To investigate the recurrence of preterm birth (PTB) among the poorest half of the Brazilian population.We conducted a population-based retrospective study in Brazil with the live births of multiparous women extracted from the CIDACS Birth Cohort between 2001 and 2015. We used multivariate logistic regression to estimate the odds of recurrent PTB in second and third births.A total of 3,528,050 live births from 1,764,025 multiparous women were analyzed. The adjusted odds for the occurrence of a PTB given a previous PTB was 2.58 (95% CI 2.53-2.62). Lower gestational age increased the odds of a subsequent PTB . Women who had two previous PTBs were at high risk of having a third . Recurrence of a PTB was more likely when the inter-birth interval was less than 12 months.In Brazil, a middle-income country, women with a previous PTB had an increased risk of a subsequent one. This association was affected by gestational age, the number of PTBs, severity of previous PTBs, and a short interval between births. Preterm births (PTBs) account for more than 10% of all births worldwide, and subsequent complications are the leading cause of death in children under the age of five , 3. GlobA previous PTB has been reportedly associated with a subsequent one. Genetic, environmental, and behavioral risk factors shared between two pregnancies may contribute to the recurrence of preterm birth through placental dysfunction, recurrent intrauterine infections, and other obstetric complications, such as diabetes and hypertension , 6. A reIn Brazil, a previous PTB has been identified as an important risk factor for subsequent PTBs . HoweverWe conducted a population-based retrospective cohort study using the Centre for Data and Knowledge Integration for Health (CIDACS) Birth Cohort. This cohort was created by linking data from the national live birth system of Brazil (Sistema de Informação sobre Nascimentos - SINASC) and the 100 million Brazilian Cohort baseline for the period between January 1, 2001 and December 31, 2015.The CIDACS Birth Cohort is composed of 24,695,617 live births. In general, the children included in the cohort were born from younger, unmarried, less educated mothers, and are more likely to be born via vaginal delivery, compared to children in the general Brazilian population. In thisWe obtained data from the Brazilian Live Births Information System (SINASC). SINASC includes information on the mother ; pregnancy information ; and information on the newborn  . The 100We linked SINASC live birth records with the 100 million Brazilian Cohort using the following variables: mother’s name, maternal age at birth, maternal date of birth, and the mother’s municipality of residence at the time of delivery. Missing, implausible names, and duplicates were excluded. The linkage was performed using CIDACS RL-Record Linkage, a novel record-linkage tool developed to link large-scale administrative datasets at CIDACS , 17. LinThe study population included live births of multiparous women aged between 14 and 49, who started to be followed up in the CIDACS Birth Cohort as nulliparous. We excluded all multiple births, live births with congenital anomalies, those weighing <500g, or with a gestational age < 22 weeks, and with information on gestational age missing. Also excluded were those with a birth date prior to the date of the mother’s entry into the cohort, and those with no sibling information  and S1.Our main study outcome was PTBs in the second and third pregnancies, defined as a live birth at less than 37 weeks of gestation. Gestational age was defined as completed weeks. Since PTB tends to recur in a subsequent delivery, we compared offspring according to the gestational age at birth of the first pregnancy. Recurrence of preterm birth for the third birth was defined as a preterm birth after a preterm birth for the first and/or second pregnancy.The following covariates were considered in the analyses: mother’s residential area , household overcrowding , mother’s self-declared race/skin color , mother’s level of education , mother’s marital status , number of prenatal visits , inter-birth interval , type of delivery  and maternal age . Household overcrowding, an important marker of poverty and social deprivation,  (The adjusted PTB odds ratio after a previous PTB was 2.58 (95% CI 2.53-2.62), compared to a first birth at term. We also observed that most of the second preterm births occurred in the same gestational age group as the first birth . Lower g1-3.83)] .Live births to women with preterm births in the two previous pregnancies (compared to those of women with two at term births) were 4.98 (95% CI 4.70-5.27) times more likely to result in a third preterm birth. The odds ratio of a third pregnancy with a premature delivery was 2.42 (95% CI 2.34-2.50) among live births of women with a first birth at term, followed by a preterm birth, and higher than among women with a preterm first birth and second birth at term 1.73 (95% CI 1.67-1.80) .The analyses stratified by the inter-birth interval showed that the shorter the interval, the greater the risk of a recurrent preterm birth. The risk of recurrence was higher in the inter-birth interval of <12 months , followed by 12 to 24 months , and ≥24 months  . We alsoAn analysis restricted to births after 2011 had similar adjusted odds ratios .In our study, live births to women with a previous PTB were over twice as likely to have a subsequent PTB, compared to those with a birth at term on their first pregnancy. In addition, the lower the gestational age at the first birth, the higher the odds of a subsequent preterm birth. The third birth of women with two previous preterm births was five times more likely to be a PTB, when compared to those with two previous at term births. For women with a history of one previous preterm and one previous term birth, the closer the last PTB, the higher the risk of a PTB in the third pregnancy.This is the study with the largest sample size to estimate the risk of preterm birth in a second or third subsequent pregnancy using a population-based approach, and conducted in a middle-income country. Although the association between PTB in a previous and subsequent pregnancy had been observed, the mechanisms underlying this association are not well understood. It has been suggested that specific maternal factors can predispose women to premature births, since they have been associated with repeated placental complications, and are more susceptible to recurrent intrauterine infections and underlying disorders between pregnancies  . SimilarThe risk of a subsequent preterm birth increased as the inter-birth interval decreased. The biological mechanisms that may explain this finding are related to the time it takes for the uterus to return to its normal state, including resolution of the inflammatory condition associated with the previous pregnancy . A furthThe results described in this study are consistent with literature , 21, 23,It is known that effective preventive measures and interventions during pregnancy can reduce the biological, social, and behavioral risk factors associated with preterm births . ServiceOur study has both strengths and weaknesses. This is the first study to assess the recurrence of PTB in a poor population of a middle-income country. The large sample size enabled the analysis of the recurrence of preterm birth in subsequent second and third births, and to perform an analysis stratified by the interval between births. However, this study has a number of limitations. Firstly, regarding the use of secondary data. The proportion of preterm births recorded on SINASC-Brazil was found to be underestimated by 15% , and misIn conclusion, our study showed an increased risk of a subsequent PTB in women who had a PTB in their previous pregnancy. This association was affected by gestational age, the number and order of previous preterm births, and the interval between births. These findings may contribute to clinical practice, the care of women with a history of previous preterm births, and to support policies for the prevention of high-risk pregnancies and preterm births. Our study highlights the importance of expanding access and the quality of prenatal care, introducing protocols for early identification, and the clinical management of women with a previous preterm birth, or who are at risk of a preterm birth, including a previous PTB birth, and applying timely therapeutic approaches. We recommend further research to analyze the impact of effective interventions in reducing PTB rates. Furthermore, studies are required in different low- and middle-income settings to uncover more evidence in such contexts, and for subsequent investigations according to PTB subtypes.Supplementary Figures and Tables


Low and high birth weight is associated with higher levels of cardiometabolic risk factors and adiposity in children and adolescents, and increases the risk of cardiovascular diseases, obesity, and early mortality later in life. Moderate-to-vigorous physical activity (MVPA) is associated with lower cardiometabolic risk factors and may mitigate the detrimental consequences of high or low birth weight. Thus, we examined whether MVPA modified the associations between birth weight and cardiometabolic risk factors in children and adolescents.We used pooled individual data from 12 cohort- or cross-sectional studies including 9,100 children and adolescents. Birth weight was measured at birth or maternally reported retrospectively. Device-measured physical activity (PA) and cardiometabolic risk factors were measured in childhood or adolescence. We tested for associations between birth weight, MVPA, and cardiometabolic risk factors using multilevel linear regression, including study as a random factor. We tested for interaction between birth weight and MVPA by introducing the interaction term in the models (birth weight x MVPA).p = 0.005) and HDL-cholesterol (mmol/l) in adolescents (p = 0.040). Sensitivity analyses suggested that some of the associations were modified by VPA, i.e., the associations between birth weight and diastolic blood pressure (mmHg) in children (p = 0.009) and LDL- cholesterol (mmol/l) (p = 0.009) and triglycerides (mmol/l) in adolescents (p = 0.028).Most of the associations between birth weight (kg) and cardiometabolic risk factors were not modified by MVPA (min/day), except between birth weight and waist circumference (cm) in children (MVPA appears not to consistently modify the associations between low birth weight and cardiometabolic risk. In contrast, MVPA may mitigate the association between higher birth weight and higher waist circumference in children. MVPA is consistently associated with a lower cardiometabolic risk across the birth weight spectrum. Optimal prenatal growth and subsequent PA are both important in relation to cardiometabolic health in children and adolescents. The developmental origin of health and disease concept (DOHaD) suggests that fetal and infant life could be critical periods for the development of cardiovascular diseases and obesity later in life . A largeLow physical activity (PA) is another important and widely recognized risk factor for cardiovascular diseases . A recenCardiovascular diseases develop gradually and they rarely manifest in childhood or adolescence; nevertheless, even at young ages early signs of disease are apparent and birth weight is inversely associated with cardiometabolic risk factors –9 and poWe hypothesized that higher moderate-to-vigorous PA (MVPA) may mitigate the associations between birth weight and cardiometabolic risk factors in children and adolescents. Examining children and adolescents may be of particular interest since interventions early in life may provide an opportunity for early intervention well before cardiovascular diseases manifest. Furthermore, device-measured PA, more specifically accelerometers, are considered the method of choice when examining associations between intensities of PA and health outcomes in children and adolescents , 18. TheWe used pooled individual data from nine studies included in the International Children’s Accelerometry Database (ICAD) , a subcoResults from three of the studies included in ICAD on the associations between birth weight and insulin and waist circumference, and effect modification of MVPA, have previously been published . In the http://www.mrc-epid.cam.ac.uk/research/studies/icad/data-harmonisation/). For the present analyses, we used data from nine ICAD-studies (ICAD 2.0). Three studies are prospective birth-cohort studies [ICAD  consists studies –27 and s studies –30. In l studies , 27, 29. studies –27, thro studies  or throu studies –30.MoBa is an ongoing prospective population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health . ParticiThe PANCS 1 study included a nationally representative sample of Norwegian 9- and 15- year-old , 22. In The ASK study is a school-based cluster randomized controlled trial carried out in 2014/15, situated in the western part of Norway . Sixty sEach collaborator in the ICAD consulted their research board to make sure sufficient ethical approval had been obtained. Written informed consent was obtained from each child’s parent prior to all testing in ICAD, the subcohort of MoBa, PANCS, and ASK, and the study protocols were approved by the Regional Committee for Medical Research Ethics.Birth weight was either measured at birth , 24–27 oEleven studies provided data on systolic- and diastolic blood pressure Table  20–23, –30. BlooClustered risk scores with different combinations of cardiometabolic risk factors are comparable , and we PA was measured at the same time-point as the cardiometabolic risk factors using uniaxial Actigraph- (model GT1M and 7164) , 21, 22 Potential confounders included in the models were parental education and sex. We further adjusted for waist circumference (model 2) and height (for systolic- and diastolic blood), measured at follow-up, to examine the direct associations. Ages at follow-up were included in the model to improve the precision of the outcomes due to the known changes in cardiometabolic risk factors with increasing age in these age-groups .Standardized methods were used to measure height and weight across all studies –30. For To harmonize the parent’s education level, a dichotomous variable was created dividing the maternal and paternal education level into (1): up to and completion of compulsory education and (2): any post-compulsory education. We further combined the parents’ education variables into one variable reflecting the highest education level by either the mother or the father.t test, Mann–Whitney two-sample test, and chi-squared statistics. We used multilevel linear regression, including study as a random factor (12 studies), to examine the associations between birth weight, MVPA, and the cardiometabolic outcomes. We adjusted all models for the highest parental education, age, and sex. When systolic- and diastolic blood pressure were modeled as the outcome we adjusted for childhood height and excluded age from the model due to the risk of collinearity. In model 2, we further adjusted analyses for waist circumference. Furthermore, to examine whether MVPA modified the associations between birth weight and the cardiometabolic outcomes we included the interaction term (birth weight x MVPA) in the models. A significant interaction indicates an additive interaction given the linearity of the model. We tested all models for the assumptions of linear regression . For the models including HOMA-IR and triglycerides, a slightly skewed distribution of the residuals was shown. However, due to the large sample size and sensitivity analyses with and without log-transformed variables showing similar results, we kept the variables not transformed in the models to ease the interpretation of the effect estimates. Furthermore, we used robust standard errors estimates due to signs of heteroscedasticity in some of the models. A formal interaction test showed no evidence of an interaction with sex on any of the associations. We conducted sensitivity analyses using VPA as an effect modifier, and sensitivity analyses where we excluded all participants with birth weight <1.5 kg, i.e., participants most likely to be born prematurely (n = 66).Only participants for whom data for birth weight, PA  and at least one cardiometabolic risk factor were available were included in the analyses (=3534 participants removed). The participant’s age distribution revealed two clusters around age 9–10 and 15–16 years. To ensure homogenous groups we therefore performed a median split dividing the participants into children (≤11.6 years old) and adolescents (>11.6 years old) for all analyses. We tested for differences between the age-groups using independent sample p < 0.10) we graphically illustrated the predicted values of the outcome variable, based on the final adjusted models with the interaction term, across values of birth weight and the 25th, 50th, and 75th percentile of MVPA/VPA. Regardless of an interaction, we also graphically illustrated the predicted values of the clustered cardiometabolic risk score in a similar manner.In case of a significant interaction (n = 116) and 19 % (n = 875) of the children and adolescents, respectively, had missing data on one or more of the included covariates. We replaced missing values using multiple imputation (MI) with Fully Conditional Specification. We imputed 20 datasets. Further details on participants with missing values, the MI-method, and results from complete case analyses are provided in Supplementary Information .The complete case analyses (Supplement File We observed that MVPA does not modify the association between a lower birth weight and an adverse cardiometabolic clustered risk in children and adolescents, nor consistently modify the associations with single risk factors. MVPA may slightly attenuate the association between higher birth weight and higher waist circumference in children. The observed effect modification of MVPA or VPA on diastolic blood pressure, LDL- cholesterol, HDL- cholesterol, and triglycerides are likely clinically insignificant.Few previous studies have examined a possible effect modification of PA on the association between low birth weight and measures of insulin resistance or risk of type 2 diabetes, with contradictory results. Findings by Ridgway et al. , are simWe observed that MVPA may modify the association between birth weight and HDL-cholesterol in adolescents, whereas the highest intensity is necessary to modify the association with LDL-cholesterol and triglycerides. It appears that a lower birth weight is associated with lower LDL-cholesterol, but also lower HDL-cholesterol in the least active, whereas it is in the opposite direction for the more active adolescents. This is in contrast to one study suggesting that an 8-week exercise program may eliminate the difference in LDL-cholesterol between low birth weight and high birth weight in young adulthood, whereas the difference in HDL-cholesterol between the two groups became significantly different first after the exercise period . The obsThe observed stronger association between birth weight and diastolic blood pressure in the most active may indicate that children with low birth weight do not respond to VPA to the same extent as children with higher birth weight. This interaction was however not observed in adolescents, nor for systolic blood pressure.The results from a few previous studies are similar to ours and suggest that self-reported PA attenuates the association between a high birth weight and abdominal adiposity  and riskA clustered cardiometabolic risk score is likely more important for future health than single risk factors . In contWe consider device-measured PA in a large study sample as an important strength of this study, but this method is also prone to misclassification. Children’s PA pattern is sporadic which makes precise measurements difficult ; thus, tThe main strengths of this study are the large and diverse sample of children and adolescents with available data on several cardiometabolic risk factors and device-measured PA analyzed in a harmonized manner.We did not observe strong evidence for a modifying effect of MVPA on the association between birth weight and cardiometabolic risk factors in children and adolescents, although it may to some degree attenuate the association between high birth weight and waist circumference in children. Higher levels of MVPA is consistently associated with a more favorable cardiometabolic risk profile across the birth weight spectrum.Supplemental material File 1


Pilar cyst mainly occurs on the scalp, but pilar cyst on the dorsum of hand has not been reported. Herein, we provide information to improve the clinical cognition of pilar cyst location.A 76-year-old man presented with a round nodule on the opisthenar of his right hand for two months without any subjective symptoms.Histological features of the lesion biopsy indicated the diagnosis of pilar cyst.Surgical resection was made under local anesthesia.Complete recovery was achieved after surgery.Pilar cyst rarely occurs on the dorsum of hand and its diagnosis depends on histopathological examinations. Surgical resection is the only way to treat it. The cysts are characterized by smooth, round nodules with solid texture and good mobility. It cannot be distinguished from epidermal cysts clinically, except that 90% of pilar cysts are found on the scalp where hair follicles are abundant. Other less common locations include face, trunk and extremities. Lesions rarely arise in palms, genitalia, axillary and groin. As far as we know, pilar cyst on the hand has rarely been reported before. The present study reports one such case on dorsum of hand in a male patient.Pilar cysts are identified by Pinkus as the keratinization of the outer root sheath of hairs,2A 76-year-old man presented with a flesh-colored, dome-shaped nodule on the opisthenar of his right hand Fig. , which h2.1There were no obvious abnormalities in blood routine, coagulation and liver and kidney function. A histopathological examination was conducted and the pathology showed that the cyst was in the reticular dermis Fig. , with su2.2Clinically, it was considered as dermatofibroma at first. Based on histopathological examination, a diagnosis of pilar cyst was made.2.3Complete excision under local anesthesia was done. After the treatment, the patient achieved complete recovery with no relapse during the 6 months of clinical follow-up.3 Compared with epidermal cysts, the lesions of pilar cyst do not have an overlying punctum and tend to be more mobile and firmed. Clinically, pilar cyst is indistinguishable from epidermal cyst, histopathological examination is indispensable to make a definite diagnosis. Pilar cysts are intradermal cysts with distinctive histological features. The cells of the cyst wall are composed of epithelial cells without obvious intercellular bridges. The inner cells suddenly transform into solid eosinophilic-staining keratin with no intervening granular layer. Nuclei of cells that fall into the cystic cavity generally disappear, but some cells still have nuclei. The cyst contents are homogeneous. Calcification exists in about 25% of cases. If the capsule wall is ruptured, a granulomatous reaction may occur. The cyst can therefore partially or completely disintegrate.Pilar cyst is derived from external root sheath of the follicular isthmus, which is also referred to as isthmus-catagen cyst. Pilar cyst accounts for 20% of epithelial cysts and the others are epidermal.,6 Shimomura et al et al propose a monoallelic mutation in phospholipase C delta 1 (PLCD1) that result in formation of multiple pilar cysts. Although pilar cyst is derived from follicular, several cases have indicated that pilar cyst can arise in non-hair bearing areas. The onset of pilar cyst was thought to be induced by inflammation and trauma.–10 In addition, infection of the human papilloma virus may be related to the pathogenesis of pilar cyst. In very rare cases, pilar cyst can develop into malignant proliferative lump which clinically present as progressively enlarged lobulated masses, similar to squamous cell carcinoma. It is very important to distinguish pilar cysts from proliferating pilar cysts, since the later can undergo malignant transformation. Complete excision of the cyst is curative, but it is not recommended to remove surgically when the cyst is inflamed. Proliferating pilar cysts might need radiation therapy and/ or chemotherapy after surgical excision.Pilar cyst often occurs in middle age with an obvious female preference and inheritance pattern of multiple cysts manifests as autosomal dominance.–16 The pilar cyst of our patient arose in the dorsum of right hand, a rare location and away from nail bed. No hair follicle was found. In other words, it can neither originate from hair follicles nor from nail matrix. Our case urges rethinking of the origin of pilar cyst. Meanwhile, it reminds dermatologist that a similar lesion on the dorsum of hand can not rule out the possibility of pilar cyst.The specificity of our case lies in its location. Compared to the incidence of scalp, pilar cyst on hand is extremely uncommon. To our knowledge, there are four cases of pilar cyst reported on hand previously, of which three are in finger tips and the other one is on the dorsum of the thumb. They were all proximal to the nail bed, which aroused the speculation that pilar cyst may be derived from nail matrix.ML and YF diagnosed and treated the patient. HH, YZ and SX conducted histopathological examination. ML and YF wrote the manuscript. All authors have approved the final article be true.


Background: There is limited data from Africa on the effect of pre- and post-natal growth and infant feeding on later body composition. This study's aim was to investigate the effect of birth weight, exclusive breastfeeding and infant growth on adolescent body composition, using data from a Ugandan birth cohort.Methods: Data was collected prenatally from pregnant women and prospectively from their resulting live offspring. Data on body composition (fat mass index [FMI] and fat free mass index [FFMI]) was collected from 10- and 11-year olds. Linear regression was used to assess the effect of birth weight, exclusive breastfeeding and infant growth on FMI and FFMI, adjusting for confounders.Results: 177 adolescents with a median age of 10.1 years were included in analysis, with mean FMI 2.9 kg/m2 (standard deviation (SD) 1.2), mean FFMI 12.8 kg/m2 (SD 1.4) and mean birth weight 3.2 kg (SD 0.5). 90 (50.9%) were male and 110 (63.2%) were exclusively breastfeeding at six weeks of age. Birth weight was associated with FMI in adolescence  , P=0.02), while exclusive breastfeeding , P=0.12), growth 0-6 months , P=0.48) and growth 6-12 months , P=0.11) were not associated with FMI among adolescents. Birth weight , P=0.01) was associated with FFMI in adolescence. Exclusive breastfeeding , P=0.62), growth 0-6 months , P= 0.10), and growth 6-12 months , P=0.97) were not associated with FFMI.Conclusions: Birth weight predicted body composition parameters in Ugandan early adolescents, however, exclusive breastfeeding at six weeks of age and growth in infancy did not. BMI - Body mass indexCI - Confidence intervalEMaBS- Entebbe Mother and Baby StudyFM - Fat massFMI - Fat mass indexFFM - Fat free massFFMI - Fat free mass indexNCDs - Non-communicable diseasesSD - Standard deviation2 and other cardiovascular diseases (CVDs)3 have escalated on the African continent over recent decades, disproportionately affecting populations at younger ages than in more affluent countries4. The rising burden of NCDs in low and middle-income countries is of public health and economic significance5, given the fragile health care systems and associated cost implications. In Africa, deaths due to NCDs are rising faster than anywhere else in the world4. An understanding of the pathways for development of NCDs in this setting is essential for informing interventions for prevention of NCDs.Previously neglected due to high burdens of infectious disease morbidity, attention paid to Non-communicable diseases (NCDs) in Africa has recently increased. Studies suggest that high blood pressure (BP)6 and early-life factors, such as pre- and post-natal growth and infant feeding, have been reported to program and alter body composition7. Sub-optimal nutrition in the fetal or infant periods triggers cellular and epigenetic changes that may affect later body composition8. Rapid growth especially in infancy may result in metabolic changes which can manifest as increased adiposity and result in later NCDs10. Thus, body composition changes might be one of the mechanisms through which early-life exposures may influence susceptibility to NCDs in adulthood.Body composition, specifically increased adiposity, is associated with risk of NCDs later in life11. Rapid weight gain and lack of exclusive breastfeeding in infancy have been associated with increased adiposity in adulthood12. Exclusive breastfeeding has also been reported to be associated with a reduction in fat mass . However, results are inconsistent, with some studies finding no evidence for the association between birth weight (low or high) and FM16 in late adolescence or adulthood, or for an impact of these early-life factors on risk of NCDs later in life18. Results as reported by some studies19 suggest mixed evidence for an association between birth weight and fat free mass  in late adolescence or adulthood.Evidence, predominantly from high-income countries, has shown that compared to normal birth weight infants, both low and high birth weight infants may bear an increased risk of adulthood obesity21 and Cameroon22 found that birth weight and linear growth were positively associated with both FM and FFM. However, the impact of early-life factors on later body composition remains understudied among populations from Africa.Few studies from Africa have investigated the relationship between birth weight, exclusive breastfeeding and growth in infancy, and body composition later in life, with tools for measuring body composition not widely available. Studies from South Africa23 Briefly, between 2003 and 2005, pregnant women attending antenatal care at Entebbe Hospital and residing in Entebbe Municipality or Katabi sub-county were enrolled into a double-blind randomised placebo-controlled trial designed to evaluate the effect of deworming treatment in pregnancy and childhood on response to childhood vaccines and infections. The trial was completed in 2011 when all children had turned five years of age. After the trial completion, the offspring continued under follow up, being seen at annual routine visits and when sick. Between 20th May 2014 and 16th June 2016, 10- and 11-year olds in the EMaBS attending the study clinic for their annual visit were enrolled into the EMaBS blood pressure study (BPS). Adolescents participated once in the BPS, on their first 10- or 11- year study visit occurring during the study period. Enrolment into the BPS was postponed for adolescents presenting with malaria  or other illness until they were free of any illness.The current study used prospectively collected data from the Entebbe Mother and Baby Study (EMaBS) birth cohort, conducted in Wakiso district, on the northern shores of Lake Victoria in Uganda. The EMaBS started life as a randomised controlled trial of anthelminthic treatment interventions. A detailed description of the trial design has been given elsewhere24. From 21st January 2015 to 23rd December 2015, additional data on body composition (FM and FFM) was collected from EMaBS participants enrolled into the BPS; outside this period the body composition analyser machine was not available. Briefly, adolescents stood barefoot on the posterior electrode base while holding strongly the two anterior electrodes handles of the segmental body composition analyser machine  as described elsewhere25. To avoid ambiguities from using body composition percentages27, height normalized indices (FMI in kg/m2 and FFMI in kg/m2) were computed and used for analysis. FMI is considered as a measure of adiposity and FFMI as a measure of lean muscle mass.The primary aim of the EMaBS BPS was to investigate whether birth weight and pre- and peri-natal exposures are important in programming BP in children in Uganda; results pertaining to this primary aim are described elsewhere28. Weight was measured at six months and then annually starting at one year of age using weighing scales . Height was measured at six months and then annually to the nearest 0.1cm using stadiometers . Information on feeding practices at six weeks of age was self-reported from the child’s mother or guardian at a six week visit. Data on adolescents’ dietary intake were collected at the time of body composition measurement, by questionnaire.For this analysis, we aimed to investigate if birth weight, exclusive breastfeeding and growth in infancy were associated with body composition (fat mass index [FMI] and fat free mass indices [FFMI]) in early adolescence. Birth weight was measured and recorded to the nearest 0.1 kg for infants delivered in Entebbe hospital using weight scales , and captured as recorded on child health cards for infants delivered elsewhere. Further details have been reported previously29 were used to compute weight for age standardised Z-scores at birth, and at six and 12 months of age. For each participant, growth for the periods 0–6 months and 6–12 months was calculated as the change in Z-score during that period. Growth in each time period  was categorised as either increased or normal growth using the cut-off of a 0.67 increase in z-score30.Study exposures were birth weight, breastfeeding status at six weeks, early infant growth (0–6 months) and late infant growth (6–12 months), while the study outcomes were FMI and FFMI at 10 or 11 years of age. Birth weight was considered for analysis as both a continuous variable and as a categorical variable , with analyses run separately for each approach. The 2006 World Health Organisation growth standardsCharacteristics of study participants were compared with those of cohort members who did not participate using t-tests and chi-squared tests. Descriptive statistics were calculated as frequencies, means and standard deviations. Spearman’s correlation was used to assess correlations of body composition indices with each other and with birth weight. Linear regression models were fitted separately for FMI and FFMI. Univariable models were first fitted, followed by multivariable models adjusting for confounders. Potential confounders considered were maternal age, body mass index (BMI), education, area of residence and HIV status; household socio-economic index  at enrolment; and offspring’s place of delivery, sex, age at body composition analysis, family history of hypertension, type of school attended, days/week animal-proteins were eaten, days/week fruits were eaten, days/week vegetables were eaten, days/week starchy foods were eaten, days/week sugared drinks were taken. Factors associated with the outcome, or with the exposure of interest were added to the model concurrently and likelihood ratio tests were used to assess adjusted associations between each variable and the outcome.32.Current BMI, which can be partitioned into FMI plus FFMI, was considered to be on the causal pathway between birth weight and FMI or FFMI, thus was not considered as a potential confounder for inclusion in regression models. Assumptions underlying the linear regression model analysis  were investigated using a combination of scatter plots, plots of residuals against fitted values, and normal probability plots. The possibility of multicollinearity due to inclusion of correlated predictor variables was assessed by investigating the change in standard error through calculating variance inflation factorsa priori confounders, household socio-economic status, age and sex were included in the final model regardless of whether associated with the exposure or outcome or not. The test for trend was used to investigate the shape of the relationship between birth weight and the outcomes. Likelihood ratio test p-values were calculated.STATA version 14.2  was used for data analysis. Interaction terms were fitted to assess whether birth weight might modify the effect of breastfeeding or increased growth on the outcomes (FMI or FFMI).For each of the main exposures, factors associated with that exposure or with the outcome at a 5% level of significance were included in the final model for that exposure. ThreeThe study was approved by the Research and Ethics Committee of the Uganda Virus Research Institute (GC/127/13/11/35), the Uganda National Council for Science and Technology (MV625) and the London School of Hygiene & Tropical Medicine (Ref:11253). Respectively, written informed consent and assent were obtained from parent/guardian and adolescents for study participation.24 at 10 or 11 years of age, and 177 (7.6%) had data on body composition taken and were included in the analysis. Of the 177 participants included, 90 (50.9%) were male; 175 (98.9%) were singleton births; and 161 (91.0%) were not exposed to maternal HIV in pregnancy (33). Regarding the key exposures, the mean birth weight was 3.2 kg (standard deviation (SD) 0.5); 13 (9.4%) had low birth weight, 92 (66.2%) normal birth weight and 34 (24.5%) high birth weight with 38 participants of unknown birth weight. In total, 110 (63.2%) were exclusively breastfed at six weeks of age; with three participants missing data on this exposure. 108 (61%) and 123 (69%) participants had information on growth between 0 and 6 months, and between 6 and 12 months, respectively ; 35 (32.4%) had increased growth in the first 6 months of life and 15 (12.2%) had increased growth between 6 and 12 months of age.Of the 2345 live born EMaBS offspring, 1119 (47.7%) enrolled into the BPSAdolescents who had body composition measured were similar to the original EMaBS cohort members who did not participate for most characteristics including maternal  characteristics at enrollment, household socio-economic status at enrollment and childhood , trial intervention [albendazole]) characteristics, except participants were more likely to be born to separated/divorced/widowed mothers  and were less likely to be born to mothers with hookworm infections in pregnancy .2 (SD 1.9), mean FMI 2.9 kg/m2 (SD 1.2) and mean FFMI 12.8 kg/m2 (SD 1.4). Among males, the mean FMI was 2.7 kg/m2 (SD 1.3) and mean FFMI was 13.3 kg/m2 (SD 1.1), while in females the mean FMI was 3.1 kg/m2 (SD 0.9) and mean FFMI was 12.4 kg/m2 (SD 1.5). Birth weight was positively correlated with both FMI  and FFMI . There was strong correlation between FMI and FFMI with r=0.517, p-value <0.001.At participation, offspring had a median age of 10.1 years (IQR: 10.0 to 10.7), mean BMI 15.8 kg/m2 per unit kilogram increase in birth weight, 95% confidence interval (CI):0.33-1.13. When birth weight was treated as a categorical variable, it showed a dose-response relationship with FMI (P-trend=0.007). Further investigation of this dose-response relationship showed no departure from linearity (P=0.92). Exclusive breastfeeding at six weeks , increased growth between birth and 6 months of age  and increased growth between 6 and 12 months  were not associated with FMI in unadjusted analysis. In multivariable analysis birth weight  remained associated with FMI; exclusive breastfeeding at six weeks , increased growth between birth and 6 months of age  and increased growth between 6 and 12 months  were not associated with FMI.The relationships between the main exposures, and FMI and FFMI are shown inBirth weight was positively associated with FFMI in unadjusted analysis , while exclusive breastfeeding at six weeks , increased growth between birth and 6 months of age  and increased growth between 6 and 12 months  were not associated with FFMI. When birth weight was analysed as a categorical variable, findings were consistent with a linear relationship with FFMI . In multivariable analysis, birth weight  remained associated with FFMI; there remained no evidence of association for the other exposures. There was no evidence that the effect of breastfeeding or growth rate on FMI or FFMI differed by sex or birth weight: for example, for FMI, p-values were 0.97, 0.47 and 0.60 for interaction between birth weight and breastfeeding, growth 0–6 months and growth 6–12 months, respectively. The corresponding interaction p-values for FFMI were 0.12, 0.13 and 0.16, respectively. For all analyses, assessment of the assumptions underlying the linear regression analysis indicated that these were met, and there was no suggestion of multicollinearity.We hypothesised that birth weight, exclusive breastfeeding and rate of growth in infancy were each associated with body composition indices among Ugandan adolescents aged 10–11 years. This study showed that birth weight was associated with both adolescent FMI and adolescent FFMI but there was no association between exclusive breastfeeding in the first six weeks or growth rate in infancy and FMI or FFMI among early adolescents.22, and from a birth cohort study among South Africans, with body composition assessed at ages 10 and 22 years34.Our findings of a positive association between birth weight and both FMI and FFMI are consistent with results from a cross-sectional study among 557 Cameroonian children aged 5–12 years35 that showed that on average, each additional month of exclusive breastfeeding reduced adiposity by 4%. The lack of association between exclusive breastfeeding in the first six weeks with adiposity or lean muscle mass development in this study supports results among 18-year-old Brazilians enrolled in a population-based birth cohort36. In our study, only 63% of mothers reported exclusive breastfeeding at six weeks but nearly all mothers [172 (97.2%)] were giving some breast milk and only 2 (1.1%) had weaned, thus a differential effect of breast milk and/or of different feeding patterns may be hard to detect in this population. A limitation of this study is that the relationship between exclusive breastfeeding in the first six months of life, as recommended by WHO, and adolescents’ body composition was not examined because data on feeding status at six months was not collected.We did not find evidence for an effect of exclusive breastfeeding in the first six weeks on FMI or FFMI. This was contrary to results reported in a meta-analysis37 which reported positive associations between growth rate and body composition. Our study was likely underpowered to detect true associations: of the 177 adolescents for whom body composition data were available, data on growth were only available for around two thirds, thus reducing the sample size for this analysis. Among participants in the larger EMaBS BPS , growth in the first two years of life was positively associated with BP in early adolescence24.There was no association between increased rate of growth in the first six months of life or from 6 to 12 months and FMI or FFMI. These findings do not support earlier studies predominantly from European counties reviewed in38. We therefore used direct measurement of body composition and the height normalised indices for FM and FFM which are reported to be more precise measures of adiposity and lean muscle, respectively26. The strong correlation between FMI and FFMI suggests that, for the Uganda adolescents participating in our study, FMI and FFMI both increase proportionally with an increase in BMI. This is reflected by the fact that birth weight was positively associated with both increased adiposity and increased lean muscle mass in early adolescence.Many studies have used body mass index (BMI) as a surrogate outcome measure for body adiposity. However, evidence to date shows that BMI creates ambiguities since it cannot specifically differentiate between FM and FFM39 and, importantly in this setting, provided a relatively inexpensive field method of body composition analysis. However, the method relies on prediction equations that are population specific to estimate the parameters of body composition. At the time of the study, there were no validated prediction equations for Uganda’s population.We used a segmental bio-electrical impendence body composition analyser to measure body composition among the study adolescents. Bio-electrical impendence has been reported to have good correlation with other methods such as dual energy absorptiometryin utero from effects due to pre-term birth because accurate data on gestational age was not available in this population.To our knowledge, this is one of the few studies from East Africa to investigate the impact of early-life factors on the body composition parameters FMI and FFMI. Strengths of the study are its cohort design and the robust methods used for measuring body composition parameters. Data on the exposures of interest and potential confounders were collected prospectively, minimizing recall and reporter bias. Exposures and confounders were determined before the BP study was conceptualized and designed. However, the possibility of residual confounding due to unmeasured variables cannot be ruled out. Some exposure information such as exclusive breastfeeding at six weeks was not available for all of the adolescents. In this study we were unable to differentiate the effects of low birth weight due to growth restrictionWhereas we have investigated the effect of two postnatal factors (rate of growth and exclusive breastfeeding) on later disease risk, further studies should investigate the effect of other postnatal factors such as current diet, age at menarche, sleep patterns/duration and the effect of an obesogenic environment on body composition. In conclusion, exclusive breastfeeding, and infant growth were not associated with body composition among early adolescents from a tropical setting. However, birth weight is a good predictor of both adiposity and lean muscle mass later in life in this setting.https://doi.org/10.6084/m9.figshare.7775669.v133Figshare: BP_Body_Comp.xlsx.This project contains the following underlying data:- BP_Body_Comp.xlsx (Body composition data from the cohort with data dictionary)https://doi.org/10.6084/m9.figshare.11363048.v1Figshare: Supplementary tables showing primary associations (crude associations between exposure variables and outcomes) Approved. No further comments.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This paper about the Entebbe Mother and Baby Study is well written explaining the important research question about the relationship between birthweight and breastfeeding of the babies with their body composition 10 years later. Fat Mass and Fat Free Mass were used. This longitudinal study is very seldom and therefore important to publish. The numbers of subjects is high and the statistical methods to reveal the relationship are up to date. Also the boxplots that are used explain to the scientific reader the results.et al. (20021) published this reliable method. With this the whole analysis can be repeated to see the effects on FM and FMM. My main question is that the adolescents used in this study differ slightly in calendar age, but between 10 and 12 the biological age can differ largely. So, the authors must take this into consideration. First, maybe they can include data about biological age. Second, there is an anthropometric method to estimate biological age: MirwaldI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thanks so much Han C.G. Kemper for your insightful comments and feedback about the manuscript. The comments have been helpful.  We have outlined our responses in line with each comment made. Comment 1: My main question is that the adolescents used in this study differ slightly in calendar age, but between 10 and 12 the biological age can differ largely. So, the authors must take this into consideration. First, maybe they can include data about biological age. Second, there is an anthropometric method to estimate biological age: Mirwald et al. (2001) published this reliable method. With this the whole analysis can be repeated to see the effects on FM and FMM.Response: Thanks for this comment. The cohort in this analysis was aged between 10 and 12 years, however the age distribution was strongly skewed towards the younger end of this range, with 60% of participants aged between 10 years and 10 years 3 months. Only 35 (20%) of participants were aged 11 years or older. The cohort age structure shows that the study sample were in their early adolescence stage where the factors of biological age might not be as pronounced compared to say 12-year olds and beyond. However, we appreciate the effect that biological age can have on study findings irrespective of calendar age. As reported by Mirwald et al (2001), to calculate maturity/ biological age, we would need data on sitting height and leg length, however, these were not collected. The current study is a secondary data analysis of the already collected data. In future work we would plan to collect data that would allow us to adjust for maturity stage as well as or instead of calendar age.   This study aimed to generate evidence about the association between birthweight, exclusive breastfeeding and infant growth with body composition at ages 10-11 years in a subsample of 177 Ugandan children that were part of a larger cohort study, the EMaBS study. The study authors collected body composition data using bioelectrical impedance and used the weight data collected at birth, at 6 and 12 months of age to assess prenatal growth (birthweight) and infant growth (a change of weight-for-age z-score between birth and 6 months and between 6 to 12 months greater than 0.67 z-scores). The study has the potential to contribute novel findings to the body of literature assessing the early in life contributions to NCD susceptibility later in life from the perspective of the Ugandan context under which the data was collected. I have made a list of comments below that could help the authors improve their work.Introduction:Second paragraph: I am unsure if it is correct to state that body composition changes represents a mechanism through which early-life exposure may influence NCDs later in life, given that our current understanding is the early life exposures affects both body composition and NCD susceptibility.Third paragraph: Fat mass is incorrectly stated as a proxy of adiposity. Measurement of fat mass is a direct measure of adiposity.  Methods:The methods section would benefit from editing and restructuring. For instance, it might be clearer to describe the three phases that this cohort has undergone in one paragraph, rather than in separate paragraphs. Describe all the measurements used for this analysis in full rather than to direct the reader to a published manuscript .Data handling  and the statistical analysis undertook are clearer when explained separately.The description of the analysis does not seem to reflect the information presented in the results section. The methods section mentions the use of correlation analysis, linear regression analysis, likelihood ration tests, etc., but these are not clearly presented in the results.Please, provide details about the standard error method mentioned to have been used to assess multicollinearity.It is unclear at what age the dietary assessment data used for the analysis was collected.et al., 20061 and Lucaset al., 19992).The authors do not present a rationale about their choice of methods for assessing infant growth namely, the arithmetic change in z-scores, given the wealth of literature discussing how this selection affects the findings and potentially generate incorrect results (for examples of this discussion see Tu  Results:Most of the information provided in the form of tables is replicated in the written narrative. Consider removing it from the narrative to make the manuscript concise and easier to read.3).The value of Figure 1 is unclear, as it is not mentioned within the narrative. Perhaps it would be more informative to present this data in a Hattori chart manner  than of fat mass (a poor electricity conductor). Furthermore, Tanita systems rely for the estimation of lean and fat mass on equations derived on populations different from that of this study. How would this have affected their findings? Would the method used to assess breastfeeding be sensitive enough to differentiate different feeding patterns, what about children that were predominantly breastfed?The authors mention segmental body composition, but the relevance of this statement is unclear.There is little discussion about the context under which the data was collected, e.g. Ugandan context where HIV is prevalent, and whether this could have or not affected their results.Given the sexual dimorphism already observed with body composition data at the ages of 10-11 years, it would be useful for the authors to discuss why they chose to not undertake separate analysis by sex.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thanks so much Carlos S. Grijalva-Eternod for your insightful comments and feedback about the manuscript. The comments have been helpful.  We have outlined our responses in line with each comment made. Comment 1: I am unsure if it is correct to state that body composition changes represents a mechanism through which early-life exposure may influence NCDs later in life, given that our current understanding is the early life exposures affects both body composition and NCD susceptibility.Response: Thank you for the comment. We have made a change to the sentence for clarity reasons. It now reads as “thus, body composition changes might be one of the mechanisms through which early-life exposures may influence susceptibility to NCDs in adulthood”. Comment 2: Fat mass is incorrectly stated as a proxy of adiposity. Measurement of fat mass is a direct measure of adiposity.Response: Thank you for this comment. We have edited the sentence which now reads as “exclusive breastfeeding has also been reported to be associated with a reduction in fat mass . Comment 3: The methods section would benefit from editing and restructuring. For instance, it might be clearer to describe the three phases that this cohort has undergone in one paragraph, rather than in separate paragraphs. Describe all the measurements used for this analysis in full rather than to direct the reader to a published manuscript .Response: Thanks for this comment, our use of paragraphs was meant to point the reader to the cohort phase in which each measurement was done, since the cohort had various phases. We have re-written this section to improve the structure and clarity. We have also included details of the methods used to assess key measures: regarding feeding it now reads “Information on feeding practices at six weeks of age was self-reported from the child’s mother or guardians at a six-week visit.” For household wealth this now reads “household socio-economic index ”.  Comment 4: The description of the analysis does not seem to reflect the information presented in the results section. The methods section mentions the use of correlation analysis, linear regression analysis, likelihood ration tests, etc., but these are not clearly presented in the results.Response: Thank you for this comment. The correlation results were reported as “Birth weight was positively correlated with both FMI  and FFMI . There was strong correlation between FMI and FFMI with r=0.517, p-value <0.001”. In addition, table 2 contains the likelihood ratio test p-values that represented the test for trend. Results from regression analysis are presented in table 2 and the supplementary tables which have been added at the end of the manuscript. We have made an addition to the statistical methods to reflect that both univariable (to assess crude associations) and multivariable (to adjust for confounders) linear regression models were run. Comment 5: Please, provide details about the standard error method mentioned to have been used to assess multicollinearityResponse: Standard errors will be inflated in the presence of multicollinearity as described in . One method of assessing this formally is to calculate variance inflation factors which indicate by how much the standard error increases in the presence of multicollinearity (with a variance inflation factor of 1 indicating no multicollinearity issues). We have expanded the text and included references.  Comment 6: It is unclear at what age the dietary assessment data used for the analysis was collected.Response: Thanks a lot for this comment. We have amended to manuscript that breastfeeding data was collected at 6 weeks of age and adolescents’ dietary assessment data were collected at the time of the blood pressure study. Comment 7: The authors do not present a rationale about their choice of methods for assessing infant growth namely, the arithmetic change in z-scores, given the wealth of literature discussing how this selection affects the findings and potentially generate incorrect results .Response: Thank you for your comment. We appreciate the risk of misinterpretation of results based on the fetal origin of disease especially if adjustment in the models is only made for current size. For comparison purposes, we used a 0.67 cut off reported by Freeman (1995) generated from a healthy and standard UK population. This represented the rate of rapid growth. Comment 8: The value of Figure 1 is unclear, as it is not mentioned within the narrative. Perhaps it would be more informative to present this data in a Hattori chart mannerResponse: Thanks for this comment: We appreciate the reviewer’s point and have removed Figure 1.  Comment 9: Table 2 is confusing. It is unclear what models were tested, whether birthweight was included twice in a model, as a continuous and/or categorical variable, or whether they are presenting separate models. This makes it difficult to assess the authors’ findings.Response: Thanks for this comment. We have added further clarification on this in both the methods and results sections, and have also added a footnote to the table which reads “In multivariable analysis, all factors shown in the table were added to the model together with the exception of birth weight as a continuous variable and birth weight as a categorical variable which were analysed separately (they were not included together in any model).” Comment 10: The authors should include a discussion about the methods used to assess the variables used for the study. For instance, bioelectrical impedance measures the electrical properties of the body with greater emphasis on lean mass (a good electricity conductor) than of fat mass (a poor electricity conductor). Furthermore, Tanita systems rely for the estimation of lean and fat mass on equations derived on populations different from that of this study. How would this have affected their findings? Would the method used to assess breastfeeding be sensitive enough to differentiate different feeding patterns, what about children that were predominantly breastfed?Response: It is true that prediction equations are population specific, but unfortunately at the time of the study there were no equations validated for Ugandan population. A recent paper by Ndagire (2018) has published prediction equations specifically for Ugandan populations. We have amended the manuscript to highlight this limitation. It now reads as “However, the method relies on prediction equations that are population specific to estimate the parameters of body composition. At the time of the study, there were no validated prediction equations for Uganda’s population.” We have also expanded our discussion of the findings related to breastfeeding to reflect the fact that due to the way the data on this were collected, we were not able to examine the effects of different feeding patterns or of longer term breastfeeding behaviour. Comment 11: The authors mention segmental body composition, but the relevance of this statement is unclear.Response: We used a segmental body composition analyser for the estimation of body composition as reflected in the methods section. We however, have edited this section of the discussion and removed the word “segmental” because we did not present body composition data by the cylindrical segment of the body from which it was measured. It now reads as “We used a segmental bio-electrical impendence body composition analyser to measure body composition among the study adolescents. Comment 12: There is little discussion about the context under which the data was collected, e.g. Ugandan context where HIV is prevalent, and whether this could have or not affected their results.Response: Thanks for the comment. We considered the role of HIV and other infectious diseases in our study. We collected data for maternal HIV status and the HIV exposure status of the offspring. In our cohort, 16 mothers tested positive for HIV during pregnancy with only 2 offspring becoming infected. Due to these small numbers, we did not include results relating to HIV in our manuscript. We also found a low prevalence of helminths and malaria in our study participants.  Comment 13: Given the sexual dimorphism already observed with body composition data at the ages of 10-11 years, it would be useful for the authors to discuss why they chose to not undertake separate analysis by sex.Response: Thanks for this comment, we investigated this question and as reported in the manuscript (last paragraph of results section), we found that “”. In light of this finding, we do not present results separately by sex. There was no evidence that the effect of breastfeeding or growth rate on FMI or FFMI differed by sex or birth weightReferenceshttps://doi.org/10.1016/j.clnesp.2018.09.004 Ndagire, C. T., Muyonga, J. H., Odur, B., & Nakimbugwe, D. (2018). Prediction equations for body composition of children and adolescents aged 8-19 years in Uganda using deuterium dilution as the reference technique. Clinical Nutrition ESPEN, 28, 103–109. The study participants were drawn from a birth cohort that was established for a different study. Originally, they had a birth cohort of 2345 live births of which 1119 (47.7%) were enrolled for another study at 10 or 11 years of age. This study used 177 (7.6%) of these who had body composition data. Although the investigators tried to show that the huge drop-out had little selection bias, it was not clear on what variables were they matching or similar (page 4). I suggest to depict the sampling process including exclusion criteria using a PRISMA flow diagram.  The data represented mainly urban (65%) and women populations with high HIV prevalence (9%); of which the offspring, 8% were exposed but uninfected while 1% were infected. The data has also included few variables as indicators of stages of puberty. However, it was not clear why the HIV and these variables were not included in the final regression model - see table 2.I don't see the purpose of Figure 1. The difference between FMI and FFMI in both sexes is obvious. One option is to show BC by sex. The investigators claim that this study is unique in East Africa (page 8); this does not seem justified. In the broader sense of East Africa, for instance, there are published articles from the Infant Anthropometry and Body Composition (iABC) cohort in Ethiopia that could have been used as reference(s) in this paper.The work is original and investigated the relationship between birth weight, exclusive breastfeeding and growth in infancy, and body composition among 10-11 year-old children (early adolescence) from Uganda. The findings will contribute to the understanding of the link between early like exposure and growth with later health and development particularly in populations living in contexts similar with Uganda.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thanks so much Dr. Tsinuel Girma for the insightful comments and feedback about the manuscript. The comments have been helpful.  We have outlined our responses in line with each comment made.Comment 1: The study participants were drawn from a birth cohort that was established for a different study. Originally, they had a birth cohort of 2345 live births of which 1119 (47.7%) were enrolled for another study at 10 or 11 years of age. This study used 177 (7.6%) of these who had body composition data. Although the investigators tried to show that the huge drop-out had little selection bias, it was not clear on what variables were they matching or similar (page 4). I suggest to depict the sampling process including exclusion criteria using a PRISMA flow diagram. Response: Thanks for this comment. We tried to explicitly explain the sampling flow in words. The PRISMA  flow diagram (which depicts the flow of information through the different phases of a systematic review) is not the most appropriate in this situation. We think that this reporting is well suited for this article.We have amended the manuscripts to include the list of variables that were compared for EMaBS participants included and not included in this study. It now reads as “...... for most characteristics including maternal  characteristics at enrollment, household socio-economic status at enrollment and childhood , trial intervention [albendazole]) characteristics, except participants were more likely to be born to separated/divorced/widowed mothers.....”.Comment 2: The data represented mainly urban (65%) and women populations with high HIV prevalence (9%); of which the offspring, 8% were exposed but uninfected while 1% were infected. The data has also included few variables as indicators of stages of puberty. However, it was not clear why the HIV and these variables were not included in the final regression model - see table 2.Response: Thanks so much for the comment. For each of the main exposures, factors crudely associated with that exposure or with the outcome at a 5% level of significance were included in the final model for that exposure. HIV was not included basing on these criteria. However, a priori confounders, household socio-economic status, age and sex were included in the model regardless of whether associated with the exposure or outcome or not. Comment 3:I don't see the purpose of Figure 1. The difference between FMI and FFMI in both sexes is obvious. One option is to show BC by sex. Response: Thanks so much for the comment. However, it is not clear to us. We definitely showed BC by sex.Comment 4:The investigators claim that this study is unique in East Africa (page 8); this does not seem justified. In the broader sense of East Africa, for instance, there are published articles from the Infant Anthropometry and Body Composition (iABC) cohort in Ethiopia that could have been used as reference(s) in this paper.Responses: Thanks for this comment and the reference. We must say, the Ethiopian study  should have been used as a reference; we just did not come across it during our literature review and submission of earlier manuscripts (February 2018) since the paper has just been recently published (June 2018). We have amended the sentence in the article to reflect that our study is one of the few studies in East Africa to investigate these relationships. Now reads “To our knowledge, this is one of the few studies from East Africa to investigate the impact of early-life factors on the body composition parameters FMI and FFMI.”


Primary outcome was the number of spontaneous deliveries. Secondary outcomes were: gestational age at delivery, type and time of delivery, use of analgesia, birth weight, and 5-min Apgar at delivery. We compared the outcomes in the two weeks after DST (cases) to the two weeks before DST (controls). (3) Results: Data showed no significant difference between the number of deliveries occurring before and after DST . Vaginal deliveries at any gestational age showed no statistical difference between the two groups . There were no significant differences in the secondary outcomes, as well. (4) Conclusions: DST has neither a significant impact on the number of deliveries nor on the obstetric variables investigated by this study.(1) Background: Although the current literature shows that daylight saving time (DST) may play a role in human health and behavior, this topic has been poorly investigated with reference to Obstetrics. The aim of this case–control study was to evaluate whether DST may influence the number of spontaneous deliveries. (2) Methods: A low-risk pregnancy cohort with spontaneous onset of labor ( The mechanism that starts the spontaneous onset of labor still remains an unanswered question in the current literature . In the As for female reproduction, disruptions of circadian rhythms, e.g., shift work, jet lag, and DST, have been associated with poorer fertility and early pregnancy outcomes . HoweverA cohort of 7415 deliveries from low risk pregnancies with spontaneous onset of labor was enrolled in this case–control study. We used the Certificate of Childbirth Assistance (CedAP) data collection from one single Italian region, Emilia-Romagna, in the period 2016–2018. We identified the exact dates of DST during each year analyzed . Age andExclusion criteria are shown in The primary outcome was defined as the number of deliveries, in particular the number of non-operative vaginal deliveries.The rationale underlying the choice of the latter outcome was to exclude, as far as possible, the conditioning acted out by the health workers and the healthcare organizational system that weighs on the birth path, in order to bring out the influences that the endocrine and central nervous systems have on childbirth.Gestational age delivery, defining preterm before 37 weeks, at term between 37 and 41 weeks, and post term beyond 41 weeks;Type of delivery: vaginal delivery, cesarean section, or operative delivery;Time of delivery: time when the birth occurred, combining three moments of the day—morning (08:00–15:59), afternoon (16:00–23:59), or night (00:00–07:59);Birth weight, divided into three distinct classes: less than 2500 g, between 2500 and 4000 g, and more than 4000 g;Five-minute Apgar at birth;Use of analgesia in labor.Secondary outcomes were:We also analyzed local data relating to individual provinces. In particular, the number of at term deliveries that occurred in the three-year period was considered in the various Emilia-Romagna provinces, in order to verify whether there were any differences related to DST. The statistical analysis of the data was performed with the “chi-square” test.We considered a 5% error with a confidence interval of 95%, standard deviation of 0.5, and z-score of 1.96.The differences in the number of deliveries across the Spring and Autumn shifts were assessed for significance using the Poisson Means Test.3. This calculation was performed for any year considered in this study.Annual birth rate was calculated as the ratio between the number of births during the two weeks pre and post DST, and the mean value of births obtained by the sum of births in the previous and the considered year divided by 2. The ratio was then multiplied by 10N = number of births during the index 2 weeks;X = year considered;x−1 = previous year;B = total births in the Emilia-Romagna region of Italy.p-value < 0.05 was considered statistically significant. IBM Statistical Package for Social Science  was used.In addition, we calculated the age and gestational age of participants. A logistic regression analysis was carried out, where the period including the two weeks pre and post DST was the dependent variable and the other parameters investigated in this study the independent ones. A 2-sided p = 0.033); on the contrary, gestational age was not different in the two groups . As regarding time of delivery, 2708 births occurred in the morning (36.5%), 2022 (27.3%) in the afternoon, and 2685 (36.2%) at night. Birth rate during the Spring and Autumn shifts in the three different years of the study are shown in The mean age of the 7415 women was 31.4 ± 5 years, and mean gestational age was 39.3 ± 1.4 weeks. Italian patients totaled 4801 (64.7%), whilst 35.3% were classified as non-Italian. The mean age of the women who delivered during the two weeks before DST were older than those who delivered during the two weeks post DST , including mode of delivery  and gestational age . The same result was obtained analyzing the number of spontaneous deliveries (p = 0.73).No statistical difference has been observed in the analysis of the number of deliveries .The differences in the number of deliveries and number of spontaneous deliveries across the Spring and Autumn shifts assessed by the Poisson Means Test were not significant . All the other investigated parameters were not associated with the period of the DST. Therefore, every year of increasing age of the delivering woman increased the risk of event occurrence during the two weeks after DST of 1.0% .The present study found no statistically significant differences in the number of deliveries that occurred during the two weeks following DST compared to those occurring prior to the DST shifts. The same result was obtained for each secondary outcome analyzed. These data confirm the findings by Laszlo et al.  also forThese results confirm data from Roizen et al.  regardinp = 0.08) and that relating to the operative deliveries (p = 0.99) do not allow us to exclude that what has been detected is not attributable to simple chance.The analysis of type of delivery was based on the hypothesis of a possible influence of DST on the secretion of melatonin ,22,23,24Another relevant finding in the secondary outcome analysis was the confirmation of previous data collected in Texas, USA, by Mancuso et al. . In our p = 0.97). It is possible to extend the same statement to the other investigated weight classes . Thus, we can conclude that DST shift does not have a statistically significant impact on newborn birth weight.Neonatal birth weight was also investigated as a secondary outcome, to test the hypothesis of a short-term, acute effect of rhythm disruption. This is because the available data in the literature refer to studies performed on shift worker women , and ship = 0.64).The rationale for the analysis of the use of epidural analgesia in periods of exposure and control was to assess whether the desynchronization induced by DST that shifts the transition could play a role in the perception of pain. In this case also, the results obtained do not allow us to exclude that the differences found are attributable to chance  retrospective observational study; (ii) lack of perinatal complications—however, we selected only very low risk pregnancies; (iii) three years represent a short period of observation; (iv) unfortunately, we did not apply the methods used by László et al.  due to tHowever, the present study has also several strengths: (i) the large sample size, (ii) the quality of the data, and (iii) the rigorous methodology. The sample analyzed is representative of women characterized by a very low risk pregnancy, and the Emilia-Romagna region is characterized by one the best regional health systems in Italy. The choice of performing the study in a specific Italian region allowed us to obtain a reliable, good, complete, uniform, and coded source of data. The Childbirth Assistance Certificate is unique for the entire regional territory, and its compilation is mandatory following each birth in the region, independent of the structure in which it takes place.This study shows no differences in the number of deliveries in the weeks following the change of time compared to those that precede it, neither during the Spring nor in the Autumn. This finding provides further confirmation to previous data obtained at quite different conditions of latitude, climate, and light exposure. It is possible that the multihormonal etiology of labor may explain this phenomenon. It would be interesting to extend the study to different latitudes and ethnicities, in order to verify whether our findings could be generalized or not.


A substantial amount of research shows that younger siblings perform worse than their older sisters and brothers in several socioeconomic outcomes, including educational achievement. Most of these studies examined stable families and excluded half-siblings. However, the increasing prevalence of multipartnered fertility implies that many children grow up in nonnuclear families. We examine whether there is evidence for birth order effects in this context, which offers an opportunity to test and potentially expand the explanatory scope of the two main theories on birth order effects. We use comprehensive Norwegian registry data to study siblings in the 1985–1998 cohorts born to mothers or fathers who parented children with at least two partners. We provide evidence for negative effects of birth order on lower secondary school grades in both cases. Children born to fathers displaying multipartnered fertility tend to have lower grades than older full siblings but perform more similarly or better compared with older half-siblings. For siblings born to mothers with the multipartnered fertility pattern, later-born siblings do worse in school compared with all older siblings. This indicates that negative birth order effects tend to operate either within or across sets of full siblings, depending on the sex of the parent displaying multipartnered fertility. We argue that these findings can be explained by a combination of resource dilution/confluence theory and sex differences in residential arrangements following union dissolutions. We also suggest an alternative interpretation: maternal resources could be more important for generating negative birth order effects.The online version of this article (10.1007/s13524-020-00905-4) contains supplementary material, which is available to authorized users. Being later-born confers a negative effect on several outcomes, including educational performance and choices  of the children in our samples, however, experienced union dissolutions. In some cases, they never lived with both parents simultaneously. Still, we argue that it is reasonable to expect negative birth order effects based on the two theories, but three key issues need to be accounted for to derive more specific predictions: (1) the distinction between overall and full biological birth order, (2) the significance of union dissolutions and family complexity, and (3) sex-differentiated parental behavior (discussed in the upcoming section on different expectations for the two samples). We will now discuss these in turn beginning with the first, which is crucial for evaluating the significance of the latter two.and father. When considering birth order in the context of multipartnered fertility, however, there are multiple fathers and/or mothers involved. We distinguish between two types of birth order to provide an adequate descriptive overview and because the implications derived from the two theories discussed will differ depending on the type of birth order.In studies of nuclear families, birth order is assigned based on the year of birth for all siblings who share the same mother overall birth order based on the year of birth for each child relative to all full and half-siblings with whom one shares the same father or mother—that is, all children from both sets. Second, we refer to the full biological birth order of each child based on a child’s year of birth relative to other full siblings parented by the same mother and father—siblings within the same set. The latter definition of birth order is similar to those used in previous studies, whereas the first is novel.First, we define the , the two oldest children in this example are assigned values of 1 and 2 based on their year of birth relative to each other. The last two born are also given the values of 1 and 2 because they are (respectively) first- and second-born in the mother’s last set. In this example, the values for the two types of birth order are identical for the two oldest children  but different for the two youngest .An example will make this important distinction more transparent. Consider our first sample, which includes siblings born to mothers who have parented children with more than one partner. Say that a mother has four biological children: two with the first father and two with the second. These four children are assigned an overall birth order from 1 to 4 based on their year of birth in ascending order. For the full biological birth order, howeverceteris paribus estimate of birth order effects. In the context of multipartnered fertility, having a higher birth order is correlated with a lower probability of having experienced a divorce as well as a higher probability of having an older half-sibling. Further, children in the second set are more likely to experience greater family stability, which could lead to confounding of negative effects of both full biological and overall birth order on educational achievement.In a nuclear family setting, parental fixed effects make it possible to provide a robust One potential complication is related to the fact that children of the first set in both our samples have experienced a union dissolution at some point, typically before the first child/children of the second set is/are born. Union dissolutions affect children’s educational achievements negatively , and what we obtain is therefore an estimate of this effect net of these offsetting mechanisms.In both the resource dilution and the confluence theory, parent-child interaction causes birth order effects. In our samples, union dissolutions have occurred, and the parent-child interaction is not the same as in nuclear families. Mothers and fathers will typically create a family environment together with their child(ren) as a couple before they split up and the child(ren) start to live with their mother, while the father may or may not still be involved. We expect this to have different implications for birth order effects in our two samples.fathers who display multipartnered fertility, siblings in each set typically grow up in parental environments that are separate most of the time. Although all children will typically spend some time with both parents, the children of the first set (in particular) will typically spend much of their upbringing with their mother and their full siblings . From the point of view of the two theories, this implies a discontinuity compared with the nuclear family context. Initially, both parents are present, which resembles the nuclear family mechanism. In the father-based sample, we expect that this will mainly give rise to birth order effects between full siblings . Although the father is present and contributes (both before and after the union dissolution) to some degree, his presence is not consistent in the same way as that of the children’s mother, with whom they typically reside. In other words, because a father displaying multipartnered fertility spends less time distributing his resources between all his children simultaneously (because his pattern of investment varies across sets with time), we are less likely to see evidence for overall birth order effects in the father-based sample. However, the combined efforts of the father and the mother within each set will give rise to full biological birth order effects.For children to mother sample, based on the same premises. In this situation, both full and half-siblings will spend more time interacting in a common environment. Although it clearly varies across time whether both the father and mother (or stepparents) are present, the mother’s continued presence provides a consistency that differs from that seen in the father-based sample. Importantly, this implies that the mechanisms specified in the two theories can operate for all full and half-siblings that share the same mother. In other words, although both mothers and fathers contribute in both samples, the residential situations allow for continuity across sets in this case that was not present in the other sample, therefore giving rise to overall birth order effects. We therefore expect birth order mechanisms to operate mainly within sets of full siblings in the father-based sample and across in the mother-based sample.We have different expectations for the This argument is based on some important premises, which are supported in existing literature. Previous evidence from Norway for the period covered by our data (children in the 1985–1998 birth cohorts) indicates that mothers tend to live with all or most of their biological children in the overwhelming majority of cases in which the parents have separated or never lived together  at the end of compulsory education (lower secondary school). The population registry enables us to link all individuals to their parents and thereby to their full siblings and half-siblings. Information on school points was obtained from a registry of educational information, which contains data on nearly all pupils during the period under observation.We use comprehensive registry data that cover the complete population of Norway. We link information on individuals from several registries through a nonidentifiable and unique ID number and study the relationship between birth order and grades  or fathers (the father-based sample) who parented at least three children in total with at least two partners. One or more of the co-parent(s) in each sample could also display multipartnered fertility. To estimate the full biological birth order, the same person must be the co-parent of at least two of these children. Information on lower secondary school points are measured at age 16 and are available from 2001 to 2014. This leaves us with information on the cohorts from 1985 to 1998. We therefore include children of fathers or mothers whose first child was born in or after 1985 and who have at least two siblings, which implies that we have information on school points for all older full and half-siblings for everyone in our sample.Individuals with younger siblings who are not old enough to have finished lower secondary school in 2014 are included in the main samples. We exclude all siblings of children with missing information on school points. Fifth-born and later-born children were also excluded because of the very small number of observations in these categories. Neither exclusion affected our main findings.N = 396,864).Our main analyses use a within-father/within-mother design, which creates additional data demands. Because the fixed-effects estimations require variation in birth order between children of the same father, the same mother, and combinations of fathers and mothers, we exclude children of plural births. These restrictions leave us with 20,776 and 19,508 observations in the father-based and mother-based samples, respectively. Additionally, we include results from a nuclear family sample that uses the same criteria but excludes those with half-siblings, and which is limited to parents with four or fewer children  or mother (mother sample). We assign a value between 1 and 4 to each child. Overall birth order is included in our models as a set of four dummy variables for those who are firstborn (reference group), second-born, third-born, and fourth-born.and father. The measure is also included as a set of dummy variables with firstborn as the reference group, and we also include dummy variables for second-born and third-born.The second type of birth order is full biological birth order of each child, similar to typical measures of birth order used in within-family studies. We assign a value to the child based on his or her year of birth relative to other full siblings parented by the same mother We measure educational success as the school point average at the completion of lower secondary school, which occurs at age 16. Lower secondary school comprises grades 8–10 and is mandatory in Norway. The school point variable combines the most important grades achieved throughout this level of schooling. Grades are awarded on a scale of 1 to 6, and both overall achievement grades and exam grades are included. The averages of all grades are calculated and then multiplied by 10 to obtain the value of the variable on a metric scale. The grading system was altered slightly in 2007, and a tendency for the average to rise somewhat during the period has been documented , 25–29 years old, 30–34 years old, 35–39 years old, and 40 years or older).Child’s year of birth is included as a set of dummy variables for each year from 1985 to 1998, with the first year as the reference category. This variable serves as a standardizer of the grade scale as well as a cohort control.Because gender is known to be important to educational outcomes, the child’s gender is included as a dummy variable to standardize the coefficients .We include measures for spacing (full samples) and cohabitation (available for children born from 1987 to 1998) in several additional analyses in the online appendix. We provide more details on these variables in the relevant tables.both types of birth order, which allows us to examine the relative importance of each type given that the other is adjusted for. This strategy is possible because the two types of birth order have different values for some children in our sample, as indicated in the examples discussed earlier in connection with the definition of the birth order measures. We use a similar specification when we estimate birth order effects for the nuclear family sample, but the clustering in the fixed effects in this model are based on combinations of mothers’ and fathers’ IDs.We estimate three main models with the grade average at the end of compulsory education as the outcome variable in all three. We use linear regression models estimated separately for each sample. Our approach enables us to apply father or mother fixed effects (depending on the sample), and we include control variables for the child’s gender, year of birth, and the age at birth of both parents. We examine the relationship between school points and the overall birth order (Model 1) and the full biological birth order (Model 2). Model 3 is similar to the first two but includes overall birth order in cases where half siblings are compared. To consider this issue, we first include a measure of the educational attainment of each co-parent as a covariate. This makes almost no difference to our estimates  is slightly more common among fathers.Because of the way our main samples were constructed, they include equal numbers of first-, second-, and third-born siblings when the Figure and full siblings in this sample.For children in the mother-based sample, having a higher overall birth order is negatively associated with educational achievement, as indicated in panel c of Fig. better in school when compared with the youngest sibling of the first set. This is most pronounced in the 1-2, 1-3, and 2-2 combinations, for which the coefficients for being second-born  and third-born (2-2) are actually positive. Table in the same set tend to perform worse compared with their older full siblings. This finding implies that children in this sample typically perform on par with or better than the closest half-sibling but worse than full siblings.In the father-based sample, the general association between overall birth order and school point average is less clear, as seen in panel a of Fig. Figure overall birth order and school achievement in panel a of Fig. full biological birth order shown in panel b of Fig. across or within sets of children depending on the sex of the parent displaying multipartnered fertility. Our third model takes both types of birth order into account simultaneously, thus providing additional evidence on this issue.Panels a, b, d, and e of Fig. father remains negatively associated with educational success. Per contra, the coefficient for the overall birth order is now small and not statistically significant. In the mother-based sample , whereas the difference between firstborns and fourth-borns is about 4 school points . The difference between first- and fourth-borns is of a magnitude similar to the sex gap in our sample.Previous studies on birth order and socioeconomic outcomes have typically studied nuclear families. However, recent demographic developments have led to increasingly complex family constellations. We acknowledge the importance of such developments by studying birth order effects on educational achievement in the context of multipartnered fertility. Based on the resource dilution and confluence theories as well as previous demographic research, we derive expectations for each type of birth order for each sample according to the sex of the parent who displayed multipartnered fertility.We ask three research questions. Our first question asks whether associations between birth order and educational achievement exist in the context of multipartnered fertility. We provide evidence for overall negative associations between both full biological and overall birth order on the school point average at the end of compulsory schooling in both samples.Our second and third research questions ask whether this association differs for full biological and overall birth order or according to the sex of the parent displaying multipartnered fertility. The answers to these two questions prove to be interrelated. In the mother-based sample, the association between overall and full biological birth order with educational achievement was negative. In the father-based sample, the overall birth order effect is weaker and less clear-cut, whereas the negative effect of full biological birth order remains important. Furthermore, when both overall and full biological birth order are included in the same model, only the overall birth order remains negatively associated with school achievements in the mother-based sample. For siblings in the father-based sample, this is the other way around, with only the full biological birth order remaining significant. We consider these associations as credible net effects of negative birth order effects due to the impact of union dissolution and family complexity, which clearly have a negative impact on school achievement.within sets of children who share the same father and across sets for siblings born to the same mother. Asking whether one’s birth order relative to all siblings is more important for upper secondary school achievement than one’s birth order relative to full siblings yields a more intuitive interpretation. For siblings born to mothers displaying multipartnered fertility, the overall birth order across sets of siblings is clearly the most important: on average, these children perform worse in school compared with their older full and half-siblings, and the difference is similar in magnitude to that found in nuclear families.We interpret these findings as indicating that the negative effects of birth order on educational achievement mainly operate full siblings but also tend to perform equally well or better compared with the closest older half-sibling. The latter finding is likely due to factors other than typical birth order mechanisms, such as union dissolutions, family complexity, and differences in union stability. Although these mechanisms are relevant in both samples, the evidence indicates that they are not trumped by negative overall birth order effects in the mother sample. Still, there is clearly room for variation across the different subgroups, and future research should investigate this further.For siblings born to fathers who display multipartnered fertility, the full biological birth order is what matters the most: these children tend to obtain fewer school points, on average, compared with older Our findings are consistent with previous research. In a supplementary analysis, Kristensen and Bjerkedal  considerOur evidence supports the theoretical expectations that we derive from the resource dilution and confluence theories. In our expectations, we distinguish between the behavior of mothers and fathers who display multipartnered fertility. We argue that children who share a mother displaying multipartnered fertility will face a more consistent environment, where birth order effects can arise across sets. Fathers, on the other hand, are less likely to live with and interact with all their children at the same time for an extended period. This implies that although the father is often present in his children’s life for some time, we will not see the same consistency across sets in the father sample, thereby making full biological birth order effects more relevant. Based on the two theories, the question is whether the resource dilution and intellectual climate are mostly relevant only for full siblings in the father-based sample or both half- and full siblings in the mother-based sample. Our findings are consistent with these expectations. The confluence and resource dilution theories are therefore able to account for birth order patterns outside a nuclear family context. Still, our results indicate that they need modification or supplementation and need to take parental sex and related behavioral differences into account.Still, other theories and mechanisms could also provide relevant explanations. A more radical interpretation is that mechanisms specific to the mother are more important in explaining the birth order effects we observe, and possibly also birth order effects documented in other contexts.This mother-centric interpretation could favor the resource dilution theory, which can easily be modified to ascribe a greater importance to maternal resources and investments. In terms of the confluence theory, this interpretation would mean that the mother has a more positive influence on the average intellectual climate of the family compared with the father. This is hard to square with the fact that the confluence theory is based on the distinction between the intellectual levels of children and adults. Additionally, controlling for spacing does not affect our overall findings , which could affect the external validity of our findings. However, previous research on multipartnered fertility has indicated that this fertility pacing is quite common for this group