Common Childhood Viruses and Pubertal Timing: The LEGACY Girls Study.

Earlier pubertal development is only partially explained by childhood body mass index; the role of other factors, such as childhood infections, is less understood. Using data from the LEGACY Girls Study (North America, 2011-2016), we prospectively examined the associations between childhood viral infections (cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus (HSV) 1, HSV2) and pubertal timing. We measured exposures based on seropositivity in premenarcheal girls (n = 490). Breast and pubic hair development were classified based on mother-reported Tanner Stage (TS) (TS2+ compared with TS1), adjusting for age, body mass index, and sociodemographic factors. The average age at first blood draw was 9.8 years (standard deviation, 1.9 years). The prevalences were 31% CMV+, 37% EBV+, 14% HSV1+, 0.4% HSV2+, and 16% for both CMV+/EBV+ coinfection. CMV+ infection without coinfection was associated with developing breasts an average of 7 months earlier (hazard ratio (HR) = 2.12, 95% confidence interval (CI): 1.32, 3.40). CMV infection without coinfection and HSV1 and/or HSV2 infection were associated with developing pubic hair 9 months later (HR = 0.41, 95% CI: 0.24, 0.71, and HR = 0.42, 95% CI: 0.22, 0.81, respectively). Infection was not associated with menarche. If replicated in larger cohorts with blood collection prior to any breast development, this study supports the hypothesis that childhood infections might play a role in altering pubertal timing.

Systematic review and meta-analysis of prolactin and iron deficiency in peripartum cardiomyopathy.

OBJECTIVES: We conducted a systematic review and meta-analysis of studies that compared levels of molecular biomarkers in women with peripartum cardiomyopathy (PPCM) to those in healthy pregnant and postpartum women to: (1) assess the evidence for prolactin (PRL) metabolism in PPCM, (2) ascertain the evidence for biomarkers of iron deficiency in PPCM, (3) identify other biomarkers associated with PPCM. METHODS: We searched Medline, Embase, Cumulated Index to Nursing and Allied Health Literature (CINAHL) and the Global Health Library from inception without language restriction for studies that compared biomarkers levels in PPCM cases to healthy controls. Pooled standardised mean difference (SMD) was generated using a random effects model for the difference in levels of biomarkers. RESULTS: Two studies assessed the association of PRL with PPCM, and reported that PPCM cases have higher levels of total PRL. No studies investigated iron metabolism in PPCM. Other biomarkers associated with PPCM included serum levels of natriuretic peptides (SMD=3.77, 95% CI 0.71 to 6.82), albumin (SMD=-0.67, 95% CI -1.01 to -0.32), C-reactive protein (SMD=1.67, 95% CI 0.22 to 3.12), selenium (SMD=-0.73, 95% CI -1.58 to 0.12), cardiac troponins (SMD=1.06, 95% CI 0.33 to 1.80), creatinine (SMD=0.51, 95% CI 0.33 to 0.69), white bloodcells (SMD=0.44, 95 % CI 0.07 to 0.82), haemoglobin (SMD=-0.45, 95% CI -0.64 to-0.26). CONCLUSIONS: More robust molecular studies are needed to explore the association between prolactin and PPCM in human subjects and to determine the extent to which iron deficiency (with or without anaemia) contributes to the risk of PPCM.