Relation between infant microbiota and autism? - Results from a national cohort sibling-design study

Research output: Contribution to journalJournal articleResearchpeer-review

BACKGROUND: Hypotheses concerning adverse effects of changes in microbiota have received much recent attention, but unobserved confounding makes them difficult to test. We investigated whether surrogate markers for potential adverse microbiota changes in infancy affected autism risk, addressing unobserved confounding using a sibling study design.

METHODS: This is a population-based, prospective cohort study including all singleton live births in Denmark from 1997 to 2010. The exposure variables were cesarean delivery and antibiotic use in the first 2 years of life. The outcome was a subsequent autism diagnosis. We used the between-within sibling model and compared it with sibling stratified Cox models and simpler standard Cox models that ignored sibship.

RESULTS: Of our study population including 671,606 children, who were followed for up to 15 years (7,341,133 person-years), 72% received antibiotics, 17.5% were delivered by cesarean, and 1.2% (8,267) developed autism. The standard Cox models predicted that both cesarean (compared to vaginal) delivery and antibiotics increased the risk of autism. In the sibling-stratified Cox model, only broader spectrum antibiotics were associated with increased risk of autism: hazard ratio (HR) 1.16 (95% confidence interval, 1.01-1.36). The between-within model estimated no exposure effects: intrapartum cesarean HR 1.06 (0.89-1.26); pre-labor cesarean HR 0.97 (0.83-1.15); exclusively penicillin HR 1.05 (0.93-1.18); broader spectrum antibiotics HR 1.05 (0.95-1.16).

CONCLUSIONS: The between-within model rendered more precise estimates than sibling-stratified Cox models, and we believe also provided more valid estimates. Results from these preferred models do not support a causal relation between antibiotic treatment during infancy, cesarean delivery, and autism.

Original languageEnglish
JournalEpidemiology (Cambridge, Mass.)
Issue number1
Pages (from-to)52-60
Number of pages9
Publication statusPublished - 2019

ID: 203376106