-density lipoprotein cholesterol,
and glucose at baseline, amongst others.
Results: During a mean follow-up of 7.9 years, 798 diabetes cases occurred. Higher TSH levels were associated
with a higher diabetes risk (hazard ratio [HR] 1.13; 95 % confidence interval [CI], 1.08–1.18, per logTSH), even
within the reference range of thyroid function (HR 1.24; 95 % CI, 1.06–1.45). Higher FT4 levels were associated
with a lower diabetes risk amongst all participants (HR 0.96; 95 % CI, 0.93–0.99, per 1 pmol/L) and in participants
within the reference range of thyroid function (HR 0.96; 95 % CI, 0.92–0.99). The risk of progression from prediabetes
to diabetes was higher with low-normal thyroid function (HR 1.32; 95 % CI, 1.06–1.64 for TSH and HR 0.91; 95 % CI,
0.86–0.97 for FT4). Absolute risk of developing diabetes type 2 in participants with prediabetes decreased from
35 % to almost 15 % with higher FT4 levels within the normal range.
Conclusions: Low and low-normal thyroid function are risk factors for incident diabetes, especially in individuals with
prediabetes. Future studies should investigate whether screening for and treatment of (subclinical) hypothyroidism is
beneficial in subjects at risk of developing diabetes.
Keywords: Type 2 diabetes, Thyroid hormone, Thyroid function, Diabetes, Prediabetes
Background
Diabetes mellitus and thyroid disease are the two most
common endocrine disorders, often co-existing in patients
[1]. The role of auto-immunity has been well-recognized
in the link between auto-immune thyroid disease and
type 1 diabetes mellitus [2]. A relation between thyroid
dysfunction and type 2 diabetes mellitus has also been
suggested, but the possible underlying mechanisms and
drivers show complex interactions [3].
Thyroid hormone is a major regulator of metabolism
and energy expenditure, is directly involved in the con-
trol of insulin secretion and glucose homeostasis [3, 4],
and has been shown to preserve beta-cell viability and
proliferation [5, 6]. Hyperthyroid individuals have an in-
creased insulin secretion [7] and higher free triiodothyron-
ine levels are specifically associated with improved insulin
secretion in individuals with prediabetes [8]. However, the
deleterious effect of thyrotoxicosis on glucose metabolism
* Correspondence: r.peeters@erasmusmc.nl
†
Equal contributors
1
Rotterdam Thyroid Center, Erasmus University Medical Center, Rotterdam,
The Netherlands
2
Department of Internal Medicine, Erasmus University Medical Center,
Rotterdam, The Netherlands
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Chaker et al. BMC Medicine (2016) 14:150
DOI 10.1186/s12916-016-0693-4
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has also been recognized for decades [9]. Excess thyroid
hormone (i.e. hyperthyroidism) causes increased liver
gluconeogenesis and peripheral insulin resistance and
is associated with glucose intolerance [10–13]. Interest-
ingly, lack of thyroid hormone is also associated with a
decrease in peripheral insulin sensitivity and glucose
intolerance [14] and treatment of hypothyroidism has
been shown to improve insulin sensitivity [14, 15].
There are several cross-sectional reports on the associ-
ation between thyroid dysfunction and diabetes, albeit
with conflicting results, with some studies reporting an
association between hyperthyroidism and type 2 dia-
betes, while others report instead an association between
hypothyroidism and diabetes. Further, one of the most
recent and largest cross-sectional studies reports no
association between thyroid dysfunction and type 2
diabetes [16]. However, cross-sectional studies have sev-
eral limitations, including lack of assessment of temporal-
ity. Only few studies have investigated the association of
thyroid function with incidence of diabetes prospectively
and all were register-based studies, again reporting
conflicting results [17–19]. As a consequence, there is
no consensus regarding whether patients with thyroid
dysfunction should be screened for diabetes. To date,
there are no prospective population-based cohort stud-
ies investigating the association across the full range of
thyroid function, including the normal range, with the
risk of diabetes. Therefore, we aimed to investigate the
association of thyroid function with the incidence of
type 2 diabetes and the progression from prediabetes to
diabetes in the Rotterdam Study, a large prospective
population-based cohort study.
Methods
The Rotterdam Study
The Rotterdam Study is a prospective population-based
cohort study that investigates the determinants and occur-
rence of age-related diseases in Ommoord, Rotterdam, the
Netherlands. The aims and design of the Rotterdam Study
have been described in detail elsewhere [20]. The Rotter-
dam Study consists of three independent cohorts: RS Co-
hort I (RSI), including 7983 participants aged ≥55 years
(baseline 1990–1993), RS Cohort II (RSII), including 3011
participants aged ≥55 years (baseline 2000–2001), and
RS Cohort III (RSIII), including 3932 participants
aged ≥45 years (baseline 2006–2008).
The Rotterdam Study has been approved by the medical
ethics committee according to the Population Screening
Act: Rotterdam Study, executed by the Ministry of Health,
Welfare and Sports of the Netherlands.
Study population
We selected data from participants from the third visit
of the first cohort (1997–1999, n= 4797) and the first
visit of the second (2000–2001, n= 3011) and third
(2006–2008, n= 3932) cohorts, if thyroid-stimulating
hormone (TSH) or free thyroxine (FT4) measurements,
which were performed in a random set of participants,
and information on diabetes were available. All partici-
pants in the present analysis provided written informed
consent to participate and to obtain information from
their treating physician. All study participants were
followed up from the day of baseline laboratory testing
to date of onset of diabetes, to death, or to January 1,
2012, whichever came first.
Assessment of thyroid function
Thyroid function was measured using the same methods
and assay for all three cohorts, and samples were col-
lected between 1997 and 2008, depending on the cohort.
TSH and FT4 measurements were performed in serum
samples stored at –80 °C (electrochemiluminescence
immunoassay for thyroxine and thyrotropin, “ECLIA”,
Roche). We determined cut-off values for the reference
range of TSH as 0.4–4.0 mIU/L and for FT4 as 11–25
pmol/L (0.86–1.94 ng/dL) according to guidelines as
well as our previous studies [21]. Thyroid peroxidase
antibody (TPOAb) levels greater than 35 kU/mL were
regarded as positive, as recommended by the assay
manufacturer (electrochemiluminescence immunoassay
for thyroid peroxidase antibodies, “ECLIA”,Roche).
Ascertainment of prediabetes and type 2 diabetes
At baseline and during follow-up, cases of prediabetes
and type 2 diabetes were ascertained through active
follow-up using general practitioners’records, hospital dis-
charge letters, and serum glucose measurements from Rot-
terdam Study visits, which take place approximately every
4 years [22]. Normoglycemia, prediabetes, and diabetes
were defined according to recent WHO guidelines [23];
normoglycemia was defined as a fasting serum glucose <
6.0 mmol/L; prediabetes was defined as a fasting serum glu-
cose > 6.0 mmol/L and < 7.0 mmol/L or a non-fasting
serum glucose > 7.7 mmol/L and < 11.1 mmol/L (when fast-
ing samples were absent); and type 2 diabetes was defined
as a fasting serum glucose ≥7.0 mmol/L, a non-fasting
serum glucose ≥11.1 mmol/L (when fasting samples were
absent), or the use of blood glucose lowering medication.
Information regarding the use of blood glucose lowering
medication was derived from both structured home inter-
views and linkage to pharmacy records. At baseline, more
than 95 % of the Rotterdam Study population was covered
by the pharmacies in the study area. All potential events of
type 2 diabetes were independently adjudicated by two
study physicians. In case of disagreement, consensus was
sought with an endocrinologist [22].
Chaker et al. BMC Medicine (2016) 14:150 Page 2 of 8
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Baseline measurements
Body mass index was calculated as body mass (kg) di-
vided by the square of the body height (m). Serum HDL
cholesterol and glucose were measured using standard
laboratory techniques. Information on tobacco smoking
was derived from baseline questionnaires. Systolic and
diastolic blood pressure was calculated as the average of
two consecutive measurements. Insulin was measured
using an immunoassay (electrochemiluminescence im-
munoassay “ECLIA”, Roche). Over 95 % of participants
were in a fasting state when blood was drawn at the
Rotterdam Study center visit. Information on medication
use was obtained from questionnaires in combination with
pharmacy records. Thyroid medication, including thyroid
hormone replacement therapy, was prescribed by par-
ticipant’s own GP or specialist and within the context
of regular treatment and blinded to measurements of
the Rotterdam Study.
Statistical methods
We used Cox-proportional hazards models to assess the
association of TSH or FT4 with incident diabetes. We
also assessed the association of thyroid function mea-
surements and incident diabetes in participants with
prediabetes separately. We first conducted these analyses
in all included participants and then only in those with
normal TSH and FT4 values, after excluding levothyroxine
users. The primary model, model 1, was adjusted for age,
sex, cohort, fasting glucose, and tobacco smoking. Model
2 was additionally adjusted for possible confounders or
intermediate factors, including fasting serum insulin, sys-
tolic blood pressure, diastolic blood pressure, use of blood
pressure lowering medication (diuretics, anti-adrenergic
agents, βblockers, calcium channel blockers, and RAAS
inhibitors), high-density lipoprotein (HDL) cholesterol
and body mass index (BMI). Adjusting for both BMI and
waist circumference showed multicollinearity in the
model, with BMI providing the best model fit. Additionally
adjusting for waist circumference next to BMI did not
provide meaningful changes in the risk estimates and
therefore waist circumference was omitted from the
model. Furthermore, we assessed the association of TSH
and FT4 tertiles in the normal reference range with
progression from prediabetes to diabetes and calculated
absolute risk estimates for the tertiles, using the covari-
ates of the multivariable model. We performed the fol-
lowing sensitivity analyses: (1) excluding participants
using levothyroxine at baseline, (2) excluding participants
using thyroid function altering medication, including
levothyroxine, anti-thyroid drugs (e.g., thiamazole), amio-
darone, and corticosteroids at baseline and follow-up, and
(3) additionally excluding participants with TSH and
FT4 values outside the normal range. We stratified by
possible effect modifiers, including age categories (cut-off
of 65 years) and sex. The natural logarithm of TSH was
used for the continuous models and results are presented
Fig. 1 Participant selection
Chaker et al. BMC Medicine (2016) 14:150 Page 3 of 8
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per doubling of TSH on average. The proportional hazards
assumption was assessed by performing Schoenfeld tests
and plots and was met for all analyses. There was no de-
parture from linearity as assessed by restricted cubic
splines or adding quadratic terms of TSH, FT4, or age to
the model. Reporting of the results is according to the
STROBE statement.
Results
We included a total of 8452 participants with thyroid
function measurements and who were free of diabetes at
baseline (Fig. 1). The mean age of the included partici-
pants was 64.9 years and 58 % were female. Baseline char-
acteristics are shown in Table 1. During a mean follow-up
of 7.9 years (standard deviation 4.0 years), 798 individuals
developed diabetes (IR 12 per 1000 person-years). Com-
pleteness of follow-up was 99.4 % [24].
Thyroid function and incident diabetes
The associated risk of developing diabetes was 1.09
times higher for every doubling of TSH levels mIU/L
(95 % confidence interval [CI], 1.06–1.12; Table 2).
Within the normal range, the risk of diabetes was 1.16
times higher with higher TSH levels. In model 2, this as-
sociation attenuated slightly (hazard ratio [HR] 1.06;
95 % CI, 1.00–1.13, Table 2). In the most adjusted model
(model 2), higher FT4 levels were associated with a de-
creased risk of diabetes (HR 0.96; 95 % CI, 0.93–0.99), also
within the normal range (HR 0.94; 95 % CI, 0.90–0.98).
Table 1 Baseline characteristics of included participants
Variable Mean (SD)*
Number of individuals in the study 8452
Age, in years 64.6 (9.7)
Female, n(%) 4899 (58.0)
BMI, kg/m
2
26.5 (4.05)
Total cholesterol, mmol/L 5.76 (1.01)
HDL cholesterol, mmol/L 1.43 (0.41)
Smoking, n(%)
Current 1742 (20.6)
Former 4020 (47.6)
Never 2691 (31.8)
Systolic blood pressure, mmHg 139 (21)
Diastolic blood pressure, mmHg 79 (11)
Antihypertensive medication use, n(%) 1881 (22.3)
TSH, median (IQR) 1.91 (1.29–2.76)
FT4, pmol/L 15.7 (2.32)
TPOAb positivity, n(%) 1119 (13.2)
Levothyroxine use, n(%) 233 (2.8)
*unless specified otherwise
TPOAb levels >35 kU/mL were regarded as positive
BMI body mass index, IQR interquartile range, FT4 free thyroxine, SD standard
deviation, TPOAb thyroid peroxidase antibodies, TSH thyroid-stimulating hormone,
nnumber
Table 2 Association between thyroid function and the risk of incident prediabetes and diabetes
Thyroid function measurements HR (95 % CI) Model 1 HR (95 % CI) Model 2 Incident cases Total participants
Incident Diabetes
Full range of measurement
TSH mIU/L 1.09 (1.06–1.12) 1.06 (1.00–1.13) 798 8447
Free T4 pmol/L 0.96 (0.93–0.99) 0.96 (0.93–0.99) 797 8446
Normal TSH and FT4 values
TSH mIU/L 1.16 (1.04–1.30) 1.14 (1.02–1.27) 685 7188
Free T4 pmol/L 0.96 (0.92–0.99) 0.94 (0.90–0.98) 685 7188
Progression from prediabetes to diabetes
Full range of measurement
TSH mIU/L 1.17 (1.07–1.27) 1.13 (1.03–1.24) 412 1337
Free T4 pmol/L 0.92 (0.89–0.97) 0.93 (0.89–0.98) 411 1336
Normal TSH and FT4 values
TSH mIU/L 1.26 (1.08–1.47) 1.21 (1.04–1.41) 358 1137
Free T4 pmol/L 0.90 (0.85–0.95) 0.91 (0.86–0.97) 358 1137
Model 1: adjusted for sex, age, smoking, fasting serum glucose levels and cohort
Model 2: adjusted for sex, age, smoking, cohort, fasting serum glucose levels, fasting serum insulin measurements, systolic blood pressure, diastolic blood
pressure, blood pressure lowering medication, HDL cholesterol, and body mass index
Normal range of TSH is defined by 0.4–4.0 mIU/L and normal range FT4 is defined by 11–25 pmol/L and participants not using levothyroxine
Results are presented as HR per doubling of TSH on average and per one increase in pmol/L of FT4
CI confidence interval, FT4 free thyroxine, HR hazard ratio, TSH thyroid-stimulating hormone
Chaker et al. BMC Medicine (2016) 14:150 Page 4 of 8
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Sensitivity analyses did not change risk estimates
meaningfully (Additional file 1: Table S1). Stratifying the
analyses by age category or sex did not show effect
modification for incident diabetes (Pfor interaction > 0.05
for all).
Thyroid function and progression of prediabetes to
diabetes
In participants with prediabetes, the associated risk of
developing diabetes was 1.13 times higher for every
doubling of TSH levels (95 % CI, 1.03–1.24; Table 2).
The risk of incident diabetes in participants with predia-
betes was 0.93 times lower with each 1 pmol/L increase
of FT4 (95 % CI, 0.89–0.98). In the normal range, the
risk of developing diabetes was 1.44 times higher (95 % CI,
1.13–1.93) when comparing the highest to the lowest ter-
tile of TSH in the normal range in model 1 (Additional file
2: Table S2). This corresponds to an absolute risk differ-
ence of 8.5 % for a follow-up of 7 years. Comparing the
highest to the lowest tertile for FT4, the HR for developing
diabetes in individuals with prediabetes was 0.63 (95 % CI,
0.48–0.82; Additional file 2: Table S2). Additionally
adjusting analyses for TPOAb positivity did not change
risk estimates meaningfully (data not shown). This cor-
responds to a 1.59 times higher risk and an absolute
risk difference of 9.6 % of progression to diabetes when
comparing the lowest to the highest tertile of FT4
(Additional file 2: Table S2). These associations attenuated
only slightly in model 2 (Fig. 2, Additional file 2: Table S2).
Absolute risk of diabetes type 2 in participants with predi-
abetes decreased from 35 % to almost 15 % with higher
FT4 levels within the normal range (Fig. 3).
Discussion
To our knowledge, this is the first prospective population-
based cohort study describing the relation between thyroid
function within the normal range and the risk of diabetes
and progression from prediabetes and type 2 diabetes.
Higher TSH levels and lower FT4 levels are associated
with an increased risk of diabetes and progression from
prediabetes to diabetes.
Fig. 2 Association of thyroid-stimulating hormone (TSH) and free thyroxine (FT4) levels in tertiles within the normal range and incident diabetes
in individuals with prediabetes. The normal range of TSH was defined as 0.4–4.0 mIU/L and of FT4 as 11–25 pmol/L (Conversion 1 pmol/L = 0.0777 ng/dL),
thyroid hormone medication users were excluded. The analyses were adjusted for sex, age, smoking, cohort, fasting glucose, serum insulin measurements,
systolic blood pressure, diastolic blood pressure, blood pressure lowering medication, cholesterol, and body mass index. AF atrial fibrillation, HR hazard ratio,
CI confidence interval
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There are no other studies addressing the relation be-
tween diabetes and thyroid function in the euthyroid
range or in individuals with prediabetes. Even though
there are many cross-sectional reports studying the preva-
lence of diabetes and thyroid dysfunction, only few have
investigated the association of thyroid function with the
occurrence of diabetes and all were register-based studies.
Our results are in contrast to a Danish nationwide registry
study by Brandt et al. [17] that reported an increased risk
of diabetes in hyperthyroid individuals, whereas we did
not find an increased risk of diabetes with higher thyroid
function. However, there are several factors that could ex-
plain these differences, including variance in the mean age
and possible iodine status of the studied population. Most
importantly, the study by Brandt et al. [17] did not include
laboratory measurements of thyroid function and there-
fore misclassification of the diagnosis of hyperthyroidism
could have occurred. Further, they did not provide es-
timates in the euthyroid range of thyroid function.
Two other register-based studies report an increased
risk of diabetes in hypothyroid individuals [18, 19] and
our results are largely in line as we find an increased
risk of diabetes in lower thyroid function.
There are several pathways that may explain the ob-
served relation between low and low-normal thyroid
function and the risk of diabetes. Overt and subclinical
hypothyroidism are associated with a decreased insulin
sensitivity and glucose tolerance, partially due to a de-
creased ability of insulin to increase glucose utilization
mainly in muscle [14, 25]. Other mechanisms, such as
downregulation of plasma membrane glucose transporters
and direct effects on insulin degradation, have also been de-
scribed [26–28]. Treatment of hypothyroidism has been
shown to restore insulin sensitivity and the secretion of glu-
coregulatory hormones [15]. Furthermore, hypothyroidism
is associated with several components of the metabolic syn-
drome and could therefore indirectly relate to the increased
risk of diabetes [29]. However, in our analyses, adjusting for
several cardiovascular risk factors and components of the
metabolic syndrome did not shift risk estimates towards
the null. Additionally, excluding participants using thyroid
hormone replacement therapy at baseline only slightly
Fig. 3 The 7-year absolute risk of progression from prediabetes to type 2 diabetes is plotted against thyroid-stimulating hormone (TSH) and free
thyroxine (FT4) values within the normal range. These analyses are adjusted for sex, age, smoking, cohort, fasting serum glucose levels, fasting
serum insulin measurements, systolic blood pressure, diastolic blood pressure, blood pressure lowering medication, high-density lipoprotein
cholesterol, and body mass index
Chaker et al. BMC Medicine (2016) 14:150 Page 6 of 8
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altered the results. Even though overt hyperthyroidism is
also associated with insulin resistance, our data show that
high and high-normal thyroid function are protective
against the development of or progression to diabetes. It
could be that insulin resistance in hyperthyroid patients is
counterbalanced by other mechanisms associated with pro-
longed thyroid hormone excess, such as improved beta-cell
function and increased insulin secretion [6]. However, the
exact pathophysiological mechanisms through which
thyroid function could affect diabetes risk in the general
population remain to be determined.
The clinical importance of these findings could be sev-
eral. First of all, the association of thyroid function with
development from prediabetes to diabetes is prominent.
Thus, individuals with a low-normal thyroid function,
which includes a large proportion of the population, are
at an even higher risk of progression from prediabetes to
diabetes. Secondly, with ageing and increasingly obese
populations, there is need for better screening and pre-
vention options for diabetes [30]. One could hypothesize
that, in individuals with prediabetes with low or low-
normal thyroid function (i.e., high TSH and low FT4),
lifestyle interventions or diabetes treatment could be
prompted in an earlier phase than those with normal or
high thyroid function. Alternatively, having prediabetes
could be an argument to start treatment of subclinical
hypothyroidism to aim for prevention of overt diabetes.
Current guidelines do not recommend or specifically ad-
dress screening of thyroid function or treatment of thyroid
dysfunction in individuals with type 2 diabetes [31, 32].
The relative risk increase of developing diabetes with
thyroid function differences is modest. However, due to
the high population risk of diabetes, the implications on
the absolute risk are large. Despite this high occurrence
of both conditions in the general population, the relation
between thyroid dysfunction and diabetes had remained
largely unexplored. Further research is needed to deter-
mine to what extend the association could be driven by
thyroid hormone-related acceleration of development of
diabetes or perhaps by other mechanisms such as a
common genetic predisposition. If our results are con-
firmed, subsequent studies could focus on screening and
prevention strategies as well as questions concerning
treatment of subclinical hypothyroidism in patients at
risk for diabetes.
Strengths of our study include the large number of in-
dividuals, the variety of available confounders adjusted
for, and the long follow-up. Furthermore, we were able
to investigate both diabetes risk as well as progression
from prediabetes to diabetes. Limitations of our study
should also be acknowledged. Residual confounding cannot
be excluded in an observational study, even with the large
number of potential confounders adjusted for in our ana-
lyses. Furthermore, the Rotterdam Study is predominantly
composed of white participants aged 45 years and older
and results may therefore not be generalizable to other
populations.
Conclusions
In conclusion, our results suggest that low and low-normal
thyroid function are related to an increased risk of diabetes.
In individuals with prediabetes and low and low-normal
thyroid function, the risk of progression to diabetes seems
more prominent. Our data provide new insights into the
magnitude of the risk of diabetes and prediabetes associated
with variations of thyroid function within the normal range.
More research is needed to confirm these current findings
in various populations. Subsequent studies could address
possible screening and treatment modalities for both
diabetes and thyroid dysfunction.
Additional files
Additional file 1: Table S1. Sensitivity analyses for association between
thyroid function and risk of diabetes. (DOCX 19 kb)
Additional file 2: Table S2. Association between thyroid function in
normal range and the risk of incident diabetes in individuals with
prediabetes. (DOCX 19 kb)
Abbreviations
CI: Confidence interval; FT4: Free thyroxine; HR: Hazard ratio; RS: Rotterdam
Study; TPOAb: Thyroid peroxidase antibodies; TSH: Thyroid-stimulating
hormone
Acknowledgments
We are grateful to the study participants, the staff from the Rotterdam Study,
and participating general practitioners and pharmacists. We would also like
to thank Mr. Wichor M. Bramer from the medical library (Medical Library,
Erasmus Medical Center, Rotterdam) for the important contribution to the
literature search.
The Rotterdam Study is supported by the Erasmus MC and Erasmus
University Rotterdam; the Netherlands Organization for Scientific Research
(NWO); the Netherlands Organization for Health Research and Development
(ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the
Netherlands Genomics Initiative (NGI); the Ministry of Education, Culture and
Science; the Ministry of Health Welfare and Sports; the European Commission
(DG XII); and the Municipality of Rotterdam. The funding sources had no
involvement in the collection, analysis, writing, interpretation, nor in the
decision to submit the paper for publication.
Prof. Dr. R. P. Peeters and L. Chaker are supported by a Zon-MWTOP grant
(nr 91212044) and an Erasmus MCMRACE grant. Dr. A. Dehghan is supported
by NWO grant (veni, 916.12.154) and the EUR Fellowship.
Funding
There was no funding obtained for this specific manuscript.
Authors’contributions
LC contributed to study design, collecting data, data analyses and writing of
the report. SL was involved in data analysis and writing of the report. TIMK
took part in the study design and writing of the report. AH was the principal
investigator and contributed to study design, data collection, and writing of
the report. OHF was the local principal investigator and participated in the
design and implementation of the study and the writing of the report. RPP
and AD were responsible for the overall supervision and contributed to data
analyses and writing of the report, and contributed equally to this work. All
authors had access to the data, commented on the report drafts, and
approved the final submitted version.
Chaker et al. BMC Medicine (2016) 14:150 Page 7 of 8
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Competing interests
Prof. O. H. Franco works at ErasmusAGE, a center for aging research across
the life course funded by Nestle Nutrition (Nestec Ltd.), Metagenics Inc., and
AXA. Nestle Nutrition (Nestec Ltd.), Metagenics Inc., and AXA had no role in
design and conduct of the study, collection, management, analysis, and
interpretation of the data, or in the preparation, review or approval of the
manuscript. The authors declare that they have no competing interests.
Author details
1
Rotterdam Thyroid Center, Erasmus University Medical Center, Rotterdam,
The Netherlands.
2
Department of Internal Medicine, Erasmus University
Medical Center, Rotterdam, The Netherlands.
3
Department of Epidemiology,
Erasmus University Medical Center, Room NA-2828, 3000CA Rotterdam, The
Netherlands.
4
Department of Epidemiology, Harvard T.H. Chan School of
Public Health, Boston, MA, USA.
Received: 15 March 2016 Accepted: 13 September 2016
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