Estimation of Causal Effect of Age at Menarche on Pubertal Height Growth Using Mendelian Randomization

Early life growth is related to pubertal growth and adult height - a QEPS-model analysis

BACKGROUND

The early life growth period, from conception to ~2 years of age, has proven crucial for later health. We hypothesized that early life growth could explain variations in pubertal growth and timing, and adult height.

METHODS

This retrospective, population-based study was conducted in Sweden. A subgroup, including individuals of all gestational ages and birth sizes (n = 4700, 50% males), from the longitudinal GrowUp1974&1990Gothenburg cohorts was used. QEPS variables were analyzed in univariate and multivariate linear regression models, separately per sex; Q-function throughout all growth periods, and specific E- and P-functions, for early life growth and pubertal growth, respectively.

RESULTS

In multivariate models, early life growth explained 37-38% of the variability in specific pubertal growth, but less so the variability in pubertal timing. Variability in adult height was explained by birth size (57-62%), early growth (66-67%), childhood growth (65-69%), and to a lesser degree by mid-parental height (35-39%). The change in height during puberty explained 8-9% of the variation in adult height.

CONCLUSION

This study indicates that early life growth is strongly associated with the variability in pubertal growth, and adult height, but not with the timing of pubertal growth.

IMPACT

Early life growth is important as it can serve as a marker for future growth, development, and health. The association between length growth during fetal life and infancy and pubertal growth and timing, and adult height, is only partly understood. Using the QEPS growth model, specific early life growth (E-function) and specific pubertal growth (P-function), including individual variations in tempo and amplitude, can be studied separately from ongoing basic growth (Q-function). This study showed that early life growth is strongly associated with and explains specific pubertal height gain and adult height but less so the timing of pubertal growth.

Mendelian randomization analysis on the impacts of age at menarche on adult height: A Taiwanese population study

BACKGROUNDS

Ample evidence supports potential influence of age at menarche (AM) on adult height (AH), but multiple confounders may affect causal estimates. To address this issue, the Mendelian randomization (MR) analysis was used to explore the causal impacts of AM on AH.

METHODS

Using data (n = 57,349) from the publicly accessible Taiwan Biobank and randomly splitting them into 2 equal-size subsets, we identified single nucleotide polymorphisms (SNPs) significantly associated with AM in the exploration subset and used these SNPs as instrumental variables to estimate the effects of instruments on AH in the validation subset based on two stage least squares (2SLS) regression. In addition, three more summary statistics-based approaches, namely inverse variance weighted (IVW), MR-Egger, and weighted median (WM) analyses, were used to verify the findings. We also performed heterogeneity and sensitivity analyses to evaluate the robustness of the results.

RESULTS

We identified 4 leading SNPs associated with AM at the genome-wide significant level, whereas rs9409082 may exert some pleiotropic effects on AH. After eliminating rs9409082, the 2SLS analysis indicated that one year delay in genetically determined AM predicted 1.5 cm height gain in adulthood (β = 1.508, 95% confidence interval [0.852, 2.163]). The causal relationship was also supported by WM (β = 1.183, [0.329, 2.038]) and IVW (β = 1.493, [0.523, 2.463]) methods.

CONCLUSIONS

Evidence from the present MR study supports a causal relationship between later AM and taller AH.

Timing of menarche and pubertal growth patterns using the QEPS growth model

Objectives

To explore the timing of menarche, postmenarcheal growth, and to investigate the impact of various variables on menarcheal age and postmenarcheal and pubertal growth.

Study Design

This longitudinal community population-based study analyzed pubertal growth and menarcheal age in 793 healthy term-born Swedish girls, a subset of the GrowUp1990Gothenburg cohort. The timing of menarche and postmenarcheal growth was related to variables from the Quadratic-Exponential-Pubertal-Stop (QEPS) growth model, birth characteristics, and parental height. Multivariable models were constructed for clinical milestones; at birth, age 7 years, pubertal growth onset, and midpuberty.

Results

Menarche aligned with 71.6% (18.8) of the QEPS model's specific pubertal growth function, at a mean age of 13.0 (1.3) years, ranging from 8.2 to 17.2 years. Postmenarcheal growth averaged 8.0 (4.9) cm, varying widely from 0.2 to 31.1 cm, decreasing with later menarche. Significant factors associated with menarcheal age included height at 7 years, childhood body-mass index, parental height, and QEPS-derived pubertal growth variables. Multivariable models demonstrated increasing explanatory power for each milestone, explaining 1% of the variance in menarcheal age at birth, 8% at age 7 years, 44% at onset of pubertal growth, and 45% at midpuberty.

Conclusions

This study underscores the strong link between pubertal growth and age at menarche. Data available at start of puberty explain 44% of the variation in menarcheal age, apparent on average 3.2 years before menarche. In addition, the study shows a previously seldom noticed wide variation in postmenarcheal height gain from 0.2 to 31.1 cm.

Interval estimate of causal effect in summary data based Mendelian randomization in the presence of winner's curse

This research focuses on the interval estimation of the causal effect of an exposure on an outcome using the summary data-based Mendelian randomization (SMR) method while accounting for the winner's curse caused by the selection of single nucleotide polymorphism instruments. This issue is understudied and is important as the point estimate is biased. Since Fieller's theorem and its variations are not suitable for constructing a confidence interval, we use the box method. This box method is known to be conservative and thus provides a lower bound on the coverage level. To assess the performance of the box method, we use simulation studies and compare it with the support interval we proposed earlier and the Wald interval derived from the SMR method. All three methods are applied to a study of causal genes for Alzheimer's disease. Overall, the box method presents an alternative for constructing interval estimates for a causal effect while addressing the winner's curse issue.

Maternal Age at Menarche Genes Determines Fetal Growth Restriction Risk

We aimed to explore the potential link of maternal age at menarche (mAAM) gene polymorphisms with risk of the fetal growth restriction (FGR). This case (FGR)-control (FGR free) study included 904 women (273 FGR and 631 control) in the third trimester of gestation examined/treated in the Departments of Obstetrics. For single nucleotide polymorphism (SNP) multiplex genotyping, 50 candidate loci of mAAM were chosen. The relationship of mAAM SNPs and FGR was appreciated by regression procedures (logistic/model-based multifactor dimensionality reduction [MB-MDR]) with subsequent in silico assessment of the assumed functionality pithy of FGR-related loci. Three mAAM-appertain loci were FGR-linked to genes such as KISS1 (rs7538038) (effect allele G-odds ratio (OR)allelic = 0.63/pperm = 0.0003; ORadditive = 0.61/pperm = 0.001; ORdominant = 0.56/pperm = 0.001), NKX2-1 (rs999460) (effect allele A-ORallelic = 1.37/pperm = 0.003; ORadditive = 1.45/pperm = 0.002; ORrecessive = 2.41/pperm = 0.0002), GPRC5B (rs12444979) (effect allele T-ORallelic = 1.67/pperm = 0.0003; ORdominant = 1.59/pperm = 0.011; ORadditive = 1.56/pperm = 0.009). The haplotype ACA FSHB gene (rs555621*rs11031010*rs1782507) was FRG-correlated (OR = 0.71/pperm = 0.05). Ten FGR-implicated interworking models were founded for 13 SNPs (pperm ≤ 0.001). The rs999460 NKX2-1 and rs12444979 GPRC5B interplays significantly influenced the FGR risk (these SNPs were present in 50% of models). FGR-related mAAM-appertain 15 polymorphic variants and 350 linked SNPs were functionally momentous in relation to 39 genes participating in the regulation of hormone levels, the ovulation cycle process, male gonad development and vitamin D metabolism. Thus, this study showed, for the first time, that the mAAM-appertain genes determine FGR risk.

Health outcomes of age at menarche in European women: a two-sample Mendelian randomization study

BACKGROUND

Observational studies have shown an association between age at menarche (AAM) and the risk of gynecological diseases. However, the causality cannot be determined due to residual confounding.

METHODS

We conducted a Mendelian randomization (MR) study to evaluate the causal effect of AAM on several gynecological diseases, including endometriosis, female infertility, pre-eclampsia or eclampsia, uterine fibroids, breast cancer, ovarian cancer, and endometrial cancer. Single nucleotide polymorphisms were used as genetic instruments. The inverse variance weighted method was used as the primary approach and several other MR models were conducted for comparison. Cochran's Q test, Egger's intercept test, and leave-one-out analysis were conducted for sensitivity analysis. Radial MR analysis was conducted when detecting the existence of heterogeneity.

RESULTS

After Bonferroni correction and thorough sensitivity analysis, we observed a robust causal effect of AAM on endometrial cancer (odds ratio: 0.80; 95% confidence interval: 0.72-0.89; P = 4.61 × 10-5) and breast cancer (odds ratio: 0.94; 95% confidence interval: 0.90-0.98; P = .003). Sensitivity analysis found little evidence of horizontal pleiotropy. The inverse variance weighted method also detected weak evidence of associations of AAM with endometriosis and pre-eclampsia or eclampsia.

CONCLUSIONS

This MR study demonstrated a causal effect of AAM on gynecological diseases, especially for breast cancer and endometrial cancer, which indicates AAM might be a promising index to use for disease screening and prevention in clinical practice. Key messages What is already known on this topic - Observational studies have reported associations between age at menarche (AAM) and a variety of gynecological diseases but the causality has not been determined. What this study adds - This Mendelian randomization study demonstrated that AAM causally affects the risk of breast cancer and endometrial cancer. How this study might affect research, practice, or policy - The findings of our study imply that AAM could be a candidate marker for early screening of populations at higher risk of breast cancer and endometrial cancer.

Maternal Age at Menarche Gene Polymorphisms Are Associated with Offspring Birth Weight

In this study, the association between maternal age at menarche (AAM)-related polymorphisms and offspring birth weight (BW) was studied. The work was performed on a sample of 716 pregnant women and their newborns. All pregnant women underwent genotyping of 50 SNPs of AAM candidate genes. Regression methods (linear and Model-Based Multifactor Dimensionality Reduction (MB-MDR)) with permutation procedures (the indicator p_perm was calculated) were used to identify the correlation between SNPs and newborn weight (transformed BW values were analyzed) and in silico bioinformatic examination was applied to assess the intended functionality of BW-associated loci. Four AAM-related genetic variants were BW-associated including genes such as POMC (rs7589318) (β_additive = 0.202/p_perm = 0.015), KDM3B (rs757647) (β_recessive = 0.323/p_perm = 0.005), INHBA (rs1079866) (β_additive = 0.110/p_perm = 0.014) and NKX2-1 (rs999460) (β_recessive = -0.176/p_perm = 0.015). Ten BW-significant models of interSNPs interactions (p_perm ≤ 0.001) were identified for 20 polymorphisms. SNPs rs7538038 KISS1, rs713586 RBJ, rs12324955 FTO and rs713586 RBJ-rs12324955 FTO two-locus interaction were included in the largest number of BW-associated models (30% models each). BW-associated AAM-linked 22 SNPs and 350 proxy loci were functionally related to 49 genes relevant to pathways such as the hormone biosynthesis/process and female/male gonad development. In conclusion, maternal AMM-related genes polymorphism is associated with the offspring BW.

Support Interval for Two-Sample Summary Data-Based Mendelian Randomization

The summary-data-based Mendelian randomization (SMR) method is gaining popularity in estimating the causal effect of an exposure on an outcome. In practice, the instrument SNP is often selected from the genome-wide association study (GWAS) on the exposure but no correction is made for such selection in downstream analysis, leading to a biased estimate of the effect size and invalid inference. We address this issue by using the likelihood derived from the sampling distribution of the estimated SNP effects in the exposure GWAS and the outcome GWAS. This likelihood takes into account how the instrument SNPs are selected. Since the effective sample size is 1, the asymptotic theory does not apply. We use a support for a profile likelihood as an interval estimate of the causal effect. Simulation studies indicate that this support has robust coverage while the confidence interval implied by the SMR method has lower-than-nominal coverage. Furthermore, the variance of the two-stage least squares estimate of the causal effect is shown to be the same as the variance used for SMR for one-sample data when there is no selection.