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PurposeThe Lifelines Reproductive Origins of Adult Health and Disease (Lifelines-ROAHD) cohort provides a uniquely detailed dataset allowing investigations of determinants of reproductive health as well as the influence of reproductive events on future health and disease of mother and child(ren). Lifelines-ROAHD cohort is embedded in the population-based Lifelines cohort study.ParticipantsIn total, 5412 women of reproductive age (20–45 years) were included in the Lifelines-ROAHD cohort, in the period 2017–2018.Findings to dateIn the population, 45.6% of the women indicated that they had a natural menstrual cycle. In total, 908 women (16.8%) consulted a healthcare provider for infertility; subsequently diagnosed fertility problems were anovulation (24.4%), male partner infertility problems (22.5%) or unexplained infertility (22.2%). Women underwent various consecutive assisted reproductive treatments, for example, ovulation induction (19.8%) or in vitro fertilisation (5.4%). In total, 2808 women experienced 6158 pregnancies and 5068 births. Adverse pregnancy outcomes were miscarriage (14.3%), ectopic pregnancy (0.9%) or termination of pregnancy or medical abortion (2.0 %). The modes of delivery were vaginal births (74.9%), instrumental vaginal births (11.9%), elective caesarean section (3.9%) and emergency caesarean section (9.3%). Birth outcomes were born alive at term (93.6%), born alive (very) pre-term (5.8%), stillbirth (0.316%) and neonatal death (0.197%). Additional data about 2660 most recent pregnancies showed that 19.9% of the women had irregular working hours. One year postpartum, women indicated adverse physical health issues in one or more domains, range 0.5%–12.5%. They also indicated adverse psychological health issues in one or more domains, range 0.7%–1.6%, and 1.6% experienced diminished sexual health.Future plansDue to the embedding of Lifelines-ROAHD cohort in the original Lifelines cohort, the women will be longitudinally followed. Additionally, we aim to collect data with a second online questionnaire aiming to complete women’s reproductive histories, by collecting data about potential first and subsequent pregnancies conceived after the date of completion of baseline Lifelines-ROAHD cohort.

Background: Self-reported cancer has been validated with heterogeneous results across populations. The aim was to assess the validity of self-reported cancer in the Lifelines population-based cohort and to search for explanations for not reporting cancer.Methods: Data from adult participants (n = 152,780) from Lifelines was linked to the Dutch-Nationwide pathology databank (PALGA), which has nearly 100% coverage of cancer diagnoses in the Netherlands and is considered as the gold standard for ascertainment of cancer diagnosis in this study. Sensitivity and positive predictive value (PPV) for self-reported cancers -reported as hand-written free text- were described. Logistic regressions analyses were performed to evaluate whether socio-demographic factors were associated with the presence of self-reported cancer when there was a diagnosis in PALGA.Results: 6611 (4.50%) participants had at least one self-reported diagnosis of cancer, where 9960 (6.97%) participants had at least one cancer diagnosis in PALGA. The sensitivity of self-reported cancer was 64.68% [95%CI:63.71–65.66], and 70.18% [95%CI:68.83–71.56] after excluding skin and cervical cancers. Skin and cervical cancers represented 61.24% of non-self-reported cancers. The overall PPV was 97.45% [95%CI:97.45–97.81], and 97.33% [95%CI:96.72–97.82] after the exclusion of skin and cervical cancers. Participants who did not self-report their cancer were more likely to be male, had longer time since diagnosis and lower educational level.Conclusion: Overall, the reports of cancer in Lifelines have a high positive predictive value and moderate sensitivity. One third of the cancers were not reported, mainly skin and cervical cancers. Male participants, those with a lower educational level and those with longer time since diagnosis were less likely to self-report a diagnosed cancer.

Purpose To compare the differences in lifestyle behaviours between cancer survivors (CSs) and cancer-free participants in a large and representative population-based cohort. Methods We included 115,257 adults from the Lifelines cohort. Cancer status was self-reported, and health behaviours were measured (e.g. body mass index [BMI]) or assessed by questionnaire (e.g. physical activity, smoking, alcohol consumption, sedentary behaviour and diet). The data were then categorised for logistic regression analysis, stratified and adjusted by sex and age ( Results CSs (5473; 4.7%) were diagnosed 9 ± 8.5 years before data collection, were older (mean age 55.4 vs 44.4 years) and more often female (66.6% vs 33.4%) than the cancer-free participants. They were also more likely to be physically active and to have a better diet, and also less likely to be alcohol drinkers; but, were more likely to have a higher BMI, be former smokers and to be sedentary. After adjustment for sex and age, however, BMI was more likely to be normal, physical activity was more likely to be higher and smoking to be prevalent in CSs. Current smoking was also significantly higher among females and those aged Conclusions In this population-based cohort, CSs have health behaviour comparable to those without a cancer diagnosis. Implications for cancer survivors Smoking cessation strategies should target all CSs, but efforts could yield greatest benefit if they target females and those younger than 55 years.

PurposeThe Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort.ParticipantsParticipants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project.Findings to date>71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.Future plansQuestionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.

Background: The LifeLines Cohort Study is a large three-generation prospective study and Biobank. Recruitment and data collection started in 2006 and follow-up is planned for 30 years. The central aim of LifeLines is to understand healthy ageing in the 21st century. Here, the study design, methods, baseline and major cardiovascular phenotypes of the LifeLines Cohort Study are presented.Methods and results: Baseline cardiovascular phenotypeswere defined in 9700 juvenile (8-18 years) and 152,180 adult (>= 18 years) participants. Cardiovascular disease (CVD) was defined using ICD-10 criteria. At least one cardiovascular risk factor was present in 73% of the adult participants. The prevalence, adjusted for the Dutch population, was determined for risk factors (hypertension (33%), hypercholesterolemia (19%), diabetes (4%), overweight (56%), and current smoking (19%)) and CVD (myocardial infarction (1.8%), heart failure (1.0%), and atrial fibrillation (1.3%)). Overall CVD prevalence increased with age from 9% in participants = 65 years. Of the participantswith hypertension, hypercholesterolemia and diabetes, respectively 75%, 96% and 41% did not receive preventive pharmacotherapy.Conclusions: The contemporary LifeLines Cohort Study provides researchers with unique and novel opportunities to study environmental, phenotypic, and genetic risk factors for CVD and is expected to improve our knowledge on healthy ageing. In this contemporary Western cohortwe identified a remarkable high percentage of untreated CVD risk factors suggesting that not all opportunities to reduce the CVD burden are utilised. (C) 2016 The Authors. Published by Elsevier Ireland Ltd.

Background Diversity in the reported prevalence of metabolically healthy obesity (MHO), suggests that modifiable factors may be at play. We evaluated differences in dietary patterns and physical activity between MHO and metabolically unhealthy obesity (MUO). Methods Cross-sectional data of 9270 obese individuals (30–69 years) of the Lifelines Cohort Study was used. MHO was defined as obesity and no metabolic syndrome risk factors and no cardiovascular disease history. MUO was defined as obesity and ≥2 metabolic syndrome risk factors. Sex-specific associations of dietary patterns (identified by principal component analysis) and physical activity with MHO were assessed by multivariable logistic regression (reference group: MUO). Analyses were adjusted for multiple covariates. Results Among 3442 men and 5828 women, 10.2% and 24.4% had MHO and 56.9% and 35.3% MUO, respectively. We generated four obesity-specific dietary patterns. Two were related to MHO, and in women only. In the highest quartile (Q) of ‘bread, potatoes and sweet snacks’ pattern, odds ratio (OR) (95% CI) for MHO was 0.52 (0.39–0.70). For the healthier pattern ‘fruit, vegetables and fish’, an OR of 1.36 (1.09–1.71) in Q3 and 1.55 (1.21–1.97) in Q4 was found for MHO. For physical activity, there was a positive association between moderate physical activity and vigorous physical activity in the highest tertile and MHO in women and men, respectively (OR 1.19 (1.01–1.41) and OR 2.02 (1.50–2.71)). Conclusion The healthier diet -characterized by ‘fruit, vegetables and fish’- and moderate physical activity in women, and vigorous physical activity in men may be related to MHO. The (refined) carbohydrate-rich ‘bread, potatoes and sweet snacks’ dietary pattern was found to counteract MHO in women. Electronic supplementary material The online version of this article (10.1186/s12937-018-0319-0) contains supplementary material, which is available to authorized users.

BACKGROUND: Physical activity (PA) is important in combating childhood obesity. Parents, and thus parental PA, could influence PA in young children. We examined whether the time spent at different intensities of PA and the type of parental PA are associated with the PA of children aged 4-7 years, and whether the associations between child-parent pairs were sex-specific.METHODS: All the participants were recruited from the Groningen Expert Center for Kids with Obesity (GECKO) birth cohort (babies born between 1 April 2006 and 1 April 2007 in Drenthe province, the Netherlands) and were aged 4-7 years during measurement. PA in children was measured using the ActiGraph GT3X (worn at least 3 days, ≥10 h per day). PA in parents was assessed using the validated SQUASH questionnaire.RESULTS: Of the N = 1146 children with valid ActiGraph data and 838 mothers and 814 fathers with valid questionnaire data, 623 child-parent pairs with complete data were analysed. More leisure time PA in mothers was associated with more time spent in moderate-to-vigorous PA (MVPA) in children (Spearman r = 0.079, P CONCLUSIONS: Higher PA in mothers, for instance in leisure activities, is related to higher PA in daughters, and more active fathers are related to more active sons. To support PA in young children, interventions could focus on the PA of the parent of the same sex as the child. Special attention may be needed for families where the parents have sedentary jobs, as children from these families seem to adopt more sedentary behaviour.

Objectives: To give appropriate dietary advice to preschool children, an estimation of their energy requirements for both maintenance and activity is needed. We compared energy requirements for maintenance, measured by indirect calorimetry against existing equations predicting these requirements in 3- to 4-year-old children. Methods: In 30 children (age 3.4 +/- 0.3) from the GECKO Drenthe cohort, height, weight, evening sleeping metabolic rate (SMR) (by indirect calorimetry), fat mass (FM), and fat-free mass (FFM) (by isotope dilution) were measured. For 25 children, a valid evening SMR was available as a measure for energy used for maintenance and growth. This SMR was compared with existing equations (Schofield, FAO/WHO/UNU, Oxford and Harris-Benedict). Correlations among SMR and weight, height, FM, and FFM were also calculated. Results: From the existing equations, significant higher values, ranging from 58 to 144 kcal/day, were calculated for the BMR compared with the measured SMR results, indicating 8% to 19% overestimation. This overestimation is higher at lower ranges of energy requirement. SMR was positively related to weight (r = 0.488, P = 0.013), height (r = 0.499, P = 0.011), and FFM (r = 0.482, P = 0.027), but not to FM (r = 0.211, P = 0.358). Conclusions: Existing equations show higher values for the energy used for maintenance in young children compared to the results of our measurements of the SMR. Energy used for maintenance is correlated with FFM and not with FM.

Objectives: To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children. Design and Methods: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. Results: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R2 = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. Conclusion: TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.

Movement by an infant during the first year of life might influence its activity level and thereby influence growth in early childhood.To examine whether the time that an infant is able to move unrestrictedly and time spent in baby seats are related to weight and waist circumference at age 9 months and growth from 9 to 24 months.In the GECKO Drenthe birth cohort, weight and height were measured in Well Baby Clinics at the ages of 9 and 24 months. Time spent moving unrestrictedly and time spent in baby seats were reported on a questionnaire at age 9 months. Children born37 weeks or with a low birthweight (2500 g) were excluded. Outcomes were defined as the Z-scores for weight-for-height, weight-for-age, and waist circumference-for-age at the ages of 9 and 24 months, and changes in Z-scores as between 9 and 24 months of age.The time an infant is able to move unrestrictedly at age 9 months was inversely related to Z-score waist circumference at 9 months, and the change in Z-scores weight-for-height and weight-for-age between the ages 9 and 24 months. For time spent in baby seats, 'never users' showed a decline in Z-score weight-for-height as compared to those who used baby seats. On the contrary, Z-score waist circumference-for-age declined in children sitting for 1h or more in baby seats.More time spent moving unrestrictedly in infancy may contribute to a healthy growth pattern.

Background Lack of physical activity is a growing problem in China, due to the fast economic development and changing living environment over the past two decades. The aim of this review is to summarize the factors related to physical activity in Chinese children and adolescents during this distinct period of development. Methods A systematic search was finished on Jan 10th, 2017, and identified 2200 hits through PubMed and Web of Science. English-language published studies were included if they reported statistical associations between factors and physical activity. Adapted criteria from the Strengthening The Reporting of OBservational studies in Epidemiology (STROBE) statement and evaluation of the quality of prognosis studies in systematic reviews (QUIPS) were used to assess the risk of bias of the included studies. Related factors that were reported in at least three studies were summarized separately for children and adolescents using a semi-quantitative method. Results Forty two papers (published 2002–2016) were included. Most designs were cross-sectional (79%), and most studies used questionnaires to assess physical activity. Sample size was above 1000 in 18 papers (43%). Thirty seven studies (88%) showed acceptable quality by methodological quality assessment. Most studies reported a low level of physical activity. Boys were consistently more active than girls, the parental physical activity was positively associated with children and adolescents’ physical activity, children in suburban/rural regions showed less activity than in urban regions, and, specifically in adolescents, self-efficacy was positively associated with physical activity. Family socioeconomic status and parental education were not associated with physical activity in children and adolescents. Conclusions The studies included in this review were large but mostly of low quality in terms of study design (cross-sectional) and methods (questionnaires). Parental physical activity and self-efficacy are promising targets for future physical activity promotion programmes. The low level of physical activity raises concern, especially in suburban/rural regions. Future research is required to enhance our understanding of other influences, such as the physical environment, especially in early childhood. Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0486-y) contains supplementary material, which is available to authorized users.

This review summarizes the association between directly assessed physical activity and adiposity in preschool children (age 1.5-6 years). It includes 17 cross-sectional and longitudinal studies that were published between January 1999 and February 2010. The association between physical activity and obesity seems to depend on the outcome measure of adiposity. In 60% (3/5) of the studies using percentage body fat, an inverse significant relationship with physical activity was found against 18% (2/11) of the studies that used body mass index as method to assess adiposity. Physical activity is inversely related to percentage body fat in preschool children. The associations between physical activity and body mass index as a measure of adiposity in preschool children remain elusive. Further studies using directly measured physical activity and percentage body fat to define adiposity are needed to draw more firm conclusions.

Background: Parental behavior can influence the development of overweight in children. The aim of this study is to examine whether parental BMI and parental physical activity are associated with BMI, waist circumference and physical activity in young children.Methods: In 3-4 year old children, weight, height and waist circumference were measured. Children's physical activity was measured in a subgroup (n = 299) using a tri-axial activity monitor, TracmorD. Data are represented as activity counts per minute (total physical activity) and as percentage of time in sedentary, light, moderate and vigorous intensity physical activity (generated from a subsample of Actigraph data using cut points from Butte et al.). Parental weight and height were self-reported and parental physical activity was assessed by the validated questionnaire SQUASH.Results: In total 1554 children (age 3.9 +/- 0.1 years, BMI 15.8 +/- 1.3 kg/m2 and waist circumference 52.4 +/- 3.5 cm) were included. Eleven percent were overweight or obese. A higher maternal BMI was related to higher levels of children's sedentary activity (r = 0.120, p = 0.04 and to lower levels of children's total and moderate physical activity (r = -0.158, p = 0.007 and r = -0.154, p = 0.008, respectively). Parental BMI was positively correlated with children's BMI and waist circumference (r = 0.20-0.27, p Conclusions: Higher maternal BMI and lower maternal physical activity levels were related to lower levels of children's physical activity. More active commuting by the mother and a lower parental BMI were related to a lower BMI of the children. Energy-balance related behavior of the parents may contribute to a healthier BMI of both preschool children and their parents.

In this study, we investigated the interplay between screen time, sleep duration, outdoor play, having a television in the bedroom and the number of televisions at home and their association with body mass index (BMI) in preschool children. All participants, 3–4 years of age (n = 759), were part of the Groningen expert center for kids with obesity (GECKO) Drenthe birth cohort. Weight and height were measured. Total screen time, number of televisions at home, a television in the bedroom, sleep duration and time of outdoor play were self-reported by parents in a questionnaire. Ordinary least square (OLS) regression-based path analysis was used to estimate direct and indirect effects on BMI in mediation models. A television in the bedroom or more televisions at home gave a higher screen time, which were associated with decreased sleep duration and resulted in higher BMI (indirect effect = 0.0115, 95 % bootstrap interval = 0.0016; 0.0368 and indirect effect = 0.0026, 95 % bootstrap interval = 0.0004; 0.0078, respectively). In contrast to the direct effect of screen time, sleep duration and a television in the bedroom on BMI, no direct effect was found for outdoor play and number or televisions at home on BMI. Conclusions: Short sleep duration, long screen time and a television in the bedroom were associated with the presence of overweight in preschool children.

Do paternal and maternal lifestyle factors influence the risk of hypertensive pregnancy complications, gestational diabetes mellitus (GDM), spontaneous preterm birth and small-for-gestational-age (SGA)?Paternal lifestyle factors do not exert an independent effect on the investigated outcomes while maternal prepregnancy BMI and maternal smoking during pregnancy influence the risk of hypertensive pregnancy complications, GDM and SGA.Maternal lifestyle factors are associated with perinatal complications, but the impact of paternal lifestyle factors is unclear.Data from the GECKO (Groningen Expert Center for Kids with Obesity) Drenthe cohort, a prospective population-based birth-cohort of children born between April 2006 and April 2007 in a northern province of The Netherlands, were analysed. The parents of 2958 children (62 of those approached) gave their consent to participate in the study and the data of 2264 (77) couples were available for analysis.All pregnant women in the Dutch province of Drenthe with an expected date of delivery between April 2006 and April 2007 were invited to participate and included during the third trimester of their pregnancy or within 6 months after delivery. All consenting couples received extensive questionnaires including lifestyle, biological and socio-demographic-related questions covering the period of 6 months prior to conception. Outcome data were obtained from midwives and hospital registries. Univariable and multivariable logistic regression analyses were used to determine the impact of the lifestyle factors on the primary outcome measures.Of all 2264 women, 241 women (10.6) developed a hypertensive pregnancy complication, 50 women (2.2) developed GDM, 79 (3.5) children were spontaneously delivered preterm and 155 children (6.8) were SGA. All paternal and maternal lifestyle factors were positively correlated. Multivariable analysis showed that paternal lifestyle factors did not have an independent influence on the investigated outcomes. Of the maternal factors, prepregnancy BMI was independently associated with an increased risk of a hypertensive disorder during pregnancy (odds ratio (OR): 1.12, 95 CI 1.091.16), a higher risk of GDM (OR BMI 23 kg/m(2), per BMI unit: 1.13, 95 CI 1.081.18) and with a decreased risk of SGA (OR per BMI point 0.94, 95 CI 0.900.99). Maternal smoking during pregnancy was significantly associated with SGA (OR 3.00, 95 CI 1.804.99) in multivariable analysis.The retrospective nature of the questionnaire may have induced recall bias. Selection bias might have occurred, as ethnic minorities were less willing to co-operate in the GECKO Drenthe study. The possibility of misclassification bias regarding the primary outcome measures cannot be ruled out. Inclusion bias might have occurred as not all questionnaires of the parents of the children participating in the GECKO Drenthe cohort were completed.Paternal lifestyle factors do not have an independent effect on the investigated adverse pregnancy outcomes. However, as paternal and maternal lifestyles are positively correlated, both partners should be involved in preconception counselling regarding the investigated outcome measures.GECKO is supported and funded by an unrestricted grant from Hutchison Whampoa Ltd, University of Groningen and Well Baby Clinic Foundation Icare, Drenthe, The Netherlands. M.A.Q.M. is supported by a research grant from the Dutch Organization for Health Research and Development (ZonMw; Prevention Program-Health Care Efficiency Research; project number 50-50110-96-518). The department of Obstetrics and Gynaecology received research grants from Merck Sharpe and Dohme BV, Ferring pharmaceuticals, Merck Serono, the Netherlands.

To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children.Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures.The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R(2) = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL.TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA.

Objective: To assess whether waist-to-height-ratio (WHtR) is a better estimate of body fat percentage (BF %) and a better indicator of cardiometabolic risk factors than BMI or waist circumference (WC) in young children.Methods: WHtR, WC and BMI were measured by trained staff according to standardized procedures. (H2O)-H-2 and (H2O)-H-2-O-18 isotope dilution were used to assess BF% in 61 children (3-7 years) from the general population, and bioelectrical impedance (Horlick equation) was used to assess BF% in 75 overweight/obese children (3-5 years). Cardiometabolic risk factors, including diastolic and systolic blood pressure, HOMA2-IR, leptin, adiponectin, triglycerides, total cholesterol, HDL- and LDL-cholesterol, TNF alpha and IL-6 were determined in the overweight/obese children.Results: In the children from the general population, after adjustments for age and gender, BMI had the highest explained variance for BF% compared to WC and WHtR (R-2 = 0.32, 0.31 and 0.23, respectively). In the overweight/obese children, BMI and WC had a higher explained variance for BF% compared to WHtR (R-2 = 0.68, 0.70 and 0.50, respectively). In the overweight/obese children, WHtR, WC and BMI were all significantly positively correlated with systolic blood pressure (r = 0.23, 0.30, 0.36, respectively), HOMA2-IR (r = 0.53, 0.62, 0.63, respectively), leptin (r = 0.70, 0.77, 0.78, respectively) and triglycerides (r = 0.33, 0.36, 0.24; respectively), but not consistently with other parameters.Conclusion: In young children, WHtR is not superior to WC or BMI in estimating BF%, nor is WHtR better correlated with cardiometabolic risk factors than WC or BMI in overweight/obese children. These data do not support the use of WHtR in young children. (C) 2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.