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Lung cancer risk prediction models are considered more accurate than the eligibility criteria based on age and smoking in identification of high-risk individuals for screening. We externally validated four lung cancer risk prediction models (Bach, Spitz, LLP, and PLCOM2012) among 20,700 ever smokers in the EPIC-Germany cohort. High-risk subjects were identified using the eligibility criteria applied in clinical trials (NELSON/LUSI, DLCST, ITALUNG, DANTE, and NLST) and the four risk prediction models. Sensitivity, specificity, and positive predictive value (PPV) were calculated based on the lung cancers diagnosed in the first 5 years of follow-up. Decision curve analysis was performed to compare net benefits. The number of high-risk subjects identified by the eligibility criteria ranged from 3,409 (NELSON/LUSI) to 1,458 (NLST). Among the eligibility criteria, the DLCST produced the highest sensitivity (64.13%), whereas the NLST produced the highest specificity (93.13%) and PPV (2.88%). The PLCOM2012 model showed the best performance in external validation (C-index: 0.81; 95% CI, 0.76–0.86; E/O: 1.03; 95% CI, 0.87–1.23) and the highest sensitivity, specificity, and PPV, but the superiority over the Bach model and the LLP model was modest. All the models but the Spitz model showed greater net benefit over the full range of risk estimates than the eligibility criteria. We concluded that all of the lung cancer risk prediction models apart from the Spitz model have a similar accuracy to identify high-risk individuals for screening, but in general outperform the eligibility criteria used in the screening trials. Cancer Prev Res; 8(9); 777–85. ©2015 AACR.

Supplementary Table 1, Figures 1-4. Supplementary Table 1. Pairwise kappa before and after overlapping age range was applied (the lower triangle) and the net reclassification index (NRI, %, the upper triangle). Supplementary Figure 1. Participants selected for LDCT screening using different eligibility criteria among ever smokers in the EPIC-Germany cohort (N = 20,700). Supplementary Figure 2. ROC curves of the lung cancer risk prediction models applied to ever smokers in the EPIC-Germany cohort (N = 20,700). Supplementary Figure 3. The cumulative distribution of 5-year absolute risk estimates in the EPIC-Germany cohort (N = 20,700) and the LUSI study (N = 33,621). Supplementary Figure 4. The cumulative distribution of 5-year absolute risk estimates before and after missing risk factors were simulated (ever smokers in EPIC-Germany, N = 20,700).

Supplementary Table 2

In Germany, it is currently recommended that women start mammographic breast cancer screening at age 50. However, recently updated guidelines state that for women younger than 50 and older than 70 years of age, screening decisions should be based on individual risk. International clinical guidelines recommend starting screening when a woman's 5-year risk of breast cancer exceeds 1.7%. We thus compared the performance of the current age-based screening practice with an alternative risk-adapted approach using data from a German population representative survey. We found that 10,498,000 German women ages 50–69 years are eligible for mammographic screening based on age alone. Applying the 5-year risk threshold of 1.7% to individual breast cancer risk estimated from a model that considers a woman's reproductive and personal characteristics, 39,000 German women ages 40–49 years would additionally be eligible. Among those women, the number needed to screen to detect one breast cancer case, NNS, was 282, which was close to the NNS = 292 among all 50- to 69-year-old women. In contrast, NNS = 703 for the 113,000 German women ages 50–69 years old with 5-year breast cancer risk Prevention Relevance:We show that a risk-based approach to mammography screening for German women can help detect breast cancer in women ages 40–49 years with increased risk and reduce screening costs and burdens for low-risk women ages 50–69 years. However, before recommending a particular implementation of a risk-based mammographic screening approach, further investigations of models and thresholds used are needed.

C-statistics by tumor histology for variable lag-times since blood donation

Purpose: About 60% of ovarian cancers are diagnosed at late stage, when 5-year survival is less than 30% in contrast to 90% for local disease. This has prompted search for early detection biomarkers. For initial testing, specimens taken months or years before ovarian cancer diagnosis are the best source of information to evaluate early detection biomarkers. Here we evaluate the most promising ovarian cancer screening biomarkers in prospectively collected samples from the European Prospective Investigation into Cancer and Nutrition study.Experimental Design: We measured CA125, HE4, CA72.4, and CA15.3 in 810 invasive epithelial ovarian cancer cases and 1,939 controls. We calculated the sensitivity at 95% and 98% specificity as well as area under the receiver operator curve (C-statistic) for each marker individually and in combination. In addition, we evaluated marker performance by stage at diagnosis and time between blood draw and diagnosis.Results: We observed the best discrimination between cases and controls within 6 months of diagnosis for CA125 (C-statistic = 0.92), then HE4 (0.84), CA72.4 (0.77), and CA15.3 (0.73). Marker performance declined with longer time between blood draw and diagnosis and for earlier staged disease. However, assessment of discriminatory ability at early stage was limited by small numbers. Combinations of markers performed modestly, but significantly better than any single marker.Conclusions: CA125 remains the single best marker for the early detection of invasive epithelial ovarian cancer, but can be slightly improved by combining with other markers. Identifying novel markers for ovarian cancer will require studies including larger numbers of early-stage cases. Clin Cancer Res; 22(18); 4664–75. ©2016 AACR.See related commentary by Skates, p. 4542

Case characteristics (median (min-max) or n (%)) by histologic subtypes in the EPIC cohort

Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case–control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone–binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor–positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. Clin Cancer Res; 23(15); 4181–9. ©2017 AACR.

Evaluation of added discriminatory ability of each biomarker using Net Reclassification Improvement (NRI)

Supplemental Table 1 shows associations between candidate biomarkers and breast cancer risk in women postmenopausal at blood collection using 5-year risk from Pfeiffer's model as a regression offset. ORs are estimated per doubling of concentration (from log2-transformed measurements) Supplemental Table 2 shows the estimated biomarker effects on risk of breast cancer, per doubling of concentration, in women postmenopausal at blood collection. The estimates provided are adjusted for Pfeiffer's risk score from the common model (i.e. mutually adjusted) and in the selected model. Supplemental Table 3 shows the performance of the full and selected models in women postmenopausal at blood collection in terms of concordance-statistic ("C"), integrated discrimination improvement index (IDI), and net-reclassification improvement (NRI continuous), each with 95%-Confidence interval and optimism-correction from 1000 bootstrap-samples.

Spearman coefficients of correlation between detection markers

Background: The aim of the long-term Heinz Nixdorf Recall Study (observation period 20 years) was to establish the extent to which computed tomography (CT) improves the predictability of cardiovascular events relative to determination of risk factors alone. Methods: In the period 2000–2003, study staff examined 4355 probands (53% of them female) aged 45–75 years with no signs of cardiovascular disease. The Atherosclerotic Cardiovascular Disease (ASCVD) score was calculated on the basis of demographic data and cardiovascular risk factors. Cardiac CT was carried out over the same period and coronary artery calcification (CAC) was graded according to the Agatston score. Results: The median duration of follow-up was 18.2 years for men and 17.8 years for women. Myocardial infarction or stroke occurred in 458 (11%) of the 4154 participants with complete data. Overall, estimation of risk using a combination of ASCVD score and CAC grade was superior to the ASCVD score alone—even after 10 and 20 years. Classification into established risk categories improved by 12.2% (95% confidence interval: [5.3%; 18.1%]). In the highest ASCVD risk category, we observed occurrence of a cardiovascular event over 20 years for 14% [5.0%; 23.1%] of probands with a CAC score = 0 but for 34.2% [27.5%; 41.4%] of those with a CAC score ≥ 400. In the lowest ASCVD risk category, an event occurred in 2.4% [1.4%; 3.7%] of probands with a CAC score = 0 and in 23.5% [2.3%; 35.8%] of those with a CAC score ≥ 400. Conclusion: Even after 20 years, individual risk prediction is improved by addition of CT-based determination of coronary artery calcification to the ASCVD score. Therefore, assessment of ASCVD risk factors should be complemented more widely by cardiac CT in the primary prevention of cardiovascular disease.

BACKGROUND: The aim of the long-term Heinz Nixdorf Recall Study (observation period 20 years) was to establish the extent to which computed tomography (CT) improves the predictability of cardiovascular events relative to determination of risk factors alone. METHODS: In the period 2000–2003, study staff examined 4355 probands (53% of them female) aged 45–75 years with no signs of cardiovascular disease. The Atherosclerotic Cardiovascular Disease (ASCVD) score was calculated on the basis of demographic data and cardiovascular risk factors. Cardiac CT was carried out over the same period and coronary artery calcification (CAC) was graded according to the Agatston score. RESULTS: The median duration of follow-up was 18.2 years for men and 17.8 years for women. Myocardial infarction or stroke occurred in 458 (11%) of the 4154 participants with complete data. Overall, estimation of risk using a combination of ASCVD score and CAC grade was superior to the ASCVD score alone—even after 10 and 20 years. Classification into established risk categories improved by 12.2% (95% confidence interval: [5.3%; 18.1%]). In the highest ASCVD risk category, we observed occurrence of a cardiovascular event over 20 years for 14% [5.0%; 23.1%] of probands with a CAC score = 0 but for 34.2% [27.5%; 41.4%] of those with a CAC score ≥ 400. In the lowest ASCVD risk category, an event occurred in 2.4% [1.4%; 3.7%] of probands with a CAC score = 0 and in 23.5% [2.3%; 35.8%] of those with a CAC score ≥ 400. CONCLUSION: Even after 20 years, individual risk prediction is improved by addition of CT-based determination of coronary artery calcification to the ASCVD score. Therefore, assessment of ASCVD risk factors should be complemented more widely by cardiac CT in the primary prevention of cardiovascular disease.

Background The optimal timing of anticoagulation following acute ischaemic stroke or TIA in patients with atrial fibrillation (AF) is a frequent challenge. Early initiation of anticoagulation can reduce the risk for recurrent ischaemic events, but may lead to an increased risk for intracerebral haemorrhage. Aim The Prospective Record of the Use of Dabigatran in Patients with Acute Stroke or TIA (PRODAST) study was initiated to investigate outcome events under antithrombotic therapy after ischaemic stroke or TIA in patients with AF. The main objective is to compare the three-month rates of major haemorrhagic events between early (≤ 7 days) versus late (> 7 days) administration of dabigatran or treatment with vitamin-K antagonists started at any time. Occurrences of ischaemic and major haemorrhagic events will be evaluated to determine the optimal time point for initiation or resumption of anticoagulation. Design and Methods PRODAST is a prospective, multicenter, observational, non-interventional post-authorization safety study. 10,000 patients with recent (≤ 1 week from index event) ischaemic stroke or TIA and non-valvular AF were recruited at 86 German sites starting in July 2015. The observational plan includes a baseline visit, documentation of data during hospitalization and a telephone-based, central follow-up at three months after the index event. The primary endpoint is the major bleeding rate within three months. Secondary endpoints include rates of recurrent ischaemic or haemorrhagic stroke, TIA, systemic embolism, myocardial infarction and death. Summary PRODAST will provide important real-world data on safety and efficacy of antithrombotic therapy after acute stroke and TIA in patients with AF.

Changes in DNA methylation identified by epigenome-wide association studies (EWAS) have been recently linked to increased lung cancer risk. However, the cellular effects of these differentially methylated positions (DMPs) are often unclear. Therefore, we investigated top differentially methylated positions identified from an EWAS study. This included a putative regulatory region of

Background: Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population. Methods: The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992–2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed. Results: The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell’s C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264–0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084–0.575)). Conclusions: LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level., German Research Foundation (DFG) AL 1784/3-1, European Commission European Commission Joint Research Centre, International Agency for Research on Cancer, Danish Cancer Society, Ligue Contre le Cancer (France), Institut Gustave Roussy (France), Mutuelle Generale de l'Education Nationale (France), Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum (DKFZ) (Germany), Federal Ministry of Education & Research (BMBF), Hellenic Health Foundation (Greece), Associazione Italiana per la Ricerca sul Cancro (AIRC), Consiglio Nazionale delle Ricerche (CNR), Netherlands Government, World Cancer Research Fund International (WCRF), Instituto de Salud Carlos III PI13/00061 PI13/01162, Junta de Andalucia, Regional Government of Asturias (Spain), Regional Government of Basque Country (Spain), Regional Government of Murcia (Spain) 6236, Regional Government of Navarra (Spain), Regional Government of Catalonia (Catalan Institute of Oncology -ICO-IDIBELL) (Spain), Swedish Cancer Society, Swedish Research Council, County Council of Skane (Sweden), County Council of Vasterbotten (Sweden), Cancer Research UK C864/A14136 C8221/A19170, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/N003284/1 MC-UU_12015/1 MR/M012190/1, Projekt DEAL

ObjectivesThe first German SARS-CoV-2 outbreak was a superspreading event in Gangelt, North Rhine-Westphalia, during indoor carnival festivities called ‘Kappensitzung’ (15 February 2020). We determined SARS-CoV-2 RT-PCR positivity rate, SARS-CoV-2-specific antibodies, and analysed the conditions and dynamics of superspreading, including ventilation, setting dimensions, distance from infected persons and behavioural patterns.DesignIn a cross-sectional epidemiological study (51 days postevent), participants were asked to give blood, pharyngeal swabs and complete self-administered questionnaires.SettingThe SARS-CoV-2 superspreading event took place during festivities in the small community of Gangelt in February 2020. This 5-hour event included 450 people (6–79 years of age) in a building of 27 m × 13.20 m × 4.20 m.ParticipantsOut of 450 event participants, 411 volunteered to participate in this study.Primary and secondary outcome measuresPrimary outcome: infection status (determined by IgG ELISA). Secondary outcome: symptoms (determined by questionnaire).ResultsOverall, 46% (n=186/404) of participants had been infected, and their spatial distribution was associated with proximity to the ventilation system (OR 1.39, 95% CI 0.86 to 2.25). Risk of infection was highly associated with age: children (OR 0.33, 95% CI 0.267 to 0.414) and young adults (age 18–25 years) had a lower risk of infection than older participants (average risk increase of 28% per 10 years). Behavioural differences were also risk associated including time spent outside (OR 0.55, (95% CI 0.33 to 0.91) or smoking (OR 0.32, 95% CI 0.124 to 0.81).ConclusionsOur findings underline the importance of proper indoor ventilation for future events. Lower susceptibility of children/young adults indicates their limited involvement in superspreading.

More women are surviving after breast cancer due to early detection and modern treatment strategies. Body weight also influences survival. We aimed to characterize associations between postdiagnosis weight change and prognosis in postmenopausal long-term breast cancer survivors. We used data from a prospective population-based patient cohort study (MARIE) conducted in two geographical regions of Germany. Breast cancer patients diagnosed 50 to 74 years of age with an incident invasive breast cancer or in situ tumor were recruited from 2002 to 2005 and followed up until June 2015. Baseline weight was ascertained at an in-person interview at recruitment and follow-up weight was ascertained by telephone interview in 2009. Delayed entry Cox proportional hazards regression was used to assess associations between relative weight change and all-cause mortality, breast cancer mortality, and recurrence-free survival. In total, 2216 patients were included. Compared to weight maintenance (within 5%), weight loss >10% increased risk of all-cause mortality (HR 2.50, 95% CI 1.61, 3.88), breast cancer mortality (HR 3.07, 95% CI 1.69, 5.60) and less so of recurrence-free survival (HR 1.43, 95% CI 0.87, 2.36). Large weight gain of >10% also increased all-cause mortality (HR 1.64, 95% CI 1.02, 2.62) and breast cancer mortality (HR 2.25, 95% CI 1.25, 4.04). Weight maintenance for up to 5 years in long-term breast cancer survivors may help improve survival and prognosis. Postdiagnosis fluctuations in body weight of greater than 10% may lead to increased mortality. Survivors should be recommended to avoid large deviations in body weight from diagnosis onwards to maintain health and prolong life.

Background: Our aim was to investigate the relationship between additional iodinated contrast medium (CM) application for acute stroke imaging and Post-Contrast Acute Kidney Injury (PC-AKI). Methods: We performed a retrospective analysis of consecutive patients with acute stroke who received a CT angiogram (CTA) with or without additional CT perfusion (CTP) at admission between 2017 and 2020. The primary endpoint was the incidence of PC-AKI. Potential causes of renal function impairment were recorded and logistic regression was performed to determine predictors of PC-AKI. Results: Of 3134 cases screened, n = 989 met the predefined inclusion criteria. PC-AKI occurred in 22 (5.4%) patients who received CTA only and 18 (3.1%) patients who received CTA and additional CTP (unadjusted OR, CI; 0.59, 0.29–1.05). In 31/40 (77.5%) patients who suffered PC-AKI, a non-CM-related cause of renal function impairment was identified. Stroke etiology (hemorrhagic vs. ischemic) and indicators of prior kidney disease were independent predictors of PC-AKI. Conclusions: Additional administration of CM for perfusion imaging in acute stroke did not show a relevant influence on the occurrence of PC-AKI. Patients with intracranial hemorrhage and/or prior kidney disease are at particular risk of developing AKI.

Little is known about circulating biomarkers of vascular injury in relation to cardiovascular disease risk. Thus, we evaluated associations between six novel markers (E-Selectin, P-Selectin, thrombomodulin, thrombopoietin, intercellular adhesion molecule 3 and GPIIb/IIIa) and established cardiovascular risk factors as well as the risk of myocardial infarction (MI) in a population-based study. Biomarkers were measured in pre-diagnostic plasma samples of a case-cohort subset of EPIC-Heidelberg (incident MI cases: n = 369, random sub-cohort: n = 2,418). Generalized Linear models were used to analyse cross-sectional associations between biomarkers and cardiovascular risk factors. Multivariable Cox Regression analyses were carried out to obtain Hazard Ratios (HRs) of MI across quartiles of biomarkers levels. Cross-sectional analyses showed that sex, smoking, alcohol consumption, diabetes and exogenous hormone use were associated with biomarker levels. However, while fibrinogen was associated with MI risk (HR per standard deviation: 2.97 [95% confidence interval: 1.61, 5.46]), none of the six novel biomarkers was associated with MI risk after multivariable adjustment. In a population-based cohort, biomarkers of vascular injury were associated with established cardiovascular risk factors, but not MI risk. The tested biomarkers may reflect pathophysiological alterations in cardiovascular disease development rather than constituting independent MI risk factors.

During COVID-19 lockdowns less people were able to fulfill the WHO recommendations on physical activity. Also, fitness centers were associated to SARS-CoV-2 superspreader events. However, the risk of infection can be strongly reduced by outdoor air ventilation. To investigate whether a reopening of fitness centers can be justified

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

In Germany, it is currently recommended that women start mammographic breast cancer screening at age 50. However, recently updated guidelines state that for women younger than 50 and older than 70 years of age, screening decisions should be based on individual risk. International clinical guidelines recommend starting screening when a woman's 5-year risk of breast cancer exceeds 1.7%. We thus compared the performance of the current age-based screening practice with an alternative risk-adapted approach using data from a German population representative survey. We found that 10,498,000 German women ages 50–69 years are eligible for mammographic screening based on age alone. Applying the 5-year risk threshold of 1.7% to individual breast cancer risk estimated from a model that considers a woman's reproductive and personal characteristics, 39,000 German women ages 40–49 years would additionally be eligible. Among those women, the number needed to screen to detect one breast cancer case, NNS, was 282, which was close to the NNS = 292 among all 50- to 69-year-old women. In contrast, NNS = 703 for the 113,000 German women ages 50–69 years old with 5-year breast cancer risk Prevention Relevance: We show that a risk-based approach to mammography screening for German women can help detect breast cancer in women ages 40–49 years with increased risk and reduce screening costs and burdens for low-risk women ages 50–69 years. However, before recommending a particular implementation of a risk-based mammographic screening approach, further investigations of models and thresholds used are needed.

Smoking has been described as a risk factor for severe COVID-19. While there is clear evidence that smoking depresses pulmonary immune function and increases the risk for respiratory infectious diseases with severe complications, recent data suggest that it also may have a protective effect from SARS-CoV-2 infection. We therefore investigated the independent impact of smoking on SARS-CoV-2 infection in two separate studies including a representative cohort study (N=988) and a COVID-19 cluster-outbreak (N=404). Cigarette consumption of >=10 cigarettes/day we observed a reduced risk of SARS-CoV-2-infection (fully adjusted OR 0.424 (95% confidence interval 0.253- 0.712). Trial Registration Number: German Clinical Trials Register (https://www.drks.de, ID: DRKS00021306) Funding: Both cohort studies were funded by the Ministry of Labor, Health, and Social Affairs of the State of North Rhine-Westphalia, Germany. Conflict of Interest: All authors declare no conflicts of interest. Ethical Approval: The study was approved by the ethics committee of the medical faculty of University Bonn (approval number 085/20).

Background The past few decades have witnessed a substantial increase in life expectancy in Western countries, leading to an increase in the prevalence of age-related chronic diseases. Several lifestyle risk factors (i.e. smoking, adiposity, unhealthy diet, heavy alcohol drinking and lack of physical activity) have been responsible for a large proportion of premature deaths, as they can affect the incidence of age-related chronic diseases. Our objective was to predict the loss of residual life expectancy (RLE) associated with these lifestyle factors, using data from the EPIC-Heidelberg cohort, linked to an up-to-date mortality registry. Methods A total of 23,324 German adults, aged 40 years and above were included (1994-1998) and followed until June 2019. A multi-adjusted parametric proportional hazard model (Gompertz hazard distribution), used to predict survival probabilities, followed by a life table approach was used. Results At age 40, being a heavy smoker (> 10 cigarettes/day) was associated with 10.5 y loss of RLE in men and 8.3 in women. Low body mass index (< 22.5 kg/m2) was associated with a RLE loss of 3.7 y in men and 1.4 y in women, while obesity was associated with 4.4 y in men and 3.8 y in women. Heavy alcohol drinking (> 4 drinks/day) was associated with a loss of 4.5 y in men, and high red/processed meat consumption (≥ 120 g/day) was associated with a loss of 1.1y in men and 2.1 y in women. Compared with an overall healthy lifestyle, combined unhealthy behaviors were associated with a loss of RLE of 21.4 y in men and 15.5 y in women. Conclusions Prevention strategies encouraging the adoption of an overall healthy lifestyle, particularly by avoiding smoking, heavy alcohol drinking, excess body fatness and reducing red/processed meat consumption, help reducing premature deaths. An extension of this project using blood biomarkers measures is ongoing. Key messages Adopting a healthy lifestyle by avoiding or reducing the exposure to risk factors might contribute to a longer life expectancy. Tobacco use, adiposity, and alcohol are probably the main modifiable lifestyle factors affecting life expectancy.

BackgroundSeasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS Corona Virus detection by PCR.MethodsWe performed a retrospective analysis of 12763 respiratory tract sample results (288 positive and 12475 negative) for non-SARS, non-MERS Corona viruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the Corona virus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors.ResultsCorona virus infections followed a seasonal pattern peaking from December to March and plunging from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent. Different automatic variable selection processes agreed to select the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased Corona virus detection rates.ConclusionsCorona virus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed. Several meteorological factors were associated with the Corona virus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the Corona virus detection rate.

PURPOSE: There are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman’s age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman’s medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use). METHODS: We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC). RESULTS: Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, E(BCRmod)/O = 1.08 (95% confidence interval: 0.95–1.21), and E(BCRAT)/O = 0.99(0.87–1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat over-estimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUC(BCRmod) = 0.61(0.58–0.63) and AUC(BCRAT) = 0.58(0.56–0.61), with similar values for 5-year follow-up. CONCLUSION: US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.

Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening.To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial.In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019.Five annual rounds of low-dose multislice CT.Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital).A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model.This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.

Endometrial cancer (EC) incidence rates vary ~10‐fold worldwide, in part due to variation in EC risk factor profiles. Using an EC risk model previously developed in the European EPIC cohort, we evaluated the prevention potential of modified EC risk factor patterns and whether differences in EC incidence between a European population and low‐risk countries can be explained by differences in these patterns. Predicted EC incidence rates were estimated over 10 years of follow‐up for the cohort before and after modifying risk factor profiles. Risk factors considered were: body mass index (BMI, kg/m2), use of postmenopausal hormone therapy (HT) and oral contraceptives (OC) (potentially modifiable); and, parity, ages at first birth, menarche and menopause (environmentally conditioned, but not readily modifiable). Modeled alterations in BMI (to all ≤23 kg/m2) and HT use (to all non‐HT users) profiles resulted in a 30% reduction in predicted EC incidence rates; individually, longer duration of OC use (to all ≥10 years) resulted in a 42.5% reduction. Modeled changes in not readily modifiable exposures (i.e., those not contributing to prevention potential) resulted in ≤24.6% reduction in predicted EC incidence. Women in the lowest decile of a risk score based on the evaluated exposures had risk similar to a low risk countries; however, this was driven by relatively long use of OCs (median = 23 years). Our findings support avoidance of overweight BMI and of HT use as prevention strategies for EC in a European population; OC use must be considered in the context of benefits and risks., What's new? Endometrial cancer rates vary considerably around the world, with incidence rates higher in Europe and North America than in parts of Asia and Africa. Here, the authors investigated how much of the risk disparity arises from modifiable factors, and how much modifying these factors could reduce cancer incidence. The 10% of European women with lowest risk had similar incidence to women in low‐risk countries, they found. Their model predicted that in European women, maintaining BMI below 23 kg/m2 and avoiding postmenopausal hormone use could reduce risk by 30%. Long‐term use of oral contraceptives could reduce risk by 42.5%.

Background Even though in situ breast cancer (BCIS) accounts for a large proportion of the breast cancers diagnosed, few studies have investigated potential risk factors for BCIS. Their results suggest that some established risk factors for invasive breast cancer have a similar impact on BCIS risk, but large population-based studies on lifestyle factors and BCIS risk are lacking. Thus, we investigated the association between lifestyle and BCIS risk within the European Prospective Investigation into Cancer and Nutrition cohort. Methods Lifestyle was operationalized by a score reflecting the adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations. The recommendations utilized in these analyses were the ones pertinent to healthy body weight, physical activity, consumption of plant-based foods, energy-dense foods, red and processed meat, and sugary drinks and alcohol, as well as the recommendation on breastfeeding. Cox proportional hazards regression was used to assess the association between lifestyle score and BCIS risk. The results were presented as hazard ratios (HR) and corresponding 95% confidence intervals (CI). Results After an overall median follow-up time of 14.9 years, 1277 BCIS cases were diagnosed. Greater adherence to the WCRF/AICR cancer prevention recommendations was not associated with BCIS risk (HR = 0.98, 95% CI 0.93–1.03; per one unit of increase; multivariable model). An inverse association between the lifestyle score and BCIS risk was observed in study centers, where participants were recruited mainly via mammographic screening and attended additional screening throughout follow-up (HR = 0.85, 95% CI 0.73–0.99), but not in the remaining ones (HR = 0.99, 95% CI 0.94–1.05). Conclusions While we did not observe an overall association between lifestyle and BCIS risk, our results indicate that lifestyle is associated with BCIS risk among women recruited via screening programs and with regular screening participation. This suggests that a true inverse association between lifestyle habits and BCIS risk in the overall cohort may have been masked by a lack of information on screening attendance. The potential inverse association between lifestyle and BCIS risk in our analyses is consistent with the inverse associations between lifestyle scores and breast cancer risk reported from previous studies.

Immuno-proteomic screening has identified several tumor-associated autoantibodies (AAb) that may have diagnostic capacity for invasive epithelial ovarian cancer, with AAbs to P53 proteins and cancer-testis antigens (CTAGs) as prominent examples. However, the early detection potential of these AAbs has been insufficiently explored in prospective studies. We performed ELISA measurements of AAbs to CTAG1A, CTAG2, P53 and NUDT11 proteins, for 194 patients with ovarian cancer and 705 matched controls from the European EPIC cohort, using serum samples collected up to 36 months prior to diagnosis under usual care. CA125 was measured using electrochemo-luminiscence. Diagnostic discrimination statistics were calculated by strata of lead-time between blood collection and diagnosis. With lead times ≤6 months, ovarian cancer detection sensitivity at 0.98 specificity (SE98) varied from 0.19 [95% CI 0.08-0.40] for CTAG1A, CTAG2 and NUDT1 to 0.23 [0.10-0.44] for P53 (0.33 [0.11-0.68] for high-grade serous tumors). However, at longer lead-times, the ability of these AAb markers to distinguish future ovarian cancer cases from controls declined rapidly; at lead times >1 year, SE98 estimates were close to zero (all invasive cases, range: 0.01-0.11). Compared to CA125 alone, combined logistic regression scores of AAbs and CA125 did not improve detection sensitivity at equal level of specificity. The added value of these selected AAbs as markers for ovarian cancer beyond CA125 for early detection is therefore limited.

CA125 is the best ovarian cancer early detection marker to date; however, sensitivity is limited and complementary markers are required to improve discrimination between ovarian cancer cases and no ...

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case–control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p

Epidemiological studies suggest that soya consumption as a source of phyto-oestrogens and isoflavones may be associated with a reduced risk of colorectal cancer. However, findings have not yet been synthesised for all groups of phyto-oestrogens. A meta-analysis was conducted to quantify the association between phyto-oestrogens and colorectal cancer risk. Relevant observational studies published up to June 2016 were identified by searching MEDLINE, EMBASE and Cochrane Library databases. Study-specific relative risks (RR) were pooled in both categorical and dose–response meta-analyses. Out of seventeen identified studies, sixteen were included in the meta-analysis. Comparing the highest with the lowest intake category, inverse associations for phyto-oestrogens overall and by subgroup were observed but were statistically significant in case–controls studies and not in cohort studies. The pooled RR in case–control studies were 0·76 (95 % CI 0·69, 0·84), 0·77 (95 % CI 0·69, 0·85) and 0·70 (95 % CI 0·56, 0·89) for phyto-oestrogens, isoflavones and lignans, respectively, whereas the corresponding pooled RR were 0·95 (95 % CI 0·85, 1·06), 0·94 (95 % CI 0·84, 1·05) and 1·00 (95 % CI 0·64, 1·57) in cohort studies. Dose–response analysis yielded an 8 % reduced risk of colorectal neoplasms for every 20 mg/d increase in isoflavones intake in Asians (pooled RR 0·92; 95 % CI 0·86, 0·97). A non-linear inverse association with colorectal cancer risk was found for lignans intake, but no association for circulating enterolactone concentrations was observed. Thus, study heterogeneity precludes a rigorous conclusion regarding an effect of high exposure to isoflavones on risk of colorectal cancer. Current evidence for an association with lignans exposure is limited. Further prospective studies, particularly evaluating lignans, are warranted to clarify the association between different phyto-oestrogens and colorectal cancer risk.

Background Physical activity (PA) before and after breast cancer diagnosis has been reported to be associated with lower mortality. However, whether changes in the activity after diagnosis impact prognosis is unclear and has not received much attention. This study aimed to examine pre- to postdiagnosis leisure-time PA and breast cancer prognosis. Methods We used data from the MARIE study, a prospective population-based patient cohort study of 3813 postmenopausal breast cancer patients, aged 50–74 at diagnosis, recruited from 2002 to 2005, re-interviewed in 2009, and followed up until June 2015. Prediagnosis PA was assessed at recruitment; postdiagnosis PA was assessed at re-interview in 2009. To examine pre- to postdiagnosis change in PA, women were categorized by pre- and postdiagnosis PA using a cut-off of 7.5 MET-h/week for meeting PA recommendations and combined into four groups: insufficiently active, increasingly active, decreasingly active, and sufficiently active. Cox regression models with delayed entry were used to assess associations between pre- to postdiagnosis patterns of PA and overall mortality (OM), breast cancer mortality (BCM), and recurrence-free survival (RFS). Additional analyses of pre- and postdiagnosis PA (no activity (reference), low activity, sufficient activity) with cancer outcomes, such as using a time-dependent model, were performed. In total, 2042 patients were included in the analyses. Results There were 206 deaths (114 from breast cancer) after a median follow-up time of 6.0 years after the 2009 interview. Compared to insufficiently active women, increasingly active women were at lower risk of OM, BCM, and RFS (HR (95%CI) of 0.50 (0.31–0.82), 0.54 (0.30–1.00), 0.58 (0.40–0.84), respectively). In sufficiently active women, associations for OM (0.75 (0.48–1.15)), BCM (0.61 (0.33–1.13)), and RFS 0.80 (0.57–1.14)) were similar to increasingly active women but attenuated, and decreasingly active women were not at lower risk for OM (0.91 (0.61–1.36)), BCM (0.80 (0.45–1.42)), and RFS (1.04 (0.76–1.43)). In time-dependent analyses, sufficient activity vs. no activity was associated with better OM (0.73 (0.57–0.93)), BCM (0.64 (0.46–0.89)), and RFS (0.82 (0.68–0.99)). Low activity was not significantly associated with prognosis. Conclusion Our data support benefits for breast cancer prognosis in being physically active pre- and postdiagnosis particularly for women who were insufficiently active prediagnosis.