Work careers in adults separated temporarily from their parents in childhood during World War II

Introduction: Traumatic experiences, such as separation from parents in childhood causing early life stress (ELS) may increase the risk of adverse long-term health outcomes and biological age-related changes. This may have an impact on work career. Our aim was to examine long term consequences of ELS due to temporary separation from parents during World War II (WWII) in relation to work career. Material and methods: The Helsinki Birth Cohort Study comprises 13,345 individuals born in Helsinki, Finland, between the years 1934-1944. From the original cohort, 1781 individuals were identified as being separated temporarily from their parents due to World War II. Information on date and type of pension was provided by the Finnish Centre for Pensions and the Social Insurance Institution of Finland. The cohort members either transitioned into old age pension at the statutory retirement age or retired earlier and transitioned into disability, unemployment, part-time pension or died before retirement. Results: Those who were separated were more likely to have transitioned into disability pension (RRR: 1.26: 95% CI: 1.06-1.48), especially due to diseases of the musculoskeletal system (OR: 1.57; 95% CI: 1.20-2.07), or into unemployment pension (RRR: 1.25; 95% CI: 1.02-1.53) compared with those not separated from their parents. Longer duration of separation was associated with early exit from the workforce compared with non-separation. Conclusions: Exposure to ELS may have an impact upon lifetime work career. Early interventions preventing exposure to ELS or mitigating its negative effects may prolong future work careers along with healthier aging across the life-span.Peer reviewe

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study

Objectives Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors. Methods We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12(+0)-13(+6). Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population. Results The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.711.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.160.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.160.6), intermediate onset (delivery between 34(+0)-36(+6) weeks of gestation) to 25.1-fold (95%CI 3.179.9) and preterm preeclampsia (delivery before 37(+0) weeks of gestation) to 16.4-fold (95%CI 2.052.4). Body mass index over 30 kg/m(2) (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.13.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.520.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.49.8), of severe preeclampsia 22.2-fold (95%CI 9.941.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.057.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.121.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia. Conclusion The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia.Peer reviewe

Predisposition to superimposed preeclampsia in women with chronic hypertension: endothelial, renal, cardiac, and placental factors in a prospective longitudinal cohort

Objective To assess the contribution of maternal and placental factors to the development of superimposed preeclampsia in women with chronic hypertension. Methods Endothelial and renal function markers were serially assessed in 90 pregnant women with chronic hypertension and controls. Results Syndecan-1 concentrations were lower at 26–27+6 weeks in women with chronic hypertension who subsequently developed superimposed preeclampsia compared with those who did not. Decreased PlGF and raised urine albumin:creatinine ratio were also associated with development of superimposed preeclampsia. Conclusion Decreased syndecan-1 and PlGF concentrations implicate endothelial glycocalyx disturbance and reduced placental angiogenic capacity, respectively, in the pathophysiology of superimposed preeclampsia

Number of women included in the cluster analysis with each risk factor.

<p>A participant may have one or more risk factors.</p

Heat map—Results of the cluster analysis.

<p>The heatmap presents the risk factors (columns) in the different clusters on the left side with black boxes. The rows correspond to the 25 clusters (C1-C25) identified on the basis of the risk factor profiles, and the sizes of the clusters are shown on the left side of the heat map (n = 226 etc.). The size (i.e. area) of the black box illustrates the proportion of women in the particular cluster with the risk factor in question. Right side of the heatmap presents the risk ratios of the outcomes. The colour of the cell represents the estimated risk ratio of the corresponding outcome in the corresponding cluster, and the colour encoding is shown on the right side of the heatmap. Those cells are colored which are significant at the nominal 5% level (see text for discussion). The exact risk ratios are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174399#pone.0174399.t005" target="_blank">Table 5</a>.</p

Delivery characteristics.

<p>Delivery characteristics.</p

Risk ratios and 95% Confidence Intervals (CIs) of the cluster analysis.

<p>The marking C1 etc. is referring to certain row in the heatmap. If the risk factor is inside brackets, only a portion of women in the cluster had that risk factor.</p