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Objective The objective of this study was to examine the associations between ultra-processed food consumption and risk of obesity among UK adults. Methods Participants aged 40–69 years at recruitment in the UK Biobank (2006–2019) with dietary intakes collected using 24-h recall and repeated measures of adiposity––body mass index (BMI), waist circumference (WC) and percentage of body fat (% BF)––were included ( N  = 22,659; median follow-up: 5 years). Ultra-processed foods were identified using the NOVA classification and their consumption was expressed as a percentage of total energy intake. Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HR) of several indicators of obesity according to ultra-processed food consumption. Models were adjusted for sociodemographic and lifestyle characteristics. Results 947 incident cases of overall obesity (BMI ≥ 30 kg/m 2 ) and 1900 incident cases of abdominal obesity (men: WC ≥ 102 cm, women: WC ≥ 88 cm) were identified during follow-up. Participants in the highest quartile of ultra-processed food consumption had significantly higher risk of developing overall obesity (HR 1.79; 95% CI 1.06─3.03) and abdominal obesity (HR 1.30; 95% CI 1.14─1.48). They had higher risk of experiencing a ≥ 5% increase in BMI (HR 1.31; 95% CI 1.20─1.43), WC (HR 1.35; 95% CI 1.25─1.45) and %BF (HR 1.14; 95% CI 1.03─1.25), than those in the lowest quartile of consumption. Conclusions Our findings provide evidence that higher consumption of ultra-processed food is strongly associated with a higher risk of multiple indicators of obesity in the UK adult population. Policy makers should consider actions that promote consumption of fresh or minimally processed foods and reduce consumption of ultra-processed foods.

Background Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. Methods The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). Results The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p <0.05. Conclusion KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

ObjectivesTo investigate whether the risk of developing an incident autoimmune disease is increased in patients with prior COVID-19 disease compared to those without COVID-19, a large cohort study was conducted.MethodA cohort was selected from German routine health care data. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through December 31, 2020. Patients were matched 1:3 to control patients without COVID-19. Both groups were followed up until June 30, 2021. We used the four quarters preceding the index date until the end of follow-up to analyze the onset of autoimmune diseases during the post-acute period. Incidence rates (IR) per 1000 person-years were calculated for each outcome and patient group. Poisson models were deployed to estimate the incidence rate ratios (IRRs) of developing an autoimmune disease conditional on a preceding diagnosis of COVID-19.ResultsIn total, 641,704 patients with COVID-19 were included. Comparing the incidence rates in the COVID-19 (IR=15.05, 95% CI: 14.69–15.42) and matched control groups (IR=10.55, 95% CI: 10.25–10.86), we found a 42.63% higher likelihood of acquiring autoimmunity for patients who had suffered from COVID-19. This estimate was similar for common autoimmune diseases, such as Hashimoto thyroiditis, rheumatoid arthritis, or Sjögren syndrome. The highest IRR was observed for autoimmune diseases of the vasculitis group. Patients with a more severe course of COVID-19 were at a greater risk for incident autoimmune disease.ConclusionsSARS-CoV-2 infection is associated with an increased risk of developing new-onset autoimmune diseases after the acute phase of infection. Key Points• In the 3 to 15 months after acute infection, patients who had suffered from COVID-19 had a 43% (95% CI: 37–48%) higher likelihood of developing a first-onset autoimmune disease, meaning an absolute increase in incidence of 4.50 per 1000 person-years over the control group.• COVID-19 showed the strongest association with vascular autoimmune diseases.

Abstract Rationale Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are common, associated with acute inflammation, and may increase subsequent cardiovascular disease (CVD) risk. Objectives Determine whether AECOPD events are associated with increased risk of subsequent CVD. Methods We performed a secondary cohort analysis of the SUMMIT (Study to Understand Mortality and Morbidity) trial, a convenience sample of current/former smokers with moderate COPD from 1,368 centers in 43 countries. All had CVD or increased CVD risk. AECOPD was defined as an increase in respiratory symptoms requiring treatment with antibiotics, systemic corticosteroids, and/or hospitalization. CVD events were a composite outcome of cardiovascular death, myocardial infarction, stroke, unstable angina, and transient ischemic attack. All CVD events were adjudicated. Cox proportional hazards models compared the hazard for a CVD event before AECOPD versus after AECOPD. Measurements and Main Results Among 16,485 participants in SUMMIT, 4,704 participants had at least one AECOPD and 688 had at least one CVD event. The hazard ratio (HR) for CVD events after AECOPD was increased, particularly in the first 30 days after AECOPD (HR, 3.8; 95% confidence interval, 2.7–5.5) and was elevated up to 1 year after AECOPD. The 30-day HR after hospitalized AECOPD was more than twofold greater (HR, 9.9; 95% confidence interval, 6.6–14.9). Conclusions In patients with COPD with CVD or risk factors for CVD, exacerbations confer an increased risk of subsequent CVD events, especially in hospitalized patients and within the first 30 days after exacerbation. Patients and clinicians should have heightened vigilance for early CVD events after AECOPD. Clinical trial registered with www.clinicaltrials.gov (NCT 01313676).

Upper gastrointestinal endoscopy for gastric cancer screening has been implemented in Japan. However, its effectiveness for gastric cancer prevention has not been fully studied. We aimed to investigate the effectiveness of endoscopic screening to reduce mortality from gastric cancer. In a large prospective population‐based cohort study including 80,272 participants, we compared the risk of mortality and incidence of gastric cancer among participants who underwent endoscopic or radiographic screening compared with those who did not undergo any screening using multivariable Cox proportional hazards models. In the 1,023,364 person‐year observation period (median; 13.0 years), 1977 cases of gastric cancer were diagnosed, and 783 patients with gastric cancer died. In the endoscopic screening group, the mortality from gastric cancer and incidence of advanced gastric cancer were reduced by 61% (hazard ratio [HR] = 0.39 [95% CI: 0.30–0.51]) and 22% (HR = 0.78 [95% CI: 0.67–0.90]), respectively. The radiographic screening reduced the mortality from gastric cancer (HR = 0.63 [95% CI: 0.54–0.73]), but its effectiveness was lower than that of endoscopic screening. In conclusion, endoscopic screening reduced the incidence of advanced gastric cancer and mortality from gastric cancer in the Japanese population. In this Japanese prospective population‐based cohort study, endoscopic screening reduced the incidence of advanced gastric cancer by 22% and mortality from gastric cancer by 61% compared with no screening. The effectiveness of endoscopic screening on reducing gastric cancer mortality was greater than that of radiographic screening. The results provide a rationale for promoting endoscopic screening for gastric cancer, which has a high mortality rate and significant public health impact in Japan.

A large-scale epigenome-wide association study identifies changes in DNA methylation associated with body mass index in blood and adipose tissue, and correlates DNA methylation sites with high risk of incident type 2 diabetes. Body fat and diabetes risk Obesity is a major risk factor for type 2 diabetes and related metabolic disorders. Genetic association studies have identified genomic loci associated with obesity, and recent studies have also suggested associations with DNA methylation. These authors report an epigenome-wide association study for body mass index (BMI), identifying an association with DNA methylation at 187 loci in blood and adipose tissue. They find that these methylation changes are secondary to adiposity and are also associated with an increased risk of developing type 2 diabetes, independent of conventional risk factors. Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances 1 , 2 . Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation 3 , 4 , 5 , 6 , a key regulator of gene expression and molecular phenotype 7 . Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P  < 1 × 10 −7 , range P  = 9.2 × 10 −8 to 6.0 × 10 −46 ; n  = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues ( P  < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci ( P  < 9.0 × 10 −6 , range P  = 5.5 × 10 −6 to 6.1 × 10 −35 , n  = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P  = 1.1 × 10 −54 ). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

The Generation R Study is a population-based prospective cohort study from fetal life until adulthood. The study is designed to identify early environmental and genetic causes and causal pathways leading to normal and abnormal growth, development and health from fetal life, childhood and young adulthood. This multidisciplinary study focuses on several health outcomes including behaviour and cognition, body composition, eye development, growth, hearing, heart and vascular development, infectious disease and immunity, oral health and facial growth, respiratory health, allergy and skin disorders of children and their parents. Main exposures of interest include environmental, endocrine, genomic (genetic, epigenetic, microbiome), lifestyle related, nutritional and socio-demographic determinants. In total, 9778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. Response at baseline was 61%, and general follow-up rates until the age of 10 years were around 80%. Data collection in children and their parents includes questionnaires, interviews, detailed physical and ultrasound examinations, behavioural observations, lung function, Magnetic Resonance Imaging and biological sampling. Genome and epigenome wide association screens are available. Eventually, results from the Generation R Study contribute to the development of strategies for optimizing health and healthcare for pregnant women and children.

Systemic inflammation markers have been linked to increased cancer risk and mortality in a number of studies. However, few studies have estimated pre-diagnostic associations of systemic inflammation markers and cancer risk. Such markers could serve as biomarkers of cancer risk and aid in earlier identification of the disease. This study estimated associations between pre-diagnostic systemic inflammation markers and cancer risk in the prospective UK Biobank cohort of approximately 440,000 participants recruited between 2006 and 2010. We assessed associations between four immune-related markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-tolymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and risk for 17 cancer sites by estimating hazard ratios (HR) using flexible parametric survival models. We observed positive associations with risk for seven out of 17 cancers with SII, NLR, PLR, and negative associations with LMR. The strongest associations were observed for SII for colorectal and lung cancer risk, with associations increasing in magnitude for cases diagnosed within one year of recruitment. For instance, the HR for colorectal cancer per standard deviation increment in SII was estimated at 1.09 (95% CI 1.02–1.16) in blood drawn five years prior to diagnosis and 1.50 (95% CI 1.24–1.80) in blood drawn one month prior to diagnosis. We observed associations between systemic inflammation markers and risk for several cancers. The increase in risk the last year prior to diagnosis may reflect a systemic immune response to an already present, yet clinically undetected cancer. Blood cell ratios could serve as biomarkers of cancer incidence risk with potential for early identification of disease in the last year prior to clinical diagnosis.

Emerging evidence suggests that living near major roads might adversely affect cognition. However, little is known about its relationship with the incidence of dementia, Parkinson's disease, and multiple sclerosis. We aimed to investigate the association between residential proximity to major roadways and the incidence of these three neurological diseases in Ontario, Canada. In this population-based cohort study, we assembled two population-based cohorts including all adults aged 20–50 years (about 4·4 million; multiple sclerosis cohort) and all adults aged 55–85 years (about 2·2 million; dementia or Parkinson's disease cohort) who resided in Ontario, Canada on April 1, 2001. Eligible patients were free of these neurological diseases, Ontario residents for 5 years or longer, and Canadian-born. We ascertained the individual's proximity to major roadways based on their residential postal-code address in 1996, 5 years before cohort inception. Incident diagnoses of dementia, Parkinson's disease, and multiple sclerosis were ascertained from provincial health administrative databases with validated algorithms. We assessed the associations between traffic proximity and incident dementia, Parkinson's disease, and multiple sclerosis using Cox proportional hazards models, adjusting for individual and contextual factors such as diabetes, brain injury, and neighbourhood income. We did various sensitivity analyses, such as adjusting for access to neurologists and exposure to selected air pollutants, and restricting to never movers and urban dwellers. Between 2001, and 2012, we identified 243 611 incident cases of dementia, 31 577 cases of Parkinson's disease, and 9247 cases of multiple sclerosis. The adjusted hazard ratio (HR) of incident dementia was 1·07 for people living less than 50 m from a major traffic road (95% CI 1·06–1·08), 1·04 (1·02–1·05) for 50–100 m, 1·02 (1·01–1·03) for 101–200 m, and 1·00 (0·99–1·01) for 201–300 m versus further than 300 m (p for trend=0·0349). The associations were robust to sensitivity analyses and seemed stronger among urban residents, especially those who lived in major cities (HR 1·12, 95% CI 1·10–1·14 for people living <50 m from a major traffic road), and who never moved (1·12, 1·10–1·14 for people living <50 m from a major traffic road). No association was found with Parkinson's disease or multiple sclerosis. In this large population-based cohort, living close to heavy traffic was associated with a higher incidence of dementia, but not with Parkinson's disease or multiple sclerosis. Health Canada (MOA-4500314182).

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full-cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI-approx) of White and Royston (2009) and the \"substantive model compatible\" (MI-SMC) method of Bartlett et al. (2015). We also apply the \"MI matched set\" approach of Seaman and Keogh (2015) to nested case-control studies, which does not require any full-cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full-cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full-cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code.