| 1. |
- Guida, Florence, et al.
(författare)
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Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins
- 2018
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Ingår i: JAMA Oncology. - : American Medical Association (AMA). - 2374-2437 .- 2374-2445. ; 4:10
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Tidskriftsartikel (refereegranskat)abstract
- Importance There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.Objective To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria.Design, Setting, and Participants Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS).Main Outcomes and Measures Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity).Results In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model.Conclusions and Relevance This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.
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| 2. |
- Lassale, Camille, et al.
(författare)
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Separate and combined associations of obesity and metabolic health with coronary heart disease : a pan-European case-cohort analysis
- 2018
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 39:5, s. 397-406
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Tidskriftsartikel (refereegranskat)abstract
- Aims: The hypothesis of 'metabolically healthy obesity' implies that, in the absence of metabolic dysfunction, individuals with excess adiposity are not at greater cardiovascular risk. We tested this hypothesis in a large pan-European prospective study.Methods and results: We conducted a case-cohort analysis in the 520 000-person European Prospective Investigation into Cancer and Nutrition study ('EPIC-CVD'). During a median follow-up of 12.2 years, we recorded 7637 incident coronary heart disease (CHD) cases. Using cut-offs recommended by guidelines, we defined obesity and overweight using body mass index (BMI), and metabolic dysfunction ('unhealthy') as ≥ 3 of elevated blood pressure, hypertriglyceridaemia, low HDL-cholesterol, hyperglycaemia, and elevated waist circumference. We calculated hazard ratios (HRs) and 95% confidence intervals (95% CI) within each country using Prentice-weighted Cox proportional hazard regressions, accounting for age, sex, centre, education, smoking, diet, and physical activity. Compared with metabolically healthy normal weight people (reference), HRs were 2.15 (95% CI: 1.79; 2.57) for unhealthy normal weight, 2.33 (1.97; 2.76) for unhealthy overweight, and 2.54 (2.21; 2.92) for unhealthy obese people. Compared with the reference group, HRs were 1.26 (1.14; 1.40) and 1.28 (1.03; 1.58) for metabolically healthy overweight and obese people, respectively. These results were robust to various sensitivity analyses.Conclusion: Irrespective of BMI, metabolically unhealthy individuals had higher CHD risk than their healthy counterparts. Conversely, irrespective of metabolic health, overweight and obese people had higher CHD risk than lean people. These findings challenge the concept of 'metabolically healthy obesity', encouraging population-wide strategies to tackle obesity.
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| 3. |
- Kroeger, Janine, et al.
(författare)
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Circulating Fetuin-A and Risk of Type 2 Diabetes : A Mendelian Randomization Analysis
- 2018
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 67:6, s. 1200-1205
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Tidskriftsartikel (refereegranskat)abstract
- Fetuin-A, a hepatic-origin protein, is strongly positively associated with risk of type 2 diabetes in human observational studies, but it is unknown whether this association is causal. Weaimed to study the potential causal relation of circulating fetuin-A to risk of type 2 diabetes in a Mendelian randomization study with single nucleotide polymorphisms located in the fetuin-A-encoding AHSG gene. We used data from eight European countries of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study including 10,020 incident cases. Plasma fetuin-A concentration was measured in a subset of 965 subcohort participants and 654 case subjects. A genetic score of the AHSG single nucleotide polymorphisms was strongly associated with fetuin-A (28% explained variation). Using the genetic score as instrumental variable of fetuin-A, we observed no significant association of a 50 mu g/mL higher fetuin-A concentration with diabetes risk (hazard ratio 1.02 [95% CI 0.97, 1.07]). Combining our results with those from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (12,171 case subjects) also did not suggest a clear significant relation of fetuin-A with diabetes risk. In conclusion, although there is mechanistic evidence for an effect of fetuin-A on insulin sensitivity and secretion, this study does not support a strong, relevant relationship between circulating fetuin-A and diabetes risk in the general population.
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| 4. |
- Li, Sherly X, et al.
(författare)
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Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries.
- 2018
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 61:6, s. 1325-1332
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Tidskriftsartikel (refereegranskat)abstract
- Gene-macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes.We investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution.No interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0-51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined.Genetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions.
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| 5. |
- Ward, Heather A., et al.
(författare)
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Meat and haem iron intake in relation to glioma in the European Prospective Investigation into Cancer and Nutrition study
- 2018
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Ingår i: European Journal of Cancer Prevention. - : Lippincott Williams & Wilkins. - 0959-8278 .- 1473-5709. ; 27:4, s. 379-383
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Tidskriftsartikel (refereegranskat)abstract
- Diets high in red or processed meat have been associated positively with some cancers, and several possible underlying mechanisms have been proposed, including iron-related pathways. However, the role of meat intake in adult glioma risk has yielded conflicting findings because of small sample sizes and heterogeneous tumour classifications. The aim of this study was to examine red meat, processed meat and iron intake in relation to glioma risk in the European Prospective Investigation into Cancer and Nutrition study. In this prospective cohort study, 408751 individuals from nine European countries completed demographic and dietary questionnaires at recruitment. Multivariable Cox proportional hazards models were used to examine intake of red meat, processed meat, total dietary iron and haem iron in relation to incident glioma. During an average follow-up of 14.1 years, 688 incident glioma cases were diagnosed. There was no evidence that any of the meat variables (red, processed meat or subtypes of meat) or iron (total or haem) were associated with glioma; results were unchanged when the first 2 years of follow-up were excluded. This study suggests that there is no association between meat or iron intake and adult glioma. This is the largest prospective analysis of meat and iron in relation to glioma and as such provides a substantial contribution to a limited and inconsistent literature.
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| 6. |
- Meidtner, Karina, et al.
(författare)
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Interaction of Dietary and Genetic Factors Influencing Body Iron Status and Risk of Type 2 Diabetes Within the EPIC-InterAct Study
- 2018
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 41:2, s. 277-285
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Meat intake has been consistently shown to be positively associated with incident type 2 diabetes. Part of that association may be mediated by body iron status, which is influenced by genetic factors. We aimed to test for interactions of genetic and dietary factors influencing body iron status in relation to the risk of incident type 2 diabetes.RESEARCH DESIGN AND METHODS: The case-cohort comprised 9,347 case subjects and 12,301 subcohort participants from eight European countries. Single nucleotide polymorphisms (SNPs) were selected from genome-wide association studies on iron status biomarkers and candidate gene studies. A ferritin-related gene score was constructed. Multiplicative and additive interactions of heme iron and SNPs as well as the gene score were evaluated using Cox proportional hazards regression.RESULTS: Higher heme iron intake (per 1 SD) was associated with higher ferritin levels (β = 0.113 [95% CI 0.082; 0.144]), but not with transferrin (−0.019 [−0.043; 0.006]) or transferrin saturation (0.016 [−0.006; 0.037]). Five SNPs located in four genes (rs1799945 [HFE H63D], rs1800562 [HFE C282Y], rs236918 [PCK7], rs744653 [SLC40A1], and rs855791 [TMPRSS6V736A]) were associated with ferritin. We did not detect an interaction of heme iron and the gene score on the risk of diabetes in the overall study population (Padd = 0.16, Pmult = 0.21) but did detect a trend toward a negative interaction in men (Padd = 0.04, Pmult = 0.03).CONCLUSIONS: We found no convincing evidence that the interplay of dietary and genetic factors related to body iron status associates with type 2 diabetes risk above the level expected from the sum or product of the two individual exposures.
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| 7. |
- van Roekel, Eline H., et al.
(författare)
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Circulating metabolites associated with alcohol intake in the european prospective investigation into cancer and nutrition cohort
- 2018
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Ingår i: Nutrients. - : MDPI AG. - 2072-6643. ; 10:5
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Tidskriftsartikel (refereegranskat)abstract
- Identifying the metabolites associated with alcohol consumption may provide insights into the metabolic pathways through which alcohol may affect human health. We studied associations of alcohol consumption with circulating concentrations of 123 metabolites among 2974 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Alcohol consumption at recruitment was self-reported through dietary questionnaires. Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQTMp180 kit). Data were randomly divided into discovery (2/3) and replication (1/3) sets. Multivariable linear regression models were used to evaluate confounder-adjusted associations of alcohol consumption withmetabolite concentrations. Metabolites significantly related to alcohol intake in the discovery set (FDR q-value < 0.05) were further tested in the replication set (Bonferroni-corrected p-value < 0.05). Of the 72metabolites significantly related to alcohol intake in the discovery set, 34 were also significant in the replication analysis, including three acylcarnitines, the amino acid citrulline, four lysophosphatidylcholines, 13 diacylphosphatidylcholines, seven acyl-alkylphosphatidylcholines, and six sphingomyelins. Our results confirmed earlier findings that alcohol consumption was associated with several lipid metabolites, and possibly also with specific acylcarnitines and amino acids. This provides further leads for future research studies aiming at elucidating the mechanisms underlying the effects of alcohol in relation to morbid conditions.
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