Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

BACKGROUND: We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test. METHODS: We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided. RESULTS: The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model. CONCLUSION: Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.

Incidence of LADA and Type 2 Diabetes in Relation to Tobacco Use and Genetic Susceptibility to Type 2 Diabetes and Related Traits: Findings From a Swedish Case-Control Study and the Norwegian HUNT Study.

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.

A cross-sectional study of inflammatory markers as determinants of circulating kynurenines in the Lung Cancer Cohort Consortium.

Circulating concentrations of metabolites (collectively called kynurenines) in the kynurenine pathway of tryptophan metabolism increase during inflammation, particularly in response to interferon-gamma (IFN-γ). Neopterin and the kynurenine/tryptophan ratio (KTR) are IFN-γ induced inflammatory markers, and together with C-reactive protein (CRP) and kynurenines they are associated with various diseases, but comprehensive data on the strength of associations of inflammatory markers with circulating concentrations of kynurenines are lacking. We measured circulating concentrations of neopterin, CRP, tryptophan and seven kynurenines in 5314 controls from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). The associations of neopterin, KTR and CRP with kynurenines were investigated using regression models. In mixed models, one standard deviation (SD) higher KTR was associated with a 0.46 SD higher quinolinic acid (QA), and 0.31 SD higher 3-hydroxykynurenine (HK). One SD higher neopterin was associated with 0.48, 0.44, 0.36 and 0.28 SD higher KTR, QA, kynurenine and HK, respectively. KTR and neopterin respectively explained 24.1% and 16.7% of the variation in QA, and 11.4% and 7.5% of HK. CRP was only weakly associated with kynurenines in regression models. In summary, QA was the metabolite that was most strongly associated with the inflammatory markers. In general, the inflammatory markers were most strongly related to metabolites located along the tryptophan-NAD axis, which may support suggestions of increased production of NAD from tryptophan during inflammation.

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

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 ).

Small RNAs are differentially expressed in autoimmune and non-autoimmune diabetes and controls.

Objective: Diabetes is a heterogeneous disease and a precise diagnosis of diabetes subgroups is necessary to initiate proper early treatment and clinical management of the disease. Circulating small RNAs (sRNAs) are potentially diagnostic biomarkers in diseases, including diabetes. Here we aimed to examine whether profiles of circulating sRNAs differed between patients with autoimmune and non-autoimmune diabetes and non-diabetic controls. Design: This cross-sectional case-control study included participants from the third survey of the HUNT study. Methods: We performed sRNA sequencing in serum from adult-onset type 1 diabetes (n = 51), type 2 diabetes (n = 50) and latent autoimmune diabetes in adult (LADA, n = 51), as well as non-diabetic HUNT3 participants as control group (n = 51). Differential expression analysis of the sRNAs was performed in R using limma-voom. Results: We identified differences in sRNA expression between autoimmune (type 1 diabetes and LADA) and non-autoimmune diabetes (type 2 diabetes) and between patients with diabetes and non-diabetic controls. Focusing on miRNA, we identified 10 differentially expressed mature miRNAs and 30 differentially expressed miRNA variants (isomiRs). We also identified significant changes within other sRNA classes, including a pronounced downregulation of a tRNA fragment in patients with diabetes compared to non-diabetic controls. We created cross-validated sRNA signatures based on the significant sRNAs that distinguished patients with diabetes from non-diabetic controls, and autoimmune from non-autoimmune diabetes, with high specificity and sensitivity. sRNA profiles did not distinguish between type 1 diabetes and LADA. Conclusions: Circulating sRNAs are differentially expressed between patients with diabetes and non-diabetic controls and between autoimmune and non-autoimmune diabetes.

Prediagnosis Leisure-Time Physical Activity and Lung Cancer Survival: A Pooled Analysis of 11 Cohorts.

BACKGROUND: Little is known about the association between physical activity before cancer diagnosis and survival among lung cancer patients. In this pooled analysis of 11 prospective cohorts, we investigated associations of prediagnosis leisure-time physical activity (LTPA) with all-cause and lung cancer-specific mortality among incident lung cancer patients. METHODS: Using self-reported data on regular engagement in exercise and sports activities collected at study enrollment, we assessed metabolic equivalent hours (MET-h) of prediagnosis LTPA per week. According to the Physical Activity Guidelines for Americans, prediagnosis LTPA was classified into inactivity, less than 8.3 and at least 8.3 MET-h per week (the minimum recommended range). Cox regression was used to estimate hazard ratios (HRs) and 95% confidence interval (CIs) for all-cause and lung cancer-specific mortality after adjustment for major prognostic factors and lifetime smoking history. RESULTS: Of 20 494 incident lung cancer patients, 16 864 died, including 13 596 deaths from lung cancer (overall 5-year relative survival rate = 20.9%, 95% CI = 20.3% to 21.5%). Compared with inactivity, prediagnosis LTPA of more than 8.3 MET-h per week was associated with a lower hazard of all-cause mortality (multivariable-adjusted HR = 0.93, 95% CI = 0.88 to 0.99), but not with lung cancer-specific mortality (multivariable-adjusted HR = 0.99, 95% CI = 0.95 to 1.04), among the overall population. Additive interaction was found by tumor stage (Pinteraction = .008 for all-cause mortality and .003 for lung cancer-specific mortality). When restricted to localized cancer, prediagnosis LTPA of at least 8.3 MET-h per week linked to 20% lower mortality: multivariable-adjusted HRs were 0.80 (95% CI = 0.67 to 0.97) for all-cause mortality and 0.80 (95% CI = 0.65 to 0.99) for lung cancer-specific mortality. CONCLUSIONS: Regular participation in LTPA that met or exceeded the minimum Physical Activity Guidelines was associated with reduced hazards of mortality among lung cancer patients, especially those with early stage cancer.

Undiagnosed diabetes: Prevalence and cardiovascular risk profile in a population-based study of 52,856 individuals. The HUNT Study, Norway.

AIMS: We investigated the current extent of undiagnosed diabetes and prediabetes and their associated cardiovascular risk profile in a population-based study. METHODS: All residents aged ≥20 years in the Nord-Trøndelag region, Norway, were invited to the HUNT4 Survey in 2017-2019, and 54% attended. Diagnosed diabetes was self-reported, and in those reporting no diabetes HbA1c was used to classify undiagnosed diabetes (≥48 mmol/mol [6.5%]) and prediabetes (39-47 mmol/mol [5.7%-6.4%]). We estimated the age- and sex-standardized prevalence of these conditions and their age- and sex-adjusted associations with other cardiovascular risk factors. RESULTS: Among 52,856 participants, the prevalence of diabetes was 6.0% (95% CI 5.8, 6.2), of which 11.1% were previously undiagnosed (95% CI 10.1, 12.2). The prevalence of prediabetes was 6.4% (95% CI 6.2, 6.6). Among participants with undiagnosed diabetes, 58% had HbA1c of 48-53 mmol/mol (6.5%-7.0%), and only 14% (i.e., 0.1% of the total study population) had HbA1c >64 mmol/mol (8.0%). Compared with normoglycaemic participants, those with undiagnosed diabetes or prediabetes had higher body mass index, waist circumference, systolic blood pressure, triglycerides and C-reactive protein but lower low-density lipoprotein cholesterol (all p < 0.001). Participants with undiagnosed diabetes had less favourable values for every measured risk factor compared with those with diagnosed diabetes. CONCLUSIONS: The low prevalence of undiagnosed diabetes suggests that the current case-finding-based diagnostic practice is well-functioning. Few participants with undiagnosed diabetes had very high HbA1c levels indicating severe hyperglycaemia. Nonetheless, participants with undiagnosed diabetes had a poorer cardiovascular risk profile compared with participants with known or no diabetes.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium.

BACKGROUND: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Epidemiology of 40 blood biomarkers of one-carbon metabolism, vitamin status, inflammation, and renal and endothelial function among cancer-free older adults.

Imbalances of blood biomarkers are associated with disease, and biomarkers may also vary non-pathologically across population groups. We described variation in concentrations of biomarkers of one-carbon metabolism, vitamin status, inflammation including tryptophan metabolism, and endothelial and renal function among cancer-free older adults. We analyzed 5167 cancer-free controls aged 40-80 years from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). Centralized biochemical analyses of 40 biomarkers in plasma or serum were performed. We fit multivariable linear mixed effects models to quantify variation in standardized biomarker log-concentrations across four factors: age, sex, smoking status, and body mass index (BMI). Differences in most biomarkers across most factors were small, with 93% (186/200) of analyses showing an estimated difference lower than 0.25 standard-deviations, although most were statistically significant due to large sample size. The largest difference was for creatinine by sex, which was - 0.91 standard-deviations lower in women than men (95%CI - 0.98; - 0.84). The largest difference by age was for total cysteine (0.40 standard-deviation increase per 10-year increase, 95%CI 0.36; 0.43), and by BMI was for C-reactive protein (0.38 standard-deviation increase per 5-kg/m2 increase, 95%CI 0.34; 0.41). For 31 of 40 markers, the mean difference between current and never smokers was larger than between former and never smokers. A statistically significant (p < 0.05) association with time since smoking cessation was observed for 8 markers, including C-reactive protein, kynurenine, choline, and total homocysteine. We conclude that most blood biomarkers show small variations across demographic characteristics. Patterns by smoking status point to normalization of multiple physiological processes after smoking cessation.

Low C-peptide together with a high glutamic acid decarboxylase autoantibody level predicts progression to insulin dependence in latent autoimmune diabetes in adults: The HUNT study.

AIM: To search for risk factors that could predict progression in latent autoimmune diabetes in adults (LADA) and compare them with those for type 2 diabetes. MATERIALS AND METHODS: This study included 175 participants with LADA (autoantibody positive, without insulin treatment ≥1 year after diagnosis) and 2331 participants with type 2 diabetes (autoantibody negative, without insulin treatment ≥1 year after diagnosis) from the HUNT2 and HUNT3 surveys. We used Cox regression models and receiver operating characteristic curve analysis to identify predictive factors for progression to insulin dependency within 10 years. RESULTS: Low C-peptide levels (<0.3 nmol/L) predicted progression to insulin dependency within 10 years in both LADA (hazard ratio [HR] 6.40 [95% CI, 2.02-20.3]) and type 2 diabetes (HR 5.01 [95% CI, 3.53-7.10]). In addition, a high glutamic acid decarboxylase autoantibody (GADA) level (HR 5.37 [95% CI, 1.17-24.6]) predicted progression in LADA. Together, these two factors had a discriminatory power between non-progressors and progressors of area under the curve (AUC) of 0.86 (95% CI, 0.80-0.93). In type 2 diabetes, younger age at diagnosis (<50 years: HR 2.83 [95% CI, 1.56-5.15]; 50-69 years: HR 2.11 [95% CI, 1.19-3.74]), high HbA1c levels (≥53 mmol/mol, HR 2.44 [95% CI, 1.72-3.46]), central obesity (HR 1.65 [95% CI, 1.06-2.55]) and a body mass index of more than 30 kg/m2 (HR 1.73 [95% CI, 1.23-2.41]) were independent predictors. Together with C-peptide they reached an AUC of 0.79 (95% CI, 0.76-0.82). CONCLUSION: Factors predicting progression to insulin dependence are partly similar and partly dissimilar between LADA and type 2 diabetes. A constellation of low C-peptide and high GADA levels identifies LADA patients who are probable to progress to insulin dependence.

Vitamin D status and risk of type 2 diabetes in the Norwegian HUNT cohort study: does family history or genetic predisposition modify the association?

INTRODUCTION: We sought to investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) level and the risk of type 2 diabetes mellitus (T2DM) in adults who participated in the Trøndelag Health Study (HUNT), and the possible effect modification by family history and genetic predisposition. RESEARCH DESIGN AND METHODS: This prospective study included 3574 diabetes-free adults at baseline who participated in the HUNT2 (1995-1997) and HUNT3 (2006-2008) surveys. Serum 25(OH)D levels were determined at baseline and classified as <50 and ≥50 nmol/L. Family history of diabetes was defined as self-reported diabetes among parents and siblings. A Polygenic Risk Score (PRS) for T2DM based on 166 single-nucleotide polymorphisms was generated. Incident T2DM was defined by self-report and/or non-fasting glucose levels greater than 11 mmol/L and serum glutamic acid decarboxylase antibody level of <0.08 antibody index at the follow-up. Multivariable logistic regression models were applied to calculate adjusted ORs with 95% CIs. Effect modification by family history or PRS was assessed by likelihood ratio test (LRT). RESULTS: Over 11 years of follow-up, 92 (2.6%) participants developed T2DM. A higher risk of incident T2DM was observed in participants with serum 25(OH)D level of<50 nmol/L compared with those of ≥50 nmol/L (OR 1.72, 95% CI 1.03 to 2.86). Level of 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in adults without family history of diabetes (OR 3.87, 95% CI 1.62 to 9.24) but not in those with a family history (OR 0.72, 95% CI 0.32 to 1.62, p value for LRT=0.003). There was no effect modification by PRS (p value for LRT>0.23). CONCLUSION: Serum 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in Norwegian adults. The inverse association was modified by family history of diabetes but not by genetic predisposition to T2DM.

Impact of road traffic noise on obesity measures: Observational study of three European cohorts.

BACKGROUND: Environmental stressors such as transport noise may contribute to development of obesity through increased levels of stress hormones, sleep deprivation and endocrine disruption. Epidemiological evidence supporting an association of road traffic noise with obesity markers is still relatively scant and confined to certain geographical regions. We aimed to examine the cross-sectional associations between road traffic noise and obesity markers in three large European cohorts involving nearly 500,000 individuals. METHODS: Three population-based cohorts (UK Biobank, Lifelines, HUNT3) were established between 2006 and 2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-h road traffic noise (Lden) for 2009 was modelled from a standardised European noise assessment framework. Residential exposures to NO2 for 2007 and PM2.5 for 2010 were estimated from Europe-wide land use regression models. Obesity markers including body mass index and waist circumference were measured at recruitment. Obesity and central obesity status were subsequently derived. Regression models were fitted in each cohort, adjusting for a harmonised set of demographic and lifestyle covariates, with further adjustments for air pollution in the main model. RESULTS: The main analyses included 412,934 participants of UK Biobank, 61,032 of Lifelines and 30,305 of HUNT3, with a mean age of 43-56 years and Lden ranging 42-89 dB(A) across cohorts. In UK Biobank, per 10 dB(A) higher of Lden: BMI was higher by 0.14kg/m2 (95%CI: 0.11-0.18), waist circumference higher by 0.27 cm (95%CI: 0.19-0.35), odds of obesity was 1.06 (95%CI: 1.04-1.08) and of central obesity was 1.05 (95%CI: 1.04-1.07). These associations were robust to most other sensitivity analyses but attenuated by further adjustment of PM2.5 or area-level socioeconomic status. Associations were more pronounced among women, those with low physical activity, higher household income or hearing impairment. In HUNT3, associations were observed for obesity or central obesity status among those exposed to Lden greater than 55 dB(A). In contrast, no or negative associations were observed in the Lifelines cohort. CONCLUSIONS: This largest study to date providing mixed findings on impacts of long-term exposure to road traffic noise on obesity, which necessitates future analyses using longitudinal data to further investigate this potentially important epidemiological link.

Physical Activity, Genetic Susceptibility, and the Risk of Latent Autoimmune Diabetes in Adults and Type 2 Diabetes.

PURPOSE: Physical activity (PA) has been linked to a reduced risk of type 2 diabetes by reducing weight and improving insulin sensitivity. We investigated whether PA is associated with a lower incidence of latent autoimmune diabetes in adults (LADA) and whether the association is modified by genotypes of human leukocyte antigen (HLA), transcription factor 7-like 2 (TCF7L2)-rs7903146, or the fat mass and obesity-associated gene, FTO-rs9939609. METHODS: We combined data from a Swedish case-control study and a Norwegian prospective study including 621 incident cases of LADA and 3596 cases of type 2 diabetes. We estimated adjusted pooled relative risks (RRs) and 95% CI of diabetes in relation to high (≥ 30 minutes of moderate activity 3 times/week) self-reported leisure time PA, compared to sedentariness. RESULTS: High PA was associated with a reduced risk of LADA (RR 0.61; CI, 0.43-0.86), which was attenuated after adjustment for body mass index (BMI) (RR 0.90; CI, 0.63-1.29). The reduced risk applied only to noncarriers of HLA-DQB1 and -DRB1 (RR 0.49; CI, 0.33-0.72), TCF7L2 (RR 0.62; CI, 0.45-0.87), and FTO (RR 0.51; CI, 0.32-0.79) risk genotypes. Adjustment for BMI attenuated but did not eliminate these associations. For type 2 diabetes, there was an inverse association with PA (RR 0.49; CI, 0.42-0.56), irrespective of genotype. MAIN CONCLUSIONS: Our findings indicate that high PA is associated with a reduced risk of LADA in individuals without genetic susceptibility.

Hours lying down per day, as a proxy for sedentary behaviour and risk of diabetes in young and middle-aged adults in Norway: an 11-year follow-up of the HUNT study.

OBJECTIVE: We aimed to examine relationship between hours lying down per day, as a proxy for sedentary behaviour and risk of diabetes in young and middle-aged adults, and to assess if leisure-time physical activity and body mass index (BMI) modified this relationship. DESIGN: A population-based prospective cohort study. SETTING: Nord-Trøndelag, Norway. PARTICIPANTS: The cohort included 17 058 diabetes-free adults, at an age of 20-55 years in 1995-1997, who were followed-up to 2006-2008. PRIMARY OUTCOME MEASURES: Incident diabetes was defined by self-report of diabetes or non-fasting glucose levels greater than 11 mmol/L at the follow-up. METHODS: Multivariable logistic regression models were used to obtain OR with 95% CI for risk of diabetes by the categories of hours lying down (≤7, 8 and ≥9 hours/day). RESULTS: 362 individuals (2.1%) developed diabetes during an average of 11-year follow-up. Individuals who reported lying down ≥9 hours/day had an adjusted OR of 1.35 (95% CI 1.01 to 1.80) for incident diabetes compared with those lying down 8 hours/day. Lying down ≤7 hours/day was not associated with the risk of diabetes. In analysis stratified by physical activity, the ORs associated with lying down ≥9 hours/day were 1.41 (95% CI 1.05 to 1.90) and 0.90 (95% CI 0.23 to 3.55), respectively, among the less active and highly active individuals (pinteraction=0.048). There was little evidence that the association differed by BMI status (pinteraction=0.62). CONCLUSIONS: Prolonged hours lying down per day was associated with an increased risk of diabetes in young and middle-aged adults. The positive association appeared to be modified by physical activity but not by BMI.