Ramadan during pregnancy and offspring age at menarche in Indonesia: a quasi-experimental study

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Nutrition is an important non-genetic factor influencing menarcheal timing, with animal models indicating a link between maternal nutrition during pregnancy and offspring pubertal onset. However, due to ethical and practical constraints, studies on humans are scarce. Our study used prenatal exposure to Ramadan to investigate the effect of maternal nutrition on offspring AAM. Due to its intermittent nature, Ramadan fast is similar to other common forms of nutritional deprivation during pregnancy, e.g. breakfast skipping. Therefore, the relevance of this research extends beyond the context of Ramadan itself. Furthermore, considering the global prevalence of Ramadan observance, understanding the link between Ramadan during pregnancy and offspring reproduction health could benefit millions of females. Methods We used data from the Indonesian Family Life Survey (1993-2014, N=8,081) and Indonesian Demographic and Health Surveys (2002-2007, N=13,241). OLS and Cox regressions were applied to compare the AAM of female Muslims who were prenatally exposed to Ramadan and those of female Muslims who were not exposed. Exposure was determined based on the overlap between each woman’s own time in utero with historical dates of Ramadan. We further subdivided this overlap into trimester-specific categories. In all analyses, we adjusted for urban-rural residence, birth month, birth year, birth year squared, and survey wave. Results No associations between Ramadan during pregnancy and AAM were found, irrespective of the pregnancy trimester overlapping with Ramadan. These results were stable when we restricted the sample to women with shorter recall periods and younger women at the time of survey. Conclusions This study adds to the limited body of human research on the effects of prenatal nutritional on AAM. Given the limitations inherent in our study, future research is encouraged to further investigate this relationship. This could include examining clinical markers of pubertal onset, as well as exploring other social indicators of female reproduction. Such studies would help deepen our understanding of the dynamics between prenatal nutrition and female reproductive outcomes. Menarche Ramadan prenatal nutrition fetal programming quasi-experiment fasting female reproduction Figures Figure 1 Plain English Summary The age at which a girl first starts menstruating (AAM), known as menarche, is an important sign of female health. It helps predict fertility, birth rates, and when menopause might start. Research has shown that maternal nutrition during pregnancy is important for AAM. Studies on animals found that diets of pregnant mothers can affect offspring pubertal onset. It is challenging to run experiments with humans because it’s not ethical to restrict pregnant women’s nutrition. Thus, there aren't many human studies, and the existing findings are mixed. To overcome this, researchers have used events like famines to study how maternal diet during pregnancy affected AAM in an almost-random design. In our study, we looked at the association between Ramadan during pregnancy and AAM. Ramadan involves fasting during the day, affecting many pregnant Muslim women. We compared AAM of female Muslims who were in the womb during Ramadan with those who were not. Using data from Indonesia, a country with a large Muslim population, we found no evidence on this link. Because Ramadan fasting is similar to other common forms of nutritional restriction, like skipping breakfast, our findings are relevant beyond just Ramadan. Understanding this connection could benefit millions of females, given the large number of Muslim births each year. Future studies should look at clinical markers of pubertal onset, exploring other social indicators of female reproductive health. Background The age at menarche (AAM), a woman's first menstrual cycle, is an important indicator of female reproductive health (1, 2). AAM predicts key indicators of fertility such as fecundity (3), birth rate (4), and age at menopause (5). Furthermore, early menarche was associated with increased risks of breast cancer, cardiovascular diseases, and mortality (6-8), while delayed menarche was linked to osteoporosis and reduced areal bone density (6). Even though genetics is an important determinant, environmental factors may also affect menarcheal timing (6). Given the limited possibility to influence genetic aspects, illuminating the mechanisms that connect non-genetic factors with AAM could provide crucial insights for mitigating the associated reproductive and general health consequences. A major non-genetic determinant of AAM is nutrition. While the evidence on the link between early childhood or adolescent diets and AAM has been well-established (9-11), research on the impacts of maternal nutrition during pregnancy on offspring AAM remains limited. Prenatal nutrition may influence AAM through dynamic changes in the hypothalamic-pituitary-gonadal (HPG) axis, which regulates reproductive hormone production (12-14). Such alteration is explained by fetal programming theory, which predicts that environmental factors in utero can cause long-term functional and structural changes in organisms (15). This hypothesis has been indicated in studies on sheep and lambs, where changes in releasing hormonal levels within the HPG axis were shown to be triggered by maternal undernutrition (16, 17). Moreover, experimental rat models suggested that maternal diet alterations during gestation may affect offspring pubertal onset (18-22). In human studies, the existing evidence on prenatal nutrition and AAM are both scarce and inconsistent. Due to ethical concerns, it is infeasible to assign nutritional restrictions to pregnant women. Furthermore, controlled-randomized trials would require long follow-up periods as menarcheal timing is observable only during adolescence. Thus, the available studies are limited with regards to study designs. An Indian study using data from a supplementary nutrition programme reported later menarche in the offspring of pregnant women who had received a balanced protein-calorie supplement during pregnancy (23). By contrast, studies on the Dutch famine, a historical severe prenatal nutritional shock, were not found to be associated with AAM (24, 25). Research in which birthweight was used as a proxy for nutritional status during gestation reported mixed results, with both higher and lower birth weights reportedly being associated with earlier AAM (9, 11). Considering the importance of menarche for female reproductive health, evidence from additional settings can yield important new insights into the prenatal nutrition – offspring AAM nexus. Our Indonesian study employed a quasi-experimental design, similar to that used in the Dutch studies; however, instead of prenatal exposure to famine, we used Ramadan during pregnancy as a proxy for maternal malnutrition to assess its impact on offspring AAM. During Ramadan, adult Muslims abstain from food and drink from sunrise to sunset, and in-utero exposure to this fasting period can be considered a form of maternal nutritional shock. Even though pregnant women can skip fasting during Ramadan by compensating for it at a later point of time or making expiatory payments to feed the poor, a very large share decide to fast (26, 27). Ramadan during pregnancy has been found to be associated with various adverse health and human capital outcomes in the offspring (28), including childhood growth (29). Such anthropometric outcomes are not only important predictors of AAM (11, 13, 30), but are also correlated with obesity, an important risk factor of earlier onset of menarche (31, 32). For pregnant female adults, dietary adaptations to Ramadan might lead to fluctuations in the levels of key reproductive biomarkers such as follicle-stimulating hormone, estrogen, progesterone, and leptin (33). While initial analyses in our previous research did not identify AAM as a mechanism linking prenatal nutritional deprivation and offspring height growth, we believe that this potential link warrants further investigation with more rigorously defined, younger and larger samples to overcome recall bias related to self-reported AAM (34). Due to its intermittent nature, Ramadan fast is a comparatively mild prenatal exposure. It is more comparable to other common forms of nutritional deprivation during pregnancy – such as breakfast skipping – than more extreme nutritional shortages such as famines. Therefore, the relevance of this research for female offspring extends beyond the context of Ramadan itself. At the same time, given that Muslim births constitute about 31% of the 130 - 140 million babies born worldwide each year (35, 36), the findings of this study could be particularly significant for Muslims of childbearing age and their healthcare providers. Methods Data Our data come from Indonesia, the country with the world’s largest Muslim population. They comprised two sources: the Indonesian Family Life Survey (IFLS), and the Indonesian Demographic and Health Survey (DHS). IFLS is a longitudinal socioeconomic and health survey that is representative of 83% of the Indonesian population (excluding a few provinces). Its sampling frame was stratified in provinces, from which the households were selected randomly for face-to-face interviews (37). We used data from all five IFLS waves, conducted in 1993/94, 1997/98, 2000, 2007/08, and 2014/15. The Indonesian DHS is a nationally representative household survey, utilizing systematic sampling in multiple stages to select individuals for interviews (38). This sampling was stratified at both national and provincial levels. The surveys were conducted every five years, and data on AAM come from the module of Young Adult Reproductive Health, available in 2002, 2007, 2012, and 2017. The IFLS sample includes 8,081 ever-married women aged 18 to 60, born between 1935 and 1997. The DHS sample consists of 24,898 never-married Muslim women aged 15 to 25, born between 1978 and 1999, with the main analysis sample being restricted to 13,104 women at the age of 18 to 25. We restricted the main sample to women who were at least 18 years old, the age by which most Indonesian females in our samples would have experienced menarche. Including younger individuals could introduce selection bias, as those with earlier menarche (potentially due to prenatal exposure to Ramadan) would have a higher probability of being included in the sample. Such bias might cause an overestimation of the effects because it would artificially lower the average AAM in the exposed group. In additional analyses that did not rely on the average AAM (see the discussion on Cox regressions below), we use an extended sample that includes all women aged 15-25. To identify prenatal exposure to Ramadan, exact dates of birth were required for our study. Unlike the IFLS, the Indonesia DHS data only provided the month and year of birth. Thus, we imputed the 15th as the day of birth for all observations in this sample. Heaping in dates of birth was not detected in both samples. Study design Age at menarche In both data sources, age at menarche was self-reported through direct interviews by trained staff (38, 39). The outcome variable AAM is continuous and measured in years. To strengthen the validity of recorded data in the sample, several adjustments were undertaken. First, we included only Muslim women with age at menarche (AAM) between 9 and 20 years, as data outside this range are likely to be misreported (10, 40-42) . Particularly in the IFLS sample, when a woman reported on AAM in more than one survey wave, only the first declared AAM was considered, provided that the difference among the stated values did not exceed 1 year. Women who stated AAM with variation of more than one year across waves are likely to be unsure about their exact age at AAM, so removing them from the sample increases the precision in our estimation. To further minimize the risk of recall bias, women over 60 years old were excluded from the sample as AAM is long ago and recall errors may increase by this age (43). Exposure: Ramadan during Pregnancy Women were classified into exposure and control groups based on whether their prenatal period overlapped with Ramadan or not. To determine exposure, we calculated 266 days (the average duration of human pregnancy from conception) backwards from the date of birth. This estimated gestational period was then compared against historical Ramadan dates. The women whose calculated 266-day-gestation coincided with a Ramadan were categorized as exposed, while the control group consisted of Muslims whose gestational period did not overlap with a Ramadan. This classification of exposure follows the standard approach in the literature on Ramadan during pregnancy (26). It does not require information on maternal fasting behavior (see “Statistical methods” for details on the quasi-experimental approach). We further differentiated the exposed group based on the pregnancy trimester during which Ramadan started. Evidence from animal studies indicates that poor nutrition specifically during early pregnancy affects the pituitary sensitivity in the HPG axis, thereby influencing pubertal timing (16, 17). Genetic studies in humans also support this finding, showing that DNA methylation, a determinant for gene expression within the HPG axis, is particularly sensitive to environmental factors during early gestation (44). In this study, the three sub-categories were defined as follows: Trimester 1 covers pregnancy days 1 to 88, trimester 2 spans days 89 to 177, and trimester 3 encompasses days 178 onwards. Note that we placed observations whose conception was calculated to have been within 21 days after the end of a Ramadan into a separate group (“probably-not-exposed”). This avoids noise in the control group since if these individuals were born post-term, they would have experienced Ramadan in early pregnancy (26). Post-term pregnancies extending more than 21 days beyond the due date are rare (45). Statistical methods We compared AAM between two groups of Muslim women: those whose prenatal period overlapped with a Ramadan, and those who were not in utero during a Ramadan. The key advantage of defining exposure based on birth dates is the quasi-random occurrence of Ramadan during a pregnancy. Previous studies on Ramadan during pregnancy in Indonesia showed that the overlap of Ramadan in pregnancy was independent of maternal background characteristics (26, 46), in contrast to the subjective maternal decision to fast during pregnancy. At the same time, to the extent that not all women whose pregnancies overlapped with a Ramadan do fast, this implies that offspring whose time in utero overlapped with a Ramadan were classified as exposed, even though their mothers did not fast in Ramadan. While this intention-to-treat set-up does not threaten the causal interpretation of our estimates, it leads to a potential bias towards zero in our analyses. Following the Islamic calendar, Ramadan dates shift 11 days backwards over the Gregorian calendar every year and will be back to the same Gregorian date after a 33-year cycle. Such “shifting-over-the-seasons” characteristics mean that any unobserved season-related confounders in the prenatal phase (e.g., seasonal food availability, likelihood of infectious diseases) are unlikely to bias our results. Therefore, researchers can infer causality through separating Ramadan effects from seasonality effects by controlling for month of birth. (26). Ordinary least squares (OLS) regressions were performed using the two main samples from IFLS and DHS. To gain insights into the relationship between prenatal Ramadan exposure and AAM, we additionally utilized Cox proportional hazard regression on the extended sample that additionally includes females aged 15-17. While OLS looks at the mean difference between the exposed and unexposed groups, Cox estimates the likelihood of experiencing menarche at any given age given the prenatal Ramadan exposure status. Thus, this model focuses on the order and timing of menarche (partial likelihood), rather than requiring the precise AAM (full likelihood approach). This is particularly meaningful regarding DHS data, since Muslims younger than 18 years old can still be included in the analysis, without leading to selection bias. This allows for a large increase in sample size and thus raises statistical power. For IFLS data, the majority of respondents were at least 18 years old at the interview, so that the Cox model has little added value. The results of Cox regressions are displayed as hazard ratios. In all analyses, Ramadan exposure was included either as a general dummy (exposed – unexposed), or as trimester-specific sub-categories. Since females living in urban areas tend to experience earlier menarche and there is a global downward trend of AAM over time (10), we adjusted the analyses for urban-rural residency, birth year and birth year squared. As previously explained, we also controlled for those women classified as “probably not exposed” and for month of birth. Additionally, survey years were included to account for unobserved confounding factors specific to the timing of data collection. The same set of covariates was used for both OLS and Cox models. Robust standard errors were applied to all analyses. Sample characteristics The mean age at menarche in both samples was approximately 13.5 years, aligning with findings from previous research on Indonesia and other low- and middle-income countries (10, 47). When including females aged 15-17 in the DHS sample, the average age at AAM artificially decreases slightly due to the fact that the mean AAM is based only on those women who already had their menarche. Descriptive statistics for each sample are summarized in Table 1. Table 1. Characteristics of Indonesian female Muslims from Indonesian DHS (2002-2017) and IFLS (1993–2015) IFLS DHS Main sample – Aged 18-60 at observation (n = 8,081) Main sample – Aged 18-25 at observation (n = 13,104) Extended sample – Aged 15-25 at observation (n = 24,898) Mean (SD)/Share Mean (SD)/Share Mean (SD)/Share Age at menarche (years) 13.66 (1.54) 13.48 (1.41) 13.29 (1.33) Living in an urban area 58.9 % 65.1 % 59.4 % Age at the survey (years) 28.25 (6.90) 20.30 (1.90) 18.3 (2.62) In utero during Ramadan 82.7 % 82.6 % 82.8 % Ram. started in trimester 1 33.1 % 31.3 % 31.8 % Ram. started in trimester 2 24.3 % 25.5 % 26.6 % Ram. started in trimester 3 25.3 % 25.8 % 24.4 % Abbreviations: IFLS, Indonesian Family Life Survey; SD, standard deviation; Ram., Ramadan; DHS, Indonesian Demographic and Health Surveys. Sensitivity checks In order to address the risk of recall bias (48), we used different subsamples to test the stability of our results: The first subsamples included only women with shorter recall intervals, i.e. max. 15 (IFLS) and 6 (DHS) years between declared age at menarche and age at interview. The second subsamples were limited to only younger women, who thus were more likely to correctly recall their AAM at the time of the interview (49), i.e. max 30 (IFLS) and 20 (DHS) years old. Results Prenatal exposure to Ramadan was not associated with menarcheal onset, neither in the IFLS nor the DHS sample, independent of the pregnancy trimester during which a Ramadan-pregnancy overlap started (Table 2). Consistent with the OLS estimates (Table 2, columns 1 and 2), the findings from the Cox analysis (Table 2, column 3) using DHS-extended data showed no difference in the likelihood of experiencing menarche between the exposed and non-exposed females at any given age. The estimated coefficients were close to zero and the hazard rates for Cox regressions close to one, and the confidence intervals did not fit with the existence of substantial effects. These findings were stable when reducing the sample to women with shorter recall intervals (Appendix A) and younger age at the time of interview (Appendix B). Table 2. Associations between in-utero exposure to Ramadan and age at menarche (in years)among female Muslims IFLS DHS (1) (2) (3) OLS OLS Cox (n=8,081) (n=13,104) (n= 24,898) Exposure categories β 95% CI β 95% CI HR 95% CI In utero during Ramadan 0.025 [-0.082 ; 0.132] -0.005 [-0.086 ; 0.075] 0.999 [0.969 ; 1.030] Exposure periods Ram. started in trimester 1 0.012 [-0.103 ; 0.128] -0.029 [-0.119 ; 0.061] 0.999 [0.966 ; 1.034] Ram. started in trimester 2 0.052 [-0.071 ; 0.174] -0.038 [-0.134 ; 0.058] 1.005 [0.970 ; 1.042] Ram. started in trimester 3 0.019 [-0.101 ; 0.138] 0.027 [-0.062 ; 0.116] 0.995 [0.962 ; 1.029] Notes: Results stem from two separate regressions per column (top panel: exposed vs. not exposed; bottom panel: classification of exposure into different pregnancy phases) that adjusted for “probably-not-exposed” women, birth year, birth year squared, rural-urban living area, month-of-birth, survey wave. Data from the Indonesian Family Life Survey (IFLS) (1993–2015) and the Indonesian Demographic and Health Surveys (DHS) (2002 - 2017). Column (2) uses the age 18-25, and column (3) the age 15-25 of the DHS-sample. Abbreviations: Ram., Ramadan; HR, hazard ratio; CI, confidence interval. β = unstandardized regression coefficient. * p<0.05, ** p<0.01, *** p<0.001 Discussion We investigated the effects of prenatal nutrition on the AAM among female Muslims using in-utero exposure to Ramadan as treatment. Using two distinct surveys from Indonesia: IFLS and DHS, we found no associations between Ramadan during pregnancy and AAM, independent of the pregnancy trimester of exposure. Our results are consistent with previous studies on the Dutch famine, in which no association between prenatal undernutrition and menarcheal timing of offspring was found (24, 25, 34). Other studies using birthweight as a proxy for maternal nutrition reported mixed results (11). One key strength of the present study is that our samples included data from a broader range of birth cohorts and more recent time periods, an important consideration given the documented decline in AAM over time (9). A few limitations in our study should be noted. Regarding the self-reported AAM, while we accounted for potential recall bias in our sensitivity analyses, the unit of measurement was in years, rather than days or weeks. This may have reduced our statistical power to detect subtler effects of Ramadan exposure during pregnancy. This was augmented by the intention-to-treat framework, with the assumption that all pregnant Muslims in our sample did fast during Ramadan. This is particularly different from the controlled set-up seen in Nandi et al (23), in which food supplementation for specific pregnant women was suggested to delay AAM among offspring. Therefore, our estimates are inherently subject to attenuation bias, potentially underestimating the true impacts of maternal nutritional shock. Furthermore, such effects might potentially be mediated by postnatal determinants of AAM, such as childhood or adolescent diets (9-11). An experimental study on rats showed that the interaction between prenatal undernutrition and postnatal high-fat diet led to altered levels of leptin and hypothalamic Kiss1, both of which are crucial for pubertal onset (50). Unlike in rats, in which pubertal status can be assessed relatively soon after prenatal shocks (18-22), AAM in humans takes place long after the prenatal exposure, allowing more space for such interactions. Apart from the pubertal onset, future studies on Ramadan during pregnancy might also look at other outcomes of female reproduction, which occur after puberty starts. For instance, previous studies have found evidence on a link between maternal undernutrition and social indicators of reproductive capacity, such as the timing of first pregnancy (23, 25, 34). However, in contrast to age at menarche, age at first childbirth does not reflect the purely biological start of reproduction. This age is socially-shaped and might itself be affected by prenatal factors. For instance, earlier Ramadan studies suggested impacts on offspring performance at school and on the labor market (46, 51-53). At the same time, education and labor market factors are known to be associated with maternal age at first birth. A promising avenue for future research into the link between prenatal factors and biological reproductive ages of women which helps overcome challenges arising from self-reported data is the use of clinical indicators. One option is to explore alternative biological markers of menarche. In particular, the body nutritional status is communicated to the HPG axis through metabolic signals such as leptin, of which fluctuating levels might affect the timing of menarche (54). An experimental study on lamb documented that poor maternal nutrition during pregnancy affects the offspring leptin level (55). Furthermore, pubertal status can also be assessed by the changing levels of associated sex hormones (e.g. oestrogen) or through clinical scales of Tanner stage (56). In fact, a few studies on female pubertal onset have looked at clinically-measured age at thelarche, the onset of breast development, which is an early indicator of puberty (57). Conclusion Contributing to the scarce body of empirical evidence on the associations between nutrition during pregnancy and the onset of menarche in the offspring, this study documents that Ramadan during pregnancy is not associated with AAM. Although some prior research has suggested a link between birthweight and AAM (9, 11) and food supplementation during pregnancy in India was found to delay AAM (23), our findings, based on Indonesian data, are consistent with previous studies on maternal undernutrition (24, 25, 34). In addition to investigating other indicators of female fertility that may partially be socially determined such as age at first childbirth, future research should consider alternative approaches to studying pubertal onset, such as using clinical markers like leptin levels or sex hormone changes, rather than relying on self-reported AAM. As a large share of pregnant Muslims fast, this topic is relevant to the large worldwide community of Muslims. At the same time, given the intermittent nature of the Ramadan fast, the relevance of such research may go beyond the health impacts on Muslims, as (intermittent) fasting is done by many non-Muslims, too. Abbreviations AAM Age at menarche HPG Hypothalamic-Pituitary-Gonadal axis IFLS Indonesian Family Life Survey DHS Demographic and Health Survey OLS Ordinary Least Squares Cox Cox Survival Analysis Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The dataset(s) supporting the conclusions of this article are publicly available at www.dhsprogram.com and www.rand.org. Datasets generated and/or analyzed during the study are available from the corresponding author on request. Disclaimer The opinions in this article are those of its authors, and do not necessarily constitute the official position of the World Health Organization. Competing interests The authors declare that they have no competing interests. Funding This project was funded by the German Research Foundation (DFG), grant number 260639091. Authors' information Van My Tran 1 , Reyn van Ewijk 1 , Fabienne Pradella 1, 2, 3 1 Johannes Gutenberg-University Mainz, Faculty of Law, Management, and Economics, Chair of Statistics and Econometrics, Jakob-Welder-Weg 4, D-55099 Mainz, Rhineland-Palatinate, Germany 2 Heidelberg University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg, Germany 3 Stanford University, Department of Medicine, Division of Primary Care and Population Health, Stanford, USA Authors' contributions VT: Formal analysis, data curation, writing – original draft; RvE: Conceptualization, Methodology, Writing – Review & Editing, Funding acquisition; FP: Conceptualization, Methodology, Writing – Review & Editing. All authors read and approved the final manuscript. References Sommer M, Sutherland C, Chandra-Mouli V. Putting menarche and girls into the global population health agenda. Reproductive health. 2015;12:1-3. DiVall SA, Radovick S. Pubertal development and menarche. 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Is there a causal relationship between obesity and puberty? The Lancet Child & adolescent health. 2019;3(1):44-54. Khoshdel A, Kheiri S, Hashemi-Dehkordi E, Nasiri J, Shabanian-Borujeni S, Saedi E. The effect of Ramadan fasting on LH, FSH, oestrogen, progesterone and leptin in pregnant women. Journal of Obstetrics and Gynaecology. 2014;34(7):634-8. Pradella F, Ewijk R. Mechanisms linking prenatal environment and linear growth: the case of Ramadan during pregnancy. American Journal of Epidemiology. 2024:kwae386. Ritchie H. How many people die and how many are born each year. Our world in Data. 2019. Center PR. The changing global religious landscape. Pew Research Center. 2017. Frankenberg E, Karoly L, Gertler P, Achmad S, Agung I, Hatmadji S, et al. The 1993 Indonesian family life survey: Overview and field report. 1995. Statistik-Bps SI-BP. National Population and Family Planning Board-BKKBN/Indonesia, Kementerian Kesehatan-Kemenkes-Ministry of Health/Indonesia, ICF International. Indonesia Demographic and Health Survey (IDHS). 2017;2018:1-606. Strauss J, Witoelar F, Sikoki B. The fifth wave of the Indonesia family life survey: overview and field report: Rand Santa Monica, CA, USA; 2016. Moelyo AG, Wulandari A, Imas O, Rahma UP, Hidayah N, Kesumaningtyas C, et al. Age at menarche and early menarche among healthy adolescents. Paediatrica Indonesiana. 2019;59(1):33-7. Batubara J, Soesanti F, van de Waal HD. Age at menarche in Indonesian girls: a national survey. Acta Med Indones. 2010;42(2):78-81. Tamakoshi K, Yatsuya H, Tamakoshi A. Early age at menarche associated with increased all-cause mortality. European journal of epidemiology. 2011;26(10):771-8. Hänninen T, Koivisto K, Reinikainen KJ, Helkala E-L, Soininen H, MYKKÄNEN L, et al. Prevalence of ageing-associated cognitive decline in an elderly population. Age and ageing. 1996;25(3):201-5. Tobi EW, Slieker RC, Stein AD, Suchiman HED, Slagboom PE, Van Zwet EW, et al. Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome. International journal of epidemiology. 2015;44(4):1211-23. Kieler H, Axelsson O, Nilsson S, Waldenströ U. The length of human pregnancy as calculated by ultrasonographic measurement of the fetal biparietal diameter. Ultrasound in Obstetrics and Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology. 1995;6(5):353-7. Majid MF. The persistent effects of in utero nutrition shocks over the life cycle: Evidence from Ramadan fasting. Journal of Development Economics. 2015;117:48-57. Coast E, Lattof SR, Strong J. Puberty and menstruation knowledge among young adolescents in low-and middle-income countries: a scoping review. International journal of public health. 2019;64(2):293-304. Lundblad MW, Jacobsen BK. The reproducibility of self-reported age at menarche: the Tromsø Study. BMC women's health. 2017;17:1-7. Must A, Phillips S, Naumova E, Blum M, Harris S, Dawson-Hughes B, et al. Recall of early menstrual history and menarcheal body size: after 30 years, how well do women remember? American journal of epidemiology. 2002;155(7):672-9. Iwasa T, Matsuzaki T, Munkhzaya M, Tungalagsuvd A, Yamasaki M, Kuwahara A, et al. The effects of prenatal undernutrition and postnatal high-fat diet on hypothalamic Kiss1 mRNA and serum leptin levels. International Journal of Developmental Neuroscience. 2015;42:76-9. Almond D, Mazumder B. Health capital and the prenatal environment: the effect of Ramadan observance during pregnancy. American Economic Journal: Applied Economics. 2011;3(4):56-85. Almond D, Mazumder B, Van Ewijk R. In utero Ramadan exposure and children’s academic performance. The Economic Journal. 2015;125(589):1501-33. Majid F, Behrman J, Mani S. Short-term and long-term distributional consequences of prenatal malnutrition and stress: using Ramadan as a natural experiment. BMJ global health. 2019;4(3):e001185. Childs GV, Odle AK, MacNicol MC, MacNicol AM. The importance of leptin to reproduction. Endocrinology. 2021;162(2):bqaa204. Hoffman M, Peck K, Forella M, Fox A, Govoni K, Zinn S. The effects of poor maternal nutrition during gestation on postnatal growth and development of lambs. Journal of animal science. 2016;94(2):789-99. Walker I, Smith C, Davies J, Inskip H, Baird J. Methods for determining pubertal status in research studies: literature review and opinions of experts and adolescents. Journal of developmental origins of health and disease. 2020;11(2):168-87. Eckert-Lind C, Busch AS, Petersen JH, Biro FM, Butler G, Bräuner EV, et al. Worldwide secular trends in age at pubertal onset assessed by breast development among girls: a systematic review and meta-analysis. JAMA pediatrics. 2020;174(4):e195881-e. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5324852","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371972190,"identity":"ca6750fe-fa38-4dd2-8480-cb695cdfd744","order_by":0,"name":"Van My Tran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3RIQsCMRTA8Y2DS4PVE/0QTwTlin4Vh3VesQgGBwaLcPU+hiBcMrxj4JUT66LJdEGbQcE7LaYdNsP+sLCxH48xQlyuf45XC6vF6k3QeL2+2lI/E8CvAyuB/IjebT+MemaaIZXDzoAjNVcbKaIxwctk1jfRGGk6YaFCL0wspKUkEERPpEaCfqYeg0z5bWYjcVmTpdglEqopSwaa+O2HhfDgPUWLbfAmmsGhIrbn86AELDAXSVHWJGdQ0FW4sRCfy+55jgsRr2XvStPFCE46M3fbGPL5lu+oagAul8vlauoFC01PfpAgQJoAAAAASUVORK5CYII=","orcid":"","institution":"Johannes Gutenberg-University Mainz","correspondingAuthor":true,"prefix":"","firstName":"Van","middleName":"My","lastName":"Tran","suffix":""},{"id":371972191,"identity":"0150e179-8ba9-45ac-accb-4451198a68bf","order_by":1,"name":"Reyn van Ewijk","email":"","orcid":"","institution":"Johannes Gutenberg-University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Reyn","middleName":"van","lastName":"Ewijk","suffix":""},{"id":371972192,"identity":"46151e06-15ca-4c83-bc62-8ea08bbe8204","order_by":2,"name":"Fabienne Pradella","email":"","orcid":"","institution":"Johannes Gutenberg-University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Fabienne","middleName":"","lastName":"Pradella","suffix":""}],"badges":[],"createdAt":"2024-10-24 10:08:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5324852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5324852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68278943,"identity":"168bdcf1-d612-4bdf-aac9-f5e4d9ce7df0","added_by":"auto","created_at":"2024-11-05 15:04:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCategories of Ramadan exposure in utero\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates an example using females born in 1996 and 1997. Diamonds indicate the date of birth. Lines represent the average length of pregnancy (266 days). Circles show the estimated date of conception. A person was considered prenatally exposed to Ramadan 1997 if her time in utero overlapped with Ramadan.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5324852/v1/24e3ec0925f066efd7aea269.png"},{"id":79067502,"identity":"51b0dc93-dc18-4d96-8e0d-e05aff9fe5e8","added_by":"auto","created_at":"2025-03-24 04:53:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":888414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5324852/v1/2bb4d8a1-0449-41de-bc3f-5330831366b0.pdf"},{"id":68277381,"identity":"08090df8-0b54-4072-9902-89bcd17157f4","added_by":"auto","created_at":"2024-11-05 14:48:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16784,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-5324852/v1/50b04c29a51cd2f0171fd4c1.docx"},{"id":68277384,"identity":"066493a2-3d66-4d4b-b31b-3deea1749e87","added_by":"auto","created_at":"2024-11-05 14:48:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17474,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-5324852/v1/5f92bbd2941f90b9c4cefaa2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ramadan during pregnancy and offspring age at menarche in Indonesia: a quasi-experimental study","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003eThe age at which a girl first starts menstruating (AAM), known as menarche, is an important sign of female health. It helps predict fertility, birth rates, and when menopause might start. Research has shown that maternal nutrition during pregnancy is important for AAM. Studies on animals found that diets of pregnant mothers can affect offspring pubertal onset. It is challenging to run experiments with humans because it\u0026rsquo;s not ethical to restrict pregnant women\u0026rsquo;s nutrition. Thus, there aren\u0026apos;t many human studies, and the existing findings are mixed. To overcome this, researchers have used events like famines to study how maternal diet during pregnancy affected AAM in an almost-random design.\u003c/p\u003e\n\u003cp\u003eIn our study, we looked at the association between Ramadan during pregnancy and AAM. Ramadan involves fasting during the day, affecting many pregnant Muslim women. We compared AAM of female Muslims who were in the womb during Ramadan with those who were not. Using data from Indonesia, a country with a large Muslim population, we found no evidence on this link.\u003c/p\u003e\n\u003cp\u003eBecause Ramadan fasting is similar to other common forms of nutritional restriction, like skipping breakfast, our findings are relevant beyond just Ramadan. Understanding this connection could benefit millions of females, given the large number of Muslim births each year. Future studies should look at clinical markers of pubertal onset, exploring other social indicators of female reproductive health.\u0026nbsp;\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eThe age at menarche (AAM), a woman's first menstrual cycle, is an important indicator of female reproductive health\u0026nbsp;(1, 2). AAM predicts key indicators of fertility such as fecundity\u0026nbsp;(3), birth rate\u0026nbsp;(4), and age at menopause\u0026nbsp;(5). Furthermore, early menarche was associated with increased risks of breast cancer, cardiovascular diseases, and mortality\u0026nbsp;(6-8), while delayed menarche was linked to osteoporosis and reduced areal bone density\u0026nbsp;(6). Even though genetics is an important determinant, environmental factors may also affect menarcheal timing\u0026nbsp;(6). Given the limited possibility to influence genetic aspects, illuminating the mechanisms that connect non-genetic factors with AAM could provide crucial insights for mitigating the associated reproductive and general health consequences.\u003c/p\u003e\n\u003cp\u003eA major non-genetic determinant of AAM is nutrition. While the evidence on the link between early childhood or adolescent diets and AAM has been well-established\u0026nbsp;(9-11), research on the impacts of maternal nutrition during pregnancy on offspring AAM remains limited. Prenatal nutrition may influence AAM through dynamic changes in the hypothalamic-pituitary-gonadal (HPG) axis, which regulates reproductive hormone production\u0026nbsp;(12-14). Such alteration is explained by fetal programming theory, which predicts that environmental factors in utero can cause long-term functional and structural changes in organisms\u0026nbsp;(15). This hypothesis has been indicated in studies on sheep and lambs, where changes in releasing hormonal levels within the HPG axis were shown to be triggered by maternal undernutrition\u0026nbsp;(16, 17). Moreover, experimental rat models suggested that maternal diet alterations during gestation may affect offspring pubertal onset\u0026nbsp;(18-22).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn human studies, the existing evidence on prenatal nutrition and AAM are both scarce and inconsistent. Due to ethical concerns, it is infeasible to assign nutritional restrictions to pregnant women. Furthermore, controlled-randomized trials would require long follow-up periods as menarcheal timing is observable only during adolescence. Thus, the available studies are limited with regards to study designs. An Indian study using data from a supplementary nutrition programme reported later menarche in the offspring of pregnant women who had received a balanced protein-calorie supplement during pregnancy\u0026nbsp;(23). By contrast, studies on the Dutch famine, a historical severe prenatal nutritional shock, were not found to be associated with AAM\u0026nbsp;(24, 25). Research in which birthweight was used as a proxy for nutritional status during gestation reported mixed results, with both higher and lower birth weights reportedly being associated with earlier AAM\u0026nbsp;(9, 11). Considering the importance of menarche for female reproductive health, evidence from additional settings can yield important new insights into the prenatal nutrition – offspring AAM nexus.\u003c/p\u003e\n\u003cp\u003eOur Indonesian study employed a quasi-experimental design, similar to that used in the Dutch studies; however, instead of prenatal exposure to famine, we used Ramadan during pregnancy as a proxy for maternal malnutrition to assess its impact on offspring AAM. During Ramadan, adult Muslims abstain from food and drink from sunrise to sunset, and in-utero exposure to this fasting period can be considered a form of maternal nutritional shock. Even though pregnant women can skip fasting during Ramadan by compensating for it at a later point of time or making expiatory payments to feed the poor, a very large share decide to fast\u0026nbsp;(26, 27). Ramadan during pregnancy has been found to be associated with various adverse health and human capital outcomes in the offspring\u0026nbsp;(28), including childhood growth\u0026nbsp;(29). Such anthropometric outcomes are not only important predictors of AAM\u0026nbsp;(11, 13, 30), but are also correlated with obesity, an important risk factor of earlier onset of menarche\u0026nbsp;(31, 32). For pregnant female adults, dietary adaptations to Ramadan might lead to fluctuations in the levels of key reproductive biomarkers such as follicle-stimulating hormone, estrogen, progesterone, and leptin\u0026nbsp;(33). While initial analyses in our previous research did not identify AAM as a mechanism linking prenatal nutritional deprivation and offspring height growth, we believe that this potential link warrants further investigation with more rigorously defined, younger and larger samples to overcome recall bias related to self-reported AAM\u0026nbsp;(34).\u003c/p\u003e\n\u003cp\u003eDue to its intermittent nature, Ramadan fast is a comparatively mild prenatal exposure. It is more comparable to other common forms of nutritional deprivation during pregnancy – such as breakfast skipping – than more extreme nutritional shortages such as famines. Therefore, the relevance of this research for female offspring extends beyond the context of Ramadan itself. At the same time, given that Muslim births constitute about 31% of the 130 - 140 million babies born worldwide each year (35, 36), the findings of this study could be particularly significant for Muslims of childbearing age and their healthcare providers.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eOur data come from Indonesia, the country with the world\u0026rsquo;s largest Muslim population. They comprised two sources: the Indonesian Family Life Survey (IFLS), and the Indonesian Demographic and Health Survey (DHS). IFLS is a longitudinal socioeconomic and health survey that is representative of 83% of the Indonesian population (excluding a few provinces). Its sampling frame was stratified in provinces, from which the households were selected randomly for face-to-face interviews\u0026nbsp;(37). We used data from all five IFLS waves, conducted in 1993/94, 1997/98, 2000, 2007/08, and 2014/15.\u003c/p\u003e\n\u003cp\u003eThe Indonesian DHS is a nationally representative household survey, utilizing systematic sampling in multiple stages to select individuals for interviews\u0026nbsp;(38). This sampling was stratified at both national and provincial levels. The surveys were conducted every five years, and data on AAM come from the module of Young Adult Reproductive Health, available in 2002, 2007, 2012, and 2017.\u003c/p\u003e\n\u003cp\u003eThe IFLS sample includes 8,081 ever-married women aged 18 to 60, born between 1935 and 1997. The DHS sample consists of 24,898 never-married Muslim women aged 15 to 25, born between 1978 and 1999, with the main analysis sample being restricted to\u0026nbsp;13,104 women at the age of 18 to 25. We restricted the main sample to women who were at least 18 years old, the age by which most Indonesian females in our samples would have experienced menarche. Including younger individuals could introduce selection bias, as those with earlier menarche (potentially due to prenatal exposure to Ramadan) would have a higher probability of being included in the sample. Such bias might cause an overestimation of the effects because it would artificially lower the average AAM in the exposed group. In additional analyses that did not rely on the average AAM (see the discussion on Cox regressions below), we use an extended sample that includes all women aged 15-25.\u003c/p\u003e\n\u003cp\u003eTo identify prenatal exposure to Ramadan, exact dates of birth were required for our study. Unlike the IFLS, the Indonesia DHS data only provided the month and year of birth. Thus, we imputed the 15th as the day of birth for all observations in this sample. Heaping in dates of birth was not detected in both samples.\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003ch4\u003eAge at menarche\u003c/h4\u003e\n\u003cp\u003eIn both data sources, age at menarche was self-reported through direct interviews by trained staff\u0026nbsp;(38, 39). The outcome variable AAM is continuous and measured in years. To strengthen the validity of recorded data in the sample, several adjustments were undertaken. First, we included only Muslim women with age at menarche (AAM) between 9 and 20 years, as data outside this range are likely to be misreported\u0026nbsp;(10, 40-42)\u0026nbsp;. Particularly in the IFLS sample, when a woman reported on AAM in more than one survey wave, only the first declared AAM was considered, provided that the difference among the stated values did not exceed 1 year. Women who stated AAM with variation of more than one year across waves are likely to be unsure about their exact age at AAM, so removing them from the sample increases the precision in our estimation. To further minimize the risk of recall bias, women over 60 years old were excluded from the sample as AAM is long ago and recall errors may increase by this age\u0026nbsp;(43).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eExposure: Ramadan during Pregnancy\u003c/h4\u003e\n\u003cp\u003eWomen were classified into exposure and control groups based on whether their prenatal period overlapped with Ramadan or not. To determine exposure, we calculated 266 days (the average duration of human pregnancy from conception) backwards from the date of birth. This estimated gestational period was then compared against historical Ramadan dates. The women whose calculated 266-day-gestation coincided with a Ramadan were categorized as exposed, while the control group consisted of Muslims whose gestational period did not overlap with a Ramadan. This classification of exposure follows the standard approach in the literature on Ramadan during pregnancy\u0026nbsp;(26). It does not require information on maternal fasting behavior (see \u0026ldquo;Statistical methods\u0026rdquo; for details on the quasi-experimental approach).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further differentiated the exposed group based on the pregnancy trimester during which Ramadan started. Evidence from animal studies indicates that poor nutrition specifically during early pregnancy affects the pituitary sensitivity in the HPG axis, thereby influencing pubertal timing (16, 17). Genetic studies in humans also support this finding, showing that DNA methylation, a determinant for gene expression within the HPG axis, is particularly sensitive to environmental factors during early gestation (44). In this study, the three sub-categories were defined as follows: Trimester 1 covers pregnancy days 1 to 88, trimester 2 spans days 89 to 177, and trimester 3 encompasses days 178 onwards. Note that we placed observations whose conception was calculated to have been within 21 days after the end of a Ramadan into a separate group (\u0026ldquo;probably-not-exposed\u0026rdquo;). This avoids noise in the control group since if these individuals were born post-term, they would have experienced Ramadan in early pregnancy (26). Post-term pregnancies extending more than 21 days beyond the due date are rare (45).\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eWe compared AAM between two groups of Muslim women: those whose prenatal period overlapped with a Ramadan, and those who were not in utero during a Ramadan. The key advantage of defining exposure based on birth dates is the quasi-random occurrence of Ramadan during a pregnancy. Previous studies on Ramadan during pregnancy in Indonesia showed that the overlap of Ramadan in pregnancy was independent of maternal background characteristics\u0026nbsp;(26, 46), in contrast to the subjective maternal decision to fast during pregnancy. At the same time, to the extent that not all women whose pregnancies overlapped with a Ramadan do fast, this implies that offspring whose time in utero overlapped with a Ramadan were classified as exposed, even though their mothers did not fast in Ramadan. While this intention-to-treat set-up does not threaten the causal interpretation of our estimates, it leads to a potential bias towards zero in our analyses.\u003c/p\u003e\n\u003cp\u003eFollowing the Islamic calendar, Ramadan dates shift 11 days backwards over the Gregorian calendar every year and will be back to the same Gregorian date after a 33-year cycle. Such \u0026ldquo;shifting-over-the-seasons\u0026rdquo; characteristics mean that any unobserved season-related confounders in the prenatal phase (e.g., seasonal food availability, likelihood of infectious diseases) are unlikely to bias our results. Therefore, researchers can infer causality through separating Ramadan effects from seasonality effects by controlling for month of birth.\u0026nbsp;(26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOrdinary least squares (OLS) regressions were performed using the two main samples from IFLS and DHS. To gain insights into the relationship between prenatal Ramadan exposure and AAM, we additionally utilized Cox proportional hazard regression on the extended sample that additionally includes females aged 15-17. While OLS looks at the mean difference between the exposed and unexposed groups, Cox estimates the likelihood of experiencing menarche at any given age given the prenatal Ramadan exposure status. Thus, this model focuses on the order and timing of menarche (partial likelihood), rather than requiring the precise AAM (full likelihood approach). This is particularly meaningful regarding DHS data, since Muslims younger than 18 years old can still be included in the analysis, without leading to selection bias. This allows for a large increase in sample size and thus raises statistical power. For IFLS data, the majority of respondents were at least 18 years old at the interview, so that the Cox model has little added value. The results of Cox regressions are displayed as hazard ratios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn all analyses, Ramadan exposure was included either as a general dummy (exposed \u0026ndash; unexposed), or as trimester-specific sub-categories. Since females living in urban areas tend to experience earlier menarche and there is a global downward trend of AAM over time\u0026nbsp;(10), we adjusted the analyses for urban-rural residency, birth year and birth year squared. As previously explained, we also controlled for those women classified as \u0026ldquo;probably not exposed\u0026rdquo; and for month of birth. Additionally, survey years were included to account for unobserved confounding factors specific to the timing of data collection. The same set of covariates was used for both OLS and Cox models. Robust standard errors were applied to all analyses.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSample characteristics\u003c/h3\u003e\n\u003cp\u003eThe mean age at menarche in both samples was approximately 13.5 years, aligning with findings from previous research on Indonesia and other low- and middle-income countries\u0026nbsp;(10, 47). When including females aged 15-17 in the DHS sample, the average age at AAM artificially decreases slightly due to the fact that the mean AAM is based only on those women who already had their menarche. Descriptive statistics for each sample are summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eCharacteristics of Indonesian female Muslims from\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIndonesian DHS (2002-2017) and IFLS (1993\u0026ndash;2015)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 46.1921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMain sample \u0026ndash;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAged 18-60 at observation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 8,081)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMain sample \u0026ndash;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAged 18-25 at observation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 13,104)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eExtended sample \u0026ndash;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAged 15-25 at observation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 24,898)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)/Share\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)/Share\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)/Share\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003eAge at menarche (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e13.66 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e13.48 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e13.29 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003eLiving in an urban area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e58.9 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e65.1 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e59.4 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003eAge at the survey (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e28.25 (6.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e20.30 (1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e18.3 (2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003eIn utero during Ramadan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e82.7 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e82.6 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e82.8 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ram. started in trimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e33.1 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e31.3 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e31.8 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ram. started in trimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e24.3 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e25.5 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e26.6 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6291%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Ram. started in trimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e25.3 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0331%;\"\u003e\n \u003cp\u003e25.8 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.1457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0132%;\"\u003e\n \u003cp\u003e24.4 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IFLS, Indonesian Family Life Survey; SD, standard deviation; Ram., Ramadan; DHS,\u0026nbsp;Indonesian Demographic and Health Surveys.\u003c/p\u003e\n\u003ch3\u003eSensitivity checks\u003c/h3\u003e\n\u003cp\u003eIn order to address the risk of recall bias (48), we used different subsamples to test the stability of our results: The first subsamples included only women with shorter recall intervals, i.e. max. 15 (IFLS) and 6 (DHS) years between declared age at menarche and age at interview. The second subsamples were limited to only younger women, who thus were more likely to correctly recall their AAM at the time of the interview (49), i.e. max 30 (IFLS) and 20 (DHS) years old.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePrenatal exposure to Ramadan was not associated with menarcheal onset, neither in the IFLS nor the DHS sample, independent of the pregnancy trimester during which a Ramadan-pregnancy overlap started (Table 2). Consistent with the OLS estimates (Table 2, columns 1 and 2), the findings from the Cox analysis (Table 2, column 3) using DHS-extended data showed no difference in the likelihood of experiencing menarche between the exposed and non-exposed females at any given age. The estimated coefficients were close to zero and the hazard rates for Cox regressions close to one, and the confidence intervals did not fit with the existence of substantial effects.\u003c/p\u003e\n\u003cp\u003eThese findings were stable when reducing the sample to women with shorter recall intervals (Appendix A) and younger age at the time of interview (Appendix B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eAssociations between in-utero exposure to Ramadan and age at menarche (in years)among female Muslims\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCox\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=8,081)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=13,104)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=\u003c/strong\u003e \u003cstrong\u003e24,898)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn utero during Ramadan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.082 ; 0.132]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.086 ; 0.075]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.969 ; 1.030]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExposure periods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ram. started in trimester 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.103 ; 0.128]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.119 ; 0.061]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.966 ; 1.034]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ram. started in trimester 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.071 ; 0.174]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.134 ; 0.058]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.970 ; 1.042]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ram. started in trimester 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.101 ; 0.138]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[-0.062 ; 0.116]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[0.962 ; 1.029]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Results stem from two separate regressions per column (top panel: exposed vs. not exposed; bottom panel: classification of exposure into different pregnancy phases) that adjusted for “probably-not-exposed” women, birth year, birth year squared, rural-urban living area, month-of-birth, survey wave.\u0026nbsp;Data from the Indonesian Family Life Survey (IFLS) (1993–2015) and the Indonesian Demographic and Health Surveys (DHS) (2002 - 2017). Column (2) uses the\u0026nbsp;age 18-25, and column (3) the age 15-25 of the DHS-sample.\u003c/p\u003e\n\u003cp\u003eAbbreviations: Ram., Ramadan; HR, hazard ratio; CI, confidence interval. β = unstandardized regression coefficient.\u003c/p\u003e\n\u003cp\u003e* p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the effects of prenatal nutrition on the AAM among female Muslims using in-utero exposure to Ramadan as treatment. Using two distinct surveys from Indonesia: IFLS and DHS, we found no associations between Ramadan during pregnancy and AAM, independent of the pregnancy trimester of exposure. Our results are consistent with previous studies on the Dutch famine, in which no association between prenatal undernutrition and menarcheal timing of offspring was found\u0026nbsp;(24, 25, 34). Other studies using birthweight as a proxy for maternal nutrition reported mixed results\u0026nbsp;(11). One key strength of the present study is that our samples included data from a broader range of birth cohorts and more recent time periods, an important consideration given the documented decline in AAM over time\u0026nbsp;(9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA few limitations in our study should be noted. Regarding the self-reported AAM, while we accounted for potential recall bias in our sensitivity analyses, the unit of measurement was in years, rather than days or weeks. This may have reduced our statistical power to detect subtler effects of Ramadan exposure during pregnancy. This was augmented by the intention-to-treat framework, with the assumption that all pregnant Muslims in our sample did fast during Ramadan. This is particularly different from the controlled set-up seen in Nandi et al\u0026nbsp;(23), in which food supplementation for specific pregnant women was suggested to delay AAM among offspring. Therefore, our estimates are inherently subject to attenuation bias, potentially underestimating the true impacts of maternal nutritional shock. Furthermore, such effects might potentially be mediated by postnatal determinants of AAM, such as childhood or adolescent diets\u0026nbsp;(9-11). An experimental study on rats showed that the interaction between prenatal undernutrition and postnatal high-fat diet led to altered levels of leptin and hypothalamic Kiss1, both of which are crucial for pubertal onset\u0026nbsp;(50). Unlike in rats, in which pubertal status can be assessed relatively soon after prenatal shocks\u0026nbsp;(18-22), AAM in humans takes place long after the prenatal exposure, allowing more space for such interactions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApart from the pubertal onset, future studies on Ramadan during pregnancy might also look at other outcomes of female reproduction, which occur after puberty starts. For instance, previous studies have found evidence on a link between maternal undernutrition and social indicators of reproductive capacity, such as the timing of first pregnancy\u0026nbsp;(23, 25, 34). However, in contrast to age at menarche, age at first childbirth does not reflect the purely biological start of reproduction. This age is socially-shaped and might itself be affected by prenatal factors. For instance, earlier Ramadan studies suggested impacts on offspring performance at school and on the labor market\u0026nbsp;(46, 51-53). At the same time, education and labor market factors are known to be associated with maternal age at first birth.\u003c/p\u003e\n\u003cp\u003eA promising avenue for future research into the link between prenatal factors and biological reproductive ages of women which helps overcome challenges arising from self-reported data is the use of clinical indicators. One option is to explore alternative biological markers of menarche. In particular, the body nutritional status is communicated to the HPG axis through metabolic signals such as leptin, of which fluctuating levels might affect the timing of menarche (54). An experimental study on lamb documented that poor maternal nutrition during pregnancy affects the offspring leptin level (55). Furthermore, pubertal status can also be assessed by the changing levels of associated sex hormones (e.g. oestrogen) or through clinical scales of Tanner stage (56). In fact, a few studies on female pubertal onset have looked at clinically-measured age at thelarche, the onset of breast development, which is an early indicator of puberty (57).\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eContributing to the scarce body of empirical evidence on the associations between nutrition during pregnancy and the onset of menarche in the offspring, this study documents that Ramadan during pregnancy is not associated with AAM. Although some prior research has suggested a link between birthweight and AAM\u0026nbsp;(9, 11)\u0026nbsp;and food supplementation during pregnancy in India was found to delay AAM\u0026nbsp;(23), our findings, based on Indonesian data, are consistent with previous studies on maternal undernutrition\u0026nbsp;(24, 25, 34).\u003c/p\u003e\n\u003cp\u003eIn addition to investigating other indicators of female fertility that may partially be socially determined such as age at first childbirth, future research should consider alternative approaches to studying pubertal onset, such as using clinical markers like leptin levels or sex hormone changes, rather than relying on self-reported AAM. As a large share of pregnant Muslims fast, this topic is relevant to the large worldwide community of Muslims. At the same time, given the intermittent nature of the Ramadan fast, the relevance of such research may go beyond the health impacts on Muslims, as (intermittent) fasting is done by many non-Muslims, too.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAM\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Age at menarche\u003c/p\u003e\n\u003cp\u003eHPG \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hypothalamic-Pituitary-Gonadal axis\u003c/p\u003e\n\u003cp\u003eIFLS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Indonesian Family Life Survey\u003c/p\u003e\n\u003cp\u003eDHS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Demographic and Health Survey\u003c/p\u003e\n\u003cp\u003eOLS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ordinary Least Squares\u003c/p\u003e\n\u003cp\u003eCox \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cox Survival Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article are publicly available at www.dhsprogram.com and www.rand.org. Datasets generated and/or analyzed during the study are available from the corresponding author on request.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDisclaimer\u003c/h3\u003e\n\u003cp\u003eThe opinions in this article are those of its authors, and do not necessarily constitute the official position of the World Health Organization.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis project was funded by the German Research Foundation (DFG), grant number 260639091.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; information\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eVan My Tran\u003csup\u003e1\u003c/sup\u003e, \u0026nbsp;Reyn van Ewijk\u003csup\u003e1\u003c/sup\u003e, Fabienne Pradella\u003csup\u003e1, 2, 3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eJohannes Gutenberg-University Mainz, Faculty of Law, Management, and Economics, Chair of Statistics and Econometrics, Jakob-Welder-Weg 4, D-55099 Mainz, Rhineland-Palatinate, Germany\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eHeidelberg University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg, Germany\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eStanford University, Department of Medicine, Division of Primary Care and Population Health, Stanford, USA\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eVT: Formal analysis, data curation, writing \u0026ndash; original draft; RvE: Conceptualization, Methodology, Writing \u0026ndash; Review \u0026amp; Editing, Funding acquisition; FP: Conceptualization, Methodology, Writing \u0026ndash; Review \u0026amp; Editing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSommer M, Sutherland C, Chandra-Mouli V. Putting menarche and girls into the global population health agenda. Reproductive health. 2015;12:1-3.\u003c/li\u003e\n\u003cli\u003eDiVall SA, Radovick S. Pubertal development and menarche. Annals of the New York Academy of Sciences. 2008;1135(1):19-28.\u003c/li\u003e\n\u003cli\u003eGuldbrandsen K, H\u0026aring;konsen LB, Ernst A, Toft G, Lyngs\u0026oslash; J, Olsen J, et al. Age of menarche and time to pregnancy. Human Reproduction. 2014;29(9):2058-64.\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;affa G, Kubicka AM, Hromada M, Kramer KL. Is the timing of menarche correlated with mortality and fertility rates? PloS one. 2019;14(4):e0215462.\u003c/li\u003e\n\u003cli\u003eMishra GD, Pandeya N, Dobson AJ, Chung H-F, Anderson D, Kuh D, et al. 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JAMA pediatrics. 2020;174(4):e195881-e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Menarche, Ramadan, prenatal nutrition, fetal programming, quasi-experiment, fasting, female reproduction","lastPublishedDoi":"10.21203/rs.3.rs-5324852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5324852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge at menarche (AAM) is a key indicator of female reproductive health, predicting fecundity, birth rate, menopausal timing, and other long-term health outcomes. Nutrition is an important non-genetic factor influencing menarcheal timing, with animal models indicating a link between maternal nutrition during pregnancy and offspring pubertal onset. However, due to ethical and practical constraints, studies on humans are scarce. Our study used prenatal exposure to Ramadan to investigate the effect of maternal nutrition on offspring AAM. Due to its intermittent nature, Ramadan fast is similar to other common forms of nutritional deprivation during pregnancy, e.g. breakfast skipping. Therefore, the relevance of this research extends beyond the context of Ramadan itself. Furthermore, considering the global prevalence of Ramadan observance, understanding the link between Ramadan during pregnancy and offspring reproduction health could benefit millions of females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used data from the Indonesian Family Life Survey (1993-2014, N=8,081) and Indonesian Demographic and Health Surveys (2002-2007, N=13,241). OLS and Cox regressions were applied to compare the AAM of female Muslims who were prenatally exposed to Ramadan and those of female Muslims who were not exposed. Exposure was determined based on the overlap between each woman’s own time in utero with historical dates of Ramadan. We further subdivided this overlap into trimester-specific categories. In all analyses, we adjusted for urban-rural residence, birth month, birth year, birth year squared, and survey wave.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo associations between Ramadan during pregnancy and AAM were found, irrespective of the pregnancy trimester overlapping with Ramadan. These results were stable when we restricted the sample to women with shorter recall periods and younger women at the time of survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adds to the limited body of human research on the effects of prenatal nutritional on AAM. Given the limitations inherent in our study, future research is encouraged to further investigate this relationship. This could include examining clinical markers of pubertal onset, as well as exploring other social indicators of female reproduction. Such studies would help deepen our understanding of the dynamics between prenatal nutrition and female reproductive outcomes.\u003c/p\u003e","manuscriptTitle":"Ramadan during pregnancy and offspring age at menarche in Indonesia: a quasi-experimental study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 14:48:49","doi":"10.21203/rs.3.rs-5324852/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af9c6eda-41d2-4e0f-bd3c-4d0eb3514bf9","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T04:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-05 14:48:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5324852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5324852","identity":"rs-5324852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}