Birth month affects lifetime disease risk: a phenome-wide method

A model investigating environmental factors that play a role in female fecundity or birth rate

OBJECTIVE

Over 12% of women in the United States have reduced fertility and/or fecundity. Environmental factors, such as temperature, and socioeconomic factors have been implicated in reducing female fecundity. The purpose of this study is to investigate the effect of environmental factors coupled with socioeconomic factors on birth rate at the country-level. We use birth rate as a proxy for female fecundity. This will enable us to identify the most important factors affecting female fecundity.

METHODS

Using country-specific data from 182 countries, we constructed a regression model of the effects of environmental and socioeconomic factors on birth rate at the country-level. Our model assesses the role of temperature, Gross Domestic Product (GDP) per capita, fine air particulate matter (PM 2.5), and prevalence of male and female Body Mass Index (BMI) > = 25 (age-standardized) on birth rate per country. Because many of these factors are inter-dependent, we include all possible two-way interaction terms to assess the role of individual factors and interactions between multiple factors in the model.

RESULTS

In the full regression model, we found that GDP per capita along with 5 interaction terms were significant after adjusting for multiple testing. Female BMI was only nominally significant. GDP per capita was independently associated with birth rate (adjusted p-value <0.001). Prevalence of BMI > = 25 age-standardized in males and females were also significant when interacting with air pollution or GDP on female fecundity (birth rate). Temperature did not affect birth rate either independently or as an interaction unless BMI was removed from the model.

CONCLUSION

A country's economic wealth was the most significant factor in predicting birth rate in a statistical model that includes environmental and socioeconomic variables. This is important for future studies investigating environmental factors involved in increasing or decreasing female fecundity.

Birth Month and Course of Recurrent Depressive Disorders in a Polish Population

BACKGROUND The aim of this study was to determine whether the specific season of the year during which the first trimester of pregnancy takes place is significantly associated with the course (intensification and frequency of occurrence) of an episode of recurrent depressive disorder in adult life. MATERIAL AND METHODS We enrolled 184 patients treated for recurrent depressive disorders. RESULTS An analysis of the results obtained indicates that the greatest number of people suffering from a major depressive episode were born in the spring and summer (from April to September), meaning that the first trimester of pregnancy occurred between October and March. However, our results were not statistically significant, perhaps due to the small size of the examined group. CONCLUSIONS The results obtained indicate that birth month may be significantly associated with the course of recurrent depressive disorders. In patients from Central Europe, the first trimester of pregnancy falling in autumn and winter seems to be significant. These results need to be interpreted with caution due to the small size of the examined group.

Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Cardiovascular Disease Risk Varies by Birth Month in Canines

The canine heart is a robust physiological model for the human heart. Recently, birth month associations have been reported and replicated in humans using clinical health records. While animals respond readily to their environment in the wild, a systematic investigation of birth season dependencies among pets and specifically canines remains lacking. We obtained data from the Orthopedic Foundation of Animals on 129,778 canines representing 253 distinct breeds. Among canines that were not predisposed to cardiovascular disease, a clear birth season relationship is observed with peak risk occurring in June-August. Our findings indicate that acquired cardiovascular disease among canines, especially those that are not predisposed to cardiovascular disease, appears birth season dependent. The relative risk of cardiovascular disease for canines not predisposed to cardiovascular disease was as high as 1.47 among July pups. The overall adjusted odds ratio, when mixed breeds were excluded, for the birth season effect was 1.02 (95% CI: 1.002, 1.047, p = 0.032) after adjusting for breed and genetic cardiovascular predisposition effects. Studying birth season effects in model organisms can help to elucidate potential mechanisms behind the reported associations.

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature

Background

Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making.

Objective

The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved.

Methods

PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET’s phenotype representation with PheKnow-Cloud’s by using PheKnow-Cloud’s experimental setup. In PIVET’s framework, we also introduce a statistical model trained on domain expert–verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner.

Results

PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET’s analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes.

Conclusions

Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample

Background

Older patients with multiple chronic conditions are often faced with increased health care needs and subsequent higher medical costs, posing significant financial burden to patients, their caregivers, and the health care system. The increasing adoption of electronic health record systems and the proliferation of clinical data offer new opportunities for prevalence studies and for population health assessment. The last few years have witnessed an increasing number of clinical research networks focused on building large collections of clinical data from electronic health records and claims to make it easier and less costly to conduct clinical research.

Objective

The aim of this study was to compare the prevalence of common chronic conditions and multiple chronic conditions in older adults between Florida and the United States using data from the OneFlorida Clinical Research Consortium and the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS).

Methods

We first analyzed the basic demographic characteristics of the older adults in 3 datasets—the 2013 OneFlorida data, the 2013 HCUP NIS data, and the combined 2012 to 2016 OneFlorida data. Then we analyzed the prevalence of each of the 25 chronic conditions in each of the 3 datasets. We stratified the analysis of older adults with hypertension, the most prevalent condition. Additionally, we examined trends (ie, overall trends and then by age, race, and gender) in the prevalence of discharge records representing multiple chronic conditions over time for the OneFlorida (2012-2016) and HCUP NIS cohorts (2003-2013).

Results

The rankings of the top 10 prevalent conditions are the same across the OneFlorida and HCUP NIS datasets. The most prevalent multiple chronic conditions of 2 conditions among the 3 datasets were—hyperlipidemia and hypertension; hypertension and ischemic heart disease; diabetes and hypertension; chronic kidney disease and hypertension; anemia and hypertension; and hyperlipidemia and ischemic heart disease. We observed increasing trends in multiple chronic conditions in both data sources.

Conclusions

The results showed that chronic conditions and multiple chronic conditions are prevalent in older adults across Florida and the United States. Even though slight differences were observed, the similar estimates of prevalence of chronic conditions and multiple chronic conditions across OneFlorida and HCUP NIS suggested that clinical research data networks such as OneFlorida, built from heterogeneous data sources, can provide rich data resources for conducting large-scale secondary data analyses.

Photoperiodic Programming of the SCN and Its Role in Photoperiodic Output

Though the seasonal response of organisms to changing day lengths is a phenomenon that has been scientifically reported for nearly a century, significant questions remain about how photoperiod is encoded and effected neurobiologically. In mammals, early work identified the master circadian clock, the suprachiasmatic nuclei (SCN), as a tentative encoder of photoperiodic information. Here, we provide an overview of research on the SCN as a coordinator of photoperiodic responses, the intercellular coupling changes that accompany that coordination, as well as the SCN's role in a putative brain network controlling photoperiodic input and output. Lastly, we discuss the importance of photoperiodic research in the context of tangible benefits to human health that have been realized through this research as well as challenges that remain.

The Use of Electronic Medical Record Data to Analyze the Association Between Atrial Fibrillation and Birth Month

OBJECTIVES

Cardiovascular disease is a condition of enormous public health concern. Recently, a population study newly revealed associations between cardiovascular diseases and birth month. Here, we investigated the association between atrial fibrillation in cardiovascular disease and birth month.

METHODS

We retrospectively extracted birth date data from 6,016 patients with atrial fibrillation (3,876 males; 2,140 females) from our electronic medical records. The number of live births in Japan fluctuates seasonally. Therefore, we corrected the number of patients for each birth month based on a Japanese population survey report. Then, a test of the significance of the association between atrial fibrillation and birth month was performed using a chi-square test. In addition, we compared the results of an analysis of patient data with that of simulated data that showed no association with birth month.

RESULTS

The deviations of birth month were not significant (overall: p = 0.631, males: p = 0.842, females: p = 0.333). The number of female patients born in the first quarter of the year was slightly higher than those born in the other quarters of the year (p = 0.030). However, by comparing the magnitudes of dispersion in the simulated data, it seems that this finding was mere coincidence.

CONCLUSION

An association between atrial fibrillation and birth month could not be confirmed in our Japanese study. However, this might be due to differences in ethnicity. Further epidemiologic studies on this topic may result in reduction of disease risk in the general population and contribute to public health.

The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies

Diagnostic codes within electronic health record systems can vary widely in accuracy. It has been noted that the number of instances of a particular diagnostic code monotonically increases with the accuracy of disease phenotype classification. As a growing number of health system databases become linked with genomic data, it is critically important to understand the effect of this misclassification on the power of genetic association studies. Here, I investigate the impact of this diagnostic code misclassification on the power of genetic association studies with the aim to better inform experimental designs using health informatics data. The trade-off between (i) reduced misclassification rates from utilizing additional instances of a diagnostic code per individual and (ii) the resulting smaller sample size is explored, and general rules are presented to improve experimental designs.

Association of neovascular age-related macular degeneration with month and season of birth in Italy

In order to investigate the influence of season and month of birth on the risk of neovascular age-related macular degeneration (n-AMD) in Italy, we evaluated the month birth and sex of all patients, recorded in the anti-vascular endothelial growth factor (VEGF) monitoring registry of the Italian Medicines Agency, born between 1925–1944, who received intravitreal anti-VEGF injections for n-AMD between January 1, 2013 and July 29, 2015. The numbers of all births in Italy in the same years, extracted from the Italian National Institute of Statistics, were used to calculate the expected number of n-AMD cases. Overall, 45,845 patients (19,207 men, 26,638 women) received intravitreal anti-VEGF for n-AMD; in the same years, 20,140,426 people (10,334,262 male, 9,806,164 female) were born in Italy. Comparing the observed number of n-AMD cases with the expected number of n- AMD cases in each season, we found that the season-specific risk for n-AMD was 2.5% higher for those born in summer (OR=1.03, Bonferroni-corrected P=0.008) and 3% lower for those born in winter (OR=0.96, Bonferroni-corrected P=0.0004). When considering the month of birth, the risk of n-AMD was 5.9% lower for people born in January (OR=0.93, Bonferroni-corrected P=0.0012). The factors causing such differences should be determined.

Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect

Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacological similarity with drugs of known fetal effect) and empirical data (i.e., derived from Electronic Health Records). Our fetal loss cohort contains 14,922 affected and 33,043 unaffected pregnancies and our congenital anomalies cohort contains 5,658 affected and 31,240 unaffected infants. We trained a random forest to classify drugs of unknown pregnancy class into harmful or safe categories, focusing on two distinct outcomes: fetal loss and congenital anomalies. Our models achieved an out-of-bag accuracy of 91% for fetal loss and 87% for congenital anomalies outperforming null models. Fifty-seven 'category C' medications were classified as harmful for fetal loss and eleven for congenital anomalies. This includes medications with documented harmful effects, including naproxen, ibuprofen and rubella live vaccine. We also identified several novel drugs, e.g., haloperidol, that increased the risk of fetal loss. Our approach provides important information on the harmfulness of 'category C' drugs. This is needed, as no FDA recommendation exists for these drugs' fetal safety.

Uncovering exposures responsible for birth season - disease effects: a global study

Objective

Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season.

Material and Methods

This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month–disease risk curves from each site in a case-control manner. Next, we correlated each birth month–disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month–exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures.

Results

Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R = 0.725, confidence interval [95% CI], 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R = 0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R = −0.816, 95% CI, −0.5767, −0.929).

Conclusion

A global study of birth month–disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.

Representing Knowledge Consistently Across Health Systems

Objectives: Electronic health records (EHRs) have increasingly emerged as a powerful source of clinical data that can be leveraged for reuse in research and in modular health apps that integrate into diverse health information technologies. A key challenge to these use cases is representing the knowledge contained within data from different EHR systems in a uniform fashion. Method: We reviewed several recent studies covering the knowledge representation in the common data models for the Observational Medical Outcomes Partnership (OMOP) and its Observational Health Data Sciences and Informatics program, and the United States Patient Centered Outcomes Research Network (PCORNet). We also reviewed the Health Level 7 Fast Healthcare Interoperability Resource standard supporting app-like programs that can be used across multiple EHR and research systems. Results: There has been a recent growth in high-impact efforts to support quality-assured and standardized clinical data sharing across different institutions and EHR systems. We focused on three major efforts as part of a larger landscape moving towards shareable, transportable, and computable clinical data. Conclusion: The growth in approaches to developing common data models to support interoperable knowledge representation portends an increasing availability of high-quality clinical data in support of research. Building on these efforts will allow a future whereby significant portions of the populations in the world may be able to share their data for research.

PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature

As the adoption of Electronic Healthcare Records has grown, the need to transform manual processes that extract and characterize medical data into automatic and high-throughput processes has also grown. Recently, researchers have tackled the problem of automatically extracting candidate phenotypes from EHR data. Since these phenotypes are usually generated using unsupervised or semi-supervised methods, it is necessary to examine and validate the clinical relevance of the generated "candidate" phenotypes. We present PheKnow-Cloud, a framework that uses co-occurrence analysis on the publicly available, online repository ofjournal articles, PubMed, to build sets of evidence for user-supplied candidate phenotypes. PheKnow-Cloud works in an interactive manner to present the results of the candidate phenotype analysis. This tool seeks to help researchers and clinical professionals evaluate the automatically generated phenotypes so they may tune their processes and understand the candidate phenotypes.

Relationship between patients' month of birth and the prevalence of chronic diseases

BACKGROUND AND OBJECTIVES

Patients' month of birth can reflect exposure to certain factors during pregnancy and the first few months of life, which could influence the onset of chronic diseases during adulthood. The aim of this study is to evaluate the association between a patient's month of birth and the presence of chronic diseases in the Spanish population, by analysing the National Health Survey for the year 2006.

PATIENTS AND METHODS

We measured the association between 27 common chronic diseases and the month of birth, estimating the odds ratios and confidence intervals at 95%, using multivariate logistical models and adjusting the results for month of birth and potentially confounding variables.

RESULTS

The sample population was made up of a total of 29,478 individuals, representing approximately 44.7 million Spanish residents on 1 January 2007. Significant associations were found between the month of birth and several chronic diseases. There is a gender-differentiated risk pattern of developing chronic diseases according to the month of birth, with more significant associations and of greater magnitude being detected among men compared to women.

CONCLUSIONS

The associations detected might reflect early exposure to environmental factors in the uterus and during the first few months of life. More specific studies are required to gain a more in-depth understanding of these associations.

Perspective: It's All About Time

New knowledge on multi-scale temporal dynamics linking nanobio-time series, seasonal changes, immune response, and gut mictobiota can milestone (neuro) science soon.

Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics

The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.

Modeling month-season of birth as a risk factor in mouse models of chronic disease: from multiple sclerosis to autoimmune encephalomyelitis

Month-season of birth (M-SOB) is a risk factor in multiple chronic diseases, including multiple sclerosis (MS), where the lowest and greatest risk of developing MS coincide with the lowest and highest birth rates, respectively. To determine whether M-SOB effects in such chronic diseases as MS can be experimentally modeled, we examined the effect of M-SOB on susceptibility of C57BL/6J mice to experimental autoimmune encephalomyelitis (EAE). As in MS, mice that were born during the M-SOB with the lowest birth rate were less susceptible to EAE than mice born during the M-SOB with the highest birth rate. We also show that the M-SOB effect on EAE susceptibility is associated with differential production of multiple cytokines/chemokines by neuroantigen-specific T cells that are known to play a role in EAE pathogenesis. Taken together, these results support the existence of an M-SOB effect that may reflect seasonally dependent developmental differences in adaptive immune responses to self-antigens independent of external stimuli, including exposure to sunlight and vitamin D. Moreover, our documentation of an M-SOB effect on EAE susceptibility in mice allows for modeling and detailed analysis of mechanisms that underlie the M-SOB effect in not only MS but in numerous other diseases in which M-SOB impacts susceptibility.-Reynolds, J. D., Case, L. K., Krementsov, D. N., Raza, A., Bartiss, R., Teuscher, C. Modeling month-season of birth as a risk factor in mouse models of chronic disease: from multiple sclerosis to autoimmune encephalomyelitis.

Hypothesis-Free Search for Connections between Birth Month and Disease Prevalence in Large, Geographically Varied Cohorts

We have sought to replicate and extend the Season-wide Association Study (SeaWAS) of Boland, et al.1 in identifying birth month-disease associations from electronic health records (EHRs). We used methodology similar to that implemented by Boland on three geographically distinct cohorts, for a total of 11.8 million individuals derived from multiple data sources. We were able to identify eleven out of sixteen literature-supported birth month associations as compared to seven of sixteen for SeaWAS. Of the nine novel cardiovascular birth month associations discovered by SeaWAS, we were able to replicate four. None of the novel non-cardiovascular associations discovered by SeaWAS emerged as significant relations in our study. We identified thirty birth month disease associations not previously reported; of those, only six associations were validated in more than one cohort. These results suggest that differences in cohort composition and location can cause consequential variation in results of hypothesis-free searches.

Do legal school leaving rules still affect schooling and earnings?

This paper quantifies whether compulsory schooling laws are still effective in the 21st century and if so, to what extent the school compulsion continues to influence individuals' educational achievement and labor market earnings. Using American Community Survey, I find that compulsory schooling laws were effective for the white men and women born in the 1930s and 1940s in the U.S.; however, they no longer produce the same seasonality effects on the educational attainment of the white cohorts who completed their educational attainment in the 2000s. I also find that the school compulsion was not binding for the older African American cohorts; however, they were effective in keeping the younger African American men at school longer.

Birth month and risk of atopic dermatitis: a nationwide population-based study

BACKGROUND

An individual's birth month has been associated with allergic diseases, but little is known about the association between birth month and atopic dermatitis (AD).

OBJECTIVE

The aim of this study was to investigate the risk of AD in children born in various months.

METHODS

Using Taiwan's National Health Insurance Research Database, we conducted a case-control study that included 31 237 AD cases and 124 948 age- and gender-matched controls without AD. Data regarding sociodemographic factors and coexisting medical conditions were collected and controlled in the multivariate logistic regression to determine the adjusted odds ratios and 95% confidence intervals for AD associated with the participant's birth month.

RESULTS

Compared with people born in May, people born in December had the highest risk of AD (OR 1.17, 95% CI 1.10-1.25), followed by people born in October (OR 1.15, 95% CI 1.08-1.22) and November (OR 1.13, 95% CI 1.06-1.20). Low income (OR 1.28), asthma (OR 1.88), allergic rhinitis (OR 1.70), psoriasis (OR 2.36), vitiligo (OR 1.99), urticaria (OR 2.14), and systemic lupus erythematosus (OR 1.91) were significant coexisting medical conditions associated with AD.

CONCLUSION

Being born in December, October, or November may be associated with an increased risk of AD. Future investigations are needed to evaluate the possible mechanism behind the association between birth month and AD.

Autumn season birth is associated with a lower frequency of diagnosis of unprovoked deep vein thrombosis in the emergency department

A significant association was described between lifetime frequency of several human diseases, including cardiovascular disorders, and birth season. We performed a retrospective study to establish whether an association exists between birth season and frequency of venous thromboembolism diagnosed in the emergency department. The study population consisted of all consecutive patients diagnosed with venous thromboembolism at the emergency department of the University Hospital of Parma (Italy) during the year 2014. A total number of 400 patients (217 women and 183 men; mean age 70 ± 18 years) received a final diagnosis of venous thromboembolism throughout the study period. The lowest frequency of diagnoses was observed in patients born in autumn, whereas a higher frequency was observed in those born in spring or summer. When compared with the frequency of births in the same geographical area, patients born in spring and summer exhibited a 30 and 25% higher risk of venous thromboembolism compared with those having autumn birth. A similar trend was observed in patients with unprovoked thrombosis, but not in those with provoked thrombosis. A subanalysis of patients with unprovoked deep vein thrombosis revealed that both spring birth (relative risk 1.49, 95% confidence interval 1.04-2.14) and summer birth (relative risk 1.46, 95% confidence interval 1.01-2.09) were significant risk factors for this condition compared with autumn birth. Although further studies are needed to confirm these original findings, it seems reasonable to hypothesize that birth season may influence the lifetime risk of venous thromboembolism, especially of unprovoked deep vein thrombosis.

Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?

In the modern era, with high-throughput technology and large data size, associational studies are actively being generated. Some have statistical and clinical validity and utility, or at least have biologically plausible relationships, while others may not. Recently, the potential effect of birth month on lifetime disease risks has been studied in a phenome-wide model. We evaluated the associations between birth month and 5 cardiovascular disease-related outcomes in an independent registry of 8,346 patients from Ontario, Canada in 1977-2014. We used descriptive statistics and logistic regression, along with model-fit and discrimination statistics. Hypertension and coronary heart disease (of primary interest) were most prevalent in those who were born in January and April, respectively, as observed in the previous study. Other outcomes showed weak or opposite associations. Ancillary analyses (based on raw blood pressures and subgroup analyses by sex) demonstrated inconsistent patterns and high randomness. Our study was based on a high risk population and could not provide scientific explanations. As scientific values and clinical implications can be different, readers are encouraged to read the original and our papers together for more objective interpretations of the potential impact of birth month on individual and public health as well as toward cumulative/total evidence in general.

Replicating Cardiovascular Condition-Birth Month Associations

Independent replication is vital for study findings drawn from Electronic Health Records (EHR). This replication study evaluates the relationship between seasonal effects at birth and lifetime cardiovascular condition risk. We performed a Season-wide Association Study on 1,169,599 patients from Mount Sinai Hospital (MSH) to compute phenome-wide associations between birth month and CVD. We then evaluated if seasonal patterns found at MSH matched those reported at Columbia University Medical Center. Coronary arteriosclerosis, essential hypertension, angina, and pre-infarction syndrome passed phenome-wide significance and their seasonal patterns matched those previously reported. Atrial fibrillation, cardiomyopathy, and chronic myocardial ischemia had consistent patterns but were not phenome-wide significant. We confirm that CVD risk peaks for those born in the late winter/early spring among the evaluated patient populations. The replication findings bolster evidence for a seasonal birth month effect in CVD. Further study is required to identify the environmental and developmental mechanisms.

Disrupted seasonal biology impacts health, food security and ecosystems

The rhythm of life on earth is shaped by seasonal changes in the environment. Plants and animals show profound annual cycles in physiology, health, morphology, behaviour and demography in response to environmental cues. Seasonal biology impacts ecosystems and agriculture, with consequences for humans and biodiversity. Human populations show robust annual rhythms in health and well-being, and the birth month can have lasting effects that persist throughout life. This review emphasizes the need for a better understanding of seasonal biology against the backdrop of its rapidly progressing disruption through climate change, human lifestyles and other anthropogenic impact. Climate change is modifying annual rhythms to which numerous organisms have adapted, with potential consequences for industries relating to health, ecosystems and food security. Disconcertingly, human lifestyles under artificial conditions of eternal summer provide the most extreme example for disconnect from natural seasons, making humans vulnerable to increased morbidity and mortality. In this review, we introduce scenarios of seasonal disruption, highlight key aspects of seasonal biology and summarize from biomedical, anthropological, veterinary, agricultural and environmental perspectives the recent evidence for seasonal desynchronization between environmental factors and internal rhythms. Because annual rhythms are pervasive across biological systems, they provide a common framework for trans-disciplinary research.

Epidemiological features and spatio-temporal clusters of hand-foot-mouth disease at town level in Fuyang, Anhui Province, China (2008-2013)

Hand-foot-mouth disease (HFMD) is a frequently occurring epidemic and has been an important cause of childhood mortality in China. Given the disease's significant impact nationwide, the epidemiological characteristics and spatio-temporal clusters in Fuyang from 2008 to 2013 were analysed in this study. The disease exhibits strong seasonality with a rising incidence. Of the reported HFMD cases, 63·7% were male and 95·2% were preschool children living at home. The onset of HFMD is age-dependent and exhibits a 12-month periodicity, with 12-, 24- and 36-month-old children being the most frequently affected groups. Across the first 60 months of life, children born in April [relative risk (RR) 8·18], May (RR 9·79) and June (RR 8·21) exhibited an elevated infection risk of HFMD relative to January-born children; the relative risk compared with the reference (January-born) group was highest for children aged 24 months born in May (RR 34·85). Of laboratory-confirmed cases, enterovirus 71 (EV71), coxsackie A16 (Cox A16) and other enteroviruses accounted for 60·1%, 7·1% and 32·8%, respectively. Spatio-temporal analysis identified one most likely cluster and several secondary clusters each year. The centre of the most likely cluster was found in different regions in Fuyang. Implications of our findings for current and future public health interventions are discussed.

In Search of 'Birth Month Genes': Using Existing Data Repositories to Locate Genes Underlying Birth Month-Disease Relationships

Prenatal and perinatal exposures vary seasonally (e.g., sunlight, allergens) and many diseases are linked with variance in exposure. Epidemiologists often measure these changes using birth month as a proxy for seasonal variance. Likewise, Genome-Wide Association Studies have associated or implicated these same diseases with many genes. Both disparate data types (epidemiological and genetic) can provide key insights into the underlying disease biology. We developed an algorithm that links 1) epidemiological data from birth month studies with 2) genetic data from published gene-disease association studies. Our framework uses existing data repositories - PubMed, DisGeNET and Gene Ontology - to produce a bipartite network that connects enriched seasonally varying biofactorss with birth month dependent diseases (BMDDs) through their overlapping developmental gene sets. As a proof-of-concept, we investigate 7 known BMDDs and highlight three important biological networks revealed by our algorithm and explore some interesting genetic mechanisms potentially responsible for the seasonal contribution to BMDDs.

ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

Numerous biological systems oscillate over time or space. Despite these oscillators' importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning. We present ZeitZeiger, a method to predict a periodic variable (e.g. time of day) from a high-dimensional observation. ZeitZeiger learns a sparse representation of the variation associated with the periodic variable in the training observations, then uses maximum-likelihood to make a prediction for a test observation. We applied ZeitZeiger to a comprehensive dataset of genome-wide gene expression from the mammalian circadian oscillator. Using the expression of 13 genes, ZeitZeiger predicted circadian time (internal time of day) in each of 12 mouse organs to within ∼1 h, resulting in a multi-organ predictor of circadian time. Compared to the state-of-the-art approach, ZeitZeiger was faster, more accurate and used fewer genes. We then validated the multi-organ predictor on 20 additional datasets comprising nearly 800 samples. Our results suggest that ZeitZeiger not only makes accurate predictions, but also gives insight into the behavior and structure of the oscillator from which the data originated. As our ability to collect high-dimensional data from various biological oscillators increases, ZeitZeiger should enhance efforts to convert these data to knowledge.

Season of birth is associated with birth weight, pubertal timing, adult body size and educational attainment: a UK Biobank study

Season of birth, a marker of in utero vitamin D exposure, has been associated with a wide range of health outcomes. Using a dataset of ∼450,000 participants from the UK Biobank study, we aimed to assess the impact of this seasonality on birth weight, age at menarche, adult height and body mass index (BMI). Birth weight, age at menarche and height, but not BMI, were highly significantly associated with season of birth. Individuals born in summer (June-July-August) had higher mean birth weight (P = 8 × 10^-10), later pubertal development (P = 1.1 × 10^-45) and taller adult height (P = 6.5 × 10^-9) compared to those born in all other seasons. Concordantly, those born in winter (December-January-February) showed directionally opposite differences in these outcomes. A secondary comparison of the extreme differences between months revealed higher odds ratios [95% confidence intervals (CI)] for low birth weight in February vs. September (1.23 [1.15-1.32], P = 4.4 × 10^-10), for early puberty in September vs. July (1.22 [1.16-1.28], P = 7.3 × 10^-15) and for short stature in December vs. June (1.09 [1.03-1.17], P = 0.006). The above associations were also seen with total hours of sunshine during the second trimester, but not during the first three months after birth. Additional associations were observed with educational attainment; individuals born in autumn vs. summer were more likely to continue in education post age 16 years (P = 1.1 × 10^-91) or attain a degree-level qualification (P = 4 × 10^-7). However, unlike other outcomes, an abrupt difference was seen between those born in August vs. September, which flank the start of the school year. Our findings provide support for the 'fetal programming' hypothesis, refining and extending the impact that season of birth has on childhood growth and development. Whilst other mechanisms may contribute to these associations, these findings are consistent with a possible role of in utero vitamin D exposure.