Anna Sijtsma

The LifeLines Cohort Study: Prevalence and treatment of cardiovascular disease and risk factors

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 30years. 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 phenotypes were defined in 9700 juvenile (8-18years) and 152,180 adult (≥18years) 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<65years to 28% in participants≥65years. Of the participants with 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 cohort we identified a remarkable high percentage of untreated CVD risk factors suggesting that not all opportunities to reduce the CVD burden are utilised.

Waist-to-height ratio, waist circumference and BMI as indicators of percentage fat mass and cardiometabolic risk factors in children aged 3–7 years

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. (2)H2O and (2)H2(18)O 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, TNFα 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.

Effects of paternal and maternal lifestyle factors on pregnancy complications and perinatal outcome. A population-based birth-cohort study: the GECKO Drenthe cohort

STUDY QUESTION 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)? SUMMARY ANSWER 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. WHAT IS KNOWN ALREADY Maternal lifestyle factors are associated with perinatal complications, but the impact of paternal lifestyle factors is unclear. STUDY DESIGN, SIZE, DURATION 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. PARTICIPANTS/MATERIALS, SETTINGS, METHOD 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. MAIN RESULTS AND THE ROLE OF CHANCE 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 1.09-1.16), a higher risk of GDM (OR BMI >23 kg/m(2), per BMI unit: 1.13, 95% CI 1.08-1.18) and with a decreased risk of SGA (OR per BMI point 0.94, 95% CI 0.90-0.99). Maternal smoking during pregnancy was significantly associated with SGA (OR 3.00, 95% CI 1.80-4.99) in multivariable analysis. LIMITATIONS, REASONS FOR CAUTION 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. WIDER IMPLICATIONS OF THE FINDINGS 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.

Is directly measured physical activity related to adiposity in preschool children

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.

Factors of physical activity among Chinese children and adolescents: a systematic review

BackgroundLack 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.MethodsA 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.ResultsForty 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.ConclusionsThe 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.

Validation of the TracmorD Triaxial Accelerometer to Assess Physical Activity in Preschool Children

Objectives: To assess validity evidence of TracmorD to determine energy used for physical activity in 3‐4‐year‐old children.

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

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

Energy requirements for maintenance and growth in 3- to 4-year-olds may be overestimated by existing equations

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.