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ObjectivesTo explore the timing of menarche, postmenarcheal growth, and to investigate the impact of various variables on menarcheal age and postmenarcheal and pubertal growth.Study DesignThis longitudinal community population-based study analyzed pubertal growth and menarcheal age in 793 healthy term-born Swedish girls, a subset of the GrowUp1990Gothenburg cohort. The timing of menarche and postmenarcheal growth was related to variables from the Quadratic-Exponential-Pubertal-Stop (QEPS) growth model, birth characteristics, and parental height. Multivariable models were constructed for clinical milestones; at birth, age 7 years, pubertal growth onset, and midpuberty.ResultsMenarche aligned with 71.6% (18.8) of the QEPS model's specific pubertal growth function, at a mean age of 13.0 (1.3) years, ranging from 8.2 to 17.2 years. Postmenarcheal growth averaged 8.0 (4.9) cm, varying widely from 0.2 to 31.1 cm, decreasing with later menarche. Significant factors associated with menarcheal age included height at 7 years, childhood body-mass index, parental height, and QEPS-derived pubertal growth variables. Multivariable models demonstrated increasing explanatory power for each milestone, explaining 1% of the variance in menarcheal age at birth, 8% at age 7 years, 44% at onset of pubertal growth, and 45% at midpuberty.ConclusionsThis study underscores the strong link between pubertal growth and age at menarche. Data available at start of puberty explain 44% of the variation in menarcheal age, apparent on average 3.2 years before menarche. In addition, the study shows a previously seldom noticed wide variation in postmenarcheal height gain from 0.2 to 31.1 cm.

Abstract To better understand DNA’s 3D folding in cell nuclei, researchers developed chromosome capture methods such as Hi-C that measure the contact frequencies between all DNA segment pairs across the genome. As Hi-C data sets often are massive, it is common to use bioinformatics methods to group DNA segments into 3D regions with correlated contact patterns, such as Topologically associated domains and A/B compartments. Recently, another research direction emerged that treats the Hi-C data as a network of 3D contacts. In this representation, one can use community detection algorithms from complex network theory that group nodes into tightly connected mesoscale communities. However, because Hi-C networks are so densely connected, several node partitions may represent feasible solutions to the community detection problem but are indistinguishable unless including other data. Because this limitation is a fundamental property of the network, this problem persists regardless of the community-finding or data-clustering method. To help remedy this problem, we developed a method that charts the solution landscape of network partitions in Hi-C data from human cells. Our approach allows us to scan seamlessly through the scales of the network and determine regimes where we can expect reliable community structures. We find that some scales are more robust than others and that strong clusters may differ significantly. Our work highlights that finding a robust community structure hinges on thoughtful algorithm design or method cross-evaluation.

Abstract New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial diagram generator in three case studies to illustrate significant changes in the organization of science, the effect of modeling network flows with memory in a citation network and distinguishing multidisciplinary from field-specific journals, and the effects of multilayer representation of a collaboration hypergraph.

Background Swedish child health services (CHS) is a free-of-charge healthcare system that reaches almost all children under the age of 6. The aim for the CHS is to improve children’s physical, psychological and social health by promoting health and development, preventing illness and detecting emerging problems early in the child’s life. The services are defined in a national programme divided into three parts: universal interventions, targeted interventions and indicated interventions.The Swedish Child Health Services Register (BHVQ) is a national Quality Register developed in 2013. The register extracts data from the child’s health record and automatically presents current data in real time. At present, the register includes 21 variables.Aim We aim to describe data available in the BHVQ and the completeness of data in BHVQ across variables.Methods Child-specific data were exported from the register, and data for children born in the regions were retrieved from Statistics Sweden to calculate coverage.Results The register includes over 110 000 children born between 2011 and 2022 from 221 child healthcare centres in eight of Sweden’s 21 regions. In seven of the eight regions, 100% of centres report data.The completeness of data differs between participating regions and birth cohorts. The average coverage for children born in 2021 is 71%.Conclusions The BHVQ is a valuable resource for evaluating Child Health Services nationally, with high coverage for the youngest children. As a result of continuous improvement of the services, the possibility to follow the development of children’s health in Sweden is possible through the register. When fully expanded, the register will be a natural and essential part of developing preventive services, improving healthcare for children below 6 years of age and a tool for developing evidence-based child health interventions.

Abstract Objectives Despite inter-individual variations in pubertal timing, growth references are conventionally constructed relative to chronological age (C-age). Thus, they are based on reference populations containing a mix of prepubertal and pubertal individuals, making them of limited use for detecting abnormal growth during adolescence. Recently we developed new types of height and weight references, with growth aligned to age at onset of the pubertal growth spurt (P-age). Here, we aim to develop a corresponding reference for pubertal BMI. Methods The QEPS-height and weight models were used to define a corresponding QEPS-BMI model. QEPS-BMI was modified by the same individual, constitutional weight–height-factor (WHF) as computed for QEPS-weight. QEPS-BMI functions were computed with QEPS weight and height functions fitted on longitudinal measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg cohort. These individual BMI functions were used to develop BMI references aligned for height at AgeP5; when 5% of specific puberty-related (P-function) height had been attained. Pubertal timing, stature at pubertal onset, and childhood BMI, were investigated in subgroups of children from the cohort GrowUp1974Gothenburg using the new references. Results References (median, standard deviation score (SDS)) were generated for total BMI (QEPS-functions), for ongoing prepubertal growth (QE-function) vs C-age, and for total BMI and separated into BMI specific to puberty (P-function) and BMI gain from ongoing basic growth (QES-functions), allowing individual growth to be aligned based on P-age. Growth in basic BMI was greater than average for children categorized as tall and/or with high-BMI at puberty-start. In children categorized as short at puberty-start, P-function-related-BMI was greater than average. Conclusions Use of these new pubertal BMI references will make it possible for the first time to consider individual variations owing to pubertal timing when evaluating BMI. This will improve the detection of abnormal changes in body composition when used in combination with pubertal height and weight references also abnormal growth. Other benefits in the clinic will include improved growth monitoring during treatment for children who are overweight/obese or underweight. Furthermore, in research settings these new references represent a novel tool for exploring human growth.

Abstract Background Growth references are traditionally constructed relative to chronological age, despite inter-individual variations in pubertal timing. A new type of height reference was recently developed allowing growth to be aligned based on onset of pubertal height growth. We here aim to develop a corresponding reference for pubertal weight. Methods To model QEPS-weight, 3595 subjects (1779 girls) from GrowUp1974Gothenburg and GrowUp1990Gothenburg were used. The QEPS-height-model was transformed to a corresponding QEPS-weight-model; thereafter, QEPS-weight was modified by an individual, constitutional weight-height-factor. Longitudinal weight and length/height measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg were then used to create weight references aligned for height at pubertal onset (the age at 5% of P-function growth, AgeP5). GrowUp1974Gothenburg subgroups based on pubertal timing, stature at pubertal onset, and childhood body composition were assessed using the references. Results References (median, SDS) for total weight (QEPS-functions), weight specific to puberty (P-function), and weight gain in the absence of specific pubertal growth (basic weight, QES-functions), allowing alignment of individual growth based on age at pubertal onset. For both sexes, basic weight was greater than average for late maturing, tall and high-BMI subgroups. The P-function-related weight was greater than average in short and lower than average in tall children, in those with high BMI, and in girls but not boys with low BMI. Conclusions New pubertal weight references allow individual variations in pubertal timing to be taken into consideration when evaluating growth. When used together with the comparable pubertal height reference, this will improve growth monitoring in clinical practice for identifying abnormal growth and serve as a valuable research tool providing insight into human growth.

Abstract Background Computerized mathematical models describing absolute and relative individual growth during puberty in both cm and standard deviation (SD)-scores are lacking. The present study aimed to fill this gap, by applying the QEPS-model that delineates mathematically the specific pubertal functions of the total growth curve. Methods Study population used was the individual growth curves of the longitudinally followed cohort GrowUp1974 Gothenburg (n = 2280). The QEPS-model describes total height as (T)otal-function: a combination of four shape-invariant growth functions, modified by time-scale and height-scale parameters: a (Q)uadratic-function for the continuous growth from fetal life to adulthood; a negative (E)xponential-function adds the rapid, declining fetal/infancy growth; a (P)ubertal-function the specific pubertal growth spurt; a (S)top-function the declining growth until adult height. A constructed variable, MathSelect, was developed for assessing data-quality. CIs and SD-scores for growth estimates were calculated for each individual. QEPS-model estimates used for pubertal growth; from the T-function: onset of puberty as minimal height velocity (AgeT ONSET ); mid-puberty as peak height velocity (AgeT PHV ); end of puberty as height velocity decreased to 1 cm/year (AgeT END ); duration of different intervals and gain (AgeT ONSET–END and Tpubgain); from the P-function: onset of puberty, estimated as growth at 1% or 5% (AgeP1 , AgeP5); mid-puberty as 50% (AgeP50) and PHV (AgeP PHV ); end of pubertal growth at 95 or 99% (AgeP95, AgeP99); duration of different intervals and pubertal gain (Ppubgain; P max ); from the QES-function: gain (QESpubgain) . Results Application of these mathematical estimates for onset, middle and end of puberty of P-function, QES-function, and T-function during puberty showed: the later the onset of puberty, the greater the adult height; pubertal gain due to the P-function growth was independent of age at onset of puberty; boys had higher total gain during puberty due to P-function growth than to QES-function growth; for girls it was reversed. Conclusions QEPS is the first growth model to provide individualized estimates of both the specific pubertal growth function and the total growth during puberty, with accompanying SD-scores and Cis for each individual. These QEPS-derived estimates enable more in-depth analysis of different aspects of pubertal growth than previously possible.