This study aimed to establish normal reference ranges of combined cardiac output (CCO) and umbilical venous flow volume (UVFV) in twin fetuses at 20 to 28 weeks of gestation and to evaluate the differences between monochorionic and dichorionic twins. CCO and UVFV were prospectively measured by ultrasound at two centers. The following exclusion criteria were applied: age <18 years or >45 years, first hospital visit at >16 weeks of gestation, monochorionic monoamniotic twin pregnancy, fetal structural or chromosomal abnormality, fetal growth restriction, twin-twin transfusion syndrome, twin anemia polycythemia sequence, and severe hypertension or renal disease were excluded. The period was divided into three groups: 20–22 weeks of gestation, 23–25 weeks of gestation, and 26–28 weeks of gestation. The CCO and UVFV were measured at least once during each period. CCO and UVFV were collected from 274 and 269 fetuses and were measured 412 and 424 times, respectively. UVFV and CCO levels increased as gestation progressed. The weight-corrected UVFV (UVFV/kg) and CCO (CCO/kg) remained stable. UVFV and CCO did not differ between monochorionic and dichorionic twin fetuses. The mean ± SD of UVFV/kg and CCO/kg were determined as 127.8 ± 31.8 and 439.4 ± 80.1 mL/kg/min, respectively. The UVFV-to-CCO ratio also remained stable from 20 to 28 weeks of gestation, ranging from 27.7% to 31.8%. The values and ranges of UVFV/kg and mean CCO/kg in twins were similar to those in singletons.

Is general intelligence (g) a reflective construct, representing a latent causal entity underlying subtest performance, or a formative construct, better understood as an aggregate variable shaped by and summarizing variation across subtests? Genetically informative data provide a framework for testing whether a construct is reflective or formative by comparing common pathway and independent pathways structural equation models (SEMs). Previous studies using biometric SEMs have predominantly supported the reflective model, with phenotypic g mediating the effects of additive genetic and environmental influences on lower level abilities. In the current study, four large genetically informed datasets (three from the US and one from the UK) were analyzed to test three competing SEM models — common pathway, independent pathways, and merged — using Confirmatory Factor Analysis (CFA). Genetic g was estimated in each sample as a latent variable derived from polygenic scores indexing educational attainment and cognitive abilities. The models were compared as follows: the common pathway model, consistent with a reflective g, included a direct path from genetic g to phenotypic g; the independent pathways model, consistent with a formative g, featured indirect paths from genetic g to phenotypic g via subtests; and the merged model incorporated both direct and indirect paths. Across all four datasets, the merged model consistently provided the best fit (based on goodness-of-fit and parsimony criteria). Phenotypic g mediated between 31% and 81% of the effects of genetic g on subtests. These findings suggest that g functions as both a reflective and formative entity.

Since the 1970s, twin birth rates have increased sharply in developed countries. In Africa, where the rate is the highest globally, its evolution and variation are poorly understood. This article aims to estimate the twinning rate in sub-Saharan African (SSA) countries throughout 1986–2016 and analyze its spatial, temporal, and ethnic variations. It also seeks to identify social and demographic factors associated with a high probability of twin births and outline a forecast of the twinning rate. We used data from 174 Demographic and Health Surveys from 42 countries. We supplemented them with the UN World Population Prospects (WPP). The twinning rate was calculated by reporting the number of twin births per thousand total births. We used logistic regression to analyze the factors associated with twin births. We projected the twinning rate based on WPP. The overall SSA twinning rate is 17.4 per 1000, but it has changed very little over time, and we expect it will grow a little between 2015 and 2050, increasing at most from 17.4 per 1000 to 18.4 per 1000. We also show significant differences in the twinning rate in SSA according to mother ethnicity. Most ethnic groups with high twinning rates belong to the large Bantu ethnic family. SSA remains the ‘land of twins’, with the twinning rate changing slowly. However, specific health policies must target twin births in SSA to address the public health challenges they present.