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Debbie A Lawlor

Researcher at University of Bristol

Publications -  1118
Citations -  118183

Debbie A Lawlor is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Body mass index. The author has an hindex of 147, co-authored 1114 publications receiving 101123 citations. Previous affiliations of Debbie A Lawlor include Southampton General Hospital & University of Vermont.

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Journal ArticleDOI

Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: individual participant meta-analysis

David Wormser, +271 more
TL;DR: Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases.
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Association of Maternal Diabetes Mellitus in Pregnancy With Offspring Adiposity Into Early Adulthood Sibling Study in a Prospective Cohort of 280 866 Men From 248 293 Families

TL;DR: Maternal diabetes mellitus has long-term consequences for greater BMI in offspring; this association is likely to be via intrauterine mechanisms, and is independent of maternal BMI in early pregnancy.

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

Felix R. Day, +215 more
TL;DR: Using 1000 Genomes Project–imputed genotype data in up to ∼370,000 women, 389 independent signals for age at menarche, a milestone in female pubertal development are identified, highlighting the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility.
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Systematic review of the association between circulating interleukin-6 (IL-6) and cancer.

TL;DR: The aim was to systematically review the epidemiologic evidence for an association of circulating interleukin-6 (IL-6), an inflammatory cytokine and cancer, and found that cancer patients' IL-6 concentrations were higher than healthy controls' in most studies, but the results of investigations comparing cancer patients and individuals with benign diseases were less consistent.
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Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

TL;DR: The use of modified weights within two-sample summary-data MR studies is proposed for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments.