OBJECTIVES: We examined the associations between birth weight and intelligence at 3 different adult ages. METHODS: The Copenhagen Perinatal Cohort is comprised of children born in Copenhagen from 1959 to 1961. Information on birth weight and ≥1 tests of intelligence was available for 4696 members of the cohort. Intelligence was assessed at a mean age of 19 years with the Børge Priens Prøve test, at age 28 years with the Wechsler Adult Intelligence Scale, and at age 50 years with the Intelligenz-Struktur-Test 2000 R. RESULTS: Birth weight was significantly associated with intelligence at all 3 follow-up assessments, with intelligence scores increasing across 4 birth weight categories and declining for the highest birth weight category. The adjusted differences between those in the 5 IQ points at all 3 follow-up assessments, corresponding to one-third of a SD. The association was stable from young adulthood into midlife,and not weaker at age 50 years. Adjustment for potential confounding factors, including infant socioeconomic status and gestational age, did not dilute the associations, and associations with intelligence were evident across the normal birth weight range and so were not accounted for by low birth weight only. CONCLUSIONS: The association between birth weight and intelligence is stable from young adulthood into midlife. These long-term cognitive consequences may imply that even small shifts in the distribution of birth size, in normal-sized infants as well, may have a large impact at the population level.

The aims of the study were to explore general trends and individual differences in cognitive changes from early adulthood to late midlife and to investigate associations between education and cognitive changes. We used data from the Lifestyle and Cognition Follow-Up Study 2015 on 1,543 Danish men born in 1950-1961. Test scores on the 78-item intelligence test used by the Danish conscription authorities, Børge Priens Prøve (BPP), completed at draft board examination (baseline, mean age = 20 years) and at follow-up (mean age = 61 years) were used to measure cognitive changes. The mean change in BPP scores was -2.94 (SD = 5.57), and a retest correlation of 0.81 between the baseline and follow-up BPP scores was observed. In spite of the substantial retest correlations, the 8.3% of the sample with statistically reliable change had a mean decline in BPP scores of -13.41 (SD = 2.56). In latent change score models adjusted for year of birth and retest interval, more years of education was associated with larger decline in BPP scores, but the association was reversed when further adjusting for baseline BPP scores. Moreover, significant interactions indicated that more years of education was associated with less cognitive decline in men with relatively low or average BPP scores at baseline, whereas no influence of education was found in men with high baseline scores. Hence, more years of education may compensate for low or average intelligence by increasing cognitive reserve in these individuals or by influencing mediating lifestyle and occupational factors. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: Screen time in children and adolescents may be linked to cardiometabolic and cardiovascular risk. Methods: We analyzed data from >1000 participants in the COPSAC2010 and COPSAC2000 (Copenhagen Prospective Studies on Asthma in Childhood) mother–child 2000 and 2010 cohorts. Discretionary screen time, reported by parents or self, was assessed in relation to a composite cardiometabolic risk score based on Z scores of waist circumference, systolic blood pressure, high‐density lipoprotein cholesterol, triglycerides, and glucose. Secondary outcomes included insulin resistance, inflammation, lipoproteins, and anthropometry. A predicted cardiovascular risk score, derived from Cox models trained on UK Biobank data, was also assessed as an outcome. We evaluated whether lifestyle factors (sleep, physical activity, diet, puberty) moderated these associations. Blood nuclear magnetic resonance metabolomics were modeled using supervised machine learning to identify a metabolic screen‐time signature. Results: Each additional hour of screen time was associated with higher cardiometabolic risk in both children (β=0.08 [0.01–0.14], P =0.021) and adolescents (β=0.13 [0.07–0.20], P =0.001). Sleep duration significantly moderated this association in both cohorts (childhood: P =0.029; adolescence: P =0.012), with higher risk among those with shorter sleep. Screen time was also associated with higher predicted cardiovascular risk in adolescence (β=0.07 [0.01–0.13], P =0.017). A screen time‐associated metabolomic signature identified in childhood was validated in adolescence (β=0.14 [0.03–0.26], P =0.014). Conclusions: Screen time was positively associated with cardiometabolic and cardiovascular risk, and these associations were stronger among children and adolescents with shorter sleep duration. These findings highlight the importance of jointly considering screen time and sleep patterns in the assessment of early‐life risk factors for cardiometabolic and cardiovascular health.