Smoking, use of smokeless tobacco, HLA genotypes and incidence of latent autoimmune diabetes in adults

Abstract

Aims/hypotheses
Smoking and use of smokeless tobacco (snus) are associated with an increased risk of type 2 diabetes. We investigated whether smoking and snus use increase the risk of latent autoimmune diabetes in adults (LADA) and elucidated potential interaction with HLA high-risk genotypes.

Methods
Analyses were based on Swedish case–control data (collected 2010–2019) with incident cases of LADA (n=593) and type 2 diabetes (n=2038), and 3036 controls, and Norwegian prospective data (collected 1984–2019) with incident cases of LADA (n=245) and type 2 diabetes (n=3726) during 1,696,503 person-years of follow-up. Pooled RRs with 95% CIs were estimated for smoking, and ORs for snus use (case–control data only). The interaction was assessed by attributable proportion (AP) due to interaction. A two-sample Mendelian randomisation (MR) study on smoking and LADA/type 2 diabetes was conducted based on summary statistics from genome-wide association studies.

Results
Smoking (RRpooled 1.30 [95% CI 1.06, 1.59] for current vs never) and snus use (OR 1.97 [95% CI 1.20, 3.24] for ≥15 box-years vs never use) were associated with an increased risk of LADA. Corresponding estimates for type 2 diabetes were 1.38 (95% CI 1.28, 1.49) and 1.92 (95% CI 1.27, 2.90), respectively. There was interaction between smoking and HLA high-risk genotypes (AP 0.27 [95% CI 0.01, 0.53]) in relation to LADA. The positive association between smoking and LADA/type 2 diabetes was confirmed by the MR study.

Conclusions/interpretation
Our findings suggest that tobacco use increases the risk of LADA and that smoking acts synergistically with genetic susceptibility in the promotion of LADA.

Data availability
Analysis codes are shared through GitHub (https://github.com/jeseds/Smoking-use-of-smokeless-tobacco-HLA-genotypes-and-incidence-of-LADA).

Graphical abstract

Full text

/Published online: 28 July 2022
Diabetologia (2023) 66:70–81
ARTICLE
Smoking, use of smokeless tobacco, HLA genotypes and incidence
of latent autoimmune diabetes in adults
Jessica Edstorp
1
&Yuxia Wei
1
&Emma Ahlqvist
2
&Lars Alfredsson
1,3
&Valdemar Grill
4
&Leif Groop
2,5
&
Bahareh Rasouli
1,6
&Elin P. Sørgjerd
7,8
&Per M. Thorsby
9,10
&Tiinamaija Tuomi
2,5,11,12,13
&
Bjørn O. Åsvold
7,8,14
&Sofia Carlsson
1
Received: 23 February 2022 /Accepted: 1 June 2022
#The Author(s) 2022
Abstract
Aims/hypotheses Smoking and use of smokeless tobacco (snus) are associated with an increased risk of type 2 diabetes. We
investigated whether smoking and snus use increase the risk of latent autoimmune diabetes in adults (LADA) and elucidated
potential interaction with HLA high-risk genotypes.
Methods Analyses were based on Swedish case–control data (collected 2010–2019) with incident cases of LADA (n=593) and
type 2 diabetes (n=2038), and 3036 controls, and Norwegian prospective data (collected 1984–2019) with incident cases of
LADA (n=245) and type 2 diabetes (n=3726) during 1,696,503 person-years of follow-up. Pooled RRs with 95% CIs were
estimated for smoking, and ORs for snus use (case–control data only). The interaction was assessed by attributable proportion
(AP) due to interaction. A two-sample Mendelian randomisation (MR) study on smoking and LADA/type 2 diabetes was
conducted based on summary statistics from genome-wide association studies.
Results Smoking (RR
pooled
1.30 [95% CI 1.06, 1.59] for current vs never) and snus use (OR 1.97 [95% CI 1.20, 3.24] for ≥15
box-years vs never use) were associated with an increased risk of LADA. Corresponding estimates for type 2 diabetes were 1.38
(95% CI 1.28, 1.49) and 1.92 (95% CI 1.27, 2.90), respectively. There was interaction between smoking and HLA high-risk
genotypes (AP 0.27 [95% CI 0.01, 0.53]) in relation to LADA. The positive association between smoking and LADA/type 2
diabetes was confirmed by the MR study.
Conclusions/interpretation Our findings suggest that tobacco use increases the risk of LADA and that smoking acts synergis-
tically with genetic susceptibility in the promotion of LADA.
Data availability Analysis codes are shared through GitHub (https://github.com/jeseds/Smoking-use-of-smokeless-tobacco-
HLA-genotypes-and-incidence-of-LADA).
*Jessica Edstorp
jessica.edstorp@ki.se
1
Institute of Environmental Medicine, Karolinska Institutet,
Stockholm, Sweden
2
Department of Clinical Sciences in Malmö, Clinical Research Centre,
Lund University, Malmö, Sweden
3
Center for Occupational and Environmental Medicine, Region
Stockholm, Stockholm, Sweden
4
Department of Clinical and Molecular Medicine, Norwegian
University of Science and Technology, Trondheim, Norway
5
Institute for Molecular Medicine Finland, Helsinki University,
Helsinki, Finland
6
Department of Global Health and Population, Harvard TH Chan
School of Public Health, Boston, MA, USA
7
HUNT Research Centre, Department of Public Health and Nursing,
NTNU, Norwegian University of Science and Technology,
Trondheim, Norway
8
Department of Endocrinology, Clinic of Medicine, St Olavs Hospital,
Trondheim, Norway
9
Hormone Laboratory, Department of Medical Biochemistry, Oslo
University Hospital, Aker, Oslo, Norway
10
Biochemical Endocrinology and Metabolism Research Group, Oslo
University Hospital, Aker, Oslo, Norway
11
Division of Endocrinology, Abdominal Center, Helsinki University
Hospital, Helsinki, Finland
12
Research Program for Diabetes and Obesity, University of Helsinki,
Helsinki, Finland
13
Folkhälsan Research Center, Helsinki, Finland
14
K.G. Jebsen Center for Genetic Epidemiology, Department of Public
Health and Nursing, NTNU, Norwegian University of Science and
Technology, Trondheim, Norway
https://doi.org/10.1007/s00125-022-05763-w
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Diabetologia (2023) 66:70–81
Keywords Gene–environment interaction .LADA .Latent autoimmune diabetes in adults .Mendelian randomisation analysis .
Smoking .Tobacco use
Abbreviations
ANDIS All New Diabetics in Scania
AP Attributable proportion due to interaction
EIRA Epidemiological Investigation of
Rheumatoid Arthritis
ESTRID Epidemiological Study of Risk Factors
for LADA and Type 2 Diabetes
GADA GAD antibody
GWAS Genome-wide association study
HUNT The Trøndelag Health Study
IV Instrumental variable
IVW Inverse-variance weighted
LADA Latent autoimmune diabetes in adults
MR Mendelian randomisation
MR-PRESSO MR pleiotropy residual sum and outlier
approach
Introduction
Smoking is associated with an increased risk of type 2 diabe-
tes [1] and a Mendelian randomisation (MR) study supports a
causal relationship [2]. The association is primarily attributed
to negative effects on insulin sensitivity [3]. In contrast, prena-
tal exposure to smoking is linked to a reduced risk of type 1
diabetes [4], possibly due to an immunosuppressive effect of
nicotine [5].
Latent autoimmune diabetes in adults (LADA) resembles
both type 1 and type 2 diabetes. It is characterised by an
autoimmune reaction that is milder than in type 1 diabetes
and progression towards insulin dependence is slower.
Genetic susceptibility is mainly manifested through the type
1 diabetes-associated HLA gene complex [6]. In addition,
individuals with LADA share phenotypic characteristics with
individuals with type 2 diabetes, including adult onset and
insulin resistance [7].
Studies on smoking and the risk of LADA are few and
conflicting. We observed a reduced risk in the Trøndelag
Health Study (HUNT) in Norway [8] and an increased risk
using Swedish case–control data [9]. Since the pathogenesis
of LADA includes both autoimmune insulitis and insulin
resistance, associations with smoking such as those seen in
both type 1 and type 2 diabetes seem biologically plausible.
The net effect may depend on genetic susceptibility to LADA
so that certain effects of smoking are augmented in those
carrying high-risk genotypes. Notably, interaction between
HLA genotypes and smoking has been observed in the aetiol-
ogy of autoimmune rheumatoid arthritis and multiple sclerosis
[10,11], although potential interaction remains to be explored
in LADA.
Studies of other types of tobacco in relation to autoimmune
diabetes are scarce. Commonly used in Scandinavia, Swedish
smokeless tobacco (snus) is high in nicotine but contains
fewer of the other harmful components found in cigarettes
[12]. Snus use has been linked to an increased risk of type 2
71
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Diabetologia (2023) 66:70–81
diabetes [13]. The only study in LADA found no association
but was hampered by small numbers [14].
Our aim was to assess the hypothesis that tobacco use
increases the risk of LADA and to investigate, for the first
time, whether tobacco use interacts with HLA high-risk geno-
types in the promotion of LADA. For these purposes, we
expand on our previous observational studies by using
updated data, including newly recruited cases from two
Scandinavian population-based studies with incident cases
of LADA. We also performed a two-sample MR study to
investigate whether the link between smoking and LADA
was causal.
Methods
The ESTRID study
Study population Epidemiological Study of Risk Factors for
LADA and Type 2 Diabetes (ESTRID) is a Swedish
population-based case–control study nested within the All
New Diabetics in Scania (ANDIS) registry and biobank
[15], aimed at characterising all incident diabetes cases in
Scania county with regard to clinical and genetic features.
Since 2010, ESTRID has enrolled incident cases of LADA
and type 2 diabetes from ANDIS. Lifestyle and demographic
information is collected by questionnaire, administered close
to diagnosis (median 4.9 months). Matched controls are
randomly selected from the general population of Scania
through incidence-density sampling. These controls respond
to the questionnaire but do not provide blood samples for
genetic analyses. Therefore, the present study includes popu-
lation-based, diabetes-free controls from the Epidemiological
Investigation of Rheumatoid Arthritis (EIRA) study [16], a
case–control study with a similar set-up to that of ESTRID.
Rheumatoid arthritis is more common among women than
men, as reflected by a larger proportion of women in the
control group of this study. For this reason, the ‘genetic’
controls are matched to cases by sex and age.
The present study is based on all cases collected in
ESTRID 2010–2019 with information on tobacco use
(LADA n=593), type 2 diabetes (n=2038) and all controls
aged ≥35 years included in EIRA 2006–2014 (n=3036). All
participants gave informed consent, and the study was
approved by the ethical review board in Stockholm.
Diabetes classification and laboratory analyses Diabetes cases
were diagnosed within the healthcare system of Scania.
Fasting glucose and C-peptide were analysed in blood
samples drawn at diagnosis [15]. An ELISA (RSR, Cardiff,
UK) measured GAD antibodies (GADA) in those diagnosed
with diabetes. Sensitivity and specificity were 0.84 and 0.98
[17], respectively, and values above 250 U/ml were censored.
Individuals with LADA were aged ≥35 years at diagnosis,
GADA positive (≥10 U/ml) and had C-peptide levels ≥0.2
nmol/l (IMMULITE 2000; Siemens Healthcare Diagnostics
Product, Llanberis, UK) or ≥0.3 nmol/l (Cobas e601; Roche
Diagnostics, Mannheim, Germany). The C-peptide criterion
separated LADA from type 1 diabetes. Type 2 diabetes was
defined as age ≥35 years, GADA negativity and C-peptide
>0.60 nmol/l (IMMULITE) or >0.72 nmol/l (Cobas).
HOMA-IR and HOMA-B were based on the relationship
between fasting plasma glucose and serum C-peptide [18].
Genetic information DNA samples from the ANDIS biobank
were analysed using iPLEX (Sequenom, San Diego, CA,
USA) or TaqMan assays (Thermo Fisher Scientific,
Carlsbad, CA, USA) [15]. Samples from the EIRA biobank
were analysed with the Illumina Global Screening array or an
Infinium Illumina 300K immunochip custom array (Illumina,
San Diego, CA, USA). Imputation was performed using
Minimac4 (https://genome.sph.umich.edu/wiki/Minimac4)
based on a Genome Reference Consortium assembly
(GRCh37/hg19).
Three SNPs tagging HLA genotypes (rs3104413,
rs2854275, rs9273363) predicted HLA-DRB1 (DR3/DR4)
and HLA-DQB1 (DQ2/DQ8) with an overall accuracy of
99.3% [19]. High genetic risk was defined as HLA genotypes
DR3/3,DR3/4,DR4/4,orhaplotypesofDR4-DQ8 or DR3-
DQ2;DR3/X,DR4/X,DRX/X (where Xis neither 3 nor 4) and
DR4-DQ7 were classified as low/intermediate risk genotypes.
The HUNT study
Study population In Trøndelag County, Norway, the entire
population aged ≥20 years have been invited to participate
in the HUNT study [20] on four occasions between 1984
and 2019 (HUNT1 1984–1986, HUNT2 1995–1997,
HUNT3 2006–2008, HUNT4 2017–2019). The study
includes questionnaires, clinical examination and blood
sampling. Eligible for analyses were all participants in
HUNT1–3 with at least one follow-up (n=94,489). After
exclusion of those with diabetes at baseline (n=2481) or with-
out smoking information (n=13,466), the analytical sample
consisted of 78,542 individuals. The study was approved by
the Norwegian Data Protection Authority and the Regional
Committee for Medical and Health Research Ethics and all
participants provided informed consent.
Diabetes classification and laboratory analyses Incident
diabetes was identified through self-report, which according
to a previous study has high validity (95% of self-reports are
confirmed by medical records) [21]. Classification was based
on age at diagnosis and GADA assessment at follow-up
(median 5 years after diagnosis). GADA was analysed at
Hormone laboratory, Oslo University Hospital, by
72
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Diabetologia (2023) 66:70–81
immunoprecipitation radioligand assay (Novo Nordisk,
Bagsværd, Denmark) in samples collected during HUNT2
and HUNT3. The sensitivity and specificity of the assay were
0.64 and 1.00, respectively (Islet Autoantibody
Standardization Program 2003). In HUNT4, GADA was
measured using ELISA (RSR) with a sensitivity and specific-
ity of 0.84 and 0.98, respectively (Islet Autoantibody
Standardization Program 2020 report), measuring range 5–
2000 U/ml, CV 9% at 9.6 U/ml and limit of quantification 5
U/ml. The GADA methods are accredited according to ISO
17025.
All individuals aged ≥35 years with GADA positivity
(≥0.08 U/ml in HUNT2 and HUNT3; ≥10 U/ml in HUNT4
[to harmonise with ESTRID cases]) were classified as having
LADA (n=245) and as having type 2 diabetes if they were
GADA negative (n=3726). We could not separate LADA
from adult-onset type 1 diabetes since C-peptide was not
measured at diagnosis and information on treatment was not
available for everyone.
Genetic information The blood samples were genotyped for
HLA-DRB1-andHLA-DQB1-associated SNPs at the
Norwegian University of Science and Technology Genomic
Core Facility, Trondheim, by HumanCoreExome, Illumina
(San Diego, CA, USA). Imputation was performed using
Minimac3 (v2.0.1, https://genome.sph.umich.edu/wiki/
Minimac3) and a customised Haplotype Reference
Consortium release 1.1 (HRC v1.1). Two SNPs were
available to infer high-risk DR3-DQ2 (rs2854275) and DR4-
DQ8 haplotypes (rs9273363). All other were classified as low/
intermediate risk genotypes.
Tobacco use and covariates
The ESTRID/EIRA and HUNT questionnaires contained
detailed questions regarding lifetime exposure to smoking,
including intensity and duration. The ESTRID/EIRA ques-
tionnaires contained corresponding questions on snus use. In
HUNT, baseline information on snus was only available in
HUNT3 and the number of exposures was too low for viable
analyses (n=5).
Smoking/snus use intensity in current users was
categorised as light/moderate (<20 cigarettes per day or <7
boxes of snus per week) or heavy (≥20 cigarettes per day or
≥7 boxes of snus per week). Smoking intensity was also
assessed continuously in current smokers. Cumulative use
was assessed in ever smokers/snus users, where one
pack/box-year equals smoking one pack of cigarettes per
day or using seven boxes of snus per week for a year. Index
date was set to 1 year prior to diagnosis/participation for
ESTRID cases and controls.
In ESTRID, information on alcohol consumption was
based on beverage-specific questions regarding the amount
and frequency consumed during the preceding year, whereas
in HUNT frequency of alcohol consumption related to the past
weeks or months. Consumption was assessed categorically,
from abstainers to high consumers. Educational level was
categorised into low (primary school), medium (upper
secondary school) or high (university). BMI (weight [kg]/
height [m
2
]) was based on anthropometric measurements in
HUNT and self-reported weight and height in ESTRID. The
questionnaires also contained information on physical activity
and family history of diabetes.
Two-sample MR study
A typical MR analysis uses uncorrelated genetic variants as
instrumental variables (IVs) for the exposure [22]. Our two-
sample MR study was conducted based on summary statistics
from a genome-wide association study (GWAS) of 2634
LADA cases and 5947 controls [23], as well as a GWAS of
26,676 type 2 diabetes cases and 132,532 controls [24]. The
IVs included 250 sentinel SNPs (independent SNPs) associ-
ated with smoking initiation (electronic supplementary mate-
rial [ESM] Table 1) in a GWAS of up to 1.2 million European
individuals [25]. Further details on the GWAS and MR
methods can be found in the ESM Methods: Two-sample
MR study; GWAS of LADA; GWAS of type 2 diabetes;
and Genetic instruments for smoking.
Statistical analysis
Differences in baseline characteristics were evaluated using
two-sided pvalues, calculated by Student’sttest for means
(±SD) of normally distributed variables (normality was
assessed by visual inspection of distribution plots), Kruskal–
Wallis test for medians (IQR) of non-normally distributed
variables and χ
2
test for proportions.
Conditional logistic regression estimated ORs with 95%
CIs of LADA/type 2 diabetes in relation to smoking, snus
use and total tobacco use in case–control data.
Corresponding HRs were derived by Cox regression in
HUNT, where study participants were followed from age at
baseline until age at end of follow-up (HUNT2, 3 or 4), diabe-
tes diagnosis, emigration or death. Models were adjusted for
age and sex (matching variables in the logistic regression; age
as underlying time scale in the Cox regression) (Model 1)
together with BMI, educational level and alcohol consump-
tion (Model 2). Exposures and covariates were updated at
each new follow-up in HUNT, if possible. Furthermore, snus
analyses were adjusted for smoking (never/former/current).
Additional adjustment for physical activity and family history
of diabetes did not change the effect estimates (<10% change
in HR/OR) and were not retained in the final models. Pooled
relative risks for smoking (RR
pooled
) were estimated through
the inverse-variance weighted (IVW) method [26].
73
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Diabetologia (2023) 66:70–81
Interaction was defined as departure from additivity of
effects and estimated as attributable proportion (AP) due to
interaction with 95% CI. AP captures how much of the disease
in the doubly exposed that can be attributed to the interaction
and was calculated by the formula: ([RR
11
–RR
10
–RR
01
+1]
/RR
11
)[27], where RR
11
is risk in doubly exposed, RR
10
is
risk in non-tobacco users with low/intermediate risk geno-
types, and RR
01
is risk in non-tobacco users with high-risk
genotypes. The reference group (RR
00
) comprised non-
tobacco users with low/intermediate risk genotypes.
To address underlying mechanisms linking tobacco use to
diabetes risk, we used multivariable linear regression to esti-
mate differences in log
e
transformed HOMA-IR and HOMA-
B in relation to tobacco use in LADA/type 2 diabetes.
Corresponding analyses of GADA were modelled using
Tobit regression, to account for censoring of GADA. The
analyses were based on case–control data, where HOMA
and GADA were assessed at time of diagnosis.
Sensitivity analyses were performed to assess the validity
of the genetic controls by re-running the main analyses with
the incidence-density sampled controls. We assessed the asso-
ciation between smoking and LADA/type 2 diabetes separate-
ly in ESTRID and HUNT, and separately for each HUNT
baseline (HUNT1, 2 or 3). Smoking and interaction with
HLA genotypes was also assessed separately in ESTRID
and HUNT.
We used the IVW method [28] to assess the potential caus-
al link between smoking initiation and LADA/type 2 diabetes
in the MR study, supplemented by other MR estimators and
three conservative analyses excluding some SNPs (see ESM
Methods: Data harmonisation and statistical analysis).
The observational analyses were performed in SAS 9.4
(SAS Institute, Cary, NC, USA) and the MR analyses were
performed using the MendelianRandomization and MR-
PRESSO (MR pleiotropy residual sum and outlier approach)
package in R 4.0.4 [29]. All statistical tests were two-sided,
with p<0.05 indicating statistical significance. Analysis codes
are shared through GitHub (https://github.com/jeseds/
Smoking-use-of-smokeless-tobacco-HLA-genotypes-and-
incidence-of-LADA).
Results
There were 838 individuals with LADA, 5764 with type 2
diabetes, 3036 controls (ESTRID) and 1,696,503 person-
years of follow-up (HUNT). Compared with individuals with
type 2 diabetes, those with LADA were less insulin resistant,
had worse beta cell function as assessed by HOMA and lower
levels of C-peptide, and were more likely to be treated with
insulin (Table 1). A higher prevalence of high-risk HLA geno-
types was seen in LADA than in type 2 diabetes and these
Table 1 Characteristics of the study participants
Characteristic ESTRID HUNT
Controls LADA Type 2 diabetes pvalue No diabetes LADA Type 2 diabetes pvalue
Individuals, n3036 593 2038 −74,326 245 3726 −
Men, % 27.8 53.0 60.0 0.002 47.9 47.8 53.2 0.10
Age at diagnosis, years
a
56.1±10.3 59.1±12.3 63.2±10.4 <0.001 −59.1±11.5 60.3±11.0 0.10
Age at baseline, years −−−−56.2±17.3 52.9±11.7 54.7±11.5 0.01
BMI, kg/m
2
25.4±4.1 28.5±5.6 31.2±5.4 <0.001 26.5±4.2 29.5±4.9 30.1±4.6 0.04
Using insulin, %
b
−39.9 5.9 <0.001 −41.0 13.3 <0.001
HLA high-risk, %
c
33.6 60.5 31.3 <0.001 29.0 48.9 27.3 <0.001
C-peptide, nmol/l −0.72 (0.45, 1.20) 1.20 (0.97, 1.60) <0.001 −0.59 (0.22, 0.99) 0.90 (0.63, 1.23) <0.001
HOMA-IR −2.79 (1.82, 4.44) 3.56 (2.73, 4.77) <0.001 −2.0 (1.1, 2.7) 2.1 (1.5, 3.0) 0.08
HOMA-B −40.6 (15.0, 69.5) 71.1 (43.9, 95.9) <0.001 −56.2 (36.5, 79.2) 58.3 (38.4, 84.0) 0.39
Data are shown as mean±SD or median (IQR) unless stated otherwise
Clinical information (C-peptide) was available for 98% of the participants in ESTRID (LADA n=583, type 2 diabetes n=1990) and HOMA was available
for 85% (LADA n=476, type 2 diabetes n=1752). C-peptide and HOMA measurements not from time of diagnosis were available for participants in
HUNT1–3 (C-peptide, LADA n=122, type 2 diabetes n=1434 [39%]; HOMA, LADA n=80, type 2 diabetes n=1005 [27%]). Genetic information was
available for 64% of the participants in ESTRID (LADA n=402, type 2 diabetes n=1289) and 92% of the participants in HUNT (LADA n=219, type 2
diabetes n=3421)
The pvalue is shown for LADA vs type 2 diabetes
a
Age at participation for controls
b
Current use of insulin
c
High-risk genotypes were defined as carriers of DR3/3,DR3/4,DR4/4,orhaplotypesofDR4-DQ8 or DR3-DQ2 (ESTRID) or carriers of at least one of
the risk variants inferring either DR3-DQ2 or DR4-DQ8 (HUNT)
74
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Diabetologia (2023) 66:70–81
genotypes conferred an RR
pooled
(95% CI) of 2.62 (2.16, 3.18)
for LADA and 0.93 (0.87, 1.00) for type 2 diabetes. All esti-
mates hereafter refer to the fully adjusted model (Model 2).
Tobacco use and LADA
The risk of LADA was increased in current smokers (RR
pooled
1.30 [95% CI 1.06, 1.59]) and even more so in current, heavy
smokers (RR
pooled
1.54 [95% CI 1.1, 2.14] for ≥20 cigarettes
per day) but not in former smokers (Table 2). For snus, OR
was estimated at 1.29 (95% CI 0.93, 1.80) in current users and
1.16 (95% CI 0.75, 1.79) in former users (Table 3). An almost
doubled OR of 1.97 (95% CI 1.20, 3.24) was seen for ≥15
box-years vs never use, but no increased risk was seen in those
with <15 box-years (Table 3). The association between
LADA and ever smoking/snus use was weak (Tables 2,3).
The combination of current smoking and snus use vs never
use was associated with a 2.46-fold (95% CI 1.50, 4.03)
increase in the risk of LADA (Table 3).
Tobacco use and type 2 diabetes
Current and former vs never smoking showed RR
pooled
1.38 (95% CI 1.28, 1.49) and RR
pooled
1.07 (95% CI
1.00, 1.14), respectively, in type 2 diabetes, and the risk
increased with number of pack-years (Table 2). Type 2
diabeteswasalsoassociatedwithcurrentandever,but
not former, snus use (Table 3). The combination of
current smoking and snus use was associated with a
1.98-fold (95% CI 1.30, 3.04) increase in the risk of type
2 diabetes (Table 3).
Tobacco use, HLA high-risk genotypes and LADA
There was additive interaction between current smoking and
high-risk HLA genotypes with RR
pooled
in those doubly
exposed estimated at 3.60 (95% CI 2.59, 5.00), and an AP
due to interaction of 0.27 (95% CI 0.01, 0.53) (Table 4).
Under the assumption of causality, this implies that 27% of
the doubly exposed cases are attributable to interaction
Table 2 Pooled relative risks with 95% CIs for LADA and type 2 diabetes in relation to smoking
Smoking habit LADA Type 2 diabetes
Cases
(n)
Controls
(n)
Person-
years
Model 1
a
RR (95% CI)
Model 2
b
RR (95% CI)
Cases
(n)
Controls
(n)
Person-
years
Model 1
a
RR (95% CI)
Model 2
b
RR (95% CI)
Smoking
Never 378 1467 729,291 1 1 2281 1467 729,291 1 1
Former 269 964 465,674 1.07 (0.89,
1.27)
1.08 (0.90,
1.29)
2084 964 465,674 1.12 (1.05,
1.20)
1.07 (1.00,
1.14)
Current 191 605 501,537 1.14 (0.94,
1.38)
1.30 (1.06,
1.59)
1399 605 501,537 1.11 (1.03,
1.20)
1.38 (1.28,
1.49)
Ever 460 1569 967,211 1.09 (0.94,
1.27)
1.16 (0.99,
1.36)
3483 1569 967,211 1.12 (1.05,
1.18)
1.20 (1.13,
1.28)
Intensity (current)
No current
smoking
647 2431 1,194,965 1 1 4365 2431 1,194,965 1 1
<20 cigarettes/day 127 497 358,396 1.00 (0.81,
1.25)
1.16 (0.93,
1.46)
920 497 358,396 1.03 (0.95,
1.12)
1.35 (1.24,
1.48)
≥20 cigarettes/day 55 98 112,955 1.54 (1.12,
2.11)
1.54 (1.11,
2.14)
425 98 112,955 1.35 (1.20,
1.50)
1.47 (1.31,
1.65)
Per 5 cigarettes 838 3036 1,696,503 1.06 (1.00,
1.14)
1.08 (1.01,
1.15)
5764 3036 1,696,503 1.03 (1.00,
1.06)
1.08 (1.05,
1.10)
Pack-years (ever)
Never 378 1467 729,291 1 1 2281 1467 729,291 1 1
<15 pack-years 229 932 527,176 1.00 (0.84,
1.20)
1.11 (0.92,
1.33)
1463 932 527,176 0.99 (0.92,
1.07)
1.12 (1.03,
1.20)
≥15 pack-years 210 637 276,814 1.26 (1.03,
1.53)
1.28 (1.04,
1.57)
1660 637 276,814 1.39 (1.29,
1.50)
1.46 (1.34,
1.58)
Per 5 pack-years 838 3036 1,696,503 1.03 (1.00,
1.07)
1.03 (1.00,
1.06)
5764 3036 1,696,503 1.06 (1.05,
1.08)
1.05 (1.04,
1.07)
a
Model 1 adjusted for age and sex
b
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption
75
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Diabetologia (2023) 66:70–81
between smoking and high-risk HLA genotypes. Similarly,
snus users with high genetic risk had an OR of 6.65 (95%
CI 3.31, 13.36), with a non-significant AP estimated at 0.42
(95% CI −0.01, 0.85) (ESM Table 2). Results from the corre-
sponding analyses for tobacco use and HLA are presented in
ESM Table 3.
Tobacco use and HOMA-IR, HOMA-B and GADA
Smoking was positively associated with HOMA-IR and
HOMA-B in both LADA and type 2 diabetes (ESM
Table 4). In LADA, every five pack-years were associ-
ated with a 2.8% higher HOMA-IR (β=0.0276,
p=0.0488). There was no significant association between
tobacco use and levels of GADA.
Sensitivity analyses
Study-specific analyses revealed a positive association
between smoking and LADA in the Swedish but not the
Norwegian study, while the association with type 2 diabetes
wasseeninbothdatasets(ESMTables5,6). The associations
remained when restricted to LADA with high (above median)
GADA levels (RR
pooled
1.45 (95% CI 1.12, 1.89) for current
smoking). Separate analyses by HUNT baseline revealed that
smoking was associated with a reduced risk of LADA in the
first wave (HUNT1) and an increased risk in later waves
(ESM Table 7). For the combination of smoking and HLA
high-risk genotypes, study-specific APs were compatible with
interaction but were not statistically significant (ESM
Tables 8,9). The association between smoking and
LADA/type 2 diabetes was similar when we used controls
Table 3 ORs with 95% CIs for the association between snus use and LADA and type 2 diabetes
Snus use LADA Type 2 diabetes
Cases (n)Controls(n) Model 1
a
OR (95% CI)
Model 2
b
OR (95% CI)
Cases (n)Controls(n) Model 1
a
OR (95% CI)
Model 2
b
OR (95% CI)
Snus use
Never 484 2684 1 1 1627 2684 1 1
Former 35 124 1.08 (0.71, 1.65) 1.16 (0.75, 1.79) 157 124 1.28 (0.97, 1.68) 1.20 (0.86, 1.67)
Current 74 228 1.19 (0.87, 1.64) 1.29 (0.93, 1.80) 254 228 1.42 (1.14, 1.77) 1.56 (1.20, 2.04)
Ever 109 352 1.15 (0.88, 1.51) 1.24 (0.94, 1.65) 411 352 1.37 (1.14, 1.64) 1.42 (1.14, 1.77)
Intensity (current)
None 519 2808 1 1 1784 2808 1 1
<7 boxes per week 59 208 1.02 (0.73, 1.43) 1.15 (0.81, 1.63) 211 208 1.23 (0.98, 1.55) 1.45 (1.10, 1.91)
≥7 boxes per week 10 17 2.15 (0.92, 4.99) 1.49 (0.59, 3.80) 33 17 2.60 (1.37, 4.92) 1.85 (0.88, 3.89)
Box-years (ever)
Never 484 2684 1 1 1627 2684 1 1
<15 box-years 73 293 0.98 (0.72, 1.32) 1.08 (0.78, 1.48) 292 293 1.21 (0.98, 1.48) 1.30 (1.02, 1.66)
≥15 box-years 36 59 1.94 (1.22, 3.10) 1.97 (1.20, 3.24) 119 59 2.08 (1.47, 2.94) 1.92 (1.27, 2.90)
Per 5 box-years 593 3036 1.11 (1.02, 1.22) 1.11 (1.01, 1.22) 2038 3036 1.15 (1.08, 1.23) 1.12 (1.04, 1.21)
Tobacco use
Never 246 1379 1 1 700 1379 1 1
Former 171 891 1.07 (0.85, 1.34) 1.11 (0.88, 1.42) 766 891 1.38 (1.18, 1.60) 1.52 (1.26, 1.83)
Current 176 766 1.24 (0.98, 1.56) 1.37 (1.08, 1.75) 572 766 1.40 (1.19, 1.65) 1.78 (1.46, 2.18)
Ever 347 1657 1.15 (0.94, 1.39) 1.23 (1.00, 1.50) 1332 1657 1.39 (1.22, 1.58) 1.63 (1.39, 1.92)
Smoking/snus use
None/none 417 2270 1 1 1466 2270 1 1
Current/none 102 538 1.20 (0.93, 1.54) 1.30 (1.00, 1.69) 318 538 1.12 (0.94, 1.33) 1.39 (1.13, 1.72)
None/current 40 161 0.87 (0.58, 1.29) 0.96 (0.63, 1.45) 166 161 1.18 (0.91, 1.53) 1.48 (1.08, 2.03)
Current/current 34 67 2.28 (1.42, 3.65) 2.46 (1.50, 4.03) 88 67 2.04 (1.42, 2.95) 1.98 (1.30, 3.04)
Data for snus use were from case−control (ESTRID) only
a
Model 1 adjusted for age and sex
b
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption; snus analyses additionally adjusted for smoking
76
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Diabetologia (2023) 66:70–81
collected within ESTRID instead of genetic controls (ESM
Table 10). Since snus use was more prevalent in men, we
restricted the analysis to men and found similar results
(ESM Table 11). Restricting the analysis to never-smokers
revealed similar associations primarily for type 2 diabetes
(ESM Table 12).
MR analyses of smoking and LADA/type 2 diabetes
Genetic predisposition to smoking initiation was associated
with a higher risk of LADA (OR 1.33 per unit [on the log
odds scale] increase in the risk of smoking [95% CI 1.02,
1.74]) and type 2 diabetes (OR 1.19 [95% CI 1.07, 1.32])
according to the IVW method (Fig. 1). We observed no strong
evidence of asymmetry in the funnel plots (ESM Figs 1,2).
The same direction of association was obtained using other
MR estimators (Fig. 1), and in the three conservative analyses
(ESM Table 13).
Discussion
Main findings
We observed an increased risk of LADA in smokers and snus
users, and confirmed that tobacco users are at increased risk of
type 2 diabetes [1]. The MR analyses provided genetic support
for a causal link between smoking initiation and LADA.
Furthermore, there was an indication of interaction between
HLA high-risk genotypes and smoking in relation to LADA,
suggesting that genetic susceptibility may accentuate the
adverse effects of smoking on the risk of LADA.
Main findings in relation to previous studies
Previous observations regarding smoking and the risk of
LADA are limited [8,9] and are based partly on the same
Scandinavian data as this study but with fewer cases and
shorter follow-up time (ESTRID data collected 2010–2014
instead of 2010–2019 and HUNT data for the period 1984–
2008 instead of 1984–2019). These results pointed to an
increased risk in smokers in the Swedish data but a decreased
risk in the Norwegian cohort. Small numbers may have
contributed to these contradictory results. Furthermore, both
protective effects of smoking on LADA (such as suppression
of autoimmunity [5]) and adverse effects (such as promotion
of insulin resistance [3]) seem possible, and the net effect may
vary by population characteristics, including genetic suscepti-
bility. In this study, the positive association between smoking
and LADA was primarily seen in the Swedish data, whereas
interaction with HLA genotypes, although not significant,
appeared similar in both datasets. The MR results supported
Table 4 Pooled RRs with 95% CIs for combinations of smoking and HLA genotypes in the risk of LADA, and pooled APs due to
interaction with 95% CI
Smoking HLA genotype Cases
(n)
Controls
(n)
Person-
years
Model 1
a
RR (95% CI)
Model 2
b
RR (95% CI)
AP (95% CI)
Ever smoking High-risk
−− 120 285 425,079 1 1
+−151 317 574,688 1.01 (0.77, 1.34) 1.07 (0.81, 1.42)
−+ 156 137 177,868 2.52 (1.88, 3.39) 2.52 (1.86, 3.41)
+ + 194 167 228,292 2.74 (2.09, 3.60) 3.10 (2.34, 4.10)
Pooled 0.19 (−0.06, 0.44)
Current smoking
−− 208 488 705,267 1 1
+−63 114 294,500 1.01 (0.73, 1.41) 1.15 (0.82, 1.60)
−+ 270 247 291,593 2.55 (2.04, 3.17) 2.59 (2.06, 3.24)
+ + 80 57 114,567 2.88 (2.10, 3.97) 3.60 (2.59, 5.00)
Pooled 0.27 (0.01, 0.53)
≥15 pack-years
−− 204 474 815,808 1 1
+−67 128 183,959 1.24 (0.89, 1.75) 1.19 (0.84, 1.69)
−+ 259 234 334,265 2.71 (2.16, 3.40) 2.73 (2.17, 3.43)
+ + 91 70 71,896 3.29 (2.38, 4.56) 3.57 (2.55, 4.99)
Pooled 0.19 (−0.10, 0.48)
a
Model 1 adjusted for age and sex
b
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption
77
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Diabetologia (2023) 66:70–81
the notion of smoking having primarily adverse effects on
LADA risk. With regard to type 2 diabetes, the results indi-
cated adverse effects of smoking, replicating a previous MR
observation [2]. Of note, twin studies have found that genetic
factors influence smoking behaviours and, furthermore, that
genes promoting nicotine dependence are distinct from those
promoting diabetes (30). We found a positive association
between smoking and HOMA-IR in LADA as well as in type
2 diabetes, whereas there was no significant association with
levels of GADA. This suggests that increasing insulin resis-
tance, and not direct effects on autoimmunity, may be the
main driver of the excess risk of LADA seen in tobacco users,
and that smoking serves as a promotor rather than a trigger in
the aetiology of LADA. Nicotine has previously been shown
to promote insulin resistance [3] and the similar risks seen in
smokers and snus users support nicotine as the main compo-
nent of tobacco products driving these associations. However,
our findings may be at odds with experimental studies show-
ing a protective effect of nicotine on autoreactivity and beta
cell survival [30,31].
We observed interaction between smoking and HLA high-
risk genotypes. Results were similar but not statistically
significant for snus use and HLA. These findings are in line
with previous studies in rheumatoid arthritis [10] and multiple
sclerosis [11]. A potential explanation for the observed
interaction in relation to LADA is that insulin resistance
speeds up the progression to manifest diabetes in individuals
with an intrinsically high rate of beta cell apoptosis [32], and
this process may be more pronounced in high-risk HLA
carriers due to abnormal immune responses to environmental
factors [7]. However, since this is the first study investigating
a potential interaction between tobacco use and HLA geno-
types in relation to autoimmune diabetes, confirmations are
clearly warranted.
Strengths and limitations
Strengths include the large number of incident LADA cases,
the use of two study populations, the population-baseddesign,
and detailed information on tobacco use, potential
confounders, clinical and genetic factors. In addition, we
performed an MR study designed to minimise confounding
bias and reverse causation, since the genetic instruments are
randomly assigned from parents and determined before the
occurrence of outcomes [33].
Self-reported information on tobacco use is a limitation
although it is correlated with blood cotinine levels [34]. The
prospective design of the HUNT study indicates that any
smoking misclassification is non-differential, which will lead
to diluted associations. Recall bias is a concern in the ESTRID
Fig. 1 MR analysis on associations of smoking with LADA and type 2
diabetes. MR-Egger (Egger regression of MR): I
2GX
=0.303, inter-
cept=0.002 and pfor directional pleiotropy=0.848 for LADA;
I
2GX
=0.321, intercept=0.002 and pfor directional pleiotropy=0.647 for
type 2 diabetes. I
2GX
is used to quantify the strength of NOME (NO
Measurement Error) violation for a set of instruments used for MR-
Egger regression. An I
2GX
much less than 1 indicates bias in the MR-
Egger causal estimate. MR-PRESSO detected no outliers for the
association between smoking and LADA and detected rs1109480 and
rs8005334 as outliers for smoking and type 2 diabetes. The outliers were
excluded from the outlier corrected estimate (pfor distortion of estimate:
0.953). The sizes of the squares are proportional to the inversevariance of
risk estimates. One of the 250 SNPs was unavailable in the GWAS
dataset of type 2 diabetes and therefore only 249 SNPs were used when
assessing the association of smoking with type 2 diabetes
78
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Diabetologia (2023) 66:70–81
study, as individuals may exaggerate or under-report their
smoking habits because of their diagnosis. However, time
between diagnosis and reporting was relatively short.
Moreover, our results regarding smoking and type 2 diabetes
were in line with previous findings based on prospective stud-
ies [1], supporting the validity of our data.
The observational association between smoking and diabe-
tes was confirmed by the MR results. The validity of these
results relies on several assumptions [35] and it is crucial that
an IV only affects the outcome through the exposure, not
through a direct pathway to the outcome or via a confounder.
The MR assumptions are difficult to test; however, the posi-
tive association between smoking and LADA/type 2 diabetes
remained in conservative analyses that excluded some poten-
tial pleiotropic SNPs. Excluding these SNPs does, however,
not necessarily mean that the SNPs indeed have pleiotropic
effects that would break the MR assumption. Finally, there
can be ‘non-compliance’in MR analyses due to epigenetic
modification of SNPs for smoking [36]. Still, such non-
compliance will most likely attenuate the observed asso-
ciation. For further discussion about MR results, see
ESM Text.
We used the presence of GADA as a criterion for LADA
but other autoantibodies may be present and individuals with
such positivity would be classified as having type 2 diabetes.
However, GADA is present in >90% of individuals with
LADA [37] and typically persist over time, although with
declining frequency [38]. The inability to separate LADA
from adult-onset type 1 diabetes in HUNT could lead to some
individuals with type 1 diabetes being misclassified as having
LADA, possibly diluting the association between smoking
and LADA. In this context it is noteworthy that the distinction
between LADA and type 1 diabetes with adult onset is not
clear-cut. The ADA proposes that LADA should be viewed as
a subtype of type 1 diabetes [39].
The specificity of the GADA assay implies that some indi-
viduals with type 2 diabetes will be misclassified as having
LADA. Importantly, smoking was associated with more auto-
immune LADA, where potential misclassification of individ-
uals with type 2 diabetes likely is minor. Finally, LADA is a
heterogeneous disease [7], and whether these findings can be
generalised to non-Scandinavian populations remains to be
investigated.
In conclusion, this study based on the combination of
observational and MR data indicates that smoking and snus
use increase the risk of LADA and suggests that the excess
risk conferred by smoking is augmented in genetically suscep-
tible individuals. The association may be attributed to an insu-
lin resistance promoting effect of nicotine. Our results suggest
that cessation of tobacco use should be a priority, not only in
individuals susceptible to type 2 diabetes but also in the
prevention of LADA. However, larger studies, as well as stud-
ies in other ethnic populations, are clearly needed. The impact
of potential interactions with other risk genotypes outside
the HLA gene complex should be explored in relation to
LADA.
Supplementary Information The online version contains peer-reviewed
but unedited supplementary material available at https://doi.org/10.1007/
s00125-022-05763-w.
Acknowledgements We thank the participants in ESTRID, ANDIS and
HUNT, as well as administrative personnel, nurses and research team
members from all the studies. Preliminary results from this work have
been presented as abstracts at the virtual SSSD meeting 2021, the virtual
55th EDEG Annual Meeting 2021, and the virtual 57th EASD Annual
Meeting 2021.
HUNT is a collaboration between HUNT Research Centre (Faculty of
Medicine and Health Sciences, Norwegian University of Science and
Technology NTNU), Trøndelag County Council, Central Norway
Regional Health Authority and the Norwegian Institute of Public Health.
Data availability The datasets analysed in the current study are avail-
able from the corresponding author upon reasonable request (ESTRID)
and with permission of the HUNT study by applying to the HUNT study
data access committee. The MR analysis used summary data and these
data are publicly available. Analysis codes are shared through GitHub
(https://github.com/jeseds/Smoking-use-of-smokeless-tobacco-HLA-
genotypes-and-incidence-of-LADA).
Funding Open access funding provided by Karolinska Institute. The
ESTRID study was funded by grants from the Swedish Research Council
(2018-03035), Research Council for Health, Working Life and Welfare
(FORTE, 2018-00337), the Novo Nordisk Foundation
(NNF19OC0057274) and the Swedish Diabetes Foundation. The post-
doctoral fellowship to BR, supported by Novo Nordisk Foundation, has grant
no. NNF17OC0027580. YW received a scholarship from the China
Scholarship Council (student no. 202006010041). EIRA was funded by
the Swedish Research Council, the Swedish Research Council for Health,
Working Life and Welfare, the Swedish Rheumatic Foundation, the AFA
Insurance Company and Stockholm County Council. ANDIS was financial-
ly supported by the Swedish Research Council and the European Research
Council Advanced Researcher grant (GA 269045) awarded to LG.
The study funders were not involved in the design of the study; the
collection, analysis, and interpretation of data; writing the report; and did
not impose any restrictions regarding the publication of the report.
Authors’relationships and activities SC declares shareholding in
Swedish Match AB to a value of 5000 euro. These were received as a gift
at birth and were originally shares in Volvo Cars Corporation. Through
investments and spin offs made by Volvo Cars, part of the shares was trans-
ferred into shares in Swedish Match by Volvo in 1996. SC has had no active
management of these shares. All other authors declare that there are no
relationships or activities that might bias, or be perceived to bias, their work.
Contribution statement Contribution to the data collection was
made by SC, BR and JE (ESTRID), LG, EA and TT
(ANDIS), LA (EIRA), and EPS, VG, PMT and BOÅ
(HUNT). YW was responsible for performing the MR
analysis. SC was responsible for conceptualising the research
objectives, designed the study and thoroughly revised the
manuscript. All authors contributed to the interpretation of
the results and critically revised and approved the final version
of the manuscript. JE developed the objectives of the study
and was responsible for drafting of the manuscript, analysing
79
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Diabetologia (2023) 66:70–81
the observational data and for the integrity of the work as a
whole.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
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otherwise in a credit line to the material. If material is not included in
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need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .
References
1. Pan A, Wang Y, Talaei M, Hu FB, Wu T (2015) Relation of active,
passive, and quitting smoking with incident type 2 diabetes: A
systematic review and meta-analysis. Lancet Diabetes Endocrinol
3(12):958–967. https://doi.org/10.1016/S2213-8587(15)00316-2
2. Yuan S, Larsson SC (2019) A causal relationship between cigarette
smoking and type 2 diabetes mellitus: A Mendelian randomization
study. Sci Rep 9(1):1–4. https://doi.org/10.1038/s41598-019-
56014-9
3. Facchini FS, Hollenbeck CB, Jeppesen J, Ida Chen YD, Reaven
GM (1992) Insulin resistance and cigarette smoking. Lancet
339(8802):1128–1130. https://doi.org/10.1016/0140-6736(92)
90730-Q
4. Edstorp J, Lampousi A, Carlsson S (2022) Parental smoking, type 1
diabetes, and islet autoantibody positivity in the offspring: A
systematic review and meta-analysis. Diabet Med (August 2021):
1–9. https://doi.org/10.1111/dme.14830
5. Sopori M (2002) Effects ofcigarette smoke on the immune system.
Nat Rev Immunol 2(May):372–377. https://doi.org/10.1038/nri803
6. Desai M, Zeggini E, Horton VA et al (2007) An association
analysis of the HLA gene region in latent autoimmune diabetes in
adults. Diabetologia 50(1):68–73. https://doi.org/10.1007/s00125-
006-0513-z
7. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L (2014) The
many faces of diabetes: A disease with increasing heterogeneity.
Lancet 383(9922):1084–1094. https://doi.org/10.1016/S0140-
6736(13)62219-9
8. Rasouli B,Grill V, Midthjell K, Ahlbom A, Andersson T, Carlsson
S (2013) Smoking is associated with reduced risk of autoimmune
diabetes inadults contrasting with increased risk in overweight men
with type 2 diabetes. Diabetes Care 36(3):604–610. https://doi.org/
10.2337/dc12-0913
9. Rasouli B,Andersson T, Carlsson PO et al (2016) Smoking and the
risk of LADA: Results from a Swedish population-based case-
control study. Diabetes Care 39(5):794–800. https://doi.org/10.
2337/dc15-2348
10. Klareskog L, Stolt P, Lundberg K et al (2006) A new model for an
etiology of rheumatoid arthritis: Smoking may trigger HLA-DR
(shared epitope)-restricted immune reactions to autoantigens modi-
fied by citrullination. Arthritis Rheum 54(1):38–46. https://doi.org/
10.1002/art.21575
11. Hedström AK, Sundqvist E, Bäärnhielm M et al (2011) Smoking
and two human leukocyte antigen genes interact to increase the risk
for multiple sclerosis. Brain 134(3):653–664. https://doi.org/10.
1093/brain/awq371
12. Österdahl BG, Jansson C, Paccou A (2004) Decreased levels of
tobacco-specific N-nitrosamines in moist snuff on the Swedish
market. J Agric Food Chem 52(16):5085–5088. https://doi.org/10.
1021/jf049931a
13. Carlsson S, AnderssonT, Araghi M et al (2017) Smokeless tobacco
(snus) is associated with an increased risk of type 2 diabetes: results
from five pooled cohorts. J Intern Med 281(4):398–406. https://doi.
org/10.1111/joim.12592
14. Rasouli B, Andersson T, Carlsson PO et al (2017) Use of Swedish
smokeless tobacco (snus) and the risk of Type 2 diabetesand latent
autoimmune diabetes of adulthood (LADA). Diabet Med 34(4):
514–521. https://doi.org/10.1111/dme.13179
15. Ahlqvist E, Storm P, Käräjämäki A et al (2018) Novel subgroups of
adult-onset diabetes and their association with outcomes: a data-
driven cluster analysis of six variables. Lancet Diabetes
Endocrinol 6(5):361–369. https://doi.org/10.1016/S2213-8587(18)
30051-2
16. Stolt P, Bengtsson C, Nordmark B et al (2003) Quantification of the
influence of cigarette smoking on rheumatoid arthritis: Results from
a population based case-control study, using incident cases. Ann
Rheum Dis 62(9):835–841. https://doi.org/10.1136/ard.62.9.835
17. Rahmati K, Lernmark Å, Becker C et al (2008) A Comparison of
Serum and EDTA Plasma in the Measurement of Glutamic Acid
Decarboxylase Autoantibodies (GADA) and Autoantibodies to
Islet Antigen-2 (IA-2A) Using the RSR Radioimmunoassay
(RIA) and Enzyme Linked Immunosorbent Assay (ELISA) Kits.
Clin Lab 54(7–8):227–235
18. The Oxford Center for Diabetes. Endocrinology & metabolism.
Diabetes Trial Unit. HOMA calculator. Available from: http://
www.dtu.ox.ac.uk/homacalculator/index.php. Accessed June 2013
19. Nguyen C, Varney MD, Harrison LC, Morahan G (2013)
Definition of high-risk type 1 diabetes HLA-DR and HLA-DQ
types using only three single nucleotide polymorphisms. Diabetes
62(6):2135–2140. https://doi.org/10.2337/db12-1398
20. Krokstad S, Langhammer A, Hveem K et al (2013) Cohort profile:
The HUNT study, Norway. Int J Epidemiol 42(4):968–977. https://
doi.org/10.1093/ije/dys095
21. Midthjell K, Holmen J, Bjorndal A, Lund-Larsen PG (1992) Is
questionnaire information valid in the study of a chronic disease
such as diabetes? The Nord-Trondelag Diabetes Study. J Epidemiol
Community Health 46(5):537–542. https://doi.org/10.1136/jech.
46.5.537
22. Burgess S, Scott RA, Timpson NJ, Smith GD, Thompson SG
(2015) Using published data in Mendelian randomization: A blue-
print for efficient identification of causal risk factors. Eur J
Epidemiol 30(7):543–552. https://doi.org/10.1007/s10654-015-
0011-z
23. Cousminer DL, Ahlqvist E, Mishra R et al (2018) First genome-
wide association study of latent autoimmune diabetes in adults
reveals novel insights linking immune and metabolic diabetes.
Diabetes Care 41(11):2396–2403. https://doi.org/10.2337/dc18-
1032
24. Scott RA, Scott LJ, MägiR et al (2017) An expanded genome-wide
association study of type 2 diabetes in Europeans. Diabetes 66(11):
2888–2902. https://doi.org/10.2337/db16-1253
25. Liu M, Jiang Y, Wedow R et al (2019) Association studies of up to
1.2 million individuals yield new insights into the genetic etiology
of tobacco and alcohol use. Nat Genet 51(2):237–244. https://doi.
org/10.1038/s41588-018-0307-5
26. Hartung J, Knapp GSB (2008) Statistical meta-analysis with appli-
cations. John Wiley & Sons, Hoboken, NJ. https://doi.org/10.1002/
9780470386347
27. Andersson T, Alfredsson L, Källberg H, Zdravkovic S, Ahlbom A
(2005) Calculating measures of biological interaction. Eur J
80
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Diabetologia (2023) 66:70–81
Epidemiol 20(7):575–579. https://doi.org/10.1007/s10654-005-
7835-x
28. Burgess S, Butterworth A, Thompson SG (2013) Mendelian
randomization analysis with multiple genetic variants using
summarized data. Genet Epidemiol 37(7):658–665. https://doi.
org/10.1002/gepi.21758
29. R Core Team (2021) R: A language and environment for statistical
computing
30. Gupta D, Lacayo AA, Greene SM, Leahy JL, Jetton TL (2018) β-
Cell mass restoration by α7 nicotinic acetylcholine receptor activa-
tion. J Biol Chem 293(52):20295–20306. https://doi.org/10.1074/
jbc.RA118.004617
31. Wang DW, Zhou RB, Yao YM et al (2010) Stimulation of α7
nicotinic acetylcholine receptor by nicotine increases suppressive
capacity of naturally occurring CD4+CD25 + regulatory T cells in
mice in vitro. J Pharmacol Exp Ther 335(3):553–561. https://doi.
org/10.1124/jpet.110.169961
32. Wilkin TJ (2001) The accelerator hypothesis: Weight gain as the
missing link between Type I and Type II diabetes. Diabetologia
44(7):914–922. https://doi.org/10.1007/s001250100548
33. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Smith GD
(2008) Mendelian randomization: Using genes as instruments for
making causal inferences in epidemiology. Stat Med 27(September
2007):1133–1163. https://doi.org/10.1002/sim.3034
34. Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay
M (2009) The accuracy of self-reported smoking: A systematic
review of the relationship between self-reported and cotinine-
assessed smoking status. Nicotine Tob Res 11(1):12–24. https://
doi.org/10.1093/ntr/ntn010
35. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG (2017)
Sensitivity analyses for robust causal inference from mendelian
randomization analyses with multiple genetic variants.
Epidemiology 28(1):30–42. https://doi.org/10.1097/EDE.
0000000000000559
36. Ogbuanu IU, Zhang H, Karmaus W (2009) Can we apply the
Mendelian randomization methodology without considering epige-
netic effects? Emerg Themes Epidemiol 6(1):1–10. https://doi.org/
10.1186/1742-7622-6-3
37. Hawa MI, Kolb H, Schloot N et al (2013) Adult-onset autoimmune
diabetes in Europe is prevalent with a broad clinical phenotype:
Action LADA 7. Diabetes Care 36(4):908–913. https://doi.org/10.
2337/dc12-0931
38. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W,
Mackay IR (1993) Antibodies to glutamic acid decarboxylase
reveal latent autoimmune diabetes mellitus in adults with a non-
insulin-dependent onset of disease. Diabetes 42(2):359–362.
https://doi.org/10.2337/diabetes.42.2.359
39. American Diabetes Association (2022) 2. Classification and
Diagnosis of Diabetes: Standards of Medical Care in Diabetes —
2022. Diabetes Care 45:17–38. https://doi.org/10.2337/dc22-S002
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