Pubertal Timing and Cardiometabolic Markers at Age 16 Years

Objective: To examine the association between pubertal timing and cardiometabolic markers among adolescents. Study design: We used data from Dutch adolescents participating in a birth cohort study. The study population for the current study consisted of 799 adolescents of whom data were available for at least 1 of the exposure variables (pubertal timing and/or age at menarche) and any of the cardiometabolic markers (waist circumference, cholesterol, blood pressure [BP], glycated hemoglobin) measured at age 16 years. Adolescents self-reported pubertal development at ages 11, 14, and 16 years. We categorized participants with early (84 girls, 88 boys), intermediate (240 girls, 211 boys), or late pubertal timing (89 girls, 85 boys). We estimated differences in cardiometabolic markers using linear regression analysis. Results: Girls with early pubertal timing had 1.54 cm larger waist circumference (95% CI .05; 3.03) and 3.98 mm Hg higher systolic BP (95% CI 1.69; 6.27) at age 16 years than girls with intermediate pubertal timing. The association with systolic BP remained after adjusting for childhood body mass index (BMI) (age 8 years) but attenuated after adjusting for BMI in adolescence (age 16 years). Boys with early pubertal timing had 0.79 mmol/mol lower glycated hemoglobin (95% CI -1.38; -0.20) than boys with intermediate pubertal timing. Conclusions: Girls with early pubertal timing had unfavorable BP levels at age 16 years, independent of BMI in childhood. Girls and boys with late pubertal timing had a tendency for lower waist circumference, but no differences in other cardiometabolic markers. Late pubertal timing does not appear to be a risk factor for unfavorable cardiometabolic markers in adolescence

Family history of myocardial infarction, stroke and diabetes and cardiometabolic markers in children

Aims/hypothesis: Despite the overlap in occurrence of cardiovascular disease (CVD) and type 2 diabetes and their risk factors, family history of these diseases has not yet been investigated simultaneously in relation to cardiometabolic markers in offspring. We examined how a family history of CVD and/or diabetes relates to cardiometabolic markers in offspring, and to what extent these diseases independently contribute to cardiometabolic markers. Methods: We used data from 1,374 12-year-old children and their parents participating in a birth cohort study in the Netherlands. Family history of CVD (myocardial infarction [MI] and stroke) and diabetes were reported by the parents. Children were classified as ‘no’, ‘moderate’ or ‘strong’ family history, based on early/late onset of disease in parents and grandparents. Cardiometabolic markers were measured at 12 years of age: waist circumference, cholesterol, blood pressure and HbA1c. Results: Compared with those with no family history, children with a strong family history of MI and/or stroke and/or diabetes (29% of the study population) had 0.13 mmol/l higher total cholesterol (TC) (95% CI 0.03, 0.23) and 0.18 higher TC/HDL-cholesterol (HDLC) ratio (95% CI 0.04, 0.32). A strong family history of MI or diabetes was independently associated with unfavourable cardiometabolic markers specific to those diseases. These associations remained after adjusting for BMI. Children with a moderate family history had no unfavourable cardiometabolic markers. Conclusions/interpretation: One-third of the children had a strong family history of CVD and/or diabetes. These children had higher TC levels and TC/HDLC ratios than children with no family history. A strong family history of MI or diabetes was independently associated with unfavourable cardiometabolic markers specific to those diseases

Time in bed, sleep quality and associations with cardiometabolic markers in children: The Prevention and Incidence of Asthma and Mite Allergy birth cohort study

We investigated associations of time in bed and multiple sleep quality characteristics with cardiometabolic markers in children. Data from the prevention and incidence of asthma and mite allergy study, a population-based prospective birth-cohort study started in 1996-1997 in the Netherlands, were analysed. In total 1481 children aged 11-12years completed a questionnaire (including questions on sleep) and underwent a medical examination. We measured body mass index, waist circumference, total- and high-density lipoprotein cholesterol, blood pressure and glycated haemoglobin. Results showed that in girls, some sleep characteristics were related to anthropometrics (body mass index, waist circumference) and cholesterol. Girls who had a long time in bed (11-12.5h) had 0.16 lower body mass index z-score (95% confidence interval -0.31; -0.01) and 0.99cm smaller waist circumference (95% confidence interval -2.01; -0.13) compared with girls who spent 10-10.5h in bed. Girls who went to bed late and rose early had 0.16mm higher total cholesterol (95% confidence interval 0.01; 0.31) and 0.08mm higher high-density lipoprotein cholesterol (95% confidence interval 0.01; 0.14) than early to bed/early rise' girls. Girls with night-time awakenings had 0.14mm higher total cholesterol (95% confidence interval 0.03; 0.25) than girls without night-time awakenings. Girls who felt sleepy/tired 1day per week had 0.10mm lower high-density lipoprotein cholesterol (95% confidence interval -0.16; -0.04) and 0.17mm higher total cholesterol/high-density lipoprotein cholesterol ratio (95% confidence interval 0.02; 0.32) than girls who did not feel sleepy. No associations were found for boys. Sleep characteristics were not related to blood pressure and glycated haemoglobin, and effect sizes of the associations in girls were small. Therefore, we consider it premature to propose that improved sleep could reduce cardiovascular risk during childhood

HR and 95% CI for overall cancer associated with increments in the components of the Healthy Diet Indicator (men and women combined).

1<p>The increment is a rounded number close to the s.d. of the component.</p>2<p>All models were stratified cohort, and adjusted for sex, age at baseline, body mass index, smoking status, education, physical activity, energy intake without energy from alcohol, and alcohol intake.</p>3<p>(en%) refers to the percentage of total energy intake excluding alcohol.</p><p>Abbreviations: s.d., standard deviation, m, milligrams, g, grams, en%, energy percentage, HR, hazard ratio, CI, confidence interval.</p

Multivariable hazards ratios (HRs) and 95% CIs of cancer according to tertiles of adherence to the Healthy Diet Indicator (HDI) in the EPIC-NL cohort.

1<p>All models were stratified by sex and cohort, and adjusted for age at baseline, body mass index, smoking status, education, physical activity, energy intake without energy from alcohol, and alcohol intake.</p>2<p>Models in women were additionally adjusted for menopausal status.</p>3<p>Smoking-related cancer included cancer of the lung, kidney, upper aero-digestive tract, stomach, pancreas, bladder, liver, and colorectal.</p>4<p>Alcohol-related cancer included cancer of the upper aero-digestive tract, breast, liver, and colorectal.</p>5<p>P for trend values were calculated using two-sided test for linear trend, treating the HDI categories as a continuous variable.</p

Baseline characteristics and number of incident cancers in the EPIC-NL cohort according to tertiles of adherence to the Healthy Diet Indicator (HDI).

1<p>HDI (range 0–7 points) included 7 components: saturated fatty acids, polyunsaturated fatty acids, cholesterol, protein, fibre, fruits and vegetables and free sugars.</p>2<p>HDI tertiles: T1: <3 points; T2∶3 points; T3: >3 points.</p

Composition of the Healthy Diet Indicator¹ (HDI) used in analyses of cancer, based on the WHO's dietary guidelines for the prevention of chronic diseases.

1<p>HDI range was 0–7 points. Tertiles of adherence to the HDI was; T1: <3 points, T2∶3 points, T3: >3 points.</p>2<p>(en%) refers to the percentage of total energy intake excluding alcohol.</p><p>Abbreviations: mg, milligrams, g, grams, en%, energy percentage.</p