Levels and trends of persistent organic pollutants in human populations living in the Arctic.

The Arctic Monitoring Assessment Program (AMAP) is tasked with monitoring and assessing the status of environmental contaminants in the Arctic, documenting levels and trends, and producing science-based assessments. The objectives of this paper are to present the current levels of persistent organic pollutants (POPs) across the Arctic, and to identify trends and knowledge gaps as detailed in the most recent AMAP Human Health Assessment Report. Many Arctic populations continue to have elevated levels of these contaminants, and the highest levels of POPs were observed in populations from Greenland, Faroe Islands, and Nunavik (Canada), as well as populations in the coastal Chukotka district (Russia) for legacy POPs only. Concentrations of most POPs are declining in Arctic populations in regions where time trends data exist, although the declines are not consistent across all regions. The exceptions are per- and polyfluoroalkyl substances, with concentrations of some long-chain PFAS such as perfluorononanoic acid increasing in populations in Nunavik, Greenland and Sweden. This paper provides a more extensive summary of levels of contaminants in adults, pregnant women, and children across the Arctic than previous AMAP human health assessments, particularly for levels of long-chain PFAS, which are currently under consideration for inclusion in the Stockholm Convention.

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models.

PURPOSE: The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information? METHODS: A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663). RESULTS: The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)). CONCLUSION: The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

On the use of the healthy lifestyle index to investigate specific disease outcomes.

The healthy lifestyle index (HLI), defined as the unweighted sum of individual lifestyle components, was used to investigate the combined role of lifestyle factors on health-related outcomes. We introduced weighted outcome-specific versions of the HLI, where individual lifestyle components were weighted according to their associations with disease outcomes. Within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined the association between the standard and the outcome-specific HLIs and the risk of T2D, CVD, cancer, and all-cause premature mortality. Estimates of the hazard ratios (HRs), the Harrell's C-index and the population attributable fractions (PAFs) were compared. For T2D, the HR for 1-SD increase of the standard and T2D-specific HLI were 0.66 (95% CI: 0.64, 0.67) and 0.43 (0.42, 0.44), respectively, and the C-index were 0.63 (0.62, 0.64) and 0.72 (0.72, 0.73). Similar, yet less pronounced differences in HR and C-index were observed for standard and outcome-specific estimates for cancer, CVD and all-cause mortality. PAF estimates for mortality before age 80 were 57% (55%, 58%) and 33% (32%, 34%) for standard and mortality-specific HLI, respectively. The use of outcome-specific HLI could improve the assessment of the role of lifestyle factors on disease outcomes, thus enhancing the definition of public health recommendations.

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study.

INTRODUCTION: Blood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis. METHODS: Serum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0-8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated. RESULTS: Fifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples. CONCLUSION: There were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Healthy lifestyle change and all-cause and cancer mortality in the European Prospective Investigation into Cancer and Nutrition cohort.

BACKGROUND: Healthy lifestyles are inversely associated with the risk of noncommunicable diseases, which are leading causes of death. However, few studies have used longitudinal data to assess the impact of changing lifestyle behaviours on all-cause and cancer mortality. METHODS: Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, lifestyle profiles of 308,497 cancer-free adults (71% female) aged 35-70 years at recruitment across nine countries were assessed with baseline and follow-up questionnaires administered on average of 7 years apart. A healthy lifestyle index (HLI), assessed at two time points, combined information on smoking status, alcohol intake, body mass index, and physical activity, and ranged from 0 to 16 units. A change score was calculated as the difference between HLI at baseline and follow-up. Associations between HLI change and all-cause and cancer mortality were modelled with Cox regression, and the impact of changing HLI on accelerating mortality rate was estimated by rate advancement periods (RAP, in years). RESULTS: After the follow-up questionnaire, participants were followed for an average of 9.9 years, with 21,696 deaths (8407 cancer deaths) documented. Compared to participants whose HLIs remained stable (within one unit), improving HLI by more than one unit was inversely associated with all-cause and cancer mortality (hazard ratio [HR]: 0.84; 95% confidence interval [CI]: 0.81, 0.88; and HR: 0.87; 95% CI: 0.82, 0.92; respectively), while worsening HLI by more than one unit was associated with an increase in mortality (all-cause mortality HR: 1.26; 95% CI: 1.20, 1.33; cancer mortality HR: 1.19; 95% CI: 1.09, 1.29). Participants who worsened HLI by more than one advanced their risk of death by 1.62 (1.44, 1.96) years, while participants who improved HLI by the same amount delayed their risk of death by 1.19 (0.65, 2.32) years, compared to those with stable HLI. CONCLUSIONS: Making healthier lifestyle changes during adulthood was inversely associated with all-cause and cancer mortality and delayed risk of death. Conversely, making unhealthier lifestyle changes was positively associated with mortality and an accelerated risk of death.

Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries.

PURPOSE: Previously reported associations of protein-rich foods with stroke subtypes have prompted interest in the assessment of individual amino acids. We examined the associations of dietary amino acids with risks of ischaemic and haemorrhagic stroke in the EPIC study. METHODS: We analysed data from 356,142 participants from seven European countries. Dietary intakes of 19 individual amino acids were assessed using validated country-specific dietary questionnaires, calibrated using additional 24-h dietary recalls. Multivariable-adjusted Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of ischaemic and haemorrhagic stroke in relation to the intake of each amino acid. The role of blood pressure as a potential mechanism was assessed in 267,642 (75%) participants. RESULTS: After a median follow-up of 12.9 years, 4295 participants had an ischaemic stroke and 1375 participants had a haemorrhagic stroke. After correction for multiple testing, a higher intake of proline (as a percent of total protein) was associated with a 12% lower risk of ischaemic stroke (HR per 1 SD higher intake 0.88; 95% CI 0.82, 0.94). The association persisted after mutual adjustment for all other amino acids, systolic and diastolic blood pressure. The inverse associations of isoleucine, leucine, valine, phenylalanine, threonine, tryptophan, glutamic acid, serine and tyrosine with ischaemic stroke were each attenuated with adjustment for proline intake. For haemorrhagic stroke, no statistically significant associations were observed in the continuous analyses after correcting for multiple testing. CONCLUSION: Higher proline intake may be associated with a lower risk of ischaemic stroke, independent of other dietary amino acids and blood pressure.

Dietary intakes of dioxins and polychlorobiphenyls (PCBs) and mortality: EPIC cohort study in 9 European countries.

Dioxins and polychlorinated biphenyls (PCBs) are toxic, endocrine disruptors and persistent chemicals for which the main exposure source is diet due to their bioaccumulation and biomagnification in food chains. Cohort studies in the general populations have reported inconsistent associations between these chemicals in serum/plasma and mortality. Our objective was to study the association between dietary intake of 17 dioxins and 35 PCBs and all-cause, cancer-specific and cardiovascular-specific mortalities were assessed in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Dietary intake of dioxins and PCBs was assessed combining EPIC food consumption data with European food contamination data provided by the European Food Safety Authority. We applied multivariable Cox regressions. The analysis included 451,390 adults (mean ± SD age:51.1 ± 9.7 years) with 46,627 deaths and a median follow-up of 17.4 years (IQR = 15.2-19.1). A U-shaped non-linear association with all-cause mortality for dietary intake of dioxins (Pnon-linearity<0.0001), DL-PCB (Pnon-linearity = 0.0001), and NDL-PCBs (Pnon-linearity<0.01) was observed. For example, the hazard ratios (95%Confidance interval) for all-cause mortality obtained with the spline model was equal to 1.03 (1.02-1.05) for low levels of intake to dioxins (7 pg TEQ/day), 0.93 (0.90-0.96) for moderate levels of intake (25 pg TEQ/day), while for high levels of intake (55 pg TEQ/day) it was 1.03 (0.97-1.09). Intake of dioxins, DL-PCBs and NDL-PCBs was not associated with cardiovascular mortality. There was no association between intakes of dioxins and cancer mortality, but a U-shaped association was observed for intake of DL-PCBs and intakes of NDL-PCBs and cancer mortality. The PCBs and dioxins are known to have endocrine disrupting properties which can lead to non-monotonic dose responses. These results need to be interpreted with caution and further studies are needed to better clarify the association between dietary intake of dioxins and PCB and mortality in the general population.

Automated calibration for stability selection in penalised regression and graphical models.

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

Prediagnostic selenium status, selenoprotein gene variants and association with breast cancer risk in a European cohort study.

Selenium (Se) may help prevent breast cancer (BC) development. Owing to limited observational evidence, we investigated whether prediagnostic Se status and/or variants in the selenoprotein genes are associated with BC risk in a large European cohort. Se status was assessed by plasma measures of Se and its major circulating proteins, selenoprotein P (SELENOP) and glutathione peroxidase 3 (GPX3), in matched BC case-control pairs (2208 for SELENOP; 1785 for GPX3 and Se) nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). Single nucleotide polymorphisms (SNPs, n = 452) in 55 selenoprotein and Se metabolic pathway genes and an additional 18 variants previously associated with Se concentrations were extracted from existing genotyping data within EPIC for 1564 case-control pairs. Multivariable-adjusted logistic regression models were used to calculate the odds ratios (ORs) and 95 % confidence intervals (CIs) of the association between Se status markers, SNP variants and BC risk. Overall, there was no statistically significant association of Se status with BC risk. However, higher GPX3 activity was associated with lower risk of premenopausal BC (4th versus 1st quartile, OR = 0.54, 95 % CI: 0.30-0.98, Ptrend = 0.013). While none of the genetic variant associations (P ≤ 0.05) retained significance after multiple testing correction, rs1004243 in the SELENOM selenoprotein gene and two SNPs in the related antioxidant TXN2 gene (rs4821494 and rs5750261) were associated with respective lower and higher risks of BC at a significance threshold of P ≤ 0.01. Fourteen SNPs in twelve Se pathway genes (P ≤ 0.01) in interaction with Se status were also associated with BC risk. Higher Se status does not appear to be associated with BC risk, although activity of the selenoenzyme GPX3 may be inversely associated with premenopausal BC risk, and SNPs in the Se pathway alone or in combination with suboptimal Se status may influence BC risk.

Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis.

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

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.

Polybrominated diphenyl ethers in type 2 diabetes mellitus cases and controls: Repeated measurements prior to and after diagnosis.

BACKGROUND: Previous studies have reported associations between certain persistent organic pollutants (POPs) and type 2 diabetes mellitus (T2DM). Polybrominated diphenyl ethers (PBDEs) are a class of POPs that are found in increasing concentrations in humans. Although obesity is a known risk factor for T2DM and PBDEs are fat-soluble, very few studies have investigated associations between PBDEs and T2DM. No longitudinal studies have assessed associations between repeated measurements of PBDE and T2DM in the same individuals and compared time trends of PBDEs in T2DM cases and controls. OBJECTIVES: To investigate associations between pre- and post-diagnostic measurements of PBDEs and T2DM and to compare time trends of PBDEs in T2DM cases and controls. METHODS: Questionnaire data and serum samples from participants in the Tromsø Study were used to conduct a longitudinal nested case-control study among 116 T2DM cases and 139 controls. All included study participants had three pre-diagnostic blood samples (collected before T2DM diagnosis in cases), and up to two post-diagnostic samples after T2DM diagnosis. We used logistic regression models to investigate pre- and post-diagnostic associations between PBDEs and T2DM, and linear mixed-effect models to assess time trends of PBDEs in T2DM cases and controls. RESULTS: We observed no substantial pre- or post-diagnostic associations between any of the PBDEs and T2DM, except for BDE-154 at one of the post-diagnostic time-points (OR = 1.65, 95% CI: 1.00, 2.71). The overall time trends of PBDE concentrations were similar for cases and controls. DISCUSSION: The study did not support PBDEs increasing the odds of T2DM, prior to or after T2DM diagnosis. T2DM status did not influence the time trends of PBDE concentrations.

A practical checklist for return of results from genomic research in the European context.

An increasing number of European research projects return, or plan to return, individual genomic research results (IRR) to participants. While data access is a data subject's right under the General Data Protection Regulation (GDPR), and many legal and ethical guidelines allow or require participants to receive personal data generated in research, the practice of returning results is not straightforward and raises several practical and ethical issues. Existing guidelines focusing on return of IRR are mostly project-specific, only discuss which results to return, or were developed outside Europe. To address this gap, we analysed existing normative documents identified online using inductive content analysis. We used this analysis to develop a checklist of steps to assist European researchers considering whether to return IRR to participants. We then sought feedback on the checklist from an interdisciplinary panel of European experts (clinicians, clinical researchers, population-based researchers, biobank managers, ethicists, lawyers and policy makers) to refine the checklist. The checklist outlines seven major components researchers should consider when determining whether, and how, to return results to adult research participants: 1) Decide which results to return; 2) Develop a plan for return of results; 3) Obtain participant informed consent; 4) Collect and analyse data; 5) Confirm results; 6) Disclose research results; 7) Follow-up and monitor. Our checklist provides a clear outline of the steps European researchers can follow to develop ethical and sustainable result return pathways within their own research projects. Further legal analysis is required to ensure this checklist complies with relevant domestic laws.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts.

BACKGROUND: Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts. METHODS: Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases. RESULTS: Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded. CONCLUSIONS: Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

Calcium Intake and Lung Cancer Risk: A Pooled Analysis of 12 Prospective Cohort Studies.

BACKGROUND: Previous studies on calcium intake and lung cancer risk reported inconsistent associations, possibly due to the differences in intake amounts and contributing sources of calcium and smoking prevalence. OBJECTIVES: We investigated the associations of lung cancer risk with intake of calcium from foods and/or supplements and major calcium-rich foods in 12 studies. METHODS: Data from 12 prospective cohort studies conducted in the United States, Europe, and Asia were pooled and harmonized. We applied the DRI to categorize calcium intake based on the recommendations and quintile distribution to categorize calcium-rich food intake. We ran multivariable Cox regression by each cohort and pooled risk estimates to compute overall HR (95% CI). RESULTS: Among 1,624,244 adult men and women, 21,513 incident lung cancer cases were ascertained during a mean follow-up of 9.9 y. Overall, the dietary calcium intake was not significantly associated with lung cancer risk; the HRs (95% CI) were 1.08 (0.98-1.18) for higher (>1.5 RDA) and 1.01 (0.95-1.07) for lower intake (<0.5 RDA) comparing with recommended intake (EAR to RDA). Milk and soy food intake were positively or inversely associated with lung cancer risk [HR (95% CI) = 1.07 (1.02-1.12) and 0.92 (0.84-1.00)], respectively. The positive association with milk intake was significant only in European and North American studies (P-interaction for region = 0.04). No significant association was observed for calcium supplements. CONCLUSIONS: In this largest prospective investigation, overall, calcium intake was not associated with risk of lung cancer, but milk intake was associated with a higher risk. Our findings underscore the importance of considering food sources of calcium in studies of calcium intake.

Increased levels of microRNA-320 in blood serum and plasma is associated with imminent and advanced lung cancer.

Lung cancer (LC) incidence is increasing globally and altered levels of microRNAs (miRNAs) in blood may contribute to identification of individuals with LC. We identified miRNAs differentially expressed in peripheral blood at LC diagnosis and evaluated, in pre-diagnostic blood specimens, how long before diagnosis expression changes in such candidate miRNAs could be detected. We identified upregulated candidate miRNAs in plasma specimens from a hospital-based study sample of 128 patients with confirmed LC and 62 individuals with suspected but confirmed negative LC (FalsePos). We then evaluated the expression of candidate miRNAs in pre-diagnostic plasma or serum specimens of 360 future LC cases and 375 matched controls. There were 1663 miRNAs detected in diagnostic specimens, nine of which met our criteria for candidate miRNAs. Higher expression of three candidates, miR-320b, 320c, and 320d, was associated with poor survival, independent of LC stage and subtype. Moreover, miR-320c and miR-320d expression was higher in pre-diagnostic specimens collected within 2 years of LC diagnosis. Our results indicated that elevated levels of miR-320c and miR-320d may be early indications of imminent and advanced LC.

Assessment of the EarlyCDT-Lung test as an early biomarker of lung cancer in ever-smokers: A retrospective nested case-control study in two prospective cohorts.

The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample <3 years prior to diagnosis. Conditional logistic regression was used to estimate the association between EarlyCDT-Lung test results and lung cancer risk. Sensitivity and specificity of the EarlyCDT-Lung test were calculated in all subjects and subgroups based on age, smoking history, lung cancer stage, sample collection time before diagnosis and year of sample collection. The overall lung cancer odds ratios were 0.89 (95% CI: 0.34-2.30) for a moderate risk EarlyCDT-Lung test result and 1.09 (95% CI: 0.48-2.47) for a high-risk test result compared to no significant test result. The overall sensitivity was 8.4% (95% CI: 4.6-14) and overall specificity was 92% (95% CI: 87-96) when considering a high-risk result as positive. Stratified analysis indicated higher sensitivity (17%, 95% CI: 7.2-32.1) in subjects with blood drawn up to 1 year prior to diagnosis. In conclusion, our study does not support a role of the EarlyCDT-Lung test in identifying the high-risk subjects in ever-smokers for lung cancer screening in the EPIC and NSHDS cohorts.

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases.

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans but with limited evidence in other ancestries. Here, we present a multi-ancestry proteome-wide MR analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the putative causal effects of 1,545 proteins on eight diseases in African (32,658) and European (1,219,993) ancestries and identified 45 and 7 protein-disease pairs with MR and genetic colocalization evidence in the two ancestries, respectively. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence in both ancestries and seven pairs with specific effects in the two ancestries separately. Integrating these MR signals with clinical trial evidence, we prioritized 16 pairs for investigation in future drug trials. Our results highlight the value of proteome-wide MR in informing the generalizability of drug targets for disease prevention across ancestries and illustrate the value of meta-analysis of biobanks in drug development.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.