The effect of birth weight on body composition: Evidence from a birth cohort and a Mendelian randomization study.

BACKGROUND: Lower birth weight is associated with diabetes although the underlying mechanisms are unclear. Muscle mass could be a modifiable link and hence a target of intervention. We assessed the associations of birth weight with muscle and fat mass observationally in a population with little socio-economic patterning of birth weight and using Mendelian randomization (MR) for validation.
METHODS: In the population-representative "Children of 1997" birth cohort (n = 8,327), we used multivariable linear regression to assess the adjusted associations of birth weight (kg) with muscle mass (kg) and body fat (%) at ~17.5 years. Genetically predicted birth weight (effect size) was applied to summary genetic associations with fat-free mass and fat mass (kg) from the UK Biobank (n = ~331,000) to obtain unconfounded estimates using inverse-variance weighting.
RESULTS: Observationally, birth weight was positively associated with muscle mass (3.29 kg per kg birth weight, 95% confidence interval (CI) 2.83 to 3.75) and body fat (1.09% per kg birth weight, 95% CI 0.54 to 1.65). Stronger associations with muscle mass were observed in boys than in girls (p for interaction 0.004). Using MR, birth weight was positively associated with fat-free mass (0.77 kg per birth weight z-score, 95% CI 0.22 to 1.33) and fat mass (0.58, 95% CI 0.01 to 1.15). No difference by sex was evident.
CONCLUSION: Higher birth weight increasing muscle mass may be relevant to lower birth weight increasing the risk of diabetes and suggests post-natal muscle mass as a potential target of intervention.

Introduction

Observationally, lower birth weight is associated with higher risk of many chronic diseases including cardiovascular disease, diabetes and poor liver function,[1-4] but is also associated with lower risk of hormone-related cancers including breast and prostate cancer.[5, 6] Although these observations are open to confounding by factors such as socio-economic position (SEP), different associations by diseases suggest some of these associations may be causal. Mendelian randomization (MR) studies, taking advantages of the random allocation of genetic endowment at conception to obtain un-confounded estimates,[7] suggest an inverse association of birth weight with diabetes,[3, 4] but practical implications for prevention are unclear given birth weight is a complex phenotype. Elucidating the pathways linking birth weight with diabetes may provide additional insights into the identification of intervention targets, since birth weight is difficult to change[8] and does not have an “optimal” definition.[9]

Observationally, birth weight is positively associated with muscle mass in both teenagers and adults.[10, 11] Randomized controlled trials shows resistance training increases muscle mass and improves Hemoglobin A1c.[12] As such, muscle mass could be a modifiable downstream effect of birth weight, partially driven by sex hormones,[13, 14] potentially with sex-specific effects, consistent with the associations of lower birth weight with lower risk of breast and prostate cancers.[5, 6] However, previous observational studies assessing the role of birth weight in muscle mass sometimes adjusted for factors on the causal pathway, such as body mass index (BMI), height and physical activity, but may not fully adjusted for SEP.[15, 16]

To clarify the role of birth weight in body composition, we conducted two analyses with different assumptions and study designs (Fig 1). First, in an observational setting, we prospectively assessed the overall and sex-specific associations of birth weight with body composition (muscle mass, grip strength, and fat percentage) in a unique population, Hong Kong’s “Children of 1997” birth cohort. In Hong Kong, the usual associations of higher SEP with higher birth weight and greater gestational age are almost absent,[17] and obesity has little socio-economic patterning in young people.[18] Therefore, Hong Kong is an ideal setting to assess the associations of birth weight and gestational age with body composition. We also assessed whether these associations differed by sex given the sex-difference in body composition since such differences are likely interpretable even when associations are confounded.[19] Second, using an MR design, we validated our findings, by assessing the associations of birth weight predicted by maternal genetics independent of fetal genetics, as a proxy of maternal intrauterine environment,[20] on body composition (fat-free mass, grip strength, and fat mass) in the largest publicly available genome wide association study (GWAS).[21] Taking advantage of the random allocation of genetic endowment at conception, MR studies provide un-confounded estimates and give the result of a lifelong difference in the risk factor between groups.[7]

Fig 1

Directed acyclic graph of the observational analysis and the Mendelian randomization analysis.

Material and methods

Ethics statement

Ethical approval for the study, including comprehensive health related analyses, was obtained from Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (HKU/HA HKW IRB). Informed written consent was obtained from the parents/guardians, or from the participant if 18 years or older, before participation in the Biobank Clinical Follow-up.

The MR study only uses published or publicly-available data. No original data were collected for the MR study. Ethical approval for each of the studies included in the investigation can be found in the original publications (including informed consent from each participant).

Observational study—The “Children of 1997” birth cohort

“Children of 1997” is a population-representative Chinese birth cohort (n = 8327), based on 88% of births in Hong Kong in April and May 1997.[22] The original study was designed to assess the associations of second-hand smoke exposure and breastfeeding with health services utilization in the first 18 months of life. Recruitment took place at all Maternal and Child Health Centers (MCHCs) in Hong Kong. Parents are strongly encouraged to take their children to the MCHCs for free preventive care and vaccinations to age 5 years. Parental and infant characteristics were obtained at recruitment. Contact was re-established in 2007. A Biobank clinical follow-up was conducted from 2013–2016 at ~17.5 years, when body composition was assessed from bio-impedance analysis using a Tanita segmental body composition monitor (Tanita BC-545, Tanita Co., Tokyo, Japan). Grip strength was measured using a Takei T.K.K.5401 GRIP D handgrip dynamometer (Takei Scientific Instruments Co. Ltd, Tokyo, Japan).

Exposure—Birth weight, gestational age-specific birth weight z-score, and gestational age

Birth weight recorded in grams was considered in kilograms and as internally generated gestational age-specific birth weight z-scores. Gestational age recorded in days was considered in weeks. Gestational age was calculated from the actual and expected dates of delivery reported by the mothers or primary caregivers at the initial MCHCs visit. The reported expected date of delivery is based on the date of the last menstrual period and any dating scans.

Outcome—Body composition

Muscle was assessed from whole-body muscle mass (kg), and dominant hand grip strength (kg). Fat mass was assessed from body fat percentage.

Mendelian randomization study

Exposure—Genetic predictors of maternal only effects on birth weight

Single nucleotide polymorphisms (SNPs) predicting maternal effects on birth weight independent of fetal genetics (z-score transformed) at genome-wide significance (p-value<5×10−8) adjusted for gestational age where available (only available in <15% of the sample) and study-specific covariates were obtained from a GWAS consisting of two components, the Early Growth Genetics (EGG) Consortium (n = 12,319, 10 studies in the EGG consortium of European descent imputed up to the HapMap 2 reference panel, and n = 7,542, 2 studies in of European descent imputed up to the HRC panel) and the UK Biobank (n = 190,406, white European). A structural equation model was used to decompose the contributions of maternal genetic and fetal effects on birth weight (264,498 individuals own birth weight and 179,360 individuals offspring birth weight).[20]

We obtained independent SNPs (R2>0.01) with the lowest p-value using the “Clumping” function of the MR-Base (TwoSampleMR) R package, with the 1000 Genomes catalog.[23] Potentially pleiotropic effects of these SNPs were obtained from up-to-date genotype to phenotype cross-references, i.e., GWAS Catalog (https://www.ebi.ac.uk/gwas/), Ensembl (http://www.ensembl.org/index.html) and Phenoscanner (http://www.phenoscanner.medschl.cam.ac.uk/). We also checked for potential pleiotropic effects and confounding of these SNPs from the Bonferroni corrected significance (12 traits × 30 SNPs, p-value<1×10−4) of their associations with alcohol consumption (past and current), smoking (past and current), physical activity (light, moderate, and vigorous), socioeconomic position (income and education), age of voice braking, age of menarche, and height in the UK Biobank summary statistics.[21]

Outcome—Genetic associations with body composition

Genetic associations with fat-free mass (kg), grip strength (kg) (left and right hand), and fat mass (kg) were obtained from the UK Biobank (~331,000 people of genetically verified white British ancestry). The genetic associations were assessed from multivariable linear regression adjusted for the first 20 principal components, sex, age, age-squared, the sex and age interaction and the sex and age-squared interaction.[21]

Statistical analyses

Observational analyses

We compared “Children of 1997” who were included and excluded on baseline characteristics using chi-squared tests, and Cohen effect sizes[24] to obtain the magnitude of the differences between groups. Cohen effect sizes are usually categorized as 0.20 for small, 0.50 for medium and 0.80 for large for continuous variables, and as 0.10 for small, 0.30 for medium and 0.50 for large for categorical variables.

The associations of muscle mass, grip strength and fat percentage with potential confounders were assessed using independent t-tests or analysis of variance for continuous variables and chi-square tests for categorical variables. We used multivariable linear regression to obtain the observational associations of birth weight, birth weight z-score and gestational age with body composition adjusting for second-hand and maternal smoking, parental education, parental occupation, household income, type of housing, and sex. We additionally adjusted for gestational age in the association of birth weight with body composition. Sex differences were assessed from the significance of interaction terms adjusted for the other potential confounding interactions with sex.

Taking missingness into account, multiple imputation and inverse probability weighting were applied.[25] Firstly, we created 20 sets of imputed data accounting for missing confounders and exposures for all participants. Secondly, logistic regression was used to predict loss-to-follow-up based on gestational age (log-transformed because of the long tail of the distribution), second-hand and maternal smoking, sex, type of housing, type of hospital at delivery, maternal migrant status, maternal age, and, breastfeeding with the lowest Akaike information criterion value. We also used the Hosmer-Lemeshow test to check model fit. Additionally, weights were checked to ensure acceptable stability. Unstable weights indicate model misspecification.[25] Lastly, we combined each inverse probability weighting effect estimator and its corresponding sandwich variance estimator according to Rubin’s Rules.[26]

Mendelian randomization

The strength of the genetic instruments was assessed from the F-statistic, obtained using an approximation (square of SNP on exposure divided by variance of SNP on exposure).[27, 28] A higher F-statistic indicates a lower risk of weak instrument bias.[27] The effects of birth weight on the outcomes were obtained from a meta-analysis of SNP-specific Wald estimates (SNP-outcome association divided by SNP-exposure association) using inverse variance weighting with multiplicative random effects assuming balanced pleiotropy. Heterogeneity of the Wald estimates was assessed from the I statistic, where a high I may indicate the presence of invalid SNPs.[29] Differences by sex were additionally assessed.[30] Power calculations were performed using the approximation that the sample size for Mendelian randomization equates to that of the same regression analysis with the sample size divided by the r2 for genetic variant on exposure.[31]

Sensitivity analyses relevant to the observational designs

A complete case analysis was conducted as a validation without taking missingness into account.

Sensitivity analyses relevant to Mendelian randomization

As sensitivity analyses, we excluded SNPs which may be invalid. These included 1)SNPs associated with potentially pleiotropic effects on muscle or fat given in Ensembl or the GWAS Catalog; 2) SNPs associated with potential confounders and/or pleiotropic effects in the UK Biobank at Bonferroni corrected significance (p-value<1×10−4) and in PhenoScanner (p-value <1×10−5).

Estimates were obtained from sensitivity analyses with different assumptions.

Specifically, we used a weighted median which may generate correct estimates if >50% of weight is contributed by valid SNPs.[32] MR-Egger was used which generates correct estimates if all the SNPs are invalid instruments as long as the instrument strength independent of direct effect assumption is satisfied.[29] A non-null intercept from MR-Egger indicates potential directional pleiotropy and an invalid inverse variance weighting estimate.[32] The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) was additionally used, which detects and corrects for pleiotropic outliers assuming >50% of the instruments are valid, balanced pleiotropy and the instrument strength independent of direct effect assumption are satisfied.[33, 34]

All statistical analyses were conducted using R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria). The R packages MendelianRandomization [35] and MRPRESSO [34] were used to generate the estimates.

Results

Children of 1997

Among the originally recruited 8327 participants, 6850 are contactable and living in Hong Kong. 3460 (51%) participated in the Biobank clinical follow-up, of which 3455 had muscle mass, grip strength or fat percentage (Fig 2). The mean and standard deviation (SD) of muscle mass, grip strength and fat percentage were 42.6kg (SD 8.8kg), 25.8kg (SD 8.3kg) and 21.7% (SD 8.8%). Boys had higher muscle mass and grip strength but lower fat percentage than girls. Body composition had little association with SEP (Table 1). Differences between participants included and excluded from the study were found for gestational age, sex, second-hand and maternal smoking exposure, and SEP using chi-squared tests, but the magnitude of these differences was small (Cohen effect size <0.15) (S1 Table).

Fig 2

Flowchart of the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016.

Table 1

Baseline characteristics muscle mass, grip strength, and fat percentage among participants in Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016.

Characteristics		Muscle mass (kg)				Grip strength (kg)				Fat percentage (%)
		No.	%	Mean (SD)	P-valuea	No.	%	Mean (SD)	P-valuea	No.	%	Mean (SD)	P-valuea
Muscle mass (kg)		3440		42.6 (8.8)
Grip strength (kg)						3444		25.8 (8.3)
Fat percentage (%)										3452		21.7 (8.8)
Sex		3440			<0.001	3444			<0.001	3452			<0.001
	Girl	1707	49.6%	35.3 (3.4)		1710	49.7%	19.9 (4.5)		1714	49.7%	28.1 (5.9)
	Boy	1733	50.4%	49.7 (6.3)		1734	50.3%	31.6 (7.0)		1738	50.3%	15.3 (6.4)
	Unknown	0	0.0%	-		0	0.0%	-		0	0.0%	-
Second-hand and maternal smoking exposure		3440			0.07	3444			0.77	3452			0.17
	None	940	27.3%	42.1 (8.4)		939	27.3%	25.6 (8.1)		943	27.3%	21.2 (8.5)
	Prenatal second-hand smoking	1275	37.1%	42.7 (8.8)		1276	37.0%	26.0 (8.4)		1276	37.0%	21.6 (9.0)
	Postnatal second-hand smoking	953	27.7%	43.0 (9.2)		956	27.8%	25.7 (8.3)		960	27.8%	22.0 (9.0)
	Maternal smoking	128	3.7%	42.7 (8.8)		128	3.7%	26.0 (8.2)		128	3.7%	22.9 (8.6)
	Unknown	144	4.2%	41.1 (8.6)		145	4.2%	25.3 (8.7)		145	4.2%	21.9 (9.0)
Highest parental education level		3440			0.06	3444			0.12	3452			0.04
	Grade< = 9	984	28.6%	42.2 (9.1)		988	28.7%	25.4 (8.3)		989	28.7%	22.2 (9.0)
	Grades 10–11	1481	43.1%	42.4 (8.6)		1483	43.1%	25.7 (8.4)		1488	43.1%	21.6 (8.8)
	Grades> = 12	959	27.9%	43.1 (8.9)		957	27.8%	26.3 (8.1)		959	27.8%	21.1 (8.7)
	Unknown	16	0.5%	39.7 (7.3)		16	0.5%	24.4 (6.8)		16	0.5%	23.9 (8.6)
Highest parental occupation		3440			0.32	3444			0.04	3452			0.12
	Ⅰ(unskilled)	98	2.8%	41.9 (9.3)		99	2.9%	25.4 (8.6)		99	2.9%	21.8 (8.1)
	Ⅱ(semiskilled)	281	8.2%	43.0 (9.0)		283	8.2%	26.4 (8.3)		285	8.3%	21.9 (8.8)
	Ⅲ(semiskilled)	503	14.6%	42.3 (9.0)		504	14.6%	25.1 (8.4)		503	14.6%	21.5 (8.8)
	Ⅲ(nonmanual skilled)	876	25.5%	42.4 (8.7)		878	25.5%	25.4 (8.1)		879	25.5%	22.2 (9.2)
	Ⅳ (managerial)	438	12.7%	43.2 (9.5)		438	12.7%	26.5 (8.5)		439	12.7%	22.2 (8.6)
	Ⅴ(professional)	794	23.1%	42.8 (8.5)		792	23.0%	26.2 (8.2)		795	23.0%	21.0 (8.5)
	Unknown	450	13.1%	42.0 (8.5)		450	13.1%	25.3 (8.4)		452	13.1%	21.5 (9.2)
Household income per head at recruitment		3440			0.07	3444			0.16	3452			0.15
	First quintile	566	16.5%	42.0 (8.5)		572	16.6%	25.6 (8.5)		571	16.5%	21.7 (8.9)
	Second quintile	613	17.8%	41.9 (9.3)		613	17.8%	25.0 (8.3)		616	17.8%	22.2 (8.7)
	Third quintile	616	17.9%	43.3 (8.8)		617	17.9%	26.1 (8.3)		618	17.9%	21.8 (9.1)
	Fourth quintile	630	18.3%	42.7 (8.9)		629	18.3%	25.9 (8.5)		630	18.3%	21.2 (8.7)
	Fifth quintile	644	18.7%	42.9 (8.6)		642	18.6%	26.1 (7.9)		645	18.7%	21.1 (8.5)
	Unknown	371	10.8%	42.6 (9.0)		371	10.8%	26.1 (8.3)		372	10.8%	22.2 (9.2)
Type of housing at recruitment		3440			0.45	3444			0.44	3452			0.36
	Public	1435	41.7%	42.5 (8.9)		1440	41.8%	25.8 (8.5)		1445	41.9%	21.9 (9.1)
	Subsidized home ownership scheme	545	15.8%	42.2 (8.8)		541	15.7%	25.2 (8.2)		544	15.8%	22.0 (8.9)
	Private	1355	39.4%	42.8 (8.8)		1358	39.4%	25.9 (8.1)		1358	39.3%	21.3 (8.5)
	Unknown	105	3.1%	41.8 (8.8)		105	3.0%	25.8 (8.7)		105	3.0%	21.2 (8.7)

a Using independent t-test or analysis of variance for continuous variables and chi-square tests for categorical variables

Observationally, birth weight and birth weight z-score were positively associated with muscle mass, grip strength, and, fat percentage. The associations were strengthened after adjusting for gestational age. Gestational age was not associated with muscle mass, grip strength or fat percentage. Associations with muscle muss differed by sex for birth weight z-score and birth weight adjusted for gestational age, with stronger associations in boys (Table 2). Similar estimates were obtained in the complete case analyses (S2 Table).

Table 2

Adjusted associations of birth weight, birth weight z-score and gestational age with body composition with inverse probability weighting (IPW) and multiple imputation (MI) in the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016.

Outcome	Exposure	Sex-adjusted as confounder		p-value of interaction with sex	Boys		Girls
		Beta	95% CI		Beta	95% CI	Beta	95% CI
Muscle mass (kg)	Birth weight (kg)	2.32	1.94 to 2.70	0.12	2.59	1.95 to 3.23	1.99	1.61 to 2.36
	Birth weight z-score	1.29	1.12 to 1.47	0.002	1.54	1.25 to 1.83	1.01	0.84 to 1.17
	Birth weight adjusted for gestational age	3.29	2.83 to 3.75	0.004	3.89	3.12 to 4.66	2.58	2.14 to 3.03
	Gestational age (week)	0.00	-0.10 to 0.10	0.25	-0.06	-0.23 to 0.12	0.07	-0.04 to 0.17
Grip strength (kg)	Birth weight (kg)	1.39	0.95 to 1.84	0.36	1.58	0.86 to 2.30	1.16	0.66 to 1.66
	Birth weight z-score	0.68	0.48 to 0.89	0.35	0.77	0.44 to 1.10	0.57	0.35 to 0.80
	Birth weight adjusted for gestational age	1.75	1.22 to 2.29	0.29	2.01	1.14 to 2.87	1.43	0.83 to 2.02
	Gestational age (week)	0.08	-0.04 to 0.20	0.89	0.09	-0.11 to 0.28	0.07	-0.06 to 0.21
Fat percentage	Birth weight (kg)	0.58	0.11 to 1.04	0.30	0.35	-0.32 to 1.01	0.85	0.20 to 1.50
	Birth weight z-score	0.44	0.23 to 0.65	0.58	0.39	0.09 to 0.69	0.51	0.22 to 0.80
	Birth weight adjusted for gestational age	1.09	0.54 to 1.65	0.69	1.00	0.20 to 1.79	1.23	0.46 to 2.00
	Gestational age (week)	-0.09	-0.22 to 0.03	0.23	-0.17	-0.34 to 0.01	-0.01	-0.19 to 0.16

Adjustment: second-hand and maternal smoking, highest parental education, parental occupation, household income, type of housing and sex.

Mendelian randomization

Genetic instruments for maternal only effects on birth weight

Altogether, 30 SNPs independently predicted effects of maternal genetics net of infant genetics on birth weight (p-value<5×10−8) in people of European ancestry.[20] The average of SNP-specific F statistics was 79, and all were >30 (S3 Table); the variance explained (r2) was 0.013. As such, the MR study had 80% power with 5% alpha to detect a difference of 0.04 of an effect size in fat-free mass and fat mass per z-score of birth weight.

Of the 30 SNPs predicting birth weight, 5 palindromic SNPs were aligned (S3 Table); 5 SNPs had potentially pleiotropic effects, i.e., (height and metabolic response) in Ensembl or the GWAS Catalog. Of the remaining 25 SNPs, 15 remained after excluding SNPs related to height, menarche, income, and basal metabolic rate in the UK Biobank (p-value<1×10−4) and in PhenoScanner (p-value <1×10−5) (S4 and S5 Tables).

Mendelian randomization estimates

Based on all 30 SNPs, genetically predicted birth weight (maternal effects net of infant effects) was positively associated with fat-free mass, fat mass, and grip strength. No sex differences were evident. After excluding 5 potentially pleiotropic SNPs, the positive associations remained, however, the associations were not robust after additionally excluding 10 potentially pleiotropic and confounded SNPs (S5 Table). Detecting and correcting for pleiotropic outliers, MR-PRESSO indicated robust positive estimates, in particular with fat mass (Fig 3). MR-Egger had wider confidence intervals but had no indication of potential pleiotropy (S5 Table).

Fig 3

Mendelian randomization estimates of the effect of genetically predicted birth weight (maternal effects net of infant effects) (per z-score) on body composition with and without potentially pleiotropic SNPs and potentially confounded SNPs using MR-PRESSO.

SNP = 30: all SNPs; SNP = 25, excluding maternal genotype related SNPs, and potential pleiotropic SNPs from GWAS catalog and Ensembl: rs560887 (G6PC2), rs2971669 (GCK), rs148982377 (ZNF789), rs2168101 (LMO1), rs10830963 (MTNR1B); excluding potential pleiotropic and/or confounded SNPs in UK Biobank in Bonferroni corrected significance (p-value<1×10−4) and in PhenoScanner (p-value<1×10−5): rs934232 (ZFP36L2), rs34471628 (DUSP1), rs9379084 (RREB1), rs6911024 (MICA), rs6995390 (ZFHX4), rs10814916 (GLIS3), rs111867185 (AGBL2), rs6487930 (IPO8), rs180438 (SLC38A4), rs597808 (ATXN2). MR-PRESSO: Mendelian randomization pleiotropy residual sum and outlier.

Discussion

Using two different designs, with different assumptions and data sources, we found consistent evidence that birth weight was positively associated with muscle mass (fat-free mass), grip strength and fat percentage (fat mass). These findings are consistent with previous observational studies,[10, 36, 37] but add by validating these observations in a setting with little socioeconomic patterning of birth weight and the use of MR.

These two study designs have contrasting limitations. First, residual confounding could not be ruled out in the observational design. SEP is hard to measure precisely and eliminate. In Hong Kong, the usual positive association of SEP with birth weight and gestational age is almost absent,[17] and SEP has little association with adiposity in young people.[18] However, other familial factors might affect birth weight and body composition.[38, 39] It is also difficult to disentangle correlated factors reliably in an observational study. Second, follow-up was incomplete (51%). Selection bias is unlikely, given no major difference between the participants with and without body composition indices. Moreover, differences by sex were observed, which are less open to confounding.[19] Third, MR studies have stringent assumptions, i.e., the genetic instruments should strongly predict the exposure, should not be confounded and should only be linked with the outcomes via the exposure. To examine the robustness of our findings, we excluded SNPs which may have pleiotropic effects or be associated with potential confounders, and the results were similar. MR-PRESSO also gave consistently positive sex-specific estimates after taking potential pleiotropy into account (Fig 3). Although some of the I were large, after excluding potentially pleiotropic and/or confounded SNPs, they became smaller. MR-Egger regression did not show directional pleiotropy even though the intercept test might be underpowered. Fourth, the overlap of the GWAS of birth weight with UK Biobank is ~90%, which might bias estimates towards the exposure-outcome association, nevertheless, the F statistic was 79 suggesting weak instrument bias is less likely.[27] Fifth, the MR study mainly pertains to people of European ancestry. However, restricting the MR study to the European ancestry could mitigate the potential confounding bias caused by hidden population structure, if the genetic associations vary by ethnic groups.[40] Ethnic differences between the MR study and the observational study is another concern, although we usually expect causal factors to act consistently across populations, unless we have evidence that the causal mechanism differs or is less relevant in some specific populations. Given the distribution of body composition varies by ethnicity, it is possible that the drivers of body composition also vary by ethnicity. However, more parsimoniously, it is likely that the drivers of body composition are similar across populations but their relevance varies. However, causes are usually consistent although not relevant in all contexts. Replicating the MR study in a Chinese population would be very helpful. Sixth, using summary statistics from different samples in the MR study means differences by age and sex could not be comprehensively assessed since no sex-specific genetic predictors of birth weight are available and hence we were only able to assess differences by sex observationally. Seventh, canalization might compensate for genetic variation in birth weight. However, whether such canalization exists is unknown. Eighth, MR provides an estimate of the effect of lifetime exposure rather than indicating the exact size of the corresponding intervention, as such it indicates an etiological pathway. Birth weight is affected by maternal and fetal genetics.[20, 41, 42] We used maternal genetics predictors net of infant genetics so the associations found with offspring body composition indicate the role of the intrauterine environment. Whether the intrauterine environment is a modifiable target of intervention, or whether subsequent consequences of the intrauterine environment would be more suitable for intervention requires investigation. Lastly, different genetic effects by generation is a concern. Given summary data was used, the genetic effects of maternal genetics net of infant genetics with offspring body composition were approximated by the genetic effects of maternal genetics net of infant genetics with maternal body composition. However, effects of genetic are likely consistent across generations.[43] We cannot rule out the possibility of the gene-environmental and/or gene-gene interactions leading to heritable epigenetic changes, which requires further exploration with individual maternal and infant genetic data.[43]

Positive associations of birth weight with body composition seem intuitive and might arise for several reasons. Development before birth is critical for skeletal muscle and adiposity. Specifically, myogenesis forms most fiber, and muscle fiber numbers do not increase after birth.[36, 44] Similarly, fat cell number is complete at birth and postnatal fat mass is mainly via increasing adipocyte size.[45, 46] Mechanisms driving differential development of muscle and fat cells before birth are unclear, but likely related to nutrition, acting via hormones. We have previously proposed that lower levels of androgens might cause higher diabetes risks via lower muscle mass.[13, 14, 47] Lower birth weight might indicate lower levels of androgens thus generating positive associations of birth weight with muscle mass and the stronger associations in men seen in both the observational and MR designs, although a difference by sex was not evident in the MR design. From an etiological perspective, a causal association of birth weight with muscle mass provides a potential mechanistic, a modifiable pathway from lower birth weight to higher diabetes risks.[3, 4, 47] Given birth weight is difficult to change, such findings suggest that muscle building might reduce diabetes risk due to lower birth weight. Such a mechanism, might also help explain a higher risk of diabetes in Asia with low prevalence of obesity, lower birth weight, and lower muscle mass than in western settings.[48-52] Asians have more than double the risk of developing diabetes than Europeans at the same BMI.[48] However, it is possible that the observed associations do not extend to the extremes of the birth weight distribution, where birth weight may be a symptom of specific pathology. Given this is likely to be rare, we do not have sufficient sample size to assess this possibility. These findings are consistent with the idea of evolutionary public health, i.e., that the trade-off of growth and reproduction against longevity may inform understanding of chronic diseases and the identification of interventions.

Conclusion

Higher birth weight might increase fat-free mass and fat mass. Our study provides some indications that low fat-free mass may explain why lower birth weight increases diabetes risk and suggests muscle building as an attractive target of intervention.

Baseline characteristics of the participants who were included (n = 3455) and excluded (n = 4872) in the analyses of the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016.

(DOCX)

Click here for additional data file.

Adjusted associations of birth weight, birth weight z-score and gestational age with body composition in complete case analysis in the Hong Kong’s “Children of 1997” birth cohort, Hong Kong, China, 1997 to 2016.

(DOCX)

Click here for additional data file.

Single nucleotide polymorphisms (SNPs) independently predicted effects of maternal genetics net of infant genetics on birth weight in Europeans from the Early Growth Genetics (EGG) Consortium (p-value<5×10−8).

(DOCX)

Click here for additional data file.

Single nucleotide polymorphisms (SNPs) with potential pleiotropic effects, and/or potential confounders from Ensembl, GWAS Catalog, PhenoScanner, and UK Biobank.

(DOCX)

Click here for additional data file.

Estimates of the effect of genetically predicted birth weight (maternal effects net of infant effects) (per z-score) on body composition with and without potentially pleiotropic single nucleotide polymorphisms (SNPs) and potentially confounded SNPs using Mendelian randomization with different methodological approaches.

(DOCX)

Click here for additional data file.

21 Jul 2019

PONE-D-19-16847

The effect of birth weight on body composition: Evidence from a birth cohort and a Mendelian randomization study

PLOS ONE

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Reviewer #1: It was a pleasure to review this comprehensive study performed by Liu and colleagues, investigating the effect of birth weight on consequent body composition. The authors are experienced with the applied methods and have taken care in the performing appropriate analyses.

The work is already close to a standard suitable for publication. My main suggestion for improvement would be some clarification regarding the interpretation of causality. I think it is important that the authors discuss the potential implications of a shared (genetic) aetiology between birth weight and consequent body composition. Keeping this in mind, is it justified that the authors should imply in their conclusion that birth weight has a causal effect on consequent body composition?

In any case, I think that the authors should also be more clear about whether they are genuinely suggesting birth weight as a target for clinical intervention - although they do state this in the last line of the conclusion, I must admit that I'm struggling to take it seriously. The practicalities of modifying birth weight is another consideration. How might this be achieved? If the authors prefer not to answer this, then they should probably remove this suggestion.

Some discussion about the range of birth weights for which these findings might apply is also warranted. Can these estimates be extrapolated to the extremes of birth weight, for example?

Finally, I wonder whether the authors might also consider replacing (or supplementing) some of the existing results tables with figures (e.g. forest plots of the estimates and their confidence intervals). The current format used for presenting the data is a little overwhelming in places and therefore difficult to follow. Figures might make things easier to digest.

Reviewer #2: The authors examined the associations of birth weight (BW) with muscle and fat mass in an observational study. They also performed a Mendelian Randomisation (MR) of BW on these measures in the UK Biobank study. I have several major concerns relating to the MR section of the paper.

The authors do not clearly state the question that they are seeking to answer with the BW-fat/muscle MR analysis. It would be helpful if the authors could provide a diagram (eg. a DAG) explaining the causal question which they seek to answer. For example, are the authors using birth weight as a proxy for intrauterine exposures which might influence muscle and fat mass? If so, the maternal birth weight-associated alleles that are not passed to the child are the appropriate instrumental variables to use. The issue of MR in this context was discussed in the latest BW GWAS paper (https://doi.org/10.1038/s41588-019-0403-1). Further papers discussing this issue are https://doi.org/10.12688/wellcomeopenres.10567.1 and https://doi.org/10.1093/ije/dyz019. Alternatively, the authors might be asking about whether the genetic variation underlying birth weight is shared with the underlying muscle and fat mass, in which case, the fetal genotype is more important. Could the authors please clarify this.

A major problem in MR with birth weight using fetal genetic instruments as the exposure is the violation of MR assumptions by the association of maternal genotype with the outcome of interest. The latest BW GWAS paper discovered additional BW associated SNPs and also quantified the independent maternal and fetal associations with BW at each of the loci. Ideally this larger list and independent fetal effect sizes should be used for the exposure SNPs, the summary statistics from these analyses adjusted for maternal/fetal genotype associations are available to download from the EGG consortium website http://egg-consortium.org/birth-weight-2019.html. These adjusted association statistics correct the SNP-exposure associations for maternal genetic effects. This still would not get around the problem of the potential violation of the MR assumptions, indeed the large attenuation of the association results when potentially maternal SNPs are excluded from the analyses suggests that failing to account for maternal effects may be affecting the results. To properly perform these analyses the SNP-outcome associations should be corrected for maternal genetic associations. If this is not possible, this limitation should be clearly stated and fully discussed in the paper. The confounding introduced by maternal genotype is briefly mentioned at the end of the limitations section, but the potential impact of this on the results and the interpretation of them is not given sufficient attention and need to be carefully discussed and made more prominent.

It is also not clear to me why the authors reduced the original list of 60 BW SNPs to 47. The list of 60 loci already represents a list of independent association signals. A lot of the SNPs which were removed seem to be correlation artefacts rather than genuine LD, for example the SNP rs61830764 (DTL) is over 50mb from the nearest signal suggesting that the correlation here doesn’t reflect real LD. The signal rs1011939 (GPR139) also is the only SNP on chromosome 16 but seems to have been dropped as “not independent” although there can be no real LD with the other signals.

Other comments

In the limitations section, point 6 leaves me confused. They state “no age-specific genetic predictors of BW are available”, but birth weight is by definition the weight at birth, there is no age variation in BW. Point 8 also leaves me a little confused. It talks about lifetime exposure, but birth weight is again defined only at birth. I suggest the authors rephrase this point as I’m not entirely sure from what they have written what they are trying to say here.

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Reviewer #1: Yes: Dipender Gill

Reviewer #2: No

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22 Aug 2019

Thank you so much indeed for these very helpful comments. We have responded all the reviewers' comments respectively and updated the manuscript and other related documents accordingly. Further details could be checked in the Response to Reviewers file and the Manuscript with Track Changes file.

Submitted filename: Response to Reviewers_Finalized.docx

Click here for additional data file.

23 Aug 2019

The effect of birth weight on body composition: Evidence from a birth cohort and a Mendelian randomization study

PONE-D-19-16847R1

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28 Aug 2019

PONE-D-19-16847R1

The effect of birth weight on body composition: Evidence from a birth cohort and a Mendelian randomization study

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