Trends in Ages at Key Reproductive Transitions in the United States, 1951–2010

AbstractBackgroundKey sexual and reproductive health milestones typically mark changing life stages with different fertility intentions and family planning needs. Knowing the typical ages at such events contributes to our understanding of changes in family formation and transition to adulthood and helps inform needs for reproductive health services.MethodsWe used data from the 1982–2010 National Surveys of Family Growth and the 1995 National Survey of Adolescent Males and event history methods to examine trends over time for women and men in the median ages at several reproductive and demographic events.FindingsWomen's reports indicate that age at menarche has changed little since 1951. Women's and men's median ages at first sex declined through the 1978 birth cohort, but increased slightly since then, to 17.8 years for women and 18.1 for men. The interval from first sex to first contraceptive use has narrowed, although Hispanic women have a longer interval. Age at first union (defined as the earlier of first marriage or first cohabiting relationship) has remained relatively stable, but the time between median age at first sex and median age at first birth has increased to 9.2 years for women and 11.4 for men. For some women and men born in the late 1970s, median age at first birth was earlier than median age at first marriage for the first time in at least the past several decades.ConclusionThe large majority of the reproductive years are spent sexually active. Thus, women have a lengthy period during which they require effective methods. In particular, the period between first sex and first childbearing has lengthened, but long-acting method use, although increasing, has not kept up with this shift. Moving the contraceptive method mix toward underutilized but highly effective contraceptive methods has the potential to reduce the unintended pregnancy rate

Sexual Knowledge, attitudes and behaviors among unmarried migrant female workers in China: a comparative analysis

<p>Abstract</p> <p>Background</p> <p>In recent years, many studies have focused on adolescent's sex-related issues in China. However, there have been few studies of unmarried migrant females' sexual knowledge, attitudes and behaviors, which is important for sexual health education and promotion.</p> <p>Methods</p> <p>A sample of 5156 unmarried migrant female workers was selected from three manufacturing factories, two located in Shenzhen and one in Guangzhou, China. Demographic data, sexual knowledge, attitudes and behaviors were assessed by self-administered questionnaires. Multivariate logistic regression analysis was conducted to examine the factors associated with premarital sexual intercourse.</p> <p>Results</p> <p>The average age of the unmarried female workers included in the sample was 20.2 years, and majority of them showed a low level of sex-related knowledge. Females from the west of China demonstrated a significant lower level of sex-related knowledge than those from the eastern or central provinces (<it>p </it>< 0.05). Approximately 13% of participants held a favorable attitude towards premarital sexual intercourse, and youths from the east/central were more likely to have favorable attitudes compared with those from the west (<it>p </it>< 0.05). About 17.0% of the unmarried female workers reported having engaged in premarital sexual intercourse, and females from the east/central were more likely to have experienced premarital sexual intercourse than those from the west (<it>p </it>< 0.05). Multivariate analysis revealed that age, education, current residential type, dating, sexual knowledge, attitudes, and pattern of communication were significantly associated with premarital sexual intercourse.</p> <p>Conclusion</p> <p>The unmarried migrant female workers lack sexual knowledge and a substantial proportion of them are engaged in premarital sexual behaviors. Interventions aimed at improving their sexual knowledge and related skills are needed.</p

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease