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Does the sex of one’s co-twin affect height and BMI in adulthood? A study of dizygotic adult twins from… Bogl, Leonie H; Jelenkovic, Aline; Vuoksimaa, Eero; Ahrenfeldt, Linda; Pietiläinen, Kirsi H; Stazi, Maria A; Fagnani, Corrado; D’Ippolito, Cristina; Hur, Yoon-Mi; Jeong, Hoe-Uk; Silberg, Judy L; Eaves, Lindon J; Maes, Hermine H; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Cutler, Tessa L; Kandler, Christian; Jang, Kerry L; Christensen, Kaare; Skytthe, Axel; Kyvik, Kirsten O; Cozen, Wendy; Hwang, Amie E; Mack, Thomas M; Derom, Catherine A; Vlietinck, Robert F; Nelson, Tracy L; Whitfield, Keith E; Corley, Robin P; Huibregtse, Brooke M; McAdams, Tom A; Eley, Thalia C; Gregory, Alice M; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Willemsen, Gonneke; Bartels, Meike; van Beijsterveldt, Toos C E M; Pang, Zengchang; Tan, Qihua; Zhang, Dongfeng; Martin, Nicholas G; Medland, Sarah E; Montgomery, Grant W; Hjelmborg, Jacob v B; Rebato, Esther; Swan, Gary E; Krasnow, Ruth; Busjahn, Andreas; Lichtenstein, Paul; Öncel, Sevgi Y; Aliev, Fazil; Baker, Laura A; Tuvblad, Catherine; Siribaddana, Sisira H; Hotopf, Matthew; Sumathipala, Athula; Rijsdijk, Fruhling; Magnusson, Patrik K E; Pedersen, Nancy L; Aslan, Anna K D; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Duncan, Glen E; Buchwald, Dedra; Tarnoki, Adam D; Tarnoki, David L; Yokoyama, Yoshie; Hopper, John L; Loos, Ruth J F; Boomsma, Dorret I; Sørensen, Thorkild I A; Silventoinen, Karri; Kaprio, Jaakko Apr 27, 2017

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RESEARCH Open AccessDoes the sex of one’s co-twin affect heightand BMI in adulthood? A study of dizygoticadult twins from 31 cohortsLeonie H. Bogl1,2*, Aline Jelenkovic3,4, Eero Vuoksimaa1, Linda Ahrenfeldt5, Kirsi H. Pietiläinen6,7, Maria A. Stazi8,Corrado Fagnani8, Cristina D’Ippolito8, Yoon-Mi Hur9, Hoe-Uk Jeong9, Judy L. Silberg10, Lindon J. Eaves10,Hermine H. Maes11, Gombojav Bayasgalan12, Danshiitsoodol Narandalai13,12, Tessa L. Cutler14, Christian Kandler15,Kerry L. Jang16, Kaare Christensen5,17, Axel Skytthe5, Kirsten O. Kyvik18,19, Wendy Cozen20,21, Amie E. Hwang20,Thomas M. Mack20,21, Catherine A. Derom22,23, Robert F. Vlietinck22, Tracy L. Nelson24, Keith E. Whitfield25,Robin P. Corley26, Brooke M. Huibregtse26, Tom A. McAdams27, Thalia C. Eley27, Alice M. Gregory28,Robert F. Krueger29, Matt McGue29, Shandell Pahlen29, Gonneke Willemsen30, Meike Bartels30,Toos C. E. M. van Beijsterveldt30, Zengchang Pang31, Qihua Tan32, Dongfeng Zhang33, Nicholas G. Martin34,Sarah E. Medland34, Grant W. Montgomery35, Jacob v. B. Hjelmborg5, Esther Rebato4, Gary E. Swan36,Ruth Krasnow37, Andreas Busjahn38, Paul Lichtenstein39, Sevgi Y. Öncel40, Fazil Aliev41,42, Laura A. Baker43,Catherine Tuvblad43,44, Sisira H. Siribaddana45,46, Matthew Hotopf47, Athula Sumathipala45,48, Fruhling Rijsdijk27,Patrik K. E. Magnusson39, Nancy L. Pedersen39, Anna K. Dahl Aslan39,49, Juan R. Ordoñana50,51,Juan F. Sánchez-Romera52,51, Lucia Colodro-Conde50,53, Glen E. Duncan54, Dedra Buchwald54, Adam D. Tarnoki55,56,David L. Tarnoki55,56, Yoshie Yokoyama57, John L. Hopper14,58, Ruth J. F. Loos59, Dorret I. Boomsma30,Thorkild I. A. Sørensen60,61, Karri Silventoinen3,62 and Jaakko Kaprio1,2AbstractBackground: The comparison of traits in twins from opposite-sex (OS) and same-sex (SS) dizygotic twin pairs isconsidered a proxy measure of prenatal hormone exposure. To examine possible prenatal hormonal influences onanthropometric traits, we compared mean height, body mass index (BMI), and the prevalence of being overweightor obese between men and women from OS and SS dizygotic twin pairs.Methods: The data were derived from the COllaborative project of Development of Anthropometrical measures inTwins (CODATwins) database, and included 68,494 SS and 53,808 OS dizygotic twin individuals above the age of20 years from 31 twin cohorts representing 19 countries. Zygosity was determined by questionnaires or DNAgenotyping depending on the study. Multiple regression and logistic regression models adjusted for cohort, age,and birth year with the twin type as a predictor were carried out to compare height and BMI in twins from OSpairs with those from SS pairs and to calculate the adjusted odds ratios and 95% confidence intervals for beingoverweight or obese.(Continued on next page)* Correspondence: leonie-helen.bogl@helsinki.fi1Institute for Molecular Medicine FIMM, University of Helsinki, P.O. Box20FI-00014 Helsinki, Finland2Department of Public Health, University of Helsinki, Helsinki, FinlandFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Bogl et al. Biology of Sex Differences  (2017) 8:14 DOI 10.1186/s13293-017-0134-x(Continued from previous page)Results: OS females were, on average, 0.31 cm (95% confidence interval (CI) 0.20, 0.41) taller than SS females. OSmales were also, on average, taller than SS males, but this difference was only 0.14 cm (95% CI 0.02, 0.27). MeanBMI and the prevalence of overweight or obesity did not differ between males and females from SS and OS twinpairs. The statistically significant differences between OS and SS twins for height were small and appeared to reflectour large sample size rather than meaningful differences of public health relevance.Conclusions: We found no evidence to support the hypothesis that prenatal hormonal exposure or postnatalsocialization (i.e., having grown up with a twin of the opposite sex) has a major impact on height and BMI inadulthood.Keywords: Prenatal hormone exposure, Opposite-sex twins, Height, Body mass index, CODATwinsBackgroundStudies in litter-bearing mammals have found that hor-mones can transfer from one fetus to adjacent fetuses inthe uterus [1]. Fetuses located between two males haveincreased concentrations of testosterone compared to fe-tuses located between two females [2], and such fetusesare heavier, suggesting that the intrauterine position mayinfluence metabolic set points involved in the regulationof body weight and fat storage [3].Whether an analogous effect exists in humans is uncer-tain; however, it has been postulated that twins influenceeach other hormonally during prenatal life because theyshare their intrauterine environment [4]. This is called thetwin testosterone transfer (TTT) hypothesis. In particular,females who develop with a male co-twin are potentiallyexposed to higher levels of prenatal testosterone, the mostpotent androgen, than females from same-sex (SS) twinpairs [5]. Support for the TTT hypothesis comes from pre-vious twin studies showing that females from opposite-sex(OS) twin pairs express more male-typical characteristics(i.e., are masculinized) in a variety of sexually dimorphictraits. One phenotype with a replicated finding ofmasculinization of females with a male co-twin is the Men-tal Rotation Test, which measures a specific cognitive abil-ity with a large sex difference favoring males [6, 7].However, also postnatal socialization effects need to beconsidered as an alternative explanation to the TTT hy-pothesis. Having grown up with a sibling of the same sexversus a sibling of the opposite sex is likely to result in dif-ferent social learning experiences and has been linked tosex-typed behavior; for example, boys with older brothersand girls with older sisters have been found to be more sextyped than children from opposite-sex sibling dyads [8].Among the few studies that have specifically comparedanthropometric and health-related variables, some differ-ences between females from OS and SS twin pairs havebeen reported, for example, for height [9], birth weight[10], body mass index (BMI) [11], dyslipidemia [11], andleukocyte telomere lengths [12]. However, it is importantto note that null reports also exist for several traits. Forexample, there are no differences between OS and SSfemale twins with regard to the prevalence of polycysticovary syndrome [13] or hormone-related cancers [14].Similarly, males from SS twin pairs could be masculinizedcompared with OS males due to additional testosteroneexposure of the male co-twin, although most studies thathave investigated co-twin effects in males have failed toidentify differences between OS and SS male twins [5, 15].Many previous studies are constrained by several limit-ing factors, including small sample size, lack of replication,and the inclusion of monozygotic (MZ) twins in the SSgroups, which is questionable due to the variation in pla-centation patterns between MZ and dizygotic (DZ) twinpairs [5]. Furthermore, publication bias is likely to haveoccurred, with non-significant results being less likely tobe published than significant ones [16]. In 2011, Tapp etal. reviewed the evidence that fetuses gestated with a maleco-twin are masculinized during the development due toprenatal androgen exposure and concluded that while ac-cumulated evidence lacks consistency, it is sufficient towarrant further research, ideally using large samples of OSand SS twin pairs [15].To this end, the present study aims to test whethertwo sexually dimorphic anthropometric traits, heightand BMI and the prevalence of overweight and obesitydiffer between females and males from OS and SS DZtwin pairs. Consistent with the TTT hypothesis, OSfemales are predicted to be taller and have a higher BMIthan SS females, and SS males are predicted to be tallerand have a higher BMI than OS males. The sample wasdrawn from the newly established CODATwins (Collab-orative project of Development of Anthropometricalmeasures in Twins) database [17], which, being based onoriginal data, avoids publication bias and is very largeand able to address the hypothesis with much greaterpower than previous studies.MethodsSampleThe CODATwins project is a major international twincollaboration that was initiated in 2013 to pool data onzygosity, weight, and height from twin cohorts acrossBogl et al. Biology of Sex Differences  (2017) 8:14 Page 2 of 12the world. A description of the project and the partici-pating twin cohorts was reported previously in detail[17]. The main CODATwins database includes 960,859height and weight measures from both MZ and DZtwins. Extreme outliers and biologically implausiblevalues for height and BMI were inspected visually foreach age decade and by sex, resulting in elimination ofless than 0.2% of the observations from the originaldatabase. For the present study, we excluded (1) all mea-surements prior to the age of 20 years to avoid con-founding by pubertal stage; (2) MZ twins (n = 165,305);(3) DZ twin individuals with missing information on OSor SS status; (4) cohorts which did not collect data fromOS twins; and (5) cohorts for which only a few OS twinswere available, i.e., if the ratio of OS to SS DZ twins was<0.15 (the expectation being unity as sex is determinedindependently in the two fetuses). As a sensitivity ana-lysis, excluding eight cohorts with 2866 twins with aratio below 0.6 rather than 0.15 did not change the re-sults (data available on request). Furthermore, if therewere multiple observations for an individual, we re-stricted the analyses to one observation per individual byusing the observation at the youngest age. The effectsizes were virtually unchanged when using older obser-vations as opposed to younger observations, and there-fore, only the latter results are presented. In total, 31cohorts from 19 countries met our inclusion criteria(Additional file 1: Tables S1 and S2). The final samplefor the present analysis consisted of 122,302 DZ twin in-dividuals of which 68,494 were from SS and 53,808 fromOS twin pairs. The median age (and interquartile range)of the participants was 44.0 (28.7–56) years for malesand 42.0 (28.0–55.9) years for females (Additional file 1:Table S1 for females and Additional file 1: Table S2 formales show these descriptive statistics by twin type andcohort). In order to examine age effects, the sample wasdivided into younger (<50 years) and older (50+ years)age groups, which is a proxy for menopausal status inwomen. Among the 66,956 females and 55,346 males, 63and 62%, respectively, were classified as younger adults.Height and weight were almost all self-reported (97%).BMI calculated as body weight in kilograms divided byheight in meters squared (kg/m2) was used as an indica-tor of adiposity. Zygosity was determined by question-naires or DNA genotyping depending on the study [17].Statistical analysisThe statistical analyses were performed using Stata (ver-sion 13.0, Stata Corporation, TX, USA) where p < 0.05was considered statistically significant. Descriptive statis-tics for the OS and SS twins are reported as means andstandard deviations (SD) separately for females andmales. Multiple regression analysis with the twin type(i.e., opposite versus same sex) as a predictor was carriedout to compare height and BMI in twins from OS pairswith those from SS pairs. Regression coefficients areshown with their 95% confidence intervals (CIs). Mar-ginal means, i.e., adjusted for cohort, age, and birth year,are presented by twin type along with CIs. Logistic re-gression models adjusted for cohort, age, and birth yearwere used to calculate the adjusted odds ratios (OR) and95% CIs for being overweight or obese with the twintype as a predictor. WHO Asian BMI cut-off points(overweight ≥23 kg/m2 and obese ≥27.5 kg/m2) wereused for Asian cohorts, and WHO standard BMI cut-offpoints (overweight ≥25 kg/m2 and obese ≥30 kg/m2)were applied for all other cohorts (from Europe, North-America, and Australia) [18]. In all regression models,SS twins were set as the reference group. The non-independence (clustering) within twin pairs was takeninto account in both multiple and logistic regressionanalyses by using the “cluster” option in Stata to yieldrobust estimators of variance [19].To assess heterogeneity across cohorts, a random-effects meta-analysis with inverse variance weighting de-rived from the DerSimonian and Laird estimator wasperformed using the user-written “metan” command inStata and visualization of forest plots. The I2 statisticwas used to examine variability in effect sizes betweencohorts. The I2 statistic estimates the proportion of vari-ation in effect sizes due to heterogeneity, whereby valuesof 25–49, 50–74, and >75% indicate low, moderate, andhigh heterogeneity, respectively [20].ResultsMales were taller (mean ± SD 178.42 ± 7.24 versus164.80 ± 6.78 cm, p < 0.001) and had a higher BMI (mean± SD 25.23 ± 3.63 versus 23.97 ± 4.50 kg/m2, p < 0.001)than females. Descriptive statistics for age, height, andBMI are shown for the OS and SS female and male twinsin Table 1. Both men and women were on average tallestin the Netherlands and shortest in Sri Lanka. Mean BMIwas lowest for women from South Korea and men fromJapan and highest for African American men and women(Additional file 1: Tables S1 and S2).Female twins with a male co-twin were on average 0.31 cmtaller than females with a female co-twin (p < 0.0001). Maletwins with a female co-twin were 0.14 cm taller than maletwins with a male co-twin (p= 0.025) (Table 2). There wereno significant differences in BMI between OS and SS femalesor between OS and SS males (Table 2). The estimates didnot differ between the younger (β=−0.04 kg/m2, 95% CI−0.13, 0.05 for females and β=−0.01 kg/m2, 95% CI −0.09,0.07 for males) or older groups (β=−0.05 kg/m2, 95% CI−0.07, 0.16 for females and β=−0.06 kg/m2, 95% CI −0.03,0.16 for males) separately.The ORs for overweight and obesity did not differ be-tween OS and SS females nor between OS and SS malesBogl et al. Biology of Sex Differences  (2017) 8:14 Page 3 of 12(Table 3). When analysed by age group, the ORs foroverweight did not differ from 1.00 for the younger(OR = 0.99, 95% CI 0.94, 1.03 for females and OR =0.99, 95% CI 0.95, 1.04 for males) or older group (OR =1.05, 95% CI 0.99, 1.11 for females and OR = 1.02, 95%CI 0.96, 1.08 for males). Females from OS twin pairswere at a reduced risk of obesity (OR = 0.90, 95% CI0.83, 0.97) in the younger but not in the older agegroup (OR = 0.98, 95% CI 0.90, 1.07), but the 95% CIsfor these two estimates were overlapping. For males,there was no difference in obesity risk in either agegroup (OR = 1.04, 95% CI 0.96, 1.13 in the younger andOR = 1.04, 95% CI 0.95, 1.14 in the older group).The random effect meta-analysis found low to moder-ate heterogeneity between cohorts, with I2 ranging from32 to 56%. The pooled regression coefficients for heightwere β = 0.31 cm (95% CI 0.10, 0.53) and β = 0.18 cm(95% CI −0.03, 0.40) in females and males, respectively(Figs. 1 and 2). For BMI, the corresponding values wereβ = 0.02 kg/m2 (95% CI −0.09, 0.14) and β = 0.04 kg/m2(95% CI −0.06, 0.13) for females and males, respectively(Figs. 3 and 4).The height differences between OS and SS twinswere not associated with the mean heights of the co-horts, showing that height differences between OS andSS twins were not greater in taller cohorts (Additionalfile 2: Figure S1). The BMI differences between OS andSS twins were not associated with the mean BMIs ofthe cohorts for males, but there was an inverse associ-ation for females, showing that OS twins tended tohave a lower BMI than SS twins with an increasingmean BMI of the cohorts (Additional file 2: Figure S2).The Spearman correlations were not significant withthe exception of BMI for females. Overall, no consist-ent pattern was observed.DiscussionUsing this newly established large-scale internationaltwin collaboration, we tested the TTT hypothesis and itspossible long-lasting influence on adult height and BMIand find little evidence in support of the hypothesis.There were no differences in mean BMI or the preva-lence of overweight or obesity between twins with a co-twin of the opposite sex and twins with a co-twin of thesame sex. Consistent with the TTT hypothesis, we ob-served that females with a male co-twin were tallerthan females with a female co-twin. However, we alsoobserved that males with a female co-twin were slightlytaller than males with a male co-twin, a finding that isopposite in direction to that predicted by the TTT hy-pothesis. Yet, our finding for males is in accordancewith previous observations on birth weight in twins.Table 1 Age and unadjusted height (cm) and BMI (kg/m2) infemales and males from same- and opposite-sex twin pairsFemales MalesSS OS SS OSNumber of individuals 39,856 27,100 28,638 26,708AgeMean 43.9 43.0 44.5 43.3SD 16.9 15.7 16.6 15.6Min 20.0 20.0 20.0 20.0Max 99.0 95.9 99.2 94.5HeightMean 164.6 165.0 178.3 178.5SD 6.8 6.7 7.2 7.2Min 135.0 135.7 147.0 145.0Max 193.0 194.0 208.3 207.0BMIMean 24.0 24.0 25.2 25.3SD 4.6 4.4 3.6 3.7Min 13.1 13.3 14.2 13.4Max 49.9 49.8 47.8 49.8SS same sex, OS opposite sex, SD standard deviation, Min minimum, Maxmaximum, BMI body mass indexTable 2 Adjusted means and regression coefficient for height (cm) and BMI (kg/m2) in females and males from same- andopposite-sex twin pairsPhenotype Twin type Mean 95% confidence intervals Regression coefficient 95% confidence intervalsHeight SS females 164.7 164.6, 164.8OS females 165.0 164.9, 165.1 0.31 cm 0.20, 0.41SS males 178.4 178.3, 178.4OS males 178.5 178.4, 178.6 0.14 cm 0.02, 0.27BMI SS females 23.95 23.90, 24.00OS females 24.00 23.05, 24.05 0.05 kg/m2 −0.02, 0.12SS males 25.20 25.16, 25.25OS males 25.26 25.22, 25.31 0.06 kg/m2 0.00, 0.12The results are adjusted for cohort, age and birth year, and the non-independence (clustering) of observations within twin pairsSS same sex, OS opposite sex, BMI body mass indexBogl et al. Biology of Sex Differences  (2017) 8:14 Page 4 of 12Males from OS twin pairs have higher growth ratesstarting from week 32 of gestation [21] and a highermean birth weight when compared with males from SSpairs [21, 22]. The increased birth weight of males witha female co-twin as compared to males with a male co-twin has been ascribed to the longer duration of gesta-tion of male-female pairs [22] and the more successfulin utero competition for nutrients of males in the pres-ence of a female, rather than a male, co-twin [23]. Birthweight is an established determinant of adult height[24], and thus, differences in fetal growth and durationof gestation that depend on the co-twin’s sex could alsocontribute to the small height differences observed inthe present analyses.Furthermore, explanations other than the in uteroenvironment need to be considered. First, postnatalsocialization effects could also contribute to differencesin anthropometric traits between OS and SS twins, sincegrowing up with a sibling of the same or opposite sex islikely to be different through sibling and parental inter-actions. However, in previous studies of non-twin sib-lings, height in childhood was unrelated to the sex of thesibling [25]. Second, because DZ twins are slightly tallerand heavier than MZ twins [26], misclassification of asmall fraction of MZ twins as SS DZ twins could havecontributed to part of the observed small differences be-tween twin-type groups. The misclassification rate ofquestionnaire-based zygosity falls below 5% when com-pared with genetic markers [27].Support for the theory that prenatal exposure to hor-mones has long-lasting effects on physiology and behaviorin later life comes mainly from animal models, in whichprenatal androgen exposure has been examined by inject-ing the developing fetuses with varying doses of testoster-one [28–32]. These animal studies have provided evidencethat prenatal androgen exposure during the organizationalperiod induces long-term alterations in metabolic function.Alteration of the epigenome during fetal developmentmight be an underlying mechanism linking prenatalandrogenization and endocrine disorders in adulthood,such as the polycystic ovary syndrome (PCOS) [33]. Pre-natal androgenization produces features of the metabolicsyndrome in rodents, including increased body weight andvisceral adiposity, impaired insulin secretion, and hepatictriglyceride content [29, 30]. These alterations could bepartly mediated by food intake, as androgenization of fe-male rats in the neonatal period leads to a feeding patternin adulthood that is more similar to that of male rats, in-cluding diminished meal number and increased meal sizeand food intake as well as increased weight [34]. Femalerhesus monkeys exposed to increased levels of testosteroneduring early-to-mid gestation develop metabolic abnormal-ities found in PCOS women, including insulin resistance,increased visceral fat accumulation, impaired pancreaticbeta-cell function, and type 2 diabetes [28, 31, 35]. Interest-ingly, the effect of prenatal androgen excess is not only re-stricted to females but is also present in prenatallyandrogenized male monkeys, who displayed by insulin re-sistance and impaired pancreatic beta-cell function despiteno changes in adult androgen levels [32].Extrapolating findings from experimental animalstudies to humans is complicated by a variety of factorsthat differ across species, including the markedly differ-ent placentation patterns and duration of pregnancy[1]. Measuring prenatal androgen exposure in humansis inherently difficult. Amniocentesis, in which a smallamount of fluid is sampled from the amniotic sac sur-rounding the developing fetus, provides the most in-formative measurement of prenatal androgen exposure.It is, however, invasive and carries a risk of miscarriageand should therefore only be performed when there ismedical need [15]. The comparison of twins from OSand SS pairs is considered a proxy measure of prenatalhormone exposure, as hormone transfer in twin preg-nancies could occur through one of the followingroutes: the maternal-fetal route (through the maternalTable 3 Prevalence and adjusted odds ratios for overweight and obesity in females and males from same- and opposite-sex twinpairsPhenotype Twin type % overweight/obese 95% confidence intervals Odds ratio 95% confidence intervalsOverweight SS females 30.2 29.7, 30.7 1.00 (reference)OS females 30.9 30.3, 31.4 1.03 0.99, 1.07SS males 47.0 46.4, 47.7 1.00 (reference)OS males 47.6 47.0, 48.2 1.02 0.99, 1.06Obesity SS females 8.45 8.15, 8.76 1.00 (reference)OS females 8.11 7.78, 8.44 0.96 0.90, 1.01SS males 8.04 7.70, 8.34 1.00 (reference)OS males 8.48 8.13, 8.83 1.06 1.00, 1.13The results are adjusted for cohort, age and birth year, and the non-independence (clustering) of observations within twin pairs. Overweight was defined as≥23 kg/m2 and obesity as ≥27.5 kg/m2 for Asian cohorts and as BMI ≥25 kg/m2 and BMI ≥30 kg/m2, respectively, for all other cohortsSS same sex, OS opposite sexBogl et al. Biology of Sex Differences  (2017) 8:14 Page 5 of 12bloodstream) or the fetal-fetal route (diffusion acrossfetal membranes). A relatively small study did notdetect differences in maternal serum steroid levelsbetween mothers who were expecting OS or SS DZtwins, making the maternal route less plausible [5]. Atbirth, females with a twin brother are not exposed tohigher androgen concentrations compared with femaleswith a twin sister [36]. Other studies of singletons havefound that maternal serum testosterone concentrationsdiffer between women carrying male and female fetuses[37, 38] and that maternal and fetal testosterone arepositively correlated [39]. Thus, it is likely that humansex steroids are transferred between twins, but directevidence for the hormone transfer to occur in humansdoes not exist.Multiple twin studies have studied whether fetusesgestated with a male co-twin are masculinized in devel-opment, and results on perception, cognition, physio-logical, and morphological outcomes are moreconsistent with the TTT hypothesis than those onbehavioral traits, including, for example, disordered eat-ing, sensation seeking and aggression [15]. Relativelyfew twin studies have studied whether anthropometryand health outcomes differ between OS and SS twins.Among recent studies, Ahrenfeldt et al. [14] found nosignificant differences in hormone-dependent cancerFig. 1 Regression coefficient and 95% CIs using a random-effects model with height as the dependent variable and twin type as the independentvariable for females. The effect size shows the increase in height of OS females as compared to dizygotic SS females. If the twin testosteronehypothesis were supported, the effect would be in the positive direction, and the effect size would be significant. The results are adjusted for ageand birth year and the non-independence (clustering) of observations within twin pairs. Squares indicate study-specific regression coefficients,and the size of the squares is proportional to the weight of each study, i.e., the inverse of the variance. The horizontal lines represent 95% CIsBogl et al. Biology of Sex Differences  (2017) 8:14 Page 6 of 12incidence between OS and SS twins using a large sam-ple of Nordic twins. Alexanderson et al. [11] found nodifferences in height, while weight, BMI, and the preva-lence of dyslipidemia were higher for women from OSpairs than SS pairs older than age 60 years using theScreening Across the Lifespan Twin study, which is alsoincluded in the CODATwins project as part of theSwedish twin studies. Loehlin and Martin [9] reporteda marginally significant increased height for femalesfrom OS compared with females from SS pairs. Gaist etal. [40] found no differences in any of the strength andanthropometric measures for middle-aged female twinsfrom the Danish population-based twin registry.The main strengths of the present study are that itincludes data from 122,302 dizygotic twin individualsfrom 31 twin studies in 19 countries, increasing thegeneralizability of the findings and reducing the type 2error. Furthermore, the narrow confidence intervals in-dicate high precision. There was low to moderate het-erogeneity across cohorts, suggesting relativelyconsistent findings across studies. Height is a classic ex-ample of a sexually dimorphic trait; on average, men aretaller than women in all human populations [41–43];thus, the large sex difference makes it worth the investi-gation in relation to the prenatal hormone transfer hy-pothesis. As the sexual dimorphism is greater for bodyFig. 2 Regression coefficient and 95% CIs using a random-effects model with height as the dependent variable and twin type as the independentvariable for males. The effect size shows the increase in height of OS males as compared to dizygotic SS males. If the twin testosterone hypothesiswere supported, the effect would be in the negative direction, and the effect size would be significant. The results are adjusted for age and birthyear and the non-independence (clustering) of observations within twin pairs. Squares indicate study-specific regression coefficients, and the sizeof the squares is proportional to the weight of each study, i.e., the inverse of the variance. The horizontal lines represent 95% CIsBogl et al. Biology of Sex Differences  (2017) 8:14 Page 7 of 12composition and body fat distribution than for BMI [44],we acknowledge the lack of more detailed adiposity mea-sures as a limitation of this study. Zygosity was self-reported and not verified by DNA testing in the majorityof studies. That almost all heights and weights were self-reported is another limitation, since perception of bodyweight has been reported to depend on sibling’s weightand sibling’s weight perceptions, and these relationshipshave been found to differ by the sex of the sibling [45].We further acknowledge the lack of data on pubertaltiming, a key developmental period that could affectadult weight and height. A recently published study re-ports the height and BMI differences between OS andSS twins to depend on breastfeeding status. Never-breastfed SS twins tended to be shorter and lighter thannever-breastfed OS twins, but breastfed SS twins wereconsistently taller and heavier than breastfed OS twinsthroughout adolescence and early adulthood [46]. Werecognize the lack of early-life nutritional data as an-other limitation, and more research is undoubtedly war-ranted in this field.ConclusionsResults from this large international twin collaborationshow that females with a twin brother were on average0.31 cm taller than females with a twin sister. Males withFig. 3 Regression coefficient and 95% CIs using a random-effects model with BMI as the dependent variable and twin type as the independentvariable for females. The effect size shows the increase in BMI of OS females as compared to dizygotic SS females. If the twin testosterone hypothesiswere supported, the effect would be in the positive direction and the effect size would be significant. The results are adjusted for age and birth yearand the non-independence (clustering) of observations within twin pairs. Squares indicate study-specific regression coefficients, and the size of thesquares is proportional to the weight of each study, i.e., the inverse of the variance. The horizontal lines represent 95% CIsBogl et al. Biology of Sex Differences  (2017) 8:14 Page 8 of 12a twin sister were also on average slightly taller thanmales with a twin brother, but this height difference wasonly 0.14 cm. Although statistically significant, the ob-served differences were of small magnitudes and there-fore of limited public health importance. Mean BMI andthe prevalence of being overweight or obese did not dif-fer between males and females from SS and OS twinpairs. Thus, the present findings provide no evidence tosupport the hypothesis that in utero exposure to testos-terone or postnatal socialization (i.e., having grown upwith a twin of the opposite sex) has a major impact onheight and BMI in later life. However, these results donot rule out the possibility that OS and SS twins differin height or BMI at other developmental periods such asprenatal development, early infancy, or during puberty.Additional filesAdditional file 1: Table S1. Sample size, mean, standard deviation andrange for age by cohort in females from same- and opposite-sex dizygotictwin pairs. Table S2. Sample size, mean, standard deviation, and range forage by cohort in males from same- and opposite-sex dizygotic twin pairs.Table S3. Sample size, mean, and standard deviation for height (cm) andBMI (kg/m2) by cohort in females from same- and opposite-sex dizygotictwin pairs. Table S4. Sample size, mean, and standard deviation for height(cm) and BMI (kg/m2) by cohort in males from same- and opposite-sexdizygotic twin pairs. (DOCX 45 kb)Fig. 4 Regression coefficient and 95% CIs using a random-effects model with BMI as the dependent variable and twin type as the independentvariable for males. The effect size shows the increase in BMI of OS males as compared to dizygotic SS males. If the twin testosterone hypothesiswere supported, the effect would be in the negative direction and the effect size would be significant. The results are adjusted for age and birthyear and the non-independence (clustering) of observations within twin pairs. Squares indicate study-specific regression coefficients, and the sizeof the squares is proportional to the weight of each study, i.e., the inverse of the variance. The horizontal lines represent 95% CIsBogl et al. Biology of Sex Differences  (2017) 8:14 Page 9 of 12Additional file 2: Figure S1. The differences in height (cm) betweenopposite sex (OS) and same sex (SS) plotted against the mean heights(cm) of the cohorts for females and males. The figure examineswhether the difference in height between OS and SS twins is greater intaller cohorts. The Spearman correlations are r = −0.16 and p = 0.39 forfemales and r = 0.10 and p = 0.59 for males. Figure S2. The differencesin BMI (kg/m2) between opposite-sex (OS) and same-sex (SS) twinsplotted against the mean BMIs (kg/m2) of the cohorts for females andmales. The figure examines whether the difference in BMI between OSand SS twins is greater in heavier cohorts. The Spearman correlationsare r = −0.39 and p = 0.029 in females and r = 0.10 and p = 0.61 for BMIin males. (DOCX 159 kb)AbbreviationsBMI: Body mass index; CI: Confidence interval; CODATwins: COllaborativeproject of Development of Anthropometrical measures in Twins;DZ: Dizygotic; MZ: Monozygotic; OR: Odds ratio; OS: Opposite sex;SD: Standard deviation; SS: Same sex; TTT: Twin testosterone transferAcknowledgementsNoneFundingThis study was conducted within the CODATwins project (Academy of Finland#266592). The lead author wishes to thank the Juho Vainio Foundation and theYrjö Jahnsson Foundation. The Australian Twin Registry is supported by aCentre of Research Excellence (grant ID 1079102) from the National Health andMedical Research Council administered by the University of Melbourne.California Twin Program was supported by The California Tobacco-RelatedDisease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the NationalInstitutes of Health (1R01ESO15150-01). The Carolina African American TwinStudy of Aging (CAATSA) was funded by a grant from the National Institute onAging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. Colorado Twin Registry isfunded by NIDA funded center grant DA011015, and Longitudinal Twin StudyHD10333; Author Huibregtse is supported by 5T32DA017637-11. Danish TwinRegistry is supported by the National Program for Research Infrastructure 2007from the Danish Agency for Science, Technology and Innovation, The ResearchCouncil for Health and Disease, the Velux Foundation, and the US NationalInstitute of Health (P01 AG08761). Netherlands Twin Register acknowledges theNetherlands Organization for Scientific Research (NWO) and MagW/ZonMWgrants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001,451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, and Spi-nozapremie 56-464-14192; VU University’s Institute for Health and Care Research(EMGO+); and the European Research Council (ERC–230374), the Avera Institute,Sioux Falls, South Dakota (USA). Data collection and analyses in Finnish twin co-horts have been supported by ENGAGE–European Network for Genetic andGenomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502,AA-00145, and AA-09203) to R J Rose, the Academy of Finland Center ofExcellence in Complex Disease Genetics (grant numbers: 213506, 129680), andthe Academy of Finland (grants 100499, 205585, 118555, 141054, 265240,263278 and 264146 to J Kaprio). K Silventoinen is supported by OsakaUniversity’s International Joint Research Promotion Program. Since its originthe East Flanders Prospective Survey has been partly supported by grants fromthe Fund of Scientific Research, Flanders and Twins, a non-profit Association forScientific Research in Multiple Births (Belgium). Waves 1–3 of Genesis 12–19were funded by the W T Grant Foundation, the University of London CentralResearch fund and a Medical Research Council Training Fellowship (G81/343)and Career Development Award (G120/635) to Thalia C. Eley. Wave 4 wassupported by grants from the Economic and Social Research Council (RES-000-22-2206) and the Institute of Social Psychiatry (06/07–11) to Alice M. Gregorywho was also supported at that time by a Leverhulme Research Fellowship (RF/2/RFG/2008/0145). Wave 5 was supported by funding to Alice M. Gregory fromGoldsmiths, University of London. Anthropometric measurements of theHungarian twins were supported by Medexpert Ltd., Budapest, Hungary. TheMurcia Twin Registry is supported by Fundación Séneca, Regional Agency forScience and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 &19479/PI/14) and Ministry of Science and Innovation Spain (PSI2009-11560 &PSI2014-56680-R). The University of Southern California Twin Study is funded bya grant from the National Institute of Mental Health (R01 MH58354). SouthKorea Twin Registry is supported by National Research Foundation of Korea(NRF-371-2011-1 B00047). S.Y. Öncel and F. Aliev are supported by KırıkkaleUniversity Research Grant: KKU, 2009/43 and TUBITAK grant 114C117.Washington State Twin Registry (formerly the University of Washington TwinRegistry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI).The West Japan Twins and Higher Order Multiple Births Registry wassupported by Grant-in-Aid for Scientific Research (B) (grant number15H05105) from the Japan Society for the Promotion of Science.Availability of data and materialsThe datasets generated and/or analysed during the current study areavailable from the corresponding author on reasonable request.Authors’ contributionsThe authors’ responsibilities were as follows—KS, Y-MH, YY, KOK, TIAS, DIB,and JK planned the study design of the CODATwins project; Y-MH, YY, KOK,DIB, TIAS, JK, LA, KHP, MAS, CF, CD, H-UK, JLS, LJE, HHM, GB, DN, TLC, CK, KLJ,KC, AS, WC, AEH, TMM, CAD, RFV, TLN, KEW, RPC, BMH, TAM, TCE, AMG, RFK,MMcGue, SP, GW, MB, TCEMvB, ZP, QT, DZ, NGM, SEM, GWM, JvBH, GES, RK,AB, PL, SYO, FA, LAB, CT, SHS, MH, AS, FR, PKEM, NLP, AKDA, JRO, JFS-R, LC-C,GED, DB, ADT, DLT, JLH, and RJF collected the data used in this study; KSand AJ were in charge of data management; JK and LHB conducted the ana-lyses; LHB wrote the first draft of the manuscript and had primary responsi-bility of for the final content; and all authors commented on the manuscriptand read and approved the final version of the manuscript. None of the au-thors reported a conflict of interest related to the study.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateThe pooled analysis of these data was approved by the ethical board of theDepartment of Public Health, University of Helsinki. The data collectionprocedures of the participating twin cohorts were approved by the localethical boards following the regulations in each country. Only anonymizeddata with non-invasive measures were delivered to the data managementcenter at University of Helsinki.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Institute for Molecular Medicine FIMM, University of Helsinki, P.O. Box20FI-00014 Helsinki, Finland. 2Department of Public Health, University ofHelsinki, Helsinki, Finland. 3Department of Social Research, University ofHelsinki, Helsinki, Finland. 4Department of Genetics, Physical Anthropologyand Animal Physiology, University of the Basque Country UPV/EHU, Leioa,Spain. 5Department of Public Health, Epidemiology, Biostatistics &Biodemography, The Danish Twin Registry, University of Southern Denmark,Odense, Denmark. 6Obesity Research Unit, Research Programs Unit,University of Helsinki, Helsinki, Finland. 7Endocrinology, Abdominal Center,Helsinki University Central Hospital and University of Helsinki, Helsinki,Finland. 8Istituto Superiore di Sanità–National Center for Epidemiology,Surveillance and Health Promotion, Rome, Italy. 9Department of Education,Mokpo National University, Jeonnam, South Korea. 10Department of Humanand Molecular Genetics, Virginia Institute for Psychiatric and BehavioralGenetics, Virginia Commonwealth University, Richmond, VA, USA.11Department of Human and Molecular Genetics, Psychiatry & Massey CancerCenter, Virginia Commonwealth University, Richmond, VA, USA. 12HealthyTwin Association of Mongolia, Ulaanbaatar, Mongolia. 13Graduate School ofBiomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.14The Australian Twin Registry, Centre for Epidemiology and Biostatistics, TheUniversity of Melbourne, Melbourne, VIC, Australia. 15Department ofPsychology, Medical School Berlin, Berlin, Germany. 16Department ofPsychiatry, University of British Columbia, Vancouver, BC, Canada.17Department of Clinical Biochemistry and Pharmacology and Department ofBogl et al. Biology of Sex Differences  (2017) 8:14 Page 10 of 12Clinical Genetics, Odense University Hospital, Odense, Denmark.18Department of Clinical Research, University of Southern Denmark, Odense,Denmark. 19Odense Patient data Explorative Network (OPEN), OdenseUniversity Hospital, Odense, Denmark. 20Department of Preventive Medicine,Keck School of Medicine of USC, University of Southern California, LosAngeles, CA, USA. 21USC Norris Comprehensive Cancer Center, Los Angeles,CA, USA. 22Centre of Human Genetics, University Hospitals Leuven, Leuven,Belgium. 23Department of Obstetrics and Gynaecology, Ghent UniversityHospitals, Ghent, Belgium. 24Department of Health and Exercise Sciences andColorado School of Public Health, Colorado State University, Fort Collins,USA. 25Psychology and Neuroscience, Duke University, Durham, NC, USA.26Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA.27Institute of Psychiatry, Psychology & Neuroscience, MRC Social, Genetic &Developmental Psychiatry Centre, King’s College London, London, UK.28Department of Psychology, Goldsmiths, University of London, London, UK.29Department of Psychology, University of Minnesota, Minneapolis, MN, USA.30Department of Biological Psychology, VU University Amsterdam,Amsterdam, Netherlands. 31Department of Noncommunicable DiseasesPrevention, Qingdao Centers for Disease Control and Prevention, Qingdao,China. 32Institute of Public Health, Epidemiology, Biostatistics andBiodemography, University of Southern Denmark, Odense, Denmark.33Department of Public Health, Qingdao University Medical College,Qingdao, China. 34Genetic Epidemiology Department, QIMR BerghoferMedical Research Institute, Brisbane, Australia. 35Molecular EpidemiologyDepartment, QIMR Berghofer Medical Research Institute, Brisbane, Australia.36Department of Medicine, Stanford Prevention Research Center, StanfordUniversity School of Medicine, Stanford, CA, USA. 37Center for HealthSciences, SRI International, Menlo Park, CA, USA. 38HealthTwiSt GmbH, Berlin,Germany. 39Department of Medical Epidemiology and Biostatistics, KarolinskaInstitutet, Stockholm, Sweden. 40Department of Statistics, Faculty of Arts andSciences, Kırıkkale University, Kırıkkale, Turkey. 41Psychology and AfricanAmerican Studies, Virginia Commonwealth University, Richmond, USA.42Faculty of Business, Karabuk University, Karabuk, Turkey. 43Department ofPsychology, University of Southern California, Los Angeles, CA, USA. 44Schoolof Law, Psychology and Social Work, Örebro University, Örebro, Sweden.45Institute of Research & Development, Battaramulla, Sri Lanka. 46Faculty ofMedicine & Allied Sciences, Rajarata University of Sri Lanka, Saliyapura, SriLanka. 47Institute of Psychiatry Psychology and Neuroscience, NIHR MentalHealth Biomedical Research Centre, South London and Maudsley NHSFoundation Trust and King’s College London, London, UK. 48ResearchInstitute for Primary Care and Health Sciences, School for Primary CareResearch (SPCR), Faculty of Health, Keele University, Staffordshire, UK.49Institute of Gerontology and Aging Research Network–Jönköping (ARN-J),School of Health and Welfare, Jönköping University, Jönköping, Sweden.50Department of Human Anatomy and Psychobiology, University of Murcia,Murcia, Spain. 51IMIB-Arrixaca, Murcia, Spain. 52Department of Developmentaland Educational Psychology, University of Murcia, Murcia, Spain. 53QIMRBerghofer Medical Research Institute, Brisbane, Australia. 54Washington StateTwin Registry, Washington State University–Health Sciences Spokane,Spokane, WA, USA. 55Department of Radiology, Semmelweis University,Budapest, Hungary. 56Hungarian Twin Registry, Budapest, Hungary.57Department of Public Health Nursing, Osaka City University, Osaka, Japan.58Department of Epidemiology, School of Public Health, Seoul NationalUniversity, Seoul, South Korea. 59The Charles Bronfman Institute forPersonalized Medicine, The Mindich Child Health and Development Institute,Icahn School of Medicine at Mount Sinai, New York, NY, USA. 60Novo NordiskFoundation Centre for Basic Metabolic Research (Section on MetabolicGenetics), and Department of Public Health, Faculty of Health and MedicalSciences, University of Copenhagen, Copenhagen, Denmark. 61Institute ofPreventive Medicine, Bispebjerg and Frederiksberg Hospitals, Copenhagen,The Capital Region, Denmark. 62Osaka University Graduate School ofMedicine, Osaka University, Osaka, Japan.Received: 23 December 2016 Accepted: 5 April 2017References1. 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J Exp Child Psychol. 2017;156:186–91.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Bogl et al. Biology of Sex Differences  (2017) 8:14 Page 12 of 12

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