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- Freisling, Heinz, et al.
(author)
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Nut intake and 5-year changes in body weight and obesity risk in adults: results from the EPIC-PANACEA study
- 2018
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In: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6207 .- 1436-6215. ; 57:7, s. 2399-2408
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Journal article (peer-reviewed)abstract
- © 2017 Springer-Verlag GmbH Germany Purpose: There is inconsistent evidence regarding the relationship between higher intake of nuts, being an energy-dense food, and weight gain. We investigated the relationship between nut intake and changes in weight over 5 years. Methods: This study includes 373,293 men and women, 25–70 years old, recruited between 1992 and 2000 from 10 European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Habitual intake of nuts including peanuts, together defined as nut intake, was estimated from country-specific validated dietary questionnaires. Body weight was measured at recruitment and self-reported 5 years later. The association between nut intake and body weight change was estimated using multilevel mixed linear regression models with center/country as random effect and nut intake and relevant confounders as fixed effects. The relative risk (RR) of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to baseline body mass index (BMI). Results: On average, study participants gained 2.1 kg (SD 5.0 kg) over 5 years. Compared to non-consumers, subjects in the highest quartile of nut intake had less weight gain over 5 years (−0.07 kg; 95% CI −0.12 to −0.02) (P trend = 0.025) and had 5% lower risk of becoming overweight (RR 0.95; 95% CI 0.92–0.98) or obese (RR 0.95; 95% CI 0.90–0.99) (both P trend < 0.008). Conclusions: Higher intake of nuts is associated with reduced weight gain and a lower risk of becoming overweight or obese.
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| 2. |
- Guida, Florence, et al.
(author)
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Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins
- 2018
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In: JAMA Oncology. - : American Medical Association (AMA). - 2374-2437 .- 2374-2445. ; 4:10
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Journal article (peer-reviewed)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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| 3. |
- Lassale, Camille, et al.
(author)
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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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In: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 39:5, s. 397-406
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Journal article (peer-reviewed)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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