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BACKGROUND/AIM: Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. METHODS: The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. RESULTS: The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10-5m-1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exp

BACKGROUND: An association between long-term exposure to fine particulate matter (PM2.5) and lung cancer has been established in previous studies. PM2.5 is a complex mixture of chemical components from various sources and little is known about whether certain components contribute specifically to the associated lung cancer risk. The present study builds on recent findings from the "Effects of Low-level Air Pollution: A Study in Europe" (ELAPSE) collaboration and addresses the potential association between specific elemental components of PM2.5 and lung cancer incidence. METHODS: We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM2.5 representing non-tail pipe emissions (copper (Cu), iron (Fe), and zinc (Zn)), long-range transport (sulfur (S)), oil burning/industry emissions (nickel (Ni), vanadium (V)), crustal material (silicon (Si)), and biomass burning (potassium (K)) to cohort participants' baseline residential address based on 100 m by 100 m grids from newly developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). RESULTS: The pooled study population comprised 306,550 individuals with 3916 incident lung cancer events during 5,541,672 person-years of follow-up. We observed a positive association between exposure to all eight components and lung cancer incidence, with adjusted HRs of 1.10 (95% CI 1.05, 1.16) per 50 ng/m3 PM2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m3 PM2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m3 PM2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m3 PM2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO2). After adjustment for PM2.5 mass, effect estimates of K, Ni, S, and

OBJECTIVE: To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines. DESIGN: Pooled analysis of eight cohorts. SETTING: Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries. PARTICIPANTS: 325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM2.5), nitrogen dioxide, ozone, and black carbon. MAIN OUTCOME MEASURES: Deaths due to natural causes and cause specific mortality. RESULTS: Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m3 in PM2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m3 increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM2.5, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m3 an increase of 5 µg/m3 in PM2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths. CONCLUSIONS: Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Glob

Contains fulltext : 214111.pdf (publisher's version ) (Open Access), INTRODUCTION: Cognitive impairment and frailty are important health determinants, independently associated with increased dementia risk. In this meta-analysis we aimed to quantify the association of the co-occurrence of cognitive impairment no dementia (CIND) and physical frailty with incident dementia. METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used when reporting this review. We performed a systematic search on PubMed, Web of Science, and Embase databases for relevant articles. Longitudinal studies enrolling individuals with both CIND and physical frailty and reporting dementia incidence were eligible. Pooled estimates were obtained through random effect models and Mantel-Haenszel weighting. RESULTS: Out of 3684 articles, five (14302 participants) were included in the meta-analysis. In comparison to participants free from frailty and CIND, the pooled hazard ratio for dementia was 3.83 (95% confidence interval [CI]: 2.64-5.56) for isolated CIND, 1.47 (95%CI: 0.89-2.40) for isolated physical frailty, and 5.36 (95%CI: 3.26-8.81) for their co-occurrence. DISCUSSION: The co-occurrence of cognitive impairment and physical frailty is a clinical marker of incident dementia.

Contains fulltext : 202861.pdf (Publisher’s version ) (Closed access), OBJECTIVES: The predictive value of frailty and comorbidity, in addition to more readily available information, is not widely studied. We determined the incremental predictive value of frailty and comorbidity for mortality and institutionalization across both short and long prediction periods in persons with dementia. DESIGN: Longitudinal clinical cohort study with a follow-up of institutionalization and mortality occurrence across 7 years after baseline. SETTING AND PARTICIPANTS: 331 newly diagnosed dementia patients, originating from 3 Alzheimer centers (Amsterdam, Maastricht, and Nijmegen) in the Netherlands, contributed to the Clinical Course of Cognition and Comorbidity (4C) Study. MEASURES: We measured comorbidity burden using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and constructed a Frailty Index (FI) based on 35 items. Time-to-death and time-to-institutionalization from dementia diagnosis onward were verified through linkage to the Dutch population registry. RESULTS: After 7 years, 131 patients were institutionalized and 160 patients had died. Compared with a previously developed prediction model for survival in dementia, our Cox regression model showed a significant improvement in model concordance (U) after the addition of baseline CIRS-G or FI when examining mortality across 3 years (FI: U = 0.178, P = .005, CIRS-G: U = 0.180, P = .012), but not for mortality across 6 years (FI: U = 0.068, P = .176, CIRS-G: U = 0.084, P = .119). In a competing risk regression model for time-to-institutionalization, baseline CIRS-G and FI did not improve the prediction across any of the periods. CONCLUSIONS: Characteristics such as frailty and comorbidity change over time and therefore their predictive value is likely maximized in the short term. These results call for a shift in our approach to prognostic modeling for chronic diseases, focusing on yearly predictions rather than a single prediction across multiple years. Our findings underline the impor

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Background: Although the definition of multimorbidity as "the simultaneous presence of two or more chronic diseases" is well established, its operationalization is not yet agreed. This study aims to provide a clinically driven comprehensive list of chronic conditions to be included when measuring multimorbidity.Methods: Based on a consensus definition of chronic disease, all four-digit level codes from the International Classification of Diseases, 10th revision (ICD-10) were classified as chronic or not by an international and multidisciplinary team. Chronic ICD-10 codes were subsequently grouped into broader categories according to clinical criteria. Last, we showed proof of concept by applying the classification to older adults from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K) using also inpatient data from the Swedish National Patient Register.Results: A disease or condition was considered to be chronic if it had a prolonged duration and either (a) left residual disability or worsening quality of life or (b) required a long period of care, treatment, or rehabilitation. After applying this definition in relation to populations of older adults, 918 chronic ICD-10 codes were identified and grouped into 60 chronic disease categories. In SNAC-K, 88.6% had >= 2 of these 60 disease categories, 73.2% had >= 3, and 55.8% had >= 4.Conclusions: This operational measure of multimorbidity, which can be implemented using either or both clinical and administrative data, may facilitate its monitoring and international comparison. Once validated, it may enable the advancement and evolution of conceptual and theoretical aspects of multimorbidity that will eventually lead to better care.

The idea that synchronous neural activity underlies cognition has driven an extensive body of research in human and animal neuroscience. Yet, insufficient data on intracranial electrical connectivity has precluded a direct test of this hypothesis in a whole-brain setting. Through the lens of memory encoding and retrieval processes, we construct whole-brain connectivity maps of fast gamma (30-100 Hz) and slow theta (3-8 Hz) spectral neural activity, based on data from 294 neurosurgical patients fitted with indwelling electrodes. Here we report that gamma networks desynchronize and theta networks synchronize during encoding and retrieval. Furthermore, for nearly all brain regions we studied, gamma power rises as that region desynchronizes with gamma activity elsewhere in the brain, establishing gamma as a largely asynchronous phenomenon. The abundant phenomenon of theta synchrony is positively correlated with a brain region's gamma power, suggesting a predominant low-frequency mechanism for inter-regional communication.

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Objectives: To explore the association between genes that may be related to human mortality, taking into account the possible contribution of morbidity, and investigate whether lifestyle behaviors may attenuate genetic risk. Design: Twenty-five-year population-based cohort study. Setting: Kungsholmen cohort, Stockholm, Sweden. Participants: Individuals aged 75 and older (N = 1,229). Measurements: The associations between single-nucleotide variations in 14 genes (previously associated with mortality or to diseases linked to mortality), relevant lifestyle risk behaviors (smoking; mental, physical, or social inactivity; moderate or poor social network), and mortality were estimated using Cox regression. Results: People with allelic variation in four genes related to cardiovascular diseases and metabolism were more likely to die: apolipoprotein (APO)C1 GG and AG carriers, APOE ɛ4 carriers, insulin-degrading enzyme (IDE) TC carriers, and phosphatidylinositol 3-kinase (PI3KCB) GG carriers. Individuals with multiple adverse alleles had 62% higher mortality rate than those with none. In contrast, people with no risk behaviors (low-risk profile) had 65% lower mortality rate than people with all examined risk behaviors (high-risk profile). Combining the genetic and environmental factors, it was found that, independent of genetic profile, individuals with a low-risk profile had up to 64% lower mortality rate than those with a moderate high– or high-risk profile and at least one genetic risk factor. Conclusion: This study supports and expands evidence that genetic variations in APOE, IDE, and PI3KCB are associated with lower mortality rate, although lifestyle behaviors can modulate their effects, Objectives: To explore the association between genes that may be related to human mortality, taking into account the possible contribution of morbidity, and investigate whether lifestyle behaviors may attenuate genetic risk. Design: Twenty-five-year population-based cohort study. Setting: Kungsholmen cohort, Stockholm, Sweden. Participants: Individuals aged 75 and older (N = 1,229). Measurements: The associations between single-nucleotide variations in 14 genes (previously associated with mortality or to diseases linked to mortality), relevant lifestyle risk behaviors (smoking; mental, physical, or social inactivity; moderate or poor social network), and mortality were estimated using Cox regression. Results: People with allelic variation in four genes related to cardiovascular diseases and metabolism were more likely to die: apolipoprotein (APO)C1 GG and AG carriers, APOE ɛ4 carriers, insulin-degrading enzyme (IDE) TC carriers, and phosphatidylinositol 3-kinase (PI3KCB) GG carriers. Individuals with multiple adverse alleles had 62% higher mortality rate than those with none. In contrast, people with no risk behaviors (low-risk profile) had 65% lower mortality rate than people with all examined risk behaviors (high-risk profile). Combining the genetic and environmental factors, it was found that, independent of genetic profile, individuals with a low-risk profile had up to 64% lower mortality rate than those with a moderate high– or high-risk profile and at least one genetic risk factor. Conclusion: This study supports and expands evidence that genetic variations in APOE, IDE, and PI3KCB are associated with lower mortality rate, although lifestyle behaviors can modulate their effects.