In this study, we report strong associations between brain white matter DTI and self-reported cognitive, somatic and emotional symptoms at 12 months post-injury in 134 MTBI patients. The anatomical distribution suggested global associations, in line with the diffuse symptomatology, although the strongest effects were found in frontal regions including the genu of the corpus callosum and the forceps minor. These findings support the hypothesis that DTI may provide increased sensitivity to the diffuse pathophysiology of MTBI and suggest an important role of advanced Magnetic Resonance Imaging (MRI) in trauma care.Identifying patients at risk of poor outcome after mild traumatic brain injury (MTBI) is essential to aid prognostics and treatment. Diffuse axonal injury (DAI) may be the primary pathologic feature of MTBI but is normally not detectable by conventional imaging technology. This lack of sensitivity of clinical imaging techniques has impeded a pathophysiologic understanding of the long-term cognitive and emotional consequences of MTBI, which often remain unnoticed and are attributed to factors other than the injury. Diffusion tensor imaging (DTI) is sensitive to microstructural properties of brain tissue and has been suggested to be a promising candidate for the detection of DAI  This conglomerate of post-concussion symptoms, often including headache, dizziness, blurred vision, sleep disturbance, fatigue, psychological distress, and reduced memory and attention, may severely impact social functioning and work participation4. Identifying patients at increased risk of poor outcome is essential to aid prognostics and optimize treatment.While the majority of patients with a mild traumatic brain injury (MTBI) show full remission5. Thus, DAI may be the primary pathologic feature of MTBI6. Unfortunately, the low sensitivity of conventional computerized tomography (CT) and MRI to detect DAI has led to a delayed understanding of the clinical MTBI symptoms, despite studies delineating trauma-related histopathology following even mild head trauma7.Since treatment and follow-up of MTBI patients is often concluded shortly after discharge from the emergency department, long-term cognitive and emotional consequences of the MTBI often remain undetected and are attributed to factors other than the injury. Although the complex and multidimensional pathophysiology is not completely understood, the penetrant and non-specific nature of the symptoms suggests close interactions between psychological and primary injury-related and secondary physiological factors. The symptoms also suggest a general and widespread involvement of brain connectivity rather than focal cortical or subcortical lesions. This hypothesis resonates well with the assumptions that the mechanic forces involved in MTBI may cause diffuse axonal injuries (DAIs), also referred to as traumatic axonal injuries (TAIs), e.g., those injuries due to traumatic shearing caused by decelerating and accelerating forces applied to the head due to a fall, traffic accident or assault8. DTI-based indices have emerged as a sensitive index of the effects of MTBI on tissue microstructure, including axonal pathology and disruption of myelin9. DTI has been suggested as a promising candidate for the detection of human DAI in vivo9 and cross-sectional associations between DTI abnormalities and functional outcome support a clinical significance12.Diffusion tensor imaging (DTI) is sensitive to the direction and magnitude of non-random water diffusion in the brain and provides a non-invasive and quantitative measurement of brain white matter microstructural properties and connectivity18. In contrast to a lower FA and higher MD, elevated FA and decreased MD have been reported at similar post-injury intervals in several studies16. However, in the chronic phase of MTBI, Lipton et al.19 reported decreased FA and increased MD in both the corpus callosum and subcortical white matter as well as in the internal capsules. Kraus et al.20 reported increased axial diffusivity (AD) and no differences in radial diffusivity (RD) in clinically heterogeneous chronic TBI patients and suggested that irreversible damage to myelin is less common in MTBI than in moderate and severe TBIs but that axonal damage is present in the chronic phase. In a meta-analysis, Eierud et al.15 found that FA was increased in studies that performed DTI within 2 weeks after MTBI and decreased when DTI was performed at longer post-injury intervals. The authors concluded that further studies focusing on DTI measures and the relationship with pre-injury status, mental health and neuropsychological functioning are needed to assess the efficacy of neuroimaging for clinical diagnosis and to guide the treatment strategies.Most reports have found DTI abnormalities in fractional anisotropy (FA) and/or mean diffusivity (MD) in the acute phase (within two weeks after MTBI) compared with those in uninjured subjects, but the direction of the effects has varied across studiesHere, we investigated white matter microstructural aberrations with DTI 12months post injury in a sample of 134 patients with MTBI. In conjunction with the literature reviewed above, the assumed injury-related mechanisms often causing DAI, and existing models suggesting a critical role of white matter microstructural characteristics in cognitive functions and mental health, we hypothesized that higher ratings of post-concussion and depressive symptoms 12 months after MTBI would be associated with lower directional coherence and higher diffusivity, manifested as lower FA and higher diffusion tensor eigenvalues. Due to the non-specific nature of the characteristic post-concussion symptoms and to increase sensitivity, our main outcome score was defined as the principal component from a principal component analysis across several clinical instruments assessing different aspects of cognitive and somatic symptoms. We hypothesized that there would be a relatively global anatomical distribution of effects in contrast to regionally specific associations but employed a methodological approach allowing for inference on a regional level to explore possible differential effects in different parts of the brain.Table Table Figure FDR = 0.0173, Cohen’s d = −0.426), MD , RD , and AD . For atlas-based ROIs, significant associations for all DTI metrics were found for the forceps minor. The left inferior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus and the genu of the corpus callosum were significant for FA, MD and RD, and the bilateral superior longitudinal fasciculus, left superior longitudinal fasciculus  and the right inferior longitudinal fasciculus were significant for MD, RD and AD . In addition, we observed strong effects of age across all DTI metrics in most ROIs and only moderate associations with sex and head coil. We revealed no major differences between the uncomplicated and complicated MTBI group except nominally significant differences with lower FA/higher RD in the body of the corpus callosum and the right uncinate in the complicated compared to the uncomplicated group. None were significant after FDR- adjustmentFigure FDR = 0.00108, Cohen’s d = 0.591) and RD  in the body and RD  in the splenium of the corpus callosum.Including QA metrics  in the models did not change the results. tSNR showed significant association with FA  and WM microstructural properties as measured using DTI in uncomplicated and complicated MTBI. In particular, patients with a higher symptom burden showed anatomically widespread reduced directional coherence and an increased magnitude of diffusion across the primary axis of the diffusion tensor, as indexed by decreased FA and increased RD, respectively. Whereas the anatomical distribution suggested primarily global associations, in line with the diffuse symptomatology, the strongest effects were found in frontal regions, including the genu of the corpus callosum and the forceps minor. Although the lack of longitudinal DTI data precludes interpretations regarding the temporal evolution from the time of injury, our results suggest a link between brain white matter microstructure and a composite measure of self-reported outcome 12 months after the injury.21, typically converging on lower FA for MTBI patients than for healthy controls23. The anatomical distribution of DTI abnormalities in our study, including the FA effects, are in line with the previous studies24. The gray-white matter junction and midline brain structures are particularly vulnerable to DAI, and the corpus callosum and dorsolateral midbrain are frequently involved because of their susceptibility to biomechanical shear-strain forces25. The localized AD effects found in this study are in line with a previous study that investigated patients in the post-acute/chronic TBI phase and found an overall increase in AD that was positively correlated with time since TBI and greater in the “mild” TBI group than in the controls24. The authors indicated that increased AD may reflect adaptive axonal recovery; however, the pathological significance of these effects remains unclear and should be assessed in relevant animal models24.Previous studies have demonstrated an important role of DTI in evaluating white matter integrity after MTBI27. However, few studies have attempted to correlate DTI abnormalities with patient self-reported symptoms28. In a recent TRACK TBI study17, a subgroup of 32 complicated MTBIs showed reduced FA in multiple tracts, whereas the uncomplicated MTBIs demonstrated no differences in DTI parameters compared to those in healthy controls in the subacute phase . The regions with reduced FA were significantly associated with functional outcome 3 and 6 months post injury. Reduced FA may occur due to axonal injury or disruption of the myelin sheath29. In line with Messe et al.18 who found that only those MTBI patients with residual behavioral and cognitive complaints demonstrated alterations in white matter integrity compared to the white matter in healthy controls, we found an association between a comprehensive composite score of persistent symptoms and DTI. We included both asymptomatic and symptomatic individuals with both uncomplicated and complicated MTBI. Contrary to studies demonstrating lower FA with greater injury severity30, we found no significant differences between uncomplicated and complicated MTBI. The lack of a significant group difference may be explained by the fact that all patients in the current study needed hospitalization in a level I trauma center and thus represented a relatively homogenous population at the more severe end of the MTBI spectrum. This intepretation is in line with Kinnunen et al.24, who reported significant white matter abnormalities in patients without microbleeds compared with age-matched controls, and indicates that further refinement of the diagnostic classification of MTBI is needed.Studies on MTBI have correlated post-concussion cognitive dysfunction with focal white matter abnormalities31, differing intervals between injury and MRI assessment in different studies, inconsistencies in reported outcome, methodological heterogeneity, and small sample sizes.Despite the sensitivity of DTI and its potential to identify patients at risk for poor long-term outcomes, to date, DTI has not yielded a validated prognostic biomarker for several reasons: a lack of longitudinal studies assessing the relationship between imaging and outcome37. In a recent study, Spitz et al. found mood disorders following TBI to be associated with reduced white matter integrity in multiple fiber bundles connecting temporal and frontal brain regions38. Our composite score included emotional symptoms and depression, reflecting the fact that mental health factors have an important role in MTBI outcome. Various outcome measures are used for MTBI. The GOSE is often used for global outcome and RPQ for self-reported symptoms. The coarseness of GOSE makes it less sensitive to subtle dysfunction, which is typically observed in patients with MTBI, and may therefore not permit sufficient differentiation of the outcome in patients with milder injuries39. RPQ is sensitive to post-concussion symptoms but is a gross outcome measure and grades common symptoms that are not specific to MTBI. Because composite outcome measures that include several dimensions of functioning are likely to increase sensitivity and are in line with a recent recommendation39, we created a composite outcome score using a principal component analysis (PCA) based on global functioning and self-reported cognitive, emotional, somatic and mental health symptoms . Further studies are needed to assess the clinical specificity of the DTI associations and to explore the interactions between predisposing and injury-related factors.Mental health, including levels of depression, anxiety and possible traumatic stress, has been associated with functional outcome after MTBI in several studies41, we observed strong effects of age across all DTI metrics in most ROIs and only moderate associations with sex. Although further research is needed to explore this hypothesis, normal age-related effects on the brain white matter integrity may leave the older brain more vulnerable to subtle injury-related processes, therefore rendering age an important prognostic factor43.Finally, in line with previous large-scale cross-sectional and longitudinal studies45. A potential caveat is that only MTBI patients requiring neurosurgical consultation at the emergency department and hospitalization were included in the present study and may therefore be in the more severe part of the mild injuries despite normal CT and MRI scan. Our results may, therefore, have the most value for more severely injured MTBI patients, and generalization of the findings to all MTBI patients should be made with caution. In addition, of the 153 patients with available DTI, 19 were discarded due to poor DTI quality, yielding a total sample of 134 patients. In our experience, excluding approximately 12% (19/153) of the available datasets due to poor quality either due to technical issues or subject motion is quite typical, in particular in clinical studies. Although excluding data based on poor data quality is inevitable, it may induce bias since subject motion and possibly other sources of noise tend to increase with increasing disease severity.The primary limitations of the current study include the cross-sectional design. Longitudinal studies are needed to determine the time course of DTI abnormalities, how they may change over time and how changes in the DTI indices are related to recovery of post-concussion symptoms. Because of the lack of a control group either without injury or with non-head injury, we were unable to determine whether the results are specific to MTBI. This is particularly pertinent given the demonstrated associations between normal variability in personality and mental health-related symptoms and DTI parameters even in presumably healthy individuals46, further studies with more frequent follow-ups, both in the short- and long-term, are needed to probe the temporal dynamics of the associations from the time of injury.Although the 12-month interval between the incident and the follow-up assessment is in line with previous recommendations47. Patients in the current study underwent MRI at approximately 12 months after injury; thus, we can be more assured that the abnormalities in the present study represent a more chronic MTBI pathology. Importantly, in line with most studies on TBI, we cannot rule out that the reported results partly reflect associations that are not related to the injury but rather reflect predisposing associations such as depression or anxiety.Indeed, the injury-to-MRI interval may be a critical factor in MTBI studies. Avariety of different biological processes within injured white matter have been postulated to vary not only according to injury severity but also at different time intervals after injury48. Also as a limitation, TBSS is only sensitive to variability in the core parts of the major white matter pathways, and future studies targeting more peripheral parts of the white matter, e.g., closer to the cortical surface, might yield a different pattern of results. Further, conventional DTI based metrics do not allow for high neurobiological specificity48, e.g., due to the influence of the complexity and degree of crossing of the fibers, and further studies are needed to characterize the exact pathophysiological mechanisms.Associations between imaging markers and clinical phenotypes may be confounded by data quality, e.g., due to increases in-scanner subject motion with increasing symptom burden. We have therefore included state-of-the art methods for the identification and correction of outlier slices, e.g., due to subject motion or other sources of noise, which we have recently demonstrated substantially increases the temporal signal-to-noise-ratio (tSNR)In conclusion, we have demonstrated associations between persistent cognitive, somatic and emotional symptoms 12 months after MTBI and WM microstructural properties as measured using DTI, suggesting that DTI can reveal a biological substrate for post-concussion symptoms and global functioning in MTBI, which may have been undetected or unrecognized in the acute injury phase. Such objective evidence of injury in MTBI may play an important role in the management of patients with persisting post-concussion symptoms.All methods were performed in accordance with relevant guidelines and regulations. All protocols were approved by the Norwegian Regional Committee for Medical Research Ethics (REC) (2010/1899) . All participants provided written informed consent, and all methods were carried out in accordance with the relevant guidelines and regulations of the REC.49. Patients were categorized as having uncomplicated or complicated MTBIs according to trauma-related intracranial structural changes observed via acute CT and MRI obtained 4 weeks post-injury. Exclusion criteria were severe mental illness (schizophrenia or bipolar disorder), progressive neurologic disease, ICD-10 diagnosis of substance dependence, contraindications for MRI, and lack of Norwegian language skills.Patients with acute MTBI admitted to Oslo University Hospital during a period from September 2011 to September 2013 were included in a prospective cohort study; 223 patients aged 16–65 years with recent (<24 hours) history of head trauma , resulting in loss of consciousness (LOC) < 30 minutes, PTA < 24 hours and GCS between 13 and 15 were included. The lowest GCS score within the first 24 hours is reported. MTBI was defined using criteria from the American Congress of Rehabilitation Medicine50.One-hundred and sixty patients returned for the 12-month follow-up including clinical assessment and multimodal MRI, of whom 134 had available DTI data of sufficient quality. We have previously reported brain volumetric and morphometric findings based on T1-weighted MRI data from the baseline assessment in an overlapping sample2) and two b = 0 volumes with reversed phase-encode (blip up/down) were acquired. Total scan time for the diffusion MRI sequence was 8 min.3 T MRI  data were obtained 12 months post-injury using two different head coils (12 channel Head/Neck/Spine (HNS) and standard 8channel GE head coil). For diffusion-weighted imaging, a 2D spin-echo whole-brain echo planar imaging pulse with the following parameters was used: repetition time (TR) = 15 s, echo time (TE) = 81.2–85.6 ms (defined as minimum), flip angle = 90°, slice thickness = 2.5 mm, field of view (FOV) = 240 × 240 mm, acquisition matrix = 96 × 96(reconstructed to128 × 128), reconstructed in-plane resolution = 1.875*1.875 mm/pix, 30 volumes with different gradient directions  sequence were performed to depict hemorrhagic or other lesions. The 12-month MRI follow-up was performed at a median time of 530 days post-injury (inter-quartil range = 235 days), and there was no major scanner upgrade in the study period.53. Processing included eddy (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/EDDY) and topup (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP)54 to correct for geometrical distortions and eddy currents. Topup uses information from the reversed phase-encode blips, yielding image pairs with distortions in opposite directions. From these pairs, we estimated the susceptibility-induced off-resonance field and combined the two images into a single corrected one. We used eddy to detect and replace slices affected by signal loss due to bulk motion during diffusion encoding, which was performed within an integrated framework along with correction for susceptibility-induced distortions, eddy currents and subject motion55. Although these processing steps have been shown to strongly increase the temporal signal-to-noise ratio (tSNR)48 and thus largely mitigate effects of subject motion and other sources of noise on data quality, no methods for data cleaning are perfect, and possible associations between data quality and clinical features may still induce spurious clinical correlations with DTI metrics. To directly test for associations between data quality and clinical outcome, for each dataset, we calculated tSNR and maximum voxel intensity outlier count (MAXVOX)56. Briefly, while MAXVOX reflects intensity-related artifacts, tSNR is a proxy for global data quality. We used these quality measures both to test for associations with clinical outcome and to confirm if clinical associations with DTI remained after including tSNR in the statistical models. FA, eigenvector and eigenvalue maps were calculated using dtifit in FSL. MD was defined as the mean of all three eigenvalues, RD as the mean of the second and third eigenvalue57, and AD as the principal eigenvalue. Voxelwise group analysis of FA, MD, AD and RD was carried out using tract-based spatial statistics (TBSS)58. FA volumes were aligned to the FMRIB58_FA template using nonlinear registration (FNIRT)60. Next, mean FA were derived and thinned to create a mean FA skeleton, representing the center of tracts common across subjects. The same procedures were applied for MD, AD and RD. We thresholded and binarized the mean FA skeleton at FA > 0.2 before the resulting data were fed into voxelwise statistics.Image analyses were done using FSL62. In particular, for each DTI measure, we derived a set of 24 features, including 20 ROIs, 4 additional features comprising the mean value across skeleton, as well as the genu, splenium and body of the corpus callosum63.To summarize data in a conventional neuroanatomical context we calculated mean DTI values across the skeleton and within regions of interest (ROIs) based on the intersection between the skeleton and the probabilistic JHU white-matter atlases64. Voxelwise non-parametric analyses were performed using Randomise65. Associations between the composite outcome score and FA, RD, MD and AD were tested using general linear models (GLM) while covarying for age, sex and head coil. The data were tested against an empirical null distribution generated across 5000 permutations in order to correct for multiple comparisons across space, and threshold free cluster enhancement (TFCE)66 was used to avoid manually defining the cluster-forming threshold. Voxel-wise maps were thresholded at p < 0.05, corrected. Average FA, MD, RD and AD within significant clusters were computed and submitted to further analysis to compute the commonly reported effect size Cohen’s d as follows: t is the t-statistic and df the degrees of freedom of the residuals. We tested the association between the ROI measures and the composite score using GLM, accounting for the effect of age, sex and head coil. We added DTI QC metrics  to test for possible confounding effects of data quality. Correction for multiple comparisons across all ROIs tested was performed following the False Discovery Rate (FDR) procedure67.Descriptive statistical analyses were performed using SPSS for Windows, version 22 . Statistical significance was reported at the 0.05 level. Descriptive statistics and PCA were also performed using Matlab  and RWe assessed key pre-injury, acute and post-injury variables. Acute clinical data were obtained from medical records and outcomes at the 12-month follow-up.Information regarding age, sex, education level, anxiety and depression (yes/no), and employment status were obtained from the clinical interview.68 assesses the conscious state, with total scores between 3 (showing no response) and 15 . Duration of PTA was assessed in the emergency department and classified into no amnesia, less than one hour and between one and 24 hours. The presence and duration of LOC were based on medical records and classified into no LOC, < 5 min and > 5 min. Causes of injury were obtained from medical records and classified as traffic accidents, falls, violence or others.GCSThe main clinical outcome variable was defined as the subject weights of the primary component from a principal component analysis (PCA) across five items from three frequently used instruments assessing key aspects of recovery and function obtained 12 months post-injury:69, recommended as the main outcome measure for TBI70. It is an 8-point ordinal scale reflecting good recovery ( > 7), moderate  and severe  disability, vegetative state (2) and death (1). The Rivermead PostConcussion Symptoms Questionnaire (RPQ) assesses frequent cognitive, emotional and somatic domains71. The sum of the subscores RPQ somatic, RPQ emotional and RPQ cognitive were used. Patient Health Questionnaire 9 (PHQ-9) was used to assess depressive symptoms72. Total score of 0–4 indicates no depression, 5–9 mild, 10–14 moderate, 15–19 moderately severe, and 20–27 severe depression, and the total score was used.GOSE measures global functions including independence, work, social and leisure activities and participation in social lifeWe used the factor explaining the highest proportion of variance. This score explained 73% of the total variance in the five scales, which were well represented in this factor , indicating that all outcome metrics contributed substantially to the composite score. A high composite score reflects poor outcome. Figure


Gestational weight gain (GWG) is an important modifiable factor known to influence fetal outcomes including birth weight and adiposity. Unlike behaviors such as smoking and alcohol consumption, the effect of GWG throughout pregnancy on fetal development and other outcomes has not been extensively studied. The aim of this study was to investigate the relationship of GWG with endocrine factors such as adiponectin, leptin, and C-reactive protein which may be associated with inflammatory response, fetal growth, and adiposity later in life. Data were obtained from the Ulm Birth Cohort Study (UBCS) and the Ulm SPATZ Health Study, two methodologically similar birth cohort studies including newborns and their mothers recruited from 11/2000–11/2001 and 04/2012–05/2013. In the two included birth cohorts we consistently observed statistically significant positive associations between GWG beginning as early as the second trimester with fetal cord blood leptin and stronger association beginning as early as the first trimester with post-delivery maternal serum leptin. Total weight gain exceeding commonly accepted recommended guidelines was consistently associated with higher leptin levels in both cord blood and post-delivery maternal serum. These results suggest a potential pathomechanistic link between fetal environment and surrogate markers of long-term health. Mounting evidence suggests fetal environment and nutrition may play a role in the origin of a number of chronic diseases manifesting later in life3Though a number of confounding factors such as offspring gender, parity, and smoking are known to influence fetal growth57One potential mechanism has been identified in studies observing associations between cord blood levels of the hormones adiponectin and leptin and pre- and post-natal growth. These hormones are involved in signaling pathways associated with growth, metabolism, and the immune system thus providing a possible link between fetal growth and disease111213161719The primary aim of this analysis was to investigate associations between GWG throughout pregnancy and both fetal (cord blood) and maternal (serum collected at birth) levels of adiponectin and leptin. As we have previously identified potential associations between leptin and birth-related factors including duration of laborData were obtained from the Ulm Birth Cohort Study (UBCS) and the Ulm SPATZ Health Study, two methodologically similar birth cohort studies including newborns and their mothers recruited from the general population shortly after delivery in the University Medical Center Ulm, Southern Germany, respectively from 11/2000–11/2001 and 04/2012–05/2013. Details can be found elsewhere22Exclusion criteria were outpatient delivery, maternal age <18 years, transfer of the newborn or the mother to intensive care immediately after delivery, and/or insufficient knowledge of the German (UBCS and SPATZ), Turkish, or Russian (both UBCS only) language. At baseline, the UBCS and SPATZ cohorts respectively included 1090 newborns of 1066 mothers  and 1006 newborns of 970 mothers . For the purposes of this analysis, the study populations were restricted to singleton full-term  newborns. In order to ensure comparability among results, we also restricted to mother-infant pairs for whom results were obtained for all three biomarkers in both cord blood and post-delivery maternal serum. Ethical approval was obtained from and all study protocols were carried out in accordance with guidelines approved by the ethics board of Ulm University . Participation was voluntary and written informed consent was obtained in each case. Exposure, outcome, and confounder definitions as well as statistical methods were identical for both studies unless specifically stated otherwise.Maternal demographic and lifestyle data including age at delivery, education (<12 years or ≥12 years), parity (first birth or greater), smoking history (within the year prior to pregnancy) were collected using a self-administered questionnaire during the hospital stay following delivery. Clinical data related to the child’s delivery including gender, birthweight, delivery mode, and duration of labor were obtained from electronic hospital records. Clinical data related to the mother’s pregnancy, including gestational weight measurements, were obtained from routine paper documentation known in Germany as “Mutterpass”, which obstetricians are required to issue to their patients when pregnancy is clinically established and which are generally updated at each clinical visit during pregnancy.Pre-pregnancy weight and height were estimated based on self-reported data documented in “Mutterpass” during the mother’s first obstetric appointment at which pregnancy was clinically established. Trimester specific and total weight gain were calculated using obstetrician-documented weight measured nearest to the end of the first (day 85), second (day 190), and third (day closest to full-term delivery) trimesters. Start of pregnancy was calculated as 280 days before the obstetrician reported full-term delivery date estimated at approximately 12 weeks gestation using fetal ultrasound measurements. As weights were seldom recorded on the trimester end-date, only weights recorded on the closest day within 14 days of the target date were used in the analysisCord blood was collected in S-Monovette 7.5 ml serum-gel tubes  by midwives or obstetricians shortly after delivery, centrifuged, and stored in a refrigerator until further processed for long-term storage at −80 °C by trained study personnel. In SPATZ, average time until long-term storage was 2.4 days (sd = 0.8 days); in UBCS the process was similar, but the time was not recorded. Maternal blood was collected during routine blood collection following delivery (after 24 h for most mothers). Adiponectin, leptin , and high-sensitivity CRP  concentrations were measured by ELISA . Biomarker measurements reported as below detection limit (SPATZ: n = 1) were imputed to the lower detection limit. Hs-CRP levels above 200 μg/L have been associated with amniotic fluid infection, therefore subjects with cord blood hs-CRP measurements above 200 μg/L were excluded from the analysisFor UBCS, adiponectin and leptin were measured in 06-07/2005 in Heidelberg, Germany, whereas hs-CRP was measured in 08-10/2009 in a research lab at Medical Center Ulm from continuously frozen stored material. For SPATZ, all three markers were measured in the latter research lab in cord blood and post-delivery maternal serum in 06-08/2013 with the same equipment and by the same technician as in UBCS. Hs-CRP levels differed between both studies with the whole distribution shifted by approximately 10 μg/L towards higher levels in UBCSTo assess the impact of missing data and restrictions, characteristics (proportions or means) of each study sample were compared to 95% confidence intervals of the characteristics within the corresponding full cohort. Chi-square and Kruskall-Wallis tests were performed to identify significant differences  in characteristics across study cohorts. Biomarkers were log transformed to account for right skewed distributions (leptin and CRP) or for comparability (adiponectin). Biomarker measurements outside 3 standard deviations from the underlying mean were considered potential outliers and excluded in sensitivity analyses. Linear regression models were used to estimate geometric means ratios (GMR) for the effect of a one standard deviation difference from the underlying mean trimester specific and total GWG and of a 5 kg change in difference from IOM recommended weight gain separately with each biomarker . Maternal age, education, parity, smoking history, and pre-pregnancy BMI, as well as, the child’s gender, birthweight, mode of delivery, and duration of labor were tested as putative confounding factors. As crude effects were small, putative confounders producing a 1% change in GMR after single adjustment of models measuring the effect of total GWG on any cord blood biomarker were selected for inclusion in adjusted models. All statistical analyses were performed by using SAS 9.4 .Following restriction to singleton full-term newborns, complete GWG and valid biomarker data were available for 540 UBCS and 412 SPATZ subjects . Due to 2) cohort compared to that observed in UBCS (16% of subjects).Overall, GWG trajectories were comparable between cohorts with the exception of obese SPATZ mothers who appeared to exhibit a lower weight gain trajectory following the first trimester . By delird trimester in SPATZ . Following adjustment for maternal smoking history and duration of labor, associations were slightly attenuated but remained significant (p < 0.05) in UBCS and for the 3in SPATZ . Pattern in UBCS . No assoth week of pregnancy compared to those classified as having “low” or “normal total pregnancy weight gain.To further explore strong associations observed between difference from IOM-recommended weight gain and leptin, we plotted weight gain trajectories for subjects who were categorized as below (“low”), within , or above (“excessive”) specified recommended total weight gain ranges 25. In boIn our analyses of two birth cohorts recruited from the general population with identical sampling and measurement methodologies and a time lag of 11 years, we observed significant positive association between GWG beginning as early as the second trimester and cord blood leptin. GWG beginning as early as the first trimester was associated with higher levels of post-delivery maternal serum leptin measured shortly after birth. Total weight gain exceeding IOM recommendations was consistently associated with higher leptin levels in both cord and post-delivery maternal serum. Finally, first trimester GWG was indicative of IOM total weight gain category. These results suggest GWG beginning as early as the first trimester might influence expression of leptin in the fetus, the newborn and possibly later in life. Furthermore, lack of consistent association between GWG and hs-CRP suggests the association with leptin was not likely associated with the inflammatory response at birth.Though we identified two previous studies reporting similar findings with cord blood outcomes, we believe we are among the first to concurrently report on associations between total and trimester-specific GWG and adiponectin, leptin, and hs-CRP levels in both cord blood and post-delivery maternal serum. Our results support those of the Rhea group, who have previously identified similar positive associations between IOM-recommended GWG and cord blood leptin27rd trimester16Interestingly, we observed similar but stronger patterns of association between GWG and leptin levels measured in post-delivery maternal serum compared to those observed in cord blood. Maternal serum leptin increases beginning in early pregnancy before peaking in the 3in-vitro study has demonstrated the possibility of cross-placental transfer of circulating leptin in human choriocarcinoma BeWo cellsThough we observed correlation between fetal and maternal leptin levels in our cohorts , it is cMaternal leptin levels in pregnancy have also been associated with pregnancy-related health conditions including gestational diabetes and pre-eclampsiaThe key strength of our study was our ability to replicate results in two geographically and methodologically similar but demographically different cohorts. Despite the demographic differences, GWG, biomarker outcomes, and overall results were similar to one another. However, it should be noted that point estimates for both cord blood and post-delivery maternal serum leptin were consistently higher in the UBCS cohort. Though we were unable to identify a specific reason for this difference, factors associated with differences between studies in GWG trajectory amongst obese mothers may warrant further exploration.As with other studies of this nature, some key limitations must also be acknowledged. First, approximately 31% of eligible UBCS mothers and 43% of eligible SPATZ mothers were excluded from our analyses due primarily to restriction to mothers with gestational weight measurements reported within 14 days of the end of each trimester and delivery. In most cases, these subjects were missing first trimester weight measurements which may have been due to a number of factors, including late establishment of pregnancy. However, no significant differences in demographic or lifestyle factors were observed due to missing data. Furthermore, several sensitivity analyses in which we included all available data for each model, less stringent restriction, and/or wider acceptable date ranges for trimester-specific weight gain estimation produced similar point estimates to reported results. Second, biomarkers were only measured upon (cord blood) and after  delivery and post-delivery maternal serum collection time varied. As GWG was only marginally associated with hs-CRP in UBCS cord blood and not associated with hs-CRP in maternal serum, it is unlikely more proximal factors at birth, such as duration of labor, affected levels of the other biomarkers. Nevertheless, we were unable to specifically determine precisely when and how biomarkers may have changed over the course of pregnancy or the possibility of reverse causality. Lastly, although specific timing of post-delivery maternal serum collection was unavailable in UBCS, we observed a weak but significant correlation between duration from delivery to blood collection and maternal adiponectin and leptin respectively in SPATZ. Adjustment for time to collection did not affect SPATZ post-delivery maternal serum model results.Despite certain limitations, our results from two independent cohorts provide strong evidence supporting an association between GWG and fetal-maternal leptin. Although GWG is likely only one of a number of factors associated with leptin synthesis or expression, our results are in line with the idea that GWG may be an important exposure during early fetal development which may potentially influence long-term health in offspring. Therefore, further research may be warranted to prove and better understand causality of the association and the potential effects on fetal development, as well as the long-term consequences of hypo- and hyper-leptinemia during gestation.How to cite this article: Logan, C. A. et al. Gestational Weight Gain and Fetal-Maternal Adiponectin, Leptin, and CRP: results of two birth cohorts studies. Sci. Rep.7, 41847; doi: 10.1038/srep41847 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14  The white matter (WM) of the human brain consists of dense bundles of neuronal axons connecting the brain's functional areas. Neural circuits thus formed allow these areas to work together as a coherent entity. Changes in WM impact these neural circuits, and are thus the subject of studies investigating pathology  model  compartment is not included, and derive explicit relations between the remaining parameters of this model and MD and FA. We call this modified version of the NODDI model the “NODDI-DTI” model because these relations formally allow extraction of neurite orientation and dispersion parameters using MD and FA from DTI. The existence of relations between NODDI-DTI and DTI parameters explains previously observed correlations between NODDI parameters and MD and FA  over the neurite distribution, where ψ is the angle between a given neurite and the main neurite orientation  to 1 —increasing τ corresponds to increasing neurite alignment—and is the average of cosBy expanding the NODDI-DTI signal model in moments, one can derive a corresponding DT  has been independently derived by Lampinen et al. .Equations (2) and (3) demonstrate a one-to-one mapping from  to , implying that, formally, NODDI-DTI parameters can be extracted from DTI data. We can predict domains within which MD and FA should lie if the NODDI-DTI model provides a valid representation: substituting ν∈ and τ∈ into Equations (2) and (3) gives the domainsValues of MD and FA lying outside of these bounds will give rise to unphysical ν and τ estimates, and could result either from errors in quantifying the DT , or from the invalidity of the NODDI-DTI model as a representation in a given voxel.b-values (multi-shell data), but this extra measurement is usually not practical  DTI data , derived in Appendix where λThe effect of failing to correct for diffusional kurtosis is much less pronounced for FA . The investigation involving the first 2 subjects was carried out in accordance with the recommendations of “Quality assurance and optimization of magnetic resonance imaging sequences and processing for non-invasive neuroimaging in human subjects,” approved by the NRES Committee London - Queen Square. The investigation involving the group of 12 subjects was carried out in accordance with the recommendations of ethics agreement number PV5141, approved by the Ärztekammer Hamburg. All subjects gave written informed consent in accordance with the Declaration of Helsinki.The data from subjects 1 and 2 were used to validate the heuristic diffusional kurtosis correction and provide preliminary validation of the stability of the NODDI-DTI model and method. The data from subjects 3–14, recorded with a different protocol, complemented the analyses of the data from subjects 1 and 2 by allowing insight into whether the behaviour of the NODDI-DTI method is stable over a wider number of subjects.2, 81 slices, 1.7 mm isotropic resolution, echo time: TE = 112 ms, volume repetition time: TR = 4, 835 ms, partial Fourier factor: 6/8, multiband factor  images per shell interleaved between the weighted acquisitions, 2 × phase encoding polarities (Anterior → Posterior/Posterior → Anterior).The first two datasets (subjects 1 and 2) were recorded using a 2D multiband spin-echo echo-planar imaging (EPI) sequence supplied by the Center for Magnetic Resonance Research, University of Minnesota .The last 12 datasets (subjects 3–14) were recorded using a twice-refocused spin-echo EPI sequence also supplied by the Center for Magnetic Resonance Research, University of Minnesota ; for details see Mohammadi et al. .Parameters were estimated only in WM voxels determined to be largely unaffected by CSF or grey matter partial volume effects. This determination was made by thresholding at 50% probability a WM probability map obtained by segmenting the first b-value shell of each dataset, and in-house SPM scripts were then used to generate ν and τ using Equations (3) and (6), respectively, from these DT parameters; we refer to these as the “NODDI-DTI method” results. For subjects 1 and 2, the ACID toolbox was also used to simultaneously estimate the diffusion and kurtosis tensors  less biased by the effects of diffusional kurtosis  simulations. Data S were simulated with the NODDI toolbox v0.9 (http://www.nitrc.org/projects/noddi_toolbox/) using the diffusion protocol of subjects 1 and 2 for parameters typical of the corpus callosum  ≈ 0.9. The main fibre orientation was taken to be along t, where ·t denotes the transpose operation. Rician noise  = |S+l+im|, where l and m were each drawn from a Gaussian distribution of mean zero and standard deviation 1/, and i is the imaginary unit. Because the intensity of the simulated b = 0 signals was unity, this choice of standard deviation gave a respective signal to noise ratio (SNR) ≈  for the b = 0 signals ]. In order to test whether the NODDI-DTI method ν estimate could be improved by taking into account the diffusional kurtosis bias explicitly, both shells of data were also fit to log) = −bMD+b2C by solving log) = t using Matlab's backslash function; this gave an MD estimate less biased by higher order diffusion moments. Estimation of spherically averaged diffusion parameters in such a manner has been reported previously  to allow analysis of the accuracy of the various methods in the presence and absence of noise. Histogram widths were those chosen by Matlab's histogram function for the SNR ≈ 5 NODDI-DTI method parameter estimates. Unphysical NODDI-DTI method parameter estimates are given as percentages of the total number of trials, and were not included when plotting the histograms.Parameters were estimated from the noise-corrupted simulated data by linearly fitting a diffusion tensor to the log-transformed first shell of data and then applying Equations (3) and (6), and also by using the NODDI toolbox to fit the NODDI-DTI model to both shells of data [converting the fitted For each subject, NODDI-DTI silver-standard results were obtained by fitting both shells of data using the NODDI toolbox with CSF volume fraction fixed at zero, followed by conversion of κ into τ using an in-house SPM script implementing Equation (1). We refer to these results as the “two-shell NODDI toolbox fitted” results, and they represent a silver-standard because two shells of data are sufficient to make inversion of multi-compartment signal models well-posed  were arbitrarily replaced with 0 in ν maps and 1/3 in τ maps. Using the SPM12 normalisation tool, the first h, is less biased than the uncorrected MD. The numerical values (mean ± one standard deviation) of the differences in Figure h); and for subject 2: 0.119 ± 0.041 (MD), 0.029 ± 0.041 (MDh). The effect of this bias on NODDI-DTI estimates is evidenced in Figure The Bland–Altman plots in Figure Simulations allowed further insight into the behaviour of the NODDI-DTI relations. Shown in Figure The similarity of parameters estimated using the NODDI-DTI method to the silver standard results can be seen in Figure Equations (3) and (6) gave unphysical parameter estimates for some voxels. For subjects 1 and 2 such unphysical ν estimates constituted 2.07% and 2.19% of the total WM voxels respectively; for τ such estimates constituted 0.27% and 0.74%. Subjects 3–14 showed more variation in terms of the numbers of failed voxels, constituting 3.65%–13.39% of WM voxels for ν, and 0.07%–0.37% of WM voxels for τ. Generally, then, the proportion of unphysical parameter estimates was higher for ν than for τ, and a strong variability in the proportion of failed voxels was evident between the different subjects.The magnitudes of the means and standard deviations of the differences between the NODDI-DTI parameter estimated using a single shell of data and the two-shell NODDI toolbox fits are shown in Figure This work has demonstrated that, with caveats to be discussed below, parameters of potential neurobiological relevance can be estimated from DTI parameters in healthy white matter using the NODDI-DTI relations, Equations (3) and (6). Correction of the estimated DTI parameters for diffusional kurtosis proved essential in relating these experimental parameters quantitatively to the underlying biology. Importantly, the improved interpretability gained through the NODDI-DTI relations is not only applicable to future DTI studies, but also to existing DTI studies.As an example of the potential use of the NODDI-DTI method in reinterpreting existing DTI studies, we apply the method superficially to the study of Scholz et al. , who demUnfortunately, the NODDI-DTI method did not always give physically plausible parameter estimates for the datasets studied herein. We posit four overlapping explanations for these unphysical estimates, related to NODDI-DTI assumptions.DTI parameters can be accurately estimated from the diffusion signal. Errors in DTI parameter estimation will lead to errors in parameters estimated using Equations (3) and (6), potentially giving unphysical parameter estimates. The b-values used likely resulted in overestimates of FA in regions of high anisotropy due to poor estimates of the smallest DT eigenvalue . Figures Assumption 2: can be heuristically corrected for diffusional kurtosis bias. Figures Assumption 3: MD Residual bias in the region around the corpus callosum of the ν difference maps in Figure The NODDI-DTI signal model is a valid representation of the diffusion signal. Several criticisms have been levelled in the literature against assumptions made by the NODDI model  (appropriately corrected using Equation 5). The lack of a CSF volume fraction could potentially be mitigated without requiring extra data acquisition by incorporating the free water elimination method  more specific neurobiological interpretation of observed microstructural changes in DTI data (including interpretation of existing datasets), and (b) simple and time efficient estimation of biophysical parameters from smaller diffusion datasets, despite limitations due to the underlying model and difficulties estimating accurate diffusion tensors.LE and SM developed the initial idea, and acquired and analysed the data. IE and SM acquired and preprocessed the data for the group of 12 subjects. LE wrote the manuscript and developed the theory, with substantial critical revisions for intellectual content and interpretation of the data provided by all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To compare the accuracy of clinical and ultrasonographic (USG) estimation of fetal weight in non-complicated, term pregnancies.Two hundred term pregnant women were included in the study. We used three formulae for the estimation of fetal weight at term; the Hadlock formula for the USG method, and two different formulas for clinical methods, maternal symphysis-fundal height and abdominal circumference at the level of umbilicus. Accuracy was determined by mean percentage error, mean absolute percentage error and proportion of estimates within 10% of actual birth weight (birth weight ±10%). Patients were divided into two groups according to actual birth weight, the normal birth weight group (2500-3999 g) and high birth weight group (≥4000 g).All three methods statistically overestimated birth weight for the high and normal birth weight groups  . For both groups, the mean absolute percentage errors of USG were smaller than for the other two clinical methods and the number of estimates were within 10% of actual birth weight for USG was greater than for the clinical methods; the differences were statistically significant (p<0.001). No statistically significant difference of accuracy was observed for all three methods for the high birth weight group . However, USG systematically underestimated birth weight in this group.For estimation of fetal birth weight in term pregnancies, ultrasonography is better than clinical methods. In the suspicion of macrosomia, it must be remembered that no method is better than any other. In addition, if ultrasonography is used, careful management is recommended because ultrasonography overestimates in this group. We evaluated the effectiveness of ultrasound and clinical examination methods for fetal weight estimation.,2. However, the use of clinical methods has decreased with the increased use of ultrasonographic (USG) for fetal weight estimation in recent years. Studies have shown that the mean absolute percentage failure rate of USG estimations generally range between 6% and 12%, and the estimation rate of actual birth weight, which was ±10%, was between 40% and 80%,4,5,6.Over the last two or three decades, fetal weight estimation has almost become a routine antepartum test in the management of high-risk pregnancies and birth. The two methods used in modern obstetrics practice are the clinical evaluation of fetus weight and ultrasound. Through the late 1960s, external clinical examination became the most frequently and widely used method for the determination of fetus weight and dimensions. Studies have shown that 80-85% of clinical estimations were between ±500 grams of the actual birth weight (ABW) and 69% were between ±10%,8. Ultrasound, as a technical, objective, and repeatable method, is generally perceived as superior to clinical estimation. Studies in the literature that compared the two methods found different results,10,11. These studies often involved estimations through evaluation of uterus size externally with use of a physician’s hands. This method is the oldest and most popular method and there have been doubts about its use because it is not objective. Therefore, it would be beneficial to use a simple, easy, cheap, but standard and objective clinical method as an alternative to USG, which is expensive and not always easy to access, especially in countries with limited financial resources for health. There are objective and standard clinical methods; however, the number of studies that evaluate the effectiveness of these methods or compare them with USG, is quite low.Various researchers have suggested that palpation of the uterus is insufficient for the estimation of fetal weightThe purpose of this study was to compare the accuracy of clinical and USG estimation of fetal weight in non-complicated term pregnancies.th 2008, and August 29th 2009, were included in the study. All pregnancies were at 37 gestational weeks with normal amniotic fluid index (50-200 mm), vertex presentations and labor had not yet begun. In a number of patienst, a vaginal examination during labor concluded with a maximum 3 cm dilatation and 60% effacement. The lowest part of the fetal head was over the interspinal line and the engagement had not yet begun. Gestational week was identified through counting the days according to the last menstrual period or a USG measurement performed before the 22nd week of pregnancy. Exclusion criteria were polyhydramnios, preterm labor, multiple pregnancy, intrauterine growth retardation, early membrane rupture, abnormal presentation, antepartum hemorrhage, eclampsia, preeclampsia, gestational diabetes mellitus (GDM), known congenital fetal anomaly, systemic disease in the mother, and placental anomalies.This prospective single-center study was approved by the local ethics committee of Etlik Zübeyde Hanım Women’s Health Teaching and Research Hospital and informed consent was obtained from all the patients. Two hundred women with term pregnancies, who were consecutively admitted to our department between November 10Patients that fulfilled the study criteria were registered on the monitoring chart including their name and surname, age, height, weight, gravida, parity and pregnancy age. Next, a clinical examination was made when they were lying on their back with their hands on both sides, legs stretched, and with an empty bladder. First, the distance between the symphysis pubis mid point of the top corner and the uterus fundus top point was measured on the naked abdomen of the mother using a non-flexible, curved tape measure (SPFU). In addition, abdominal circumferences of the mother at the umbilicus level (AP) were measured in centimeters. Ultrasound predicted fetus weight was then measured in grams and recorded. All USG measurements were performed using a Logic P 5 USG device, which had a 3200 MHz trans-abdominal probe; a three-parameter Hadlock formula  was used. All sonography and clinical measurements were performed on the same day by the same researcher, who was in his fifth year as an assistant.For the BPD measurement, the greatest distance between the outside corner of the front parietal bone and the inner corner of the back parietal bone was measured at a right angle using electronic indicators on the frozen image, where falx cerebri echo was observed at the mid-line, the thalamus were monitored symmetrically on both sides; the cavum septum pellucidum was ⅓ of the distance from the frontal occipital. On the same platform, occipitofrontal diameter (OFD) was measured from the outside corner of the frontal bone to the outside corner of the occipital bone, parallel to the falx cerebri with a right angle to the BPD. Cephalic index (BPD/OFDx100) was calculated in order to exclude dolichocephaly and brachiocephalic. The cephalic index value was included in the study and was between 0.77 and 0.83. Where all long axes of the femur were monitored, the distance between the two ends of the diaphysis was measured for FL, without including the femoral head and distal epiphysis. With respect to the AC measurement, first, using the fetal spine or fetal aorta, the direction of the longitudinal axis of the fetal body was identified. The fetal umbilical vein was obtained, along with a circular and symmetric abdominal image at a right angle to these structures at the level of the stomach. On the platform, where the umbilical vein was monitored at the front ⅓ it gave the portal branch, the stomach was monitored without monitoring the heart and bladder, and a chamber surrounding the skin echo from the outside corner to the other outside corner was created and measurements were performed. Following the recording of the three biometric data, fetal weight was automatically measured and recorded using the USG device. The mother was monitored in the delivery room.Two formulas were used for clinical estimations. In the first formula, SPFU and AP were multiplied in centimeters and TFW was calculated in grams.Formula 1: TFW (g)=SPFU (cm) x AP (cm)The Jonson formula was used as the second formula.Formula 2: TFW (g)=(SPFU (cm) - n) x 155 . Where the weight of the mother was equal to more than 91 kg, 1 cm was reduced from the fundus height.Fetal weight estimations obtained through the three different methods were registered in the monitoring chart. Finally, a nurse measured actual birth weight in grams using an electronic scale within the first 10 minutes after birth; actual weight was recorded along with the time between examinations and birth.The patients in the study were divided into two groups according to the postpartum measured actual fetal birth weight; group 1 with actual birth weight of 2500-3999 g and group 2 with actual birth weight t equal to or more than 4000 g. In order to determine the performance of USG: formula 1 and formula 2 were used to predict intrauterine fetal weight at the prenatal stage; and mean failure percentage (MFP) [ x 100/actual birth weight]; mean absolute failure percentage (MAFP)  x 100/actual birth weight) values; and the percentage of estimations that could predict actual birth weight within ±5%, ±5.1-10.0%, ±10.1-20.0%, and more than 20.0% of failure were calculated separately for each method in all three groups. In all groups, the amount of failure in estimations made using the Bland-Altman method (bias) and 95% confidence intervals of the fitness level were calculated. Furthermore, estimation rates belonging to the standard deviation value of 1 and standard deviation value of ±375 g were calculated in all groups for formula 1 and formula 2.Two hundred women whose pregnancies were at the 37th gestational week with normal amniotic fluid index (50-200 mm), vertex presentations, and labor had not yet begun, were included in the study. Demographic characteristics of the women are shown in Birth weight was less than 4000 grams in 88% (n=176) of the women and more than 4000 grams in 12% (n=24). In the group where actual birth weight was less than 4000 g, and fetal weights were estimated with USG, formula 1 and formula 2 were statistically significantly higher , respectively. Birth weights estimated with formula 1 and formula 2 were statistically significantly higher than birth weights estimated with USG (p<0.001). In addition, birth weights estimated with formula 2 were statistically significantly higher than birth weights estimated with formula 1 (p<0.001) .In the group where the actual birth weight was 4000 g and higher, birth weight estimated with USG was statistically significant and a mean of 201 g less than the actual birth weight (p<0.001). Birth weight estimated with formula 1 was a mean 39 g less than actual birth weight and was not found to be statistically significant (p=1.000). Birth weight estimated with formula 2 was a mean 48 g less than actual birth weight and again was not found to be statistically significant . The dif,10 others reported that the success of both methods was similar or that the clinical method was better,12. The differences in the conclusions might have been influenced by non-homogenous samples: term and preterm fetuses were evaluated together, maternal demographic characteristics were different, small sample sizes, different sonographic and clinical methods used, and variations in definitions and diversity of statistical analyses performed. It is important to ensure standardization in such studies because of the redundancy of reasons that cause differences. In order to do this, in our study we included pregnant women who were in the 37th week or later stage of pregnancy, whose actual birth weight is 2500 g and higher, who are singular, in vertex presentation, with Anxiety sensitivity index (ASI) within normal limits, no fetal macroscopic anomalies detected before and after birth, no known maternal systemic disease and complications, and who were not yet in labor or whose labor had just started. Principally, we aimed to assess ultrasound and clinical examination of fetal weight estimation in term pregnancies under normal conditions.Although some researchers found sonographic estimations better than clinical estimations,,10. When we looked at the success rates of clinical estimations, 80-85% of estimations were within ±500 g of the actual birth weight and 55-72% of estimations were within ±10% of the actual birth weight; the MAPE varied between 7.5% and 19.8% in term pregnancies,2.In studies of fetal weight estimation, generally two statistical parameters have been used in the evaluation of effectiveness; Mean absolute percentage error (MAPE) (with Mean error percentage) and estimation rates were within ±5-10% of actual birth weight. In the literature, it was observed that the MAPE given for the success of sonographic estimations was between 6% and 12% and the estimation rate within ±10% was between 40% and 80%In our study, it was observed that all three methods estimated birth weight systematically higher in the group in which actual birth weight was less than 4000 g. According to the Bland-Altman method, the differences among groups were found to be statistically significant. The order of most successful estimations was USG >formula 1 >formula 2. In the group in which birth weight was 4000 g and higher, both MAPE values and rates of estimation within ±5% and ±10% ranges were found to be similar. As in the group with normal birth weight, USG was most successful, formula 1 was on moderately successful, and formula 2 was least successful in terms of estimation within acceptable limits for all participants.In the group in which birth weight was higher than 4000 g, the MAPE values of all three methods were lower and estimation rates within ±10% were higher compared with the other two groups. However, it was observed that MAPE values (p=0.452 and p=0.369), and estimation rates of actual birth weight within ±10% (p=0.432 and p=0.632) were similar for USG and formula 1 in both <4000 g and ≥4000 g groups; there was no statistically significant difference between them. However, although the MAPE values were found to be lower for formula 2 in the ≥4000 g group compared with the <4000 g group (p=0.025), the estimation rate within ±10% was found to be significantly higher (p=0.046). Consequently, it can be said that formula 2 was successful with large fetuses.,14,15. Sherman et al. compared external examination and USG (Shepard-Hadlock) in 1717 fetuses with a mean birth weight of 3334 g, 85% of which were term and had no membrane rupture. The authors observed that USG systematically estimated lower in the 2500-4000 g range and the estimation rate within ±10% was 70.6%. There were no systematic errors in the clinical method whose rate was 75.1%; the authors concluded that the clinical method was superior to USG for normal-weight fetuses. For 4000 g and higher, they found that estimation rates within ±10% were 58.8% for USG and 61.3% for the clinical method; both methods estimated systematically lower but there was no statistically significant difference between the two methods. The lowest error and highest estimation rates with both methods were obtained in the group of fetuses with normal weight. Baum et al. reported that USG was not superior to the clinical and mother’s estimation, and Watson et al. reported that there was no difference between the two methods, even in extreme term weights. In two different studies that compared the two methods, error rates were found to be less than 10% and no superiority of one method over the other was reported. One of the few studies in which the effectiveness of the clinical method compared to USG in terms of fetal weight estimation was carried out by Shittu et al. in Nigeria in 2004. In their study of 100 pregnant women, which resembles our population, the authors compared Hadlock 2 and formula 1 and reported that neither of the two methods was superior the other in the whole sample of 2500-3999 g and 4000 g and higher fetuses. In all three groups, systematically lower estimations were made with USG and higher estimations were made with clinical methods. In Shittu et al. study, the number of fetuses that weighed 4000 g and higher was 17. When we compared the MAPE values and the estimation rates within ±10% for 24 patients in the same group in our study with those found by Shittu et al., it was observed that better results were obtained in large fetuses with both methods, except in the ±10% ratio for formula 1  by measuring SPFY and reported that 72.3% of their measurements were within ±10% of actual birth weights. The estimation rate of Görgen et al. for this difference was 75.6%. In our study, the estimation rate within ±10% for formula 2 was 56%, the estimation rate for ±375 g level was 62%, and for 1 SD value of ±430 g was 78.6%, which were similar to the other two studies.Görgen et al.Physicians will not always encounter pregnant women with standard characteristics as in this study. Therefore, it is necessary to investigate the effectiveness of USG in different pregnant populations. These can be listed as the most frequently encountered preterm labor, fetuses lighter than 2500 g, oligohydramnios, early membrane rupture, preterm premature rupture of membranes, breech presentation, maternal obesity, and GDM. reported that sonographic estimations, carrying out the labor in active and latent phase, and presence of membrane rupture had no significant impact on the success of estimation. Benacerraf et al. reported that oligohydramnios and polihydramnios did not affect the estimation success following their study of 1301 fetuses, whereas Barnhard et al. suggested that estimations should be made before amniotomy because oligohydramnios significantly reduces the success of sonographic and clinical estimations. Scioscio et al. determined no difference between cephalic and non-cephalic presentations. Blann reported that cervical dilatation and head level were not effective on sonographic and clinical estimation.In our study, labor did not begin in all groups and engagement of fetal head had not occurred in the latent-or early phase. Amniotic membranes were intact, ASI was within normal limits, and all fetuses were in vertex presentation. In a study of 218 fetuses, Çelik on 1301 pregnant women, intervals varied between 0-7 days as in our study, and 80% of the women gave birth between the 1st and 5th days. The additional time did not affect the results. No corrections were made for additional time because all of the births in our study occurred within 7 days and 94% occurred within the first 48 hours.In our study, ultrasonographic and clinical measurements were carried out on the same day and all deliveries occurred within seven days of the estimation. Some 90.5% of the deliveries occurred on the same day and no corrections were made for time difference. The weight of fetuses increased by a mean 25-30 g a day and 200-250 g a week during term. In the study of Benacerraf et al.These results will be valuable and useful in groups with similar characteristics to the patients in this study. However, pregnant women with these characteristics will not be encountered in daily practice. Therefore, studies should be performed that compare USG and objective clinical examination methods, various commonly used ultrasound methods, and include various obstetric populations such as preterm labor, membrane rupture, various labor phases, GDM, non-vertex presentations.


Lack of legal identification documents can impose major challenges for children in low- and middle-income countries (LMICs). The aim of this study was to investigate the association between not having a birth certificate and young children’s physical growth and developmental outcomes in LMICs.We combined nationally representative data from the Multiple Indicator Cluster Surveys in 31 LMICs. For our measure of birth registration, primary caregivers reported on whether the child had a birth certificate. Early child outcome measures focused on height-for-age z-scores (HAZ), weight-for-age z-scores (WAZ), weight-for-height z-scores (WHZ), and standardized scores of the Early Childhood Development Index (ECDI) for a subsample of children aged 36–59 months. We used linear regression models with country fixed effects to estimate the relationship between birth registration and child outcomes. In fully adjusted models, we controlled for a variety of child, caregiver, household, and access to child services covariates, including cluster-level fixed effects.In the total sample, 34.7% of children aged 0–59 months did not possess a birth certificate. After controlling for covariates, not owning a birth certificate was associated with lower HAZ , WAZ , and ECDI z-scores  among children aged 36–59 months.Our findings document links between birth registration and children’s early growth and development outcomes. Efforts to increase birth registration may be promising for promoting early childhood development in LMICs.The online version of this article (10.1186/s12889-018-5598-z) contains supplementary material, which is available to authorized users. The United Nations Convention on the Rights of the Child entitles every child to be registered immediately after birth . Birth rEnsuring that all children survive and thrive, receive good health care and education, and have equal chances to achieve their full developmental potential during their early years are also key pillars of the SDGs , 6. MoreLack of birth registration violates children’s fundamental rights, including their right to nationality, and may also hinder young children’s access to targeted health services and social welfare programs (e.g. cash transfer schemes) and enrollment in school –17. WithTo date, much of the global literature on birth registration has been at a macro-level: arguing principally from a rights-based legal approach and emphasizing the normative importance of birth registration , or advoWe used data from UNICEF’s Multiple Indicator Cluster Survey (MICS), an international household survey program that collects information about the health, nutrition, education, and development of children in LMICs. The MICS is unique for collecting and monitoring ECD in a standardized and comparable way across LMICs, and remains the primary data source to measure and monitor ECD outcomes. We combined all nationally representative surveys from MICS rounds 4 and 5 (2010–2014) that were publicly available prior to January, 2017. We restricted our sample to children who had data on birth registration and either data on anthropometric outcomes or data on the Early Childhood Development Index (ECDI), which is primarily collected for preschool children aged 36–59 months.We examined four child outcomes relating to early nutrition and development: height-for-age z-scores (HAZ), weight-for-age z-scores (WAZ), weight-for-height z-scores (WHZ), and ECDI z-scores. Z-scores for anthropometric measures were computed using the 2006 WHO Multi-center Growth Reference Study standards . BiologiEarly child development was measured using the ECDI. Developed by UNICEF for 3- and 4-year-olds surveyed in the MICS household survey program, the ECDI is comprised of 10 caregiver-reported, dichotomously-scored questions to assess 4 developmental domains: cognitive, socioemotional, literacy-numeracy, and physical development. These 10 items were determined through multi-country field tests, validity, and reliability studies, and deliberation with experts . This poOur primary independent variable of interest was lack of a birth certificate. In the MICS questionnaire, two items directly capture birth registration: first, caregivers are asked to show the interviewer the child’s birth certificate. If a birth certificate is not available, caregivers are asked whether the child ever had a birth certificate, and if not, whether the child’s birth had been registered with the civil authorities. For our empirical analysis, we created a no birth certificate indicator variable, which was coded 1 if the child did not ever have a birth certificate and 0 if the child currently had or previously had a birth certificate.We adjusted for a variety of child-, caregiver-, and household-level covariates. Child characteristics included age (in months) and sex . Caregiver characteristics included maternal and paternal highest level of education , maternal age (5-year age categories from 15 to 49 years), and maternal marital status . Household characteristics included household wealth index  and plaWe conducted a complete case analysis upon verifying that missingness was not systematic. We specified a series of four linear regression models with varying controls for potential confounders and mediators to estimate the association between lack of birth registration and each of the four outcome variables of interest: HAZ, WAZ, WHZ, and ECDI z-score among children aged 36–59 months. Model 1 only adjusted for child age, sex, and country fixed effects. Model 2 further adjusted for all caregiver- and household-level demographic and socioeconomic covariates . Model 3 further adjusted for variables representing utilization of services that may relate to both birth registration and ECD outcomes (children’s vaccinations and early childhood education programs). Finally, Model 4 additionally included primary sampling unit (PSU)/cluster-level fixed effects, which can account for other observable and unobservable differences in socioeconomic, environmental, and institutional characteristics of local enumeration areas that are common to all respondents from that area . Standard errors across all models were clustered at the PSU-level to account for the complex MICS survey design. All analyses were conducted using Stata version 13 .First, to assess whether pooled findings were robust, we conducted separate country-specific models (fully adjusted Model 4) for the associations between lack of birth registration and each of the four child outcome variables; and employed random-effects meta-regressions to re-estimate a pooled effect that accounts for the varying sample sizes across country surveys. Second, to examine whether not having a birth certificate was related to child outcomes as early as in the first 3 years of life and whether the magnitude of these associations increased by child age (categorized in 12-month age groups) we re-specified Model 4, excluding early childhood education attendance as a utilization of service covariate. This allowed us to explore the associations between not having a birth certificate and children’s HAZ and WAZ outcomes in a separate sample of younger children aged 0–35 months, for whom anthropometric data, but not the ECDI, were available.N = 65,425, 29.4% of original sample) that were excluded due to missing data on full covariates. Sample characteristics for the total sample of children are presented in Table A total of 157,336 children aged 0 to 59 months from 31 countries were represented in the full sample. No significant differences were detected between the complete cases in the analytic sample and the incomplete cases (Approximately one in three children under-5 (34.7%) did not possess a birth certificate. The average proportion of children without a birth certificate varied across countries, ranging from as low 0.2 and 0.5% in Ukraine and Thailand, respectively,  to as high as 80.8 and 95.0% in Guinea Bissau and Malawi, respectively , with 26.2% of children classified as stunted. The mean WAZ for children was − 0.58 (SD = 1.4), with 13.7% of infants exhibiting underweight. The mean WHZ for children was − 0.01 (SD = 1.4), with 7.0% classified as wasted. The mean ECDI z-score was 0.00 (SD = 1.0).Of this total sample, we primarily focused on 54,916 children aged 36 to 59 months in 24 LMICs, for whom full information was available across all variables of interest. Table Overall pooled estimates based on meta-regression (using Model 4) were robust and comparable in magnitude to findings from pooled analyses , the overall associations between a lack of birth certificate and children’s HAZ and WAZ were smaller in magnitude, but also remained significant  explained nearly half of the unadjusted associations between birth registration and poor early child growth and development outcomes. Above and beyond household socioeconomic factors, children’s utilization of early health and learning services  explained approximately a quarter to a third of the remaining association between birth registration and child outcomes. Interestingly, we found that additionally controlling for cluster characteristics did not add explanatory power, thereby minimizing the possibility that these associations are due to community characteristics within clusters within countries. Prior studies have also documented links between birth registration and children’s healthcare utilization, school enrollment and completion, and participation in social services  –14. Our However, we found that significant associations persisted between birth registration and child outcomes, which were unexplained by the covariates and cluster-fixed effects included in our models. One possible explanation could be that registration reflects some degree of parental investment in the child. If completing the registration process is arduous, parents who register their children may be those who have more time or financial resources that they are able to spend on their children  –42, or tWhile our results support robust associations in this pooled sample, it is important to note that the opportunities afforded by a birth certificate vary considerably across country contexts. For instance, in Vietnam a birth certificate is necessary to enroll in both preschool and primary school, while in Sierra Leone and India, national policy mandates that birth certificates are not formally required at any stage of the education system . MoreoveDespite these policy differences across countries, our pooled findings indicate a significant negative relationship between birth registration and child growth and development outcomes across LMICs, and the potential role of socioeconomic factors in explaining a part of this relationship. This suggests important links between birth registration, social protection, and early child health and education services, especially for the children living in the poorest households. Research from Ghana has affirmed the benefits of incorporating birth registration into community health care and child health campaigns . MoreoveBirth registration and the estimation of a child’s age is central to the very measurement of early childhood outcomes: a precise measurement of age is needed to accurately measure HAZ and WAZ among children under-5. Comandini and co-authors describe the negative effects of measurement error and age heaping in misestimating HAZ and WAZ, especially among children without a birth certificate , 19. EffThere are several limitations to this analysis. First, we were only able to pool data from countries for which the MICS data were available; therefore, our results may not be representative of LMICs as a whole. Second, although we adjusted for a range of covariates and include cluster-level fixed effects, we were unable to control for other important variables, such as data on facility birth, maternal autonomy, and access to other services. Third, both the ECDI and birth registration were caregiver reported and may be susceptible to recall bias. Fourth, measures of nutritional status (e.g. height or weight for age) could be prone to bias among unregistered children, as their age cannot be verified . FinallyThis study highlights gaps in birth registration for young children in LMICs and finds that not having a birth certificate is negatively associated with early child growth and development outcomes. Early child health, nutrition, and education programs and policies should consider integrating birth registration – and child protection more broadly – in order to ensure that every child is legally recognized and has a fair chance to achieve her full developmental potential.Additional file 1:Country mean values for proportion of children not having a birth certificate, children’s HAZ, WAZ, WHZ, and ECDI z-score values among children aged 0–59 months. (DOCX 96 kb)Additional file 2:Figure of pooled and country-specific associations between no birth certificate and HAZ among children aged 36–59 months based on meta-regression model. (DOCX 718 kb)Additional file 3:Figure of pooled and country-specific associations between no birth certificate and WAZ among children aged 36–59 months based on meta-regression model. (DOCX 732 kb)Additional file 4:Figure of pooled and country-specific associations between no birth certificate and ECDI z-score among children aged 36–59 months based on meta-regression model. (DOCX 749 kb)Additional file 5:Figure of pooled and country-specific associations between no birth certificate and WHZ among children aged 36–59 months based on meta-regression model. (DOCX 715 kb)Additional file 6:Sensitivity analysis of the associations between no birth certificate and HAZ and WAZ for children aged 0–35 months; and stratified by child age groups (12 month age groups). (DOCX 79 kb)


Background: Undernutrition in early life underlies 45% of child deaths globally. Stunting malnutrition  also has long-term negative effects on childhood development. Linear growth deficits accrue in the first 1000 d of life. Understanding the patterns and timing of linear growth faltering or recovery during this period is critical to inform interventions to improve infant nutritional status.Objective: We aimed to identify the pattern and determinants of linear growth trajectories from birth through 24 mo of age in a cohort of Zimbabwean infants.Design: We performed a secondary analysis of longitudinal data from a subset of 3338 HIV-unexposed infants in the Zimbabwe Vitamin A for Mothers and Babies trial. We used k-means clustering for longitudinal data to identify linear growth trajectories and multinomial logistic regression to identify covariates that were associated with each trajectory group.Results: For the entire population, the mean length-for-age z score declined from −0.6 to −1.4 between birth and 24 mo of age. Within the population, 4 growth patterns were identified that were each characterized by worsening linear growth restriction but varied in the timing and severity of growth declines. In our multivariable model, 1-U increments in maternal height and education and infant birth weight and length were associated with greater relative odds of membership in the least–growth restricted groups (A and B) and reduced odds of membership in the more–growth restricted groups (C and D). Male infant sex was associated with reduced odds of membership in groups A and B but with increased odds of membership in groups C and D.Conclusion: In this population, all children were experiencing growth restriction but differences in magnitude were influenced by maternal height and education and infant sex, birth weight, and birth length, which suggest that key determinants of linear growth may already be established by the time of birth. This trial was registered at clinicaltrials.gov as NCT00198718. ChildreAn estimated 20% of linear growth faltering occurs in utero . Althougwww.thousanddays.org)  , 26. Howclinicaltrials.gov; NCT00198718) study, which is a randomized, placebo-controlled trial of peripartum vitamin A supplementation that has been previously described . Thus, only 3338 HIV-negative mother-infant pairs were followed for >12 mo . Gestational age was estimated with the use of the method described by Capurro et al.  (We used ta (KML)  to identta (KML) . Becauseta (KML) . An incrk-means clustering requires complete data, we applied a data-imputation strategy with the use of multiple imputation by chained equations  had a high frequency of missing values . We opteWe used ANOVA and chi-square tests to determine whether sociodemographic and epidemiologic characteristics varied across trajectory groups. For variables with significant heterogeneity across groups, we performed pairwise tests for group differences with the use of generalized linear regression.P values were corrected for multiple testing with the use of the Bonferroni method. All analyses were performed with R version 3.1.2 software with the use of kml . Mothers had a mean age of 24.2 y  at enrollment and a mean parity of 2.0 . The majority of infants were born at term , and approximately one-half of the infants were boys . Infant feeding at age 3 mo was predominantly mixed breastfeeding ; exclusive breastfeeding was infrequent . One-fifth (19.0%) of infants were stunted (LAZ <−2) by age 12 mo, and almost one-third (29.3%) of infants were stunted by age 24 mo  in which group A exhibited better growth and group D exhibited poorer growth. The 50 complete data sets were retained for further analyses. The optimal number of clusters chosen did not differ greatly with the cluster-quality index used. In 50 data sets, the proportions of infants identified as belonging to groups A–D were 21.4% , 31.3% , 24.3% , and 23.0% , respectively. The 4 groups were largely separated at birth when 5.4%, 2.5%, 24.3%, and 16.9% of infants were already stunted in the 4 respective groups. In all 4 groups, the LAZ declined between birth and 24 mo of age, but the rate and timing of decline varied between groups (KML identified 4 groups (hereafter termed groups A–D) as the optimal number of clusters in 50 complete data sets (100.0%)  by 24 mo of age . The pre1) that observed and imputed mean values would have differed within each group and 2) that overall group means would have more-closely tracked the imputed values than the observed values if the imputations were driving the group trajectories .A comparison of observed and imputed mean trajectories in each group illustrated that the imputation of unobserved values did not create subgroups with different growth curves. We would have expected Supplemental Figure 4). The mean trajectories in these groups  corresponded well with the patterns that were identified with the use of 24 mo of data, and there was a substantial overlap in the assignment of infants to equivalent 12-mo and 24-mo groups .An analysis of data that was restricted to the first 12 mo of life also identified 4 clusters as being optimal . Finally, to investigate whether the 4 groups that were identified could be explained by the season of birth, we generated box plots of birth weight by the month of birth for each group . The decline in birth weight from groups A to D was consistent across the month of birth, thereby indicating that the season of birth did not explain the clusters.To investigate factors that distinguished group membership, we report our multiple regression model with the use of group C as the referent in In this longitudinal birth cohort of HIV-unexposed mother-infant pairs from Zimbabwe, the mean LAZ was suboptimal at birth and subsequently declined between birth and 24 mo of age. This pattern of linear growth failure was consistent with survey data from countries in Africa and Asia . HoweverP = 0.003) and weighed less . The study did not report maternal determinants of growth.To our knowledge, only one other publication has grouped infants according to their individual linear growth trajectories . The stuz scores  (Although we did not investigate stunting (LAZ <−2) as an outcome, our findings were consistent with the literature on risk factors for stunting. Analyses of multicountry data have shown 2.90-times greater odds  of stunting in 12- to 60-mo-old children who were born with LBW than in children with normal birth weight  and gain, 0.040) , 44 or m, 0.040)  have als, 0.040) , 43, 51., 0.040) , which a, 0.040) , 54. We Birth weight and length reflect the effect of the intrauterine environment on fetal growth  and the Overall, these results suggest that transgenerational effects are an important determinant of growth trajectories, and interventions targeting pregnant mothers as well as girls entering their reproductive years are required. Nutritional supplementation of girls aged 7–15 y  and duriHowever, our finding that the mean LAZ sharply declined between birth and 24 mo of age in all clusters reaffirmed the importance of postnatal interventions. Even mild stunting (LAZ ≥−2 but <−1) is associated with increased risk of mortality from infections . Thus, pThe application of multiple imputations allowed us to retain all available subjects in our analyses and to avoid potential bias from the exclusion of infants without complete data through 24 mo of follow-up. The imputed data were very similar to the observed data at each visit. The determination of the correct number of clusters is fundamental to a cluster analysis, but there is currently no optimal method to validate the choice of a final cluster number . We usedIn conclusion, we performed our analyses in a large, HIV-unexposed, urban birth cohort from Zimbabwe who were followed from birth through 2 y of age and identified 4 patterns of postnatal linear growth. The following 5 antenatal factors explained the probability of infant membership in any given group: maternal height and education and infant birth length, birth weight, and sex. These results show the importance of focusing on multisectoral interventions to improve the education, health, and nutritional status of females before pregnancy to improve transgenerational effects on infant growth. However, the observation that LAZ trajectories sharply decline between birth and 24 mo of age, even in the group with the best antenatal factors, shows the concomitant importance of postnatal stunting-prevention interventions.