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


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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 ( Graphical abstract
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/Published online: 28 July 2022
Diabetologia (2023) 66:7081
Smoking, use of smokeless tobacco, HLA genotypes and incidence
of latent autoimmune diabetes in adults
Jessica Edstorp
&Yuxia Wei
&Emma Ahlqvist
&Lars Alfredsson
&Valdemar Grill
&Leif Groop
Bahareh Rasouli
&Elin P. Sørgjerd
&Per M. Thorsby
&Tiinamaija Tuomi
Bjørn O. Åsvold
&Sofia Carlsson
Received: 23 February 2022 /Accepted: 1 June 2022
#The Author(s) 2022
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 casecontrol data (collected 20102019) with incident cases of LADA (n=593) and
type 2 diabetes (n=2038), and 3036 controls, and Norwegian prospective data (collected 19842019) 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 (casecontrol 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
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 (
*Jessica Edstorp
Institute of Environmental Medicine, Karolinska Institutet,
Stockholm, Sweden
Department of Clinical Sciences in Malmö, Clinical Research Centre,
Lund University, Malmö, Sweden
Center for Occupational and Environmental Medicine, Region
Stockholm, Stockholm, Sweden
Department of Clinical and Molecular Medicine, Norwegian
University of Science and Technology, Trondheim, Norway
Institute for Molecular Medicine Finland, Helsinki University,
Helsinki, Finland
Department of Global Health and Population, Harvard TH Chan
School of Public Health, Boston, MA, USA
HUNT Research Centre, Department of Public Health and Nursing,
NTNU, Norwegian University of Science and Technology,
Trondheim, Norway
Department of Endocrinology, Clinic of Medicine, St Olavs Hospital,
Trondheim, Norway
Hormone Laboratory, Department of Medical Biochemistry, Oslo
University Hospital, Aker, Oslo, Norway
Biochemical Endocrinology and Metabolism Research Group, Oslo
University Hospital, Aker, Oslo, Norway
Division of Endocrinology, Abdominal Center, Helsinki University
Hospital, Helsinki, Finland
Research Program for Diabetes and Obesity, University of Helsinki,
Helsinki, Finland
Folkhälsan Research Center, Helsinki, Finland
K.G. Jebsen Center for Genetic Epidemiology, Department of Public
Health and Nursing, NTNU, Norwegian University of Science and
Technology, Trondheim, Norway
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Diabetologia (2023) 66:7081
Keywords Geneenvironment interaction .LADA .Latent autoimmune diabetes in adults .Mendelian randomisation analysis .
Smoking .Tobacco use
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
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 casecontrol 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
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Diabetologia (2023) 66:7081
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.
The ESTRID study
Study population Epidemiological Study of Risk Factors for
LADA and Type 2 Diabetes (ESTRID) is a Swedish
population-based casecontrol 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
casecontrol 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 20102019 with information on tobacco use
(LADA n=593), type 2 diabetes (n=2038) and all controls
aged 35 years included in EIRA 20062014 (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 (
based on a Genome Reference Consortium assembly
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 19841986, HUNT2 19951997,
HUNT3 20062008, HUNT4 20172019). The study
includes questionnaires, clinical examination and blood
sampling. Eligible for analyses were all participants in
HUNT13 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
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Diabetologia (2023) 66:7081
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
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,
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
]) 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 Studentsttest 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
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 casecontrol 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
) were estimated through
the inverse-variance weighted (IVW) method [26].
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Diabetologia (2023) 66:7081
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
)[27], where RR
is risk in doubly exposed, RR
risk in non-tobacco users with low/intermediate risk geno-
types, and RR
is risk in non-tobacco users with high-risk
genotypes. The reference group (RR
) 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
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 casecontrol 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 (
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
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
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, %
39.9 5.9 <0.001 41.0 13.3 <0.001
HLA high-risk, %
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
HUNT13 (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
Age at participation for controls
Current use of insulin
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)
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Diabetologia (2023) 66:7081
genotypes conferred an RR
(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
1.30 [95% CI 1.06, 1.59]) and even more so in current, heavy
smokers (RR
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
1.38 (95% CI 1.28, 1.49) and RR
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
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
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
Model 1
RR (95% CI)
Model 2
RR (95% CI)
Model 1
RR (95% CI)
Model 2
RR (95% CI)
Never 378 1467 729,291 1 1 2281 1467 729,291 1 1
Former 269 964 465,674 1.07 (0.89,
1.08 (0.90,
2084 964 465,674 1.12 (1.05,
1.07 (1.00,
Current 191 605 501,537 1.14 (0.94,
1.30 (1.06,
1399 605 501,537 1.11 (1.03,
1.38 (1.28,
Ever 460 1569 967,211 1.09 (0.94,
1.16 (0.99,
3483 1569 967,211 1.12 (1.05,
1.20 (1.13,
Intensity (current)
No current
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,
1.16 (0.93,
920 497 358,396 1.03 (0.95,
1.35 (1.24,
20 cigarettes/day 55 98 112,955 1.54 (1.12,
1.54 (1.11,
425 98 112,955 1.35 (1.20,
1.47 (1.31,
Per 5 cigarettes 838 3036 1,696,503 1.06 (1.00,
1.08 (1.01,
5764 3036 1,696,503 1.03 (1.00,
1.08 (1.05,
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,
1.11 (0.92,
1463 932 527,176 0.99 (0.92,
1.12 (1.03,
15 pack-years 210 637 276,814 1.26 (1.03,
1.28 (1.04,
1660 637 276,814 1.39 (1.29,
1.46 (1.34,
Per 5 pack-years 838 3036 1,696,503 1.03 (1.00,
1.03 (1.00,
5764 3036 1,696,503 1.06 (1.05,
1.05 (1.04,
Model 1 adjusted for age and sex
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption
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Diabetologia (2023) 66:7081
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
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
OR (95% CI)
Model 2
OR (95% CI)
Cases (n)Controls(n) Model 1
OR (95% CI)
Model 2
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 casecontrol (ESTRID) only
Model 1 adjusted for age and sex
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption; snus analyses additionally adjusted for smoking
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Diabetologia (2023) 66:7081
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).
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 20102014
instead of 20102019 and HUNT data for the period 1984
2008 instead of 19842019). 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
Model 1
RR (95% CI)
Model 2
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)
Model 1 adjusted for age and sex
Model 2 adjusted for age, sex, BMI, educational level and alcohol consumption
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Diabetologia (2023) 66:7081
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
=0.303, inter-
cept=0.002 and pfor directional pleiotropy=0.848 for LADA;
=0.321, intercept=0.002 and pfor directional pleiotropy=0.647 for
type 2 diabetes. I
is used to quantify the strength of NOME (NO
Measurement Error) violation for a set of instruments used for MR-
Egger regression. An I
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
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Diabetologia (2023) 66:7081
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-compliancein 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
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
Supplementary Information The online version contains peer-reviewed
but unedited supplementary material available at
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
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.
Authorsrelationships 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
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Diabetologia (2023) 66:7081
the observational data and for the integrity of the work as a
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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
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):14.
3. Facchini FS, Hollenbeck CB, Jeppesen J, Ida Chen YD, Reaven
GM (1992) Insulin resistance and cigarette smoking. Lancet
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):
5. Sopori M (2002) Effects ofcigarette smoke on the immune system.
Nat Rev Immunol 2(May):372377.
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):6873.
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):10841094.
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):604610.
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):794800.
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):3846.
11. Hedström AK, Sundqvist E, ärnhielm M et al (2011) Smoking
and two human leukocyte antigen genes interact to increase the risk
for multiple sclerosis. Brain 134(3):653664.
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):50855088.
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):398406. https://doi.
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):
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):361369.
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):835841.
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(78):227235
18. The Oxford Center for Diabetes. Endocrinology & metabolism.
Diabetes Trial Unit. HOMA calculator. Available from: http:// 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
20. Krokstad S, Langhammer A, Hveem K et al (2013) Cohort profile:
The HUNT study, Norway. Int J Epidemiol 42(4):968977. https://
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):537542.
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):543552.
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):23962403.
24. Scott RA, Scott LJ, MägiR et al (2017) An expanded genome-wide
association study of type 2 diabetes in Europeans. Diabetes 66(11):
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):237244. https://doi.
26. Hartung J, Knapp GSB (2008) Statistical meta-analysis with appli-
cations. John Wiley & Sons, Hoboken, NJ.
27. Andersson T, Alfredsson L, Källberg H, Zdravkovic S, Ahlbom A
(2005) Calculating measures of biological interaction. Eur J
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Diabetologia (2023) 66:7081
Epidemiol 20(7):575579.
28. Burgess S, Butterworth A, Thompson SG (2013) Mendelian
randomization analysis with multiple genetic variants using
summarized data. Genet Epidemiol 37(7):658665. https://doi.
29. R Core Team (2021) R: A language and environment for statistical
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):2029520306.
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):553561. https://doi.
32. Wilkin TJ (2001) The accelerator hypothesis: Weight gain as the
missing link between Type I and Type II diabetes. Diabetologia
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
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):1224. https://
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):3042.
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):110.
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):908913.
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):359362.
39. American Diabetes Association (2022) 2. Classification and
Diagnosis of Diabetes: Standards of Medical Care in Diabetes
2022. Diabetes Care 45:1738.
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... To date, lifestyle risk factors are scarcely investigated in LADA, nor is their potential interaction with genetic susceptibility (1). However, we recently observed an increased risk of LADA in smokers and users of Swedish smokeless tobacco (snus), using data from two observational studies and a Mendelian randomization study (4), and the excess risk was comparable to that seen in type 2 diabetes (5). Moreover, smoking was associated with insulin resistance in LADA, which is in line with observations in type 2 diabetes (6). ...
... Adverse effects of smoking and nicotine exposure on insulin sensitivity have been documented in experimental studies (28,29). In line with this, we recently showed that smoking, snus, and total tobacco use are positively associated with HOMA-IR in both LADA and type 2 diabetes (4). Against this background, it is interesting that the strongest evidence of interaction was observed between smoking and IR-GRS in LADA. ...
Objective: Smoking and Swedish smokeless tobacco (snus) are associated with latent autoimmune diabetes in adults (LADA) and type 2 diabetes (T2D). Our aim was to investigate whether genetic susceptibility to T2D, insulin resistance (IR), and insulin secretion (IS) aggravate these associations. Research design and methods: We used data from two population-based Scandinavian studies with case subjects with LADA (n = 839) and T2D (n = 5,771), matched control subjects (n = 3,068), and 1,696,503 person-years at risk. Pooled, multivariate relative risks (RR) with 95% CI were estimated for smoking/genetic risk scores (T2D-GRS, IS-GRS, and IR-GRS), and ORs for snus or tobacco/GRS (case-control data). We estimated additive (proportion attributable to interaction [AP]) and multiplicative interaction between tobacco use and GRS. Results: The RR of LADA was elevated in high IR-GRS heavy smokers (≥15 pack-years; RR 2.01 [CI 1.30, 3.10]) and tobacco users (≥15 box/pack-years; RR 2.59 [CI 1.54, 4.35]) compared with low IR-GRS individuals without heavy use, with evidence of additive (AP 0.67 [CI 0.46, 0.89]; AP 0.52 [CI 0.21, 0.83]) and multiplicative (p = 0.003; p = 0.034) interaction. In heavy users, there was additive interaction between T2D-GRS and smoking, snus, and total tobacco use. The excess risk conferred by tobacco use did not differ across GRS categories in T2D. Conclusions: Tobacco use may confer a higher risk of LADA in individuals with genetic susceptibility to T2D and insulin resistance, whereas genetic susceptibility does not seem to influence the increased T2D incidence associated with tobacco use.
... In a meta-analysis study, a significant association between passive smoking and type-2 diabetes was observed [14]. Additionally, a recent study found that smoking acts in synergy with genetic susceptibility to promote latent autoimmune diabetes in adults (LADA) [15]. ...
Introduction: In recent years, there has been a surge in research focusing on the link between smoking and insulin resistance in the context of obesity and diabetes. In this study, our objective was to investigate the relationship between smoking and insulin resistance. Materials and methods: This is a case-control study. The case and control groups were formed using the hospital patient information database and clinically randomized using data obtained, including age, gender, height, and weight. The case group for this study consisted of smokers, whereas the control group consisted of non-smokers. Chi-square tests were used to compare numbers and rates, and independent sample t-tests were used for the averages. Binary logistic regression analysis was performed between the case and control groups. Results: According to logistic regression analysis, the odds ratio for non-smokers was 0.59 (0.31-1.14). The risk of insulin resistance is decreased by 41% non-significantly in non-smokers. The odds ratio for age was 1.03 (1.01-1.05). When the age variable increases by one unit, the risk of insulin resistance increase by 1.03 times. Conclusion: Our study found no significant relationship between smoking and insulin resistance in healthy individuals. The relationship between smoking and insulin resistance, as reported in the scientific literature, may be suggestive of an association in which smoking exacerbates insulin resistance as a result of other contributing factors rather than serving as a direct causal factor. Further studies are warranted to elucidate the potential mechanisms underlying this association fully.
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Background: LADA is a common form of diabetes described as a mix between type 1 and type 2 diabetes. Understanding of how genes and environmental factors interact in the development of LADA is central for future efforts to prevent the disease. This review aims to synthesize the literature on lifestyle factors linked to LADA risk and discuss their potential interaction with genetic susceptibility. Findings: Current knowledge on environmental risk factors for LADA is primarily based on observational data from Scandinavian populations. Increasing evidence suggest that lifestyle factors promoting type 2 diabetes such as obesity, sedentariness, low birth weight and smoking, is implicated in the risk of LADA. Data from mendelian randomization studies support that the link between LADA and obesity, low birth weight and smoking is causal. Limited evidence indicates that dietary factors including consumption of red meat, coffee and sweetened beverages may increase the risk while consumption of alcohol and omega-3 fatty acids may reduce the risk. Several lifestyle factors, including smoking and obesity, seem to interact with human leukocyte antigen genes associated with autoimmunity, conferring much stronger effects on disease risk among those exposed to both factors. Summary: Available studies suggest that lifestyle modification has the potential for prevention of LADA, particularly for individuals with high risk of disease such as those with genetic susceptibility. Research into risk factors of LADA is however limited, confirmations are warranted, many factors remain to be explored, and there is a need for intervention studies to assess causality.
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Aims: Our aim was to synthesize current evidence on the association between parental smoking and incidence of type 1 diabetes and islet antibody positivity (IA) in the offspring by conducting a systematic review and meta-analysis. Methods: We searched Medline, Embase, and Cochrane Library until January 21, 2021, for human studies with parental tobacco use as exposure, type 1 diabetes or IA as outcome, and hazard, risk, or odds ratios as effect estimates. Summary relative risks (RR) and 95% confidence intervals (CI) were estimated with random-effects models. Heterogeneity was quantified with the I2 statistic, bias with the ROBINS-I tool, and certainty of evidence with the GRADE tool. Results: We identified 535 records of which 23 were eligible including 25 927 cases of type 1 diabetes. Maternal smoking during pregnancy was associated with a reduced risk of type 1 diabetes (n=22, RR 0.78, CI 0.71-0.86, I2 =69%). Including only studies with low to moderate risk of bias indicated similar results with less heterogeneity (n=14, RR 0.73, CI 0.68-0.79, I2 =44%). The certainty of evidence was graded as high. There was no clear association between type 1 diabetes and neither maternal (n=6, RR 0.95, CI 0.78-1.14, I2 =0%) nor paternal (n=6, RR 0.90, 0.70-1.17, I2 =68%) smoking during childhood. Furthermore, the association between maternal smoking during pregnancy and IA was weak (n=4, RR 0.86, CI 0.44-1.65, I2 =71%). Conclusions: Maternal smoking during pregnancy may reduce the risk of type 1 diabetes in the offspring. Further studies are needed to elucidate potential mechanisms underlying this association.
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The causality between smoking and type 2 diabetes is unclear. We conducted a two-sample Mendelian randomization study to explore the causal relationship between smoking initiation and type 2 diabetes. Summary-level data for type 2 diabetes were obtained from a meta-analysis of 32 genome-wide association studies (DIAbetes Genetics Replication And Meta-analysis consortium), which included 898 130 individuals of European ancestry. Totally, 377 single-nucleotide polymorphisms associated with smoking initiation at genome wide significance threshold (p < 5 × 10−8) were identified from the hitherto largest genome-wide association study on smoking. The inverse-variance weighted, weighted median, MR-Egger regression, and MR-PRESSO approaches were used to analyze the data. Genetically predicted smoking initiation was associated with type 2 diabetes with an odds ratio of 1.28 (95% confidence interval, 1.20, 1.37; p = 2.35 × 10−12). Results were consistent across sensitivity analyses and there was no evidence of horizontal pleiotropy. This study provides genetic evidence supporting a causal association between the smoking initiation and type 2 diabetes. Reducing cigarette smoking initiation can now be even more strongly recommended for type 2 diabetes prevention.
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Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6,7,8,9,10,11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.
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Although it is well established how nutrients, growth factors, and hormones impact functional ß-cell mass (BCM), the influence of the central nervous system in this regard and, especially in the context of islet immune modulation, has been understudied. Here we have investigated the expression and activity of pancreatic islet α7 nicotinic acetylcholine receptor (α7nAChR) in islet anti-inflammatory and pro-survival signaling. Systemic administration of α7nAChR agonists in mice improves glucose tolerance and curtails streptozotocin (STZ)-induced hyperglycemia by retaining BCM, in part, through maintaining Pdx1 and MafA expression and reducing apoptosis. α7nAChR activation of mouse islets ex vivo leads to reduced inflammatory drive through a JAK2-STAT3 pathway that couples with CREB/Irs2/Akt survival signaling. Since the vagus nerve conveys anti-inflammatory signals to immune cells of the spleen and other non-neural tissues in the viscera by activating α7nAChRs, our study suggests a novel role for ß-cell α7nAChRs that function to maintain ß-cell survival and mass homeostasis through modulating islet cytokine and PI3-kinase dependent signaling pathways. Exploiting this pathway may have therapeutic potential for treating autoimmune diabetes.
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To characterise type 2 diabetes (T2D) associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D cases and 132,532 controls of European ancestry after imputation using the 1000 Genomes multi-ethnic reference panel. Promising association signals were followed-up in additional data sets (of 14,545 or 7,397 T2D cases and 38,994 or 71,604 controls). We identified 13 novel T2D-associated loci (p<5×10(-8)), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common SNVs. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion, and in adipocytes, monocytes and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.
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Background: Smoking and nicotine exposure increase insulin resistance and the risk of type 2 diabetes. Swedish smokeless tobacco (snus) is high in nicotine, and its use is prevalent in Scandinavian countries, but few studies have investigated snus use in relation to diabetes risk. Objective: To explore the association between snus use and risk of type 2 diabetes using pooled data from five cohorts. Methods: Analyses were based on prospective studies conducted between 1990 and 2013 including 54 531 never-smoking men and 2441 incident cases of type 2 diabetes identified through screening, self-reporting and hospital and prescription registries. Hazard ratios (HRs) and 95% confidence intervals (CIs) were assessed and adjusted for age, body mass index, educational level, alcohol consumption and physical activity. Results: Compared to never users, the HR of type 2 diabetes was 1.15 (95% CI: 1.00-1.32) in current users of snus. In individuals consuming 5-6 boxes per week, the HR was 1.42 (95% CI: 1.07-1.87); in those consuming ≥7 boxes per week, the HR was 1.68 (95% CI: 1.17-2.41). Each additional box of snus consumed per week yielded an HR of 1.08 (95% CI: 1.01-1.16). Conclusion: Our findings indicate that high consumption of snus is a risk factor for type 2 diabetes. The risk was similar to that in smokers, implying that smokers will not reduce their risk of type 2 diabetes by changing to snus use. The results also support the notion that nicotine increases the risk of type 2 diabetes.
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction ( Readers who wish to comment on the Standards of Care are invited to do so at
Objective: Latent autoimmune diabetes in adults (LADA) shares clinical features with both type 1 and type 2 diabetes; however, there is ongoing debate regarding the precise definition of LADA. Understanding its genetic basis is one potential strategy to gain insight into appropriate classification of this diabetes subtype. Research design and methods: We performed the first genome-wide association study of LADA in case subjects of European ancestry versus population control subjects (n = 2,634 vs. 5,947) and compared against both case subjects with type 1 diabetes (n = 2,454 vs. 968) and type 2 diabetes (n = 2,779 vs. 10,396). Results: The leading genetic signals were principally shared with type 1 diabetes, although we observed positive genetic correlations genome-wide with both type 1 and type 2 diabetes. Additionally, we observed a novel independent signal at the known type 1 diabetes locus harboring PFKFB3, encoding a regulator of glycolysis and insulin signaling in type 2 diabetes and inflammation and autophagy in autoimmune disease, as well as an attenuation of key type 1-associated HLA haplotype frequencies in LADA, suggesting that these are factors that distinguish childhood-onset type 1 diabetes from adult autoimmune diabetes. Conclusions: Our results support the need for further investigations of the genetic factors that distinguish forms of autoimmune diabetes as well as more precise classification strategies.
Background: Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis. Methods: We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations. Findings: We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes. Interpretation: We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes. Funding: Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.