A discussion of statistical methods to characterize early growth and its impact on bone mineral content later in childhood

Background Many statistical methods are available to model longitudinal growth data and relate derived summary measures to later outcomes. Aim To apply and compare commonly used methods to a realistic scenario including pre- and postnatal data, missing data and confounders. Subjects and methods Data were collected from 753 offspring in the Southampton Women’s Survey with measurements of bone mineral content (BMC) at age 6 years. Ultrasound measures included crown-rump length (11 weeks’ gestation) and femur length (19 and 34 weeks’ gestation); postnatally, infant length (birth, 6 and 12 months) and height (2 and 3 years) were measured. A residual growth model, two-stage multilevel linear spline model, joint multilevel linear spline model, SITAR and a growth mixture model were used to relate growth to 6-year BMC. Results Results from the residual growth, two-stage and joint multilevel linear spline models were most comparable: an increase in length at all ages was positively associated with BMC, the strongest association being with later growth. Both SITAR and the growth mixture model demonstrated that length was positively associated with BMC. Conclusions Similarities and differences in results from a variety of analytic strategies need to be understood in the context of each statistical methodology

Data_Sheet_1_Regression discontinuity design for the study of health effects of exposures acting early in life.pdf

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables’ cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.</p

MOESM1 of Live birth rates and perinatal outcomes when all embryos are frozen compared with conventional fresh and frozen embryo transfer: a cohort study of 337,148 in vitro fertilisation cycles

Additional file 1 : Figure S1. Risk ratios of perinatal outcomes following first live-birth within a cycle, for segmented cycles compared with non-segmented cycles, in 82,561 singleton live-births from 202,968 women undergoing 337,148 cycles of IVF. * adjusted. Figure S2. Risk ratios of perinatal outcomes following first live-birth within a cycle, for segmented cycles compared with non-segmented cycles, in 18,685 live-births as a result of single embryo transfer from 202,968 women undergoing 337,148 cycles of IVF. Very preterm birth and very low birth weight are not shown due to fewer than 8 incidences in segmented cycles. Figure S3. Live-birth rate ratios for segmented cycles compared with non-segmented cycles, in 81,682 cycles of IVF between 1 January 2011 and 31 December 2012. FigureS4. Risk ratios of perinatal outcomes following first live-birth within a cycle, for segmented cycles compared with non-segmented cycles, in 26,094 live-births from 81,682 cycles of IVF between 1 January 2011 and 31 December 2012. Figure S5. Live-birth rate ratios for segmented cycles compared with non-segmented cycles, stratified according to stage of embryo in 329,621 cycles of IVF. Figure S6. Live-birth rate ratios from the first embryo transfer (fresh in segmented cycles compared with frozen in non-segmented cycles), in 202,968 women undergoing 337,148 cycles of IVF. Figure S7. Risk ratios of perinatal outcomes following live-birth from the first embryo transfer (fresh in segmented cycles compared with frozen in non-segmented cycles), in 96,098 live-births from 202,968 women undergoing 337,148 cycles of IVF. Figure S8. Risk ratios of perinatal outcomes following first live-birth within a cycle, excluding live-birth following a fresh embryo transfer, for segmented cycles compared with non-segmented cycles, in 10,928 live-births from 202,968 women undergoing 337,148 cycles of IVF

Sedentary time in relation to cardio-metabolic risk factors: differential associations for self-report vs accelerometry in working age adults

Background Sedentary behaviour has been proposed to be detrimentally associated with cardio-metabolic risk independently of moderate to vigorous physical activity (MVPA). However, it is unclear how the choice of sedentary time (ST) indicator may influence such associations. The main objectives of this study were to examine the associations between ST and a set of cardio-metabolic risk factors [waist, body mass index (BMI), systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol, glycated haemoglobin] and whether these associations differ depending upon whether ST is assessed by self-report or objectively by accelerometry. Methods Multiple linear regression was used to examine the above objectives in a cross-sectional study of 5948 adults (2669 men) aged 16–65 years with self-reported measures of television time, other recreational sitting and occupational sitting or standing. In all, 1150 (521 men) participants had objective (accelerometry) data on ST as well. Results Total self-reported ST showed multivariable-adjusted (including for MVPA) associations with BMI [(unstandardized beta coefficients corresponding to the mean difference per 10 min/day greater ST: 0.035 kg/m2; 95% CI: 0.027–0.044), waist circumference (0.083 cm; 0.062–0.105), systolic (0.024 mmHg; 0.000–0.049) and diastolic blood pressure (0.023 mmHg; 0.006–0.040) and total cholesterol (0.004 mmol/l; 0.001–0.006)]. Similar associations were observed for TV time, whereas non-TV self-reported ST showed consistent associations with the two adiposity proxies (BMI/waist circumference) and total cholesterol. Accelerometry-assessed ST was only associated with total cholesterol (0.010 mmol/l; 0.001–0.018). Conclusions In this study, ST was associated consistently with cardio-metabolic risk only when it was measured by self-report

Adjusting for collider bias in genetic association studies using instrumental variable methods

Genome-wide association studies have provided many genetic markers that can be used as instrumental variables to adjust for confounding in epidemiological studies. Recently, the principle has been applied to other forms of bias in observational studies, especially collider bias that arises when conditioning or stratifying on a variable that is associated with the outcome of interest. An important case is in studies of disease progression and survival. Here, we clarify the links between the genetic instrumental variable methods proposed for this problem and the established methods of Mendelian randomisation developed to account for confounding. We highlight the critical importance of weak instrument bias in this context and describe a corrected weighted least-squares procedure as a simple approach to reduce this bias. We illustrate the range of available methods on two data examples. The first, waist-hip ratio adjusted for body-mass index, entails statistical adjustment for a quantitative trait. The second, smoking cessation, is a stratified analysis conditional on having initiated smoking. In both cases, we find little effect of collider bias on the primary association results, but this may propagate into more substantial effects on further analyses such as polygenic risk scoring and Mendelian randomisation

Additional file 1 of Quantitative bias analysis in practice: review of software for regression with unmeasured confounding

Additional file 1

Additional file 2 of Quantitative bias analysis in practice: review of software for regression with unmeasured confounding

Additional file 2

Additional file 3: of The range of peripapillary retinal nerve fibre layer and optic disc parameters in children aged up to but not including 18Â years of age, as measured by optical coherence tomography: protocol for a systematic review

Prisma-P checklist. (DOCX 36 kb

Additional file 2: of Associations of sex hormone-binding globulin and testosterone with genome-wide DNA methylation

Table S1. EWAS results of total testosterone in childhood in males (P value< 0.005). Table S2. EWAS results of total testosterone in adolescence in males (P value< 0.005). Table S3. EWAS results of total testosterone in adolescence in females (P value< 0.005). Table S4. EWAS results of SHBG in childhood in males (P value< 0.005). Table S5. EWAS results of SHBG in adolescence in males (P value< 0.005). Table S6. EWAS results of SHBG in adolescence in females (P value< 0.005). Table S7. EWAS results of bioavailable testosterone in childhood in males (P value< 0.005). Table S8. EWAS results of bioavailable testosterone in adolescence in males (P value< 0.005). Table S9. EWAS results of the rs12150660 variant in childhood in males (P value < 0.005). Table S10. EWAS results of the rs12150660 variant in childhood in females (P value< 0.005). Table S11. EWAS results of the rs12150660 variant in adolescence in males (P value< 0.005). Table S12. EWAS results of the rs12150660 variant in adolescence in females (P value< 0.005). (XLSX 1551 kb

Additional file1: of Associations of sex hormone-binding globulin and testosterone with genome-wide DNA methylation

Supplementary Methods of ALSPAC data description and Results Table S1. SHBG, total testosterone and bioavailable testosterone measures available in ALSPAC. Values presented for all ALSPAC participants regardless of DNA methylation data availability. Figure S1. Distributions of SHBG in males at 9.9, 17.8 years and in females at 15.5 years. Graphs include only individuals with available DNA methylation data in ARIES. Figure S2. Distributions of total testosterone in males at 9.9 and 17.8 years and in females at 15.5 years. Graphs include only individuals with available DNA methylation data in ARIES. Figure S3. Distributions of bioavailable testosterone in males at 9.9 and 17.8 years. Graphs include only individuals with available DNA methylation data in ARIES. Figure S4. QQ plots of SHBG EWAS at 9.9 and 17.8 years in males and 15.5 years in females. Figure S5. QQ plots of total testosterone EWAS at 9.9 and 17.8 years in males and 15.5 years in females. Figure S6. QQ plots of total testosterone EWAS at 9.9 and 17.8 years in males. Figure S7. QQ-plots of EWAS models of rs12150660 in childhood and adolescence, stratified by sex. (DOCX 472 kb