MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Iron status in Dutch and Finnish blood donor and general populations: A cross-cohort comparison study.

BACKGROUND AND OBJECTIVES: Blood donors are at risk of developing iron deficiency (ID) (ferritin <15 µg/L, World Health Organization definition). Blood services implement different strategies to mitigate this risk. Although in Finland risk group-based iron supplementation is in place, no iron supplementation is provided in the Netherlands. We aim to describe differences in ferritin levels and ID prevalence in donor and general populations in these countries. MATERIALS AND METHODS: Six cohorts, stratified based on sex, and for women age, in the Netherlands and Finland were used to evaluate differences in ferritin levels and ID between donor populations (Donor InSight-III and FinDonor 10,000) and general populations (Prevention of Renal and Vascular End-Stage Disease [PREVEND], FinRisk 1997 and Health 2000) and newly registered Dutch donors. Multivariable logistic regression was used to quantify associations of various explanatory factors with ID. RESULTS: In total, 13,443 Dutch and 13,933 Finnish subjects were included. Donors, except for women aged ≤50 years old in Finland, had lower median ferritin levels compared with the general population and new donors. Dutch regular blood donors had higher or similar prevalence of ID as compared with the Dutch general population, including new donors. In contrast, Finnish donors showed similar prevalence of ID compared with the general population, except for a markedly lower prevalence in ≤50-year-old women who routinely receive iron supplements when donating. CONCLUSION: Iron status in blood donors differs from that in the general population. The Finnish blood service donor management policy, for example, iron supplementation for risk groups, seemingly protects young female blood donors from developing ID.

An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources.

BACKGROUND: In the United States, the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer's Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources. METHOD: In this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across NACC and ADNI, and performed value harmonization for the mapped data elements with inconsistent permissible values. Then we built an Alzheimer's Disease Data Element Ontology (ADEO) to model the mapped data elements in NACC and ADNI. We further developed a prototype cross-cohort query system to search patient cohorts across NACC and ADNI. RESULTS: After manual review, we found 172 mappings between NACC and ADNI. These 172 mappings were further used to construct common concepts in ADEO. Our data element mapping and harmonization resulted in five files storing common concepts, variables in NACC and ADNI, mappings between variables and common concepts, permissible values of categorical type data elements, and coding inconsistency harmonization, respectively. Our cross-cohort query system consists of three core architectural elements: a web-based interface, an advanced query engine, and a backend MongoDB database. CONCLUSIONS: In this work, ADEO has been specifically designed to facilitate data harmonization and cross-cohort query of NACC and ADNI data resources. Although our prototype cross-cohort query system was developed for exploring NACC and ADNI, its backend and frontend framework has been designed and implemented to be generally applicable to other domains for querying patient cohorts from multiple heterogeneous data sources.

The EU Child Cohort Network's core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables.

The Horizon2020 LifeCycle Project is a cross-cohort collaboration which brings together data from multiple birth cohorts from across Europe and Australia to facilitate studies on the influence of early-life exposures on later health outcomes. A major product of this collaboration has been the establishment of a FAIR (findable, accessible, interoperable and reusable) data resource known as the EU Child Cohort Network. Here we focus on the EU Child Cohort Network's core variables. These are a set of basic variables, derivable by the majority of participating cohorts and frequently used as covariates or exposures in lifecourse research. First, we describe the process by which the list of core variables was established. Second, we explain the protocol according to which these variables were harmonised in order to make them interoperable. Third, we describe the catalogue developed to ensure that the network's data are findable and reusable. Finally, we describe the core data, including the proportion of variables harmonised by each cohort and the number of children for whom harmonised core data are available. EU Child Cohort Network data will be analysed using a federated analysis platform, removing the need to physically transfer data and thus making the data more accessible to researchers. The network will add value to participating cohorts by increasing statistical power and exposure heterogeneity, as well as facilitating cross-cohort comparisons, cross-validation and replication. Our aim is to motivate other cohorts to join the network and encourage the use of the EU Child Cohort Network by the wider research community.

Decline in the negative association between low birth weight and cognitive ability.

Low birth weight predicts compromised cognitive ability. We used data from the 1958 National Child Development Study (NCDS), the 1970 British Cohort Study (BCS), and the 2000-2002 Millennium Cohort Study (MCS) to analyze how this association has changed over time. Birth weight was divided into two categories, <2,500 g (low) and 2,500-4,500 g (normal) and verbal cognitive ability was measured at the age of 10 or 11 y. A range of maternal and family characteristics collected at or soon after the time of birth were considered. Linear regression was used to analyze the association between birth weight and cognitive ability in a baseline model and in a model that adjusted for family characteristics. The standardized difference (SD) in cognitive scores between low-birth-weight and normal-birth-weight children was large in the NCDS [-0.37 SD, 95% confidence interval (CI): -0.46, -0.27] and in the BCS (-0.34, 95% CI: -0.43, -0.25) cohorts, and it was more than halved for children born in the MCS cohort (-0.14, 95% CI: -0.22, -0.06). The adjustment for family characteristics did not explain the cross-cohort differences. The results show that the association between low birth weight and decreased cognitive ability has declined between the 1950s and 1970s birth cohorts and the 2000--2002 birth cohort, despite a higher proportion of the low-birth-weight babies having a very low birth weight (<1,500 g) in the more recent birth cohort. Advancements in obstetric and neonatal care may have attenuated the negative consequences associated with being born small.

Secular changes in the association between advanced maternal age and the risk of low birth weight: A cross-cohort comparison in the UK.

Existing studies provide contradictory evidence concerning the association between child health and advanced maternal age. A potential explanation for the lack of consensus on this issue is changes over time in the costs and benefits of giving birth at an advanced age. This is the first study to investigate secular changes in the characteristics of older mothers and in the association between advanced maternal age and child health. We use data from four UK cohort studies, covering births from 1958 to 2001, and use low birth weight (LBW) as a marker for child health. We find that across successive birth cohorts, the negative association between advanced maternal age and LBW becomes progressively weaker; and that this pattern is partially explained by secular changes in the characteristics of older mothers. Our results suggest that associations between maternal age and child outcomes are tied to a specific population and point in time.

Exploring educational disparities in risk of preterm delivery: a comparative study of 12 European birth cohorts.

BACKGROUND: An association between education and preterm delivery has been observed in populations across Europe, but differences in methodology limit comparability. We performed a direct cross-cohort comparison of educational disparities in preterm delivery based on individual-level birth cohort data. METHODS: The study included data from 12 European cohorts from Denmark, England, France, Lithuania, the Netherlands, Norway, Italy, Portugal, and Spain. The cohorts included between 2434 and 99 655 pregnancies. The association between maternal education and preterm delivery (22-36 completed weeks of gestation) was reported as risk ratios, risk differences, and slope indexes of inequality with 95% confidence intervals (CIs). RESULTS: Singleton preterm live delivery proportion varied between 3.7% and 7.5%. There were large variations between the cohorts in the distribution of education and maternal characteristics. Nevertheless, there were similar educational differences in risk of preterm delivery in 8 of the 12 cohorts with slope index of inequality varying between 2.2 [95% CI 1.1, 3.3] and 4.0 [95% CI 1.4, 6.6] excess preterm deliveries per 100 singleton deliveries among the educationally most disadvantaged, and risk ratio between the lowest and highest education category varying from 1.4 [95% CI 1.1, 1.8] to 1.9 [95% CI 1.2, 3.1]. No associations were found in the last four cohorts. CONCLUSIONS: Educational disparities in preterm delivery were found all over Europe. Despite differences in the distributions of education and preterm delivery, the results were remarkably similar across the cohorts. For those few cohorts that did not follow the pattern, study and country characteristics did not explain the differences.

Factors influencing innovation competence among children and adolescents in China - A multilevel, cross-cohort study.

Innovation competence is an essential core literacy skill for 21st century students. While some research exists on innovation competence in college students, there has been relatively little examination of the factors influencing this competence in children and adolescents aged 10 to 15. This study evaluated innovation competence among students from Suzhou, China, focusing on four key social and emotional skills: creativity, curiosity, cooperation, and responsibility. Data from the Organization for Economic Cooperation and Development were utilized for this analysis. Hierarchical linear modelling was applied to analyze potential factors at both individual and school levels influencing innovation competence across family and school environments. We calculated a t-test statistic to compare factors between the two cohorts. Factors significantly influencing children and adolescents' innovation competence included socio-economic status, time spent engaging in online gaming, time spent browsing the Internet for information, and the perceived cooperative climate atschool. Gender significantly influenced only adolescents' innovation competence, while teachers' disruptive behaviors had an impact solely on children's innovation competence. Apart from time spent engaging in online gaming and browsing the Internet for information, the effects of other variables showed significant differences between the groups. The findings highlight the need for targeted support from families, schools, and society to foster students' innovation competence.

Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning.

BACKGROUND: Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease. OBJECTIVE: This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts. METHODS: Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses. RESULTS: Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities. CONCLUSIONS: This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.

Dementia severity and weight loss: a comparison across eight cohorts. The 10/66 study.

BACKGROUND: We aimed to investigate the association between dementia severity and weight loss in countries with low and middle incomes, where most prevalent cases reside. METHODS: Cross-sectional catchment area surveys were performed in Cuba, Mexico, Venezuela, Peru, Dominican Republic, Puerto Rico, China, and India. In 16,538 older adults (≥65 years of age), significant weight loss was self-reported and confirmed by an informant. We conducted neuropsychological testing and clinical and neurological assessments. Dementia severity was determined by applying a validated algorithm and was quantified by the Clinical Dementia Rating Scale. RESULTS: The characteristics of those who reported weight loss varied across countries. In Poisson models, after controlling for relevant covariates and for waist and arm circumferences, dementia severity was associated with reported weight loss (pooled prevalence ratios [95% confidence intervals {CI}] 2.19 [1.98, 2.41]; 3.81 [3.35, 4.33]; and 5.18 [4.41, 6.10] for CDR 0.5, 1, and 2/3, respectively, compared with CDR 0). The association increased linearly through stages of dementia severity in all countries (P for trend < .001), and between-country heterogeneity was minimal. CONCLUSIONS: We found a strong gradient effect in the direct association between dementia severity and reported weight loss, homogeneous across sites from eight countries, consistent with mechanistic data on the role of neurodegenerative processes on energy balance and with dietary changes due to disease severity. Considering the well-recognized effect of weight loss on morbidity and mortality and the large number affected by dementia in less resourced countries, amelioration of weight loss in dementia patients should be considered with priority in these settings.

An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts.

Introduction: Polygenic risk scores (PRSs) have great clinical potential for detecting late-onset diseases such as Alzheimer's disease (AD), allowing the identification of those most at risk years before the symptoms present. Although many studies use various and complicated machine learning algorithms to determine the best discriminatory values for PRSs, few studies look at the commonality of the Single Nucleotide Polymorphisms (SNPs) utilized in these models. Methods: This investigation focussed on identifying SNPs that tag blocks of linkage disequilibrium across the genome, allowing for a generalized PRS model across cohorts and genotyping panels. PRS modeling was conducted on five AD development cohorts, with the best discriminatory models exploring for a commonality of linkage disequilibrium clumps. Clumps that contributed to the discrimination of cases from controls that occurred in multiple cohorts were used to create a generalized model of PRS, which was then tested in the five development cohorts and three further AD cohorts. Results: The model developed provided a discriminability accuracy average of over 70% in multiple AD cohorts and included variants of several well-known AD risk genes. Discussion: A key element of devising a polygenic risk score that can be used in the clinical setting is one that has consistency in the SNPs that are used to calculate the score; this study demonstrates that using a model based on commonality of association findings rather than meta-analyses may prove useful.

Life Course Socioeconomic Position and Cognitive Aging Trajectories: A Cross-National Cohort Study in China and England.

Background and Objectives: Cross-national research on cognitive aging inequality has largely concentrated on Western countries. It is unclear whether socioeconomic position (SEP) has similar effects on cognitive decline in emerging economies. We compared the association between life course SEP and cognitive function trajectories between China and England, the largest nation under state socialism and one of the oldest capitalist countries. Research Design and Methods: This cross-cohort study examined participants aged 50 years and older from the China Health and Retirement Longitudinal Study (n = 12,832) and the English Longitudinal Study of aging (n = 8,875). Cognition z-scores were derived using comparable measures of memory and time orientation on 4 occasions. Life course SEP was self-reported by participants at baseline. Seven- to 8-year trajectories of cognition z-scores were estimated using latent growth curve modeling. Country- and gender-specific associations between childhood/adolescent deprivation, education, material wealth, and home ownership were evaluated in relation to model intercept (baseline level) and linear slope (annual rate of change) of cognition. Results: After multivariable adjustment, education was positively associated with the greatest differences in baseline cognition across country and gender. Education was further linked to a slower rate of cognitive decline (z-score units per year); but compared with those with low education, Chinese men (b = 0.032) and women (b = 0.065) with high education had significantly slower declines than English men (b = -0.004) and women (b = 0.010) with high education. Discussion and Implications: Despite substantial between-cohort differences in downstream and upstream determinants of dementia, education provided the greatest benefits to cognitive aging in England but particularly in China.

Investigating the association of traditional and non-traditional tobacco product use with subclinical and clinical cardiovascular disease: The Cross-Cohort Collaboration-Tobacco working group rationale, design, and methodology.

While the impact of combustible cigarette smoking on cardiovascular disease (CVD) is well-established, the longitudinal association of non-traditional tobacco products with subclinical and clinical CVD has not been fully explored due to: 1) limited data availability; and 2) the lack of well-phenotyped prospective cohorts. Therefore, there is the need for sufficiently powered well-phenotyped datasets to fully elucidate the CVD risks associated with non-cigarette tobacco products. The Cross-Cohort Collaboration (CCC)-Tobacco is a harmonized dataset of 23 prospective cohort studies predominantly in the US. A priori defined variables collected from each cohort included baseline characteristics, details of traditional and non-traditional tobacco product use, inflammatory markers, and outcomes including subclinical and clinical CVD. The definitions of the variables in each cohort were systematically evaluated by a team of two physician-scientists and a biostatistician. Herein, we describe the method of data acquisition and harmonization and the baseline sociodemographic and risk profile of participants in the combined CCC-Tobacco dataset. The total number of participants in the pooled cohort is 322782 (mean age: 59.7 ± 11.8 years) of which 76% are women. White individuals make up the majority (73.1%), although there is good representation of other race and ethnicity groups including African American (15.6%) and Hispanic/Latino individuals (6.4%). The prevalence of participants who never smoked, formerly smoked, and currently smoke combustible cigarettes is 50%, 36%, and 14%, respectively. The prevalence of current and former cigar, pipe, and smokeless tobacco is 7.3%, 6.4%, and 8.6%, respectively. E-cigarette use was measured only in follow-up visits of select studies, totaling 1704 former and current users. CCC-Tobacco is a large, pooled cohort dataset that is uniquely designed with increased power to expand knowledge regarding the association of traditional and non-traditional tobacco use with subclinical and clinical CVD, with extension to understudied groups including women and individuals from underrepresented racial-ethnic groups.

Weakening of the cognition and height association from 1957 to 2018: Findings from four British birth cohort studies.

Background: Taller individuals have been repeatedly found to have higher scores on cognitive assessments. Recent studies have suggested that this association can be explained by genetic factors, yet this does not preclude the influence of environmental or social factors that may change over time. We thus tested whether the association changed across time using data from four British birth cohorts (born in 1946, 1958, 1970, and 2001). Methods: In each cohort height was measured and cognition via verbal reasoning, vocabulary/comprehension, and mathematical tests; at ages 10/11 and 14/17 years (N=41,418). We examined associations between height and cognition at each age, separately in each cohort, and for each cognitive test administered. Linear and quantile regression models were used. Results: Taller participants had higher mean cognitive assessment scores in childhood and adolescence, yet the associations were weaker in later (1970 and 2001) cohorts. For example, the mean difference in height comparing the highest with lowest verbal cognition scores at 10/11 years was 0.57 SD (95% CI = 0.44-0.70) in the 1946 cohort, yet 0.30 SD (0.23-0.37) in the 2001 cohort. Expressed alternatively, there was a reduction in correlation from 0.17 (0.15-0.20) to 0.08 (0.06-0.10). This pattern of change in the association was observed across all ages and cognition measures used, was robust to adjustment for social class and parental height, and modeling of plausible missing-not-at-random scenarios. Quantile regression analyses suggested that these differences were driven by differences in the lower centiles of height, where environmental influence may be greatest. Conclusions: Associations between height and cognitive assessment scores in childhood-adolescence substantially weakened from 1957-2018. These results support the notion that environmental and social change can markedly weaken associations between cognition and other traits. Funding: DB is supported by the Economic and Social Research Council (grant number ES/M001660/1); DB and LW by the Medical Research Council (MR/V002147/1). The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit [MC_UU_00011/1]. NMD is supported by an Norwegian Research Council Grant number 295989. VM is supported by the CLOSER Innovation Fund WP19 which is funded by the Economic and Social Research Council (award reference: ES/K000357/1) and Economic and Social Research Council (ES/M001660/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance.

Antibiotic treatment can lead to a loss of diversity of gut microbiota and may adversely affect gut microbiota composition and host health. Previous studies have indicated that the recovery of gut microbes from antibiotic-induced disruption may be guided by specific microbial species. We expect to predict recovery or non-recovery using these crucial species or other indices after antibiotic treatment only when the gut microbiota changes. This study focused on this prediction problem using a novel ensemble learning framework to identify a set of common and reasonably predictive recovery-associated bacterial species (p-RABs), enabling us to predict the host microbiome recovery status under broad-spectrum antibiotic treatment. Our findings also propose other predictive indicators, suggesting that higher taxonomic and functional diversity may correlate with an increased likelihood of successful recovery. Furthermore, to explore the validity of p-RABs, we performed a metabolic support analysis and identified Akkermansia muciniphila and Bacteroides uniformis as potential key supporting species for reconstruction interventions. Experimental results from a C57BL/6J male mouse model demonstrated the effectiveness of p-RABs in facilitating intestinal microbial reconstitution. Thus, we proved the reliability of the new p-RABs and validated a practical intervention scheme for gut microbiota reconstruction under antibiotic disturbance.

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers.

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

Better together: Advancing life course research through multi-cohort analytic approaches.

Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.

Age and sex trends in depressive symptoms across middle and older adulthood: Comparison of the Canadian Longitudinal Study on Aging to American and European cohorts.

BACKGROUND: The literature suggests depressive symptoms differ in a non-linear fashion across adulthood and are more commonly reported in women as compared to men. Whether these trends are observed across countries in population-based cohorts is unclear. METHODS: Cross-sectional observational study of approximately 138,000 women and men between the ages of 45 and 95 from three population-based cohorts representing Canadian, European, and American populations. Age, gender, educational attainment and annual income were assessed in each cohort. Depressive symptoms were assessed by the Center for Epidemiological Studies Depression Scale in the US and Canadian cohorts, and by the EURO-D in the European cohort. RESULTS: Across all three cohorts, non-linear age trends and gender differences were observed in the report of depressive symptoms, independent from educational attainment and annual income effects. The non-linear age trends reflected a negative association between depressive symptoms and age during midlife and then a positive association in late life. Females reported greater depressive symptoms than males; however, an interaction between gender and age was also observed in the Canadian and European cohorts. Among Canadians, the gender differences were largest after age 70, whereas among Europeans, gender differences where largest among those approximately aged 60. LIMITATIONS: Limitations include: 1) the cross-sectional nature of the study, resulting in age differences potentially reflecting cohort effects rather than a developmental process; and 2) the use of different depressive symptoms measures across cohorts. CONCLUSIONS: Characterization of depressive symptoms over mid and late adulthood in women and men provides insights into potential focal points for intervention and allocation of resources.

Using a cross-cohort comparison design to test the role of maternal smoking in pregnancy in child mental health and learning: evidence from two UK cohorts born four decades apart.

BACKGROUND: Maternal smoking in pregnancy is associated with low birth weight (LBW), child conduct problems, hyperactivity and lower cognitive attainment, but associations may reflect measured and unmeasured confounding. Cross-cohort designs can aid causal inference through comparison of associations across populations with different confounding structures. We compared associations between maternal smoking in pregnancy and child conduct and hyperactivity problems, cognition and LBW across two cohorts born four decades apart. METHODS: Two national UK cohorts born in 1958 (n = 12 415) and 2000/01 (n = 11 800) were compared. Maternal smoking in pregnancy and child birth weight was assessed at or shortly after birth. Parents rated children's conduct problems and hyperactivity, and children completed standardized tests of reading and mathematics. RESULTS: Maternal smoking in pregnancy was less common and more strongly associated with social disadvantage in 2000/01 compared with 1958 (interactions P < 0.001). Maternal smoking in pregnancy was robustly and equivalently associated with infant LBW in both cohorts [interactions: boys odds ratio (OR) = 1.01 (0.89, 1.16), P = 0.838; girls OR = 1.01 (0.91, 1.17), P = 0.633]. Maternal smoking was more strongly associated with conduct problems, hyperactivity and reading in the 2000/01 cohort (interactions P < 0.001). CONCLUSIONS: Marked cross-cohort change in associations between maternal smoking and child conduct problems, hyperactivity and reading highlights the likely role of confounding factors. In contrast, association with LBW was unaffected by change in prevalence of maternal smoking and patterns of confounding. The study highlights the utility of cross-cohort designs in helping triangulate conclusions about the role of putative causal risk factors in observational epidemiology.