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Record W4392194024 · doi:10.1111/ppe.13061

Is it time to re‐think how we look for teratogenic effects in exposure cohort studies?

2024· article· en· W4392194024 on OpenAlexaffabout
Jan M. Friedman

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCohortCohort studyInternal medicine

Abstract

fetched live from OpenAlex

In this issue of Paediatric and Perinatal Epidemiology, Segovia Chacón and her associates1 used linked data on 1,100,000 live births from the Swedish Birth Defects and Health Registries to ask whether estimates of the prevalence of major congenital anomalies differ by age of the child, a question that colours how we interpret birth defect rates observed in exposure cohort studies. The authors found that the observed prevalence of all major malformations and many anatomic classes of major malformations do increase monotonically from birth to 5 years of age. However, for all major congenital anomalies and most anatomic classes of major malformations, the increase is much smaller after 1 year of age than between birth and 1 year. Based on these observations, Segovia Chacón and her colleagues make the reasonable and practical suggestion that measuring the frequency of major congenital anomalies diagnosed by 1 year of age is sufficient for most studies looking for an association between maternal drug treatment during pregnancy and infant malformations. Finding that the prevalence of major congenital anomalies increases from birth through early childhood is, of course, no surprise. Similar observations have been made repeatedly in other pharmacoepidemiological studies for over 50 years. The current study and Nordic records linkage studies generally are distinguished from many other investigations by the consistent quality of their data and their ability to link individual records accurately. These factors permit a wide variety of outcomes to be identified on a population-wide basis for many years after birth. The current study employs a well-established and standardised design, but this design does not reflect a contemporary understanding of developmental biology and teratogenic mechanisms. For example, the outcomes of interest are ‘major congenital malformations’ as a group, as well as anatomic classes of major malformations. The range of malformations included as ‘major’ and the grouping of anomalies into anatomic classes are inconsistent with current knowledge. ‘Major’ congenital anomalies are defined as structural changes that have significant medical, surgical, social or cosmetic consequences for the affected individual.2 Thus, ‘major congenital malformations’ include not only the lethal conditions of Anencephaly (International Classification of Diseases [ICD] version 10 code Q00.0) and Craniorachischisis (ICD-10 Q00.1) but also Absence and agenesis of lacrimal apparatus (ICD-10 Q10.4), i.e., blocked tear duct. Although Absence and agenesis of lacrimal apparatus is included as a major malformation with a cumulative detection rate that increases with age in Segovia Chacón's Table 1, this anomaly is usually completely treatable by massaging the tear duct in babies under 6 months of age or by surgical probing if massage does not work. The grouping of major malformations into anatomic classes ignores the fact that teratogenic exposures are more likely to produce a recurrent pattern of multiple minor and major congenital anomalies throughout the body than several different major malformations within a single anatomic class. It would, therefore, make more sense to group anomalies by suggested ‘syndrome’ pattern or postulated pathogenic mechanism than by anatomic class.3 The authors' concern about whether some cases of microcephaly are acquired rather than congenital illustrates the issue of anomaly classification, but the problem goes deeper than age at diagnosis. Microcephaly is sometimes not really a malformation (i.e., an alteration of a primary developmental process) but rather a disruption (i.e., the breakdown of an originally normal developmental process),3 as in the severe microcephaly that occurs with Zika virus embryopathy.4 In other cases, microcephaly may be a manifestation of universal growth impairment that affects height and weight to a similar degree as head circumference. In addition, the traditional focus of birth defects registry studies on major malformations excludes not only recurrent patterns of minor abnormalities and generalised impairment of growth but also neurodevelopmental abnormalities. All of these problems are important components of many teratogenic embryopathies. Neurodevelopmental abnormalities, such as severe intellectual disability or autism, are a particular concern for many pregnant persons who are worried about the possible adverse effects of an exposure on their embryo or foetus. Nordic linked-records systems provide an exceptional opportunity to study the effects of maternal gestational exposures on neurobehavioral outcomes in the children,5-7 but neurodevelopmental abnormalities are rarely included with congenital anomalies in studies of possible teratogenic effects of maternal pregnancy exposures. Another area in which the standard methodology of birth defects registry studies is out of date relates to how chromosomal abnormalities and genetic diseases are considered. Segovia Chacón et al. found that the proportion of children with chromosomal or other genetic anomalies ranged from 0.16% at birth to 0.32% at 3 years of age. The use of exome and genome sequencing has dramatically increased the number of patients in whom a genetic cause can be recognised, and it seems likely that the proportion of children with recognised chromosomal or other genetic abnormalities is much greater today than it was when data collection for this study began. Even at 0.32%, the proportion of genetic disorders recognised in the Swedish Medical Birth Register is an order of magnitude less than the estimated prevalence of individuals with genetically caused rare diseases.8 Segovia Chacón's study excluded children with chromosomal or other genetic anomalies from the group with major malformations unless the child also had a major structural anomaly. The reasoning behind this exclusion was that children with genetic disorders have a higher prevalence of ‘nongenetic’ malformations, and maternal medication use is not likely to cause genetic changes in the embryo/foetus. It certainly is true that constitutional genetic mutations cannot be caused by a teratogenic exposure that occurs after implantation, but we now know that most major malformations have a complex origin, with both genetic and nongenetic (e.g., teratogenic) predisposing factors. Embryos with a genetic predisposition to a malformation may, therefore, act as ‘canaries in the coal mine’, being at greatly increased risk of developing a malformation after suffering a teratogenic exposure in comparison with embryos without a genetic predisposition. By analysing cases with a genetic condition such as a chromosomal abnormality or Mendelian syndrome as a separate group, rather than excluding them from a study, it may be possible to detect a signal of teratogenic risk with fewer total exposures. Nordic record-linkage studies have been a cornerstone of pharmacoepidemiology for many years. Updating the design of these studies in consideration of our contemporary understanding of how teratogenic effects occur and can be recognised in humans could make these studies more valuable. Jan M. Friedman is a Professor of Medical Genetics at the University of British Columbia, Vancouver, Canada. He is a medical geneticist and clinical teratologist. Dr. Friedman established the TERIS (Teratogen Information Service) knowledgebase in 1984 and was its principal author for 35 years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.330
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes2
Has abstractyes

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