Children of the 90s: Coming of age

Informatics Methodology Used in the Web-Based Portal of the NASCITA Cohort Study: Development and Implementation Study

Background

Many diseases occurring in adults can be pinned down to early childhood and birth cohorts are the optimal means to study this connection. Birth cohorts have contributed to the understanding of many diseases and their risk factors.

Objective

To improve the knowledge of the health status of Italian children early on and how it is affected by social and health determinants, we set up a longitudinal, prospective, national-level, population-based birth cohort, the NASCITA study (NAscere e creSCere in ITAlia). The main aim of this cohort is to evaluate physical, cognitive, and psychological development; health status; and health resource use in the first 6 years of life in newborns, and potential associated factors. A web-based system was set up with the aim to host the cohort; provide ongoing information to pediatricians and to families; and facilitate accurate data input, monitoring, and analysis. This article describes the informatics methodology used to set up and maintain the NASCITA cohort with its web-based platform, and provides a general description of the data on children aged over 7 months.

Methods

Family pediatricians were contacted for participation in the cohort and enrolled newborns from April 2019 to July 2020 at their first well-child visit. Information collected included basic data that are part of those routinely collected by the family pediatricians, but also parental data, such as medical history, characteristics and lifestyle, and indoor and outdoor environment. A specific web portal for the NASCITA cohort study was developed and an electronic case report form for data input was created and tested. Interactive data charts, including growth curves, are being made available to pediatricians with their patients’ data. Newsletters covering the current biomedical literature on child cohorts are periodically being put up for pediatricians, and, for parents, evidence-based information on common illnesses and problems in children.

Results

The entire cohort population consists of 5166 children, with 139 participating pediatricians, distributed throughout Italy. The number of children enrolled per pediatrician ranged from 1 to 100. The 5166 enrolled children represent 66.55% (5166/7763) of the children born in all of 2018 covered by the same pediatricians participating in the cohort. The number of children aged over 7 months at the time of these analyses, and for whom the most complete data were available upon initial analyses, was 4386 (2226/4381 males [50.81%] and 142/4370 twins [3.25%]). The age of the mothers at birth of the 4386 children ranged from 16 to 54 years. Most newborns’ mothers (3758/4367, 86.05%) were born in Italy, followed by mothers born in Romania (101/4367, 2.31%), Albania (75/4367, 1.72%), and Morocco (60/4367, 1.37%). Concerning the newborns, 138/4386 (3.15%) were born with malformations and 352/4386 (8.03%) had a disease, most commonly neonatal respiratory distress syndrome (n=52), neonatal jaundice (n=46), and neonatal hypoglycemia (n=45).

Conclusions

The NASCITA cohort is well underway and the population size will permit significant conclusions to be drawn. The key role of pediatricians in obtaining clinical data directly, along with the national-level representativity, will make the findings even more solid. In addition to promoting accurate data input, the multiple functions of the web portal, with its interactive platform, help maintain a solid relationship with the pediatricians and keep parents informed and interested in participating.

Trial Registration

ClinicalTrials.gov NCT03894566; https://clinicaltrials.gov/ct2/show/NCT03894566

Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort

Antibodies against pathogens provide information on exposure to infectious agents and are meaningful measures of past and present infection. Antibodies were measured in the plasma of children that are the offspring in a population-based birth cohort, the Avon Longitudinal Study of Parents and Children (ALSPAC). Plasma was collected during clinics at age 5, 7, 11 and 15 years. The antigens examined include: fungal ( Saccharomyces cerevisiae); protozoan ( Toxoplasma gondii and surface antigen 1 of  T. gondii); herpes viruses (cytomegalovirus, Epstein-Barr virus, herpes simplex virus type 1); common colds (influenza virus subtypes H1N1 and H3N2); other antigens (measles); animal (feline herpes virus, Theiler's virus); bacteria ( Helicobacter pylori); dietary antigens (bovine casein alpha protein, bovine casein beta protein). Alongside the depth of data available within the ALSPAC cohort, this longitudinal resource will enable the investigation of the association between infections and a wide variety of outcomes.

Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort

Antibodies against pathogens provide information on exposure to infectious agents and are meaningful measures of past and present infection. Antibodies were measured in the plasma of children that are the offspring in a population-based birth cohort, the Avon Longitudinal Study of Parents and Children (ALSPAC). Plasma was collected during clinics at age 5, 7, 11 and 15 years. The antigens examined include: fungal ( Saccharomyces cerevisiae); protozoan ( Toxoplasma gondii and surface antigen 1 of  T. gondii); herpes viruses (cytomegalovirus, Epstein-Barr virus, herpes simplex virus type 1); common colds (influenza virus subtypes H1N1 and H3N2); other antigens (measles); animal (feline herpes virus, Theiler’s virus); bacteria ( Helicobacter pylori); dietary antigens (bovine casein alpha protein, bovine casein beta protein). Alongside the depth of data available within the ALSPAC cohort, this longitudinal resource will enable the investigation of the association between infections and a wide variety of outcomes.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Demographics of dogs, cats, and rabbits attending veterinary practices in Great Britain as recorded in their electronic health records

BACKGROUND

Understanding the distribution and determinants of disease in animal populations must be underpinned by knowledge of animal demographics. For companion animals, these data have been difficult to collect because of the distributed nature of the companion animal veterinary industry. Here we describe key demographic features of a large veterinary-visiting pet population in Great Britain as recorded in electronic health records, and explore the association between a range of animal's characteristics and socioeconomic factors.

RESULTS

Electronic health records were captured by the Small Animal Veterinary Surveillance Network (SAVSNET), from 143 practices (329 sites) in Great Britain. Mixed logistic regression models were used to assess the association between socioeconomic factors and species and breed ownership, and preventative health care interventions. Dogs made up 64.8% of the veterinary-visiting population, with cats, rabbits and other species making up 30.3, 2.0 and 1.6% respectively. Compared to cats, dogs and rabbits were more likely to be purebred and younger. Neutering was more common in cats (77.0%) compared to dogs (57.1%) and rabbits (45.8%). The insurance and microchipping relative frequency was highest in dogs (27.9 and 53.1%, respectively). Dogs in the veterinary-visiting population belonging to owners living in least-deprived areas of Great Britain were more likely to be purebred, neutered, insured and microchipped. The same association was found for cats in England and for certain parameters in Wales and Scotland.

CONCLUSIONS

The differences we observed within these populations are likely to impact on the clinical diseases observed within individual veterinary practices that care for them. Based on this descriptive study, there is an indication that the population structures of companion animals co-vary with human and environmental factors such as the predicted socioeconomic level linked to the owner's address. This 'co-demographic' information suggests that further studies of the relationship between human demographics and pet ownership are warranted.

Babies Galore; or recent findings and future perspectives of pregnancy cohorts with a focus on immunity

Population-based pregnancy cohorts recruiting women before or at the moment of childbirth allow a longitudinal follow-up on children's health later in life. Important findings arising from pregnancy cohorts are discussed in the present review. These insights have led to revised guidelines on how to minimize disease risks in children, e.g., in the context of chronic immune diseases including allergies and asthma. Moreover, insights from pregnancy cohorts also unveiled a collateral effect of pregnancy on maternal immunity, mirrored by an ameliorated course of certain autoimmune diseases, but also an increased risk of infection with influenza A virus. Future pregnancy cohort studies are still required to close gaps in knowledge on how parameters involved in the developmental origin of health or poor immunity observed in children later in life are operational. We discuss here features that should be covered by future pregnancy cohort studies. Expected insights from such studies will then lay the foundation for biomarker discovery and offer opportunities for interventions to ameliorate adverse immune responses in humans.

Origin stories from a regional placenta tissue collection

Twenty-three years ago when women and their children were recruited to a longitudinal genetic epidemiological study during pregnancy, placentas were collected at birth. This paper explores the history of a regional placenta biobank and contemporary understandings of its value for the constitution of a research population. We draw on interviews with some of the mothers and those responsible for the establishment and curation of the placenta collection in order to explore the significance and meaning of the collection for them. Given its capacity to stand in for the study cohort of mothers and children, we argue that the material significance of the placenta biobank as a research tool seems far less important than the work it does in constituting a population. The stories about this collection may be understood within the wider context of developments in biobanking and the bioeconomy.

Beliefs about infant regulation, early infant behaviors and maternal postnatal depressive symptoms

BACKGROUND

Young infants may have irregular sleeping and feeding patterns. Such regulation difficulties are known correlates of maternal depressive symptoms. Parental beliefs about their role in regulating infant behaviors also may play a role. We investigated the association of depressive symptoms with infant feeding/sleeping behaviors, parent regulation beliefs, and the interaction of the two.

METHOD

In 2006, 272 mothers of infants aged up to 24 weeks completed a questionnaire about infant behavior and regulation beliefs. Participants were recruited from general medical practices and child health clinics in Brisbane, Australia. Depressive symptomology was measured using the Edinburgh Postnatal Depression Scale. Other measures were adapted from the ALSPAC study.

RESULTS

Regression analyses were run controlling for partner support, other support, life events, and a range of demographic variables. Maternal depressive symptoms were associated with infant sleeping and feeding problems but not regulation beliefs. The most important infant predictor was sleep behaviors with feeding behaviors accounting for little additional variance. An interaction between regulation beliefs and sleep behaviors was found. Mothers with high regulation beliefs were more susceptible to postnatal depressive symptoms when infant sleep behaviors were problematic.

CONCLUSION

Mothers of young infants who expect greater control are more susceptible to depressive symptoms when their infant presents challenging sleep behavior.

The hypergenome in inheritance and development

Both theory and experimentation suggest that during development, the DNA of multicellular organisms, recognized as graced with a lifelong intrinsic stability, is instead target of several modifications (point mutations, larger structural variations, epigenetic marks) and partner of complex interactions with non-DNA moieties (RNAs and proteins). Some of these modifications probably affect a fraction of the genome larger than standard point mutations and are more likely to respond to environmental cues. Thus, the traditional concepts of gene and genome need revision: the structure serving as depository of the overall bioinformation of the cell is more dynamic and less homogeneous than allowed for by the Central Dogma, since in addition to DNA, it includes also RNA and proteins. Each of the individual contributors as well as their stoichiometry undergo modifications. Compared to the traditional unidimensional and static genome, the resulting dynamic aggregate could be more competent to cope with different regulatory requirements: its structural variations may respond to unscheduled macro- and microenvironmental stresses as well as to scheduled genetic programs. A detailed assessment of these variations in time and space should provide a basis for a deeper comprehension of the phenotypic changes punctuating the organism's physio-pathological development, aging and transgenerational transmission. The variations of such information storage-delivery system may interest also the germ cells: the inheritance of parental traits and hence their evolutionary transmission would be affected. For the structure featuring all these properties, we propose the term 'hypergenome' to underscore the dynamic composition of a complex nucleoprotein responsive to both unpredictable environmental stimuli and internal built-in programs.

The role of longitudinal cohort studies in epigenetic epidemiology: challenges and opportunities

Longitudinal cohort studies are ideal for investigating how epigenetic patterns change over time and relate to changing exposure patterns and the development of disease. We highlight the challenges and opportunities in this approach.