Cross-Sectional and Longitudinal Associations Between Psychosocial Well-Being and Cardiometabolic Markers in European Children and Adolescents

ABSTRACT Objective Research examining aspects of positive mental health as potential predictors of cardiometabolic health in young populations is scarce. We investigated the associations between psychosocial well-being and waist circumference (WAIST), blood pressure (BP), the homeostasis model assessment for insulin resistance, triglycerides, and high-density lipoprotein cholesterol considering life-style factors as mediators. Methods Data of European children and adolescents participating in the baseline (2007/2008), first follow-up (FU1; 2009/2010) and second follow-up (FU2; 2013/2014) examinations of the IDEFICS/I.Family study were used (ncross-sectional = 6519; nlongitudinal = 1393). A psychosocial well-being score was calculated from 16 items on emotional well-being, self-esteem, and social relationships (0–48 points). Cardiometabolic markers were transformed to age- and sex-specific and, in case of BP, also height-specific z scores. Life-style factors included diet, physical activity, sleep, and electronic media use. Applying path analysis, we obtained unstandardized estimates of direct and indirect effects of well-being on cardiometabolic markers. Results Cross-sectionally, well-being score showed a negative direct and a negative indirect effect through life-style factors on WAIST z score (estimate per 4-point increase, −0.051 [p = .001] and −0.014 [p < .001], respectively). Longitudinally, positive changes in well-being score between baseline and FU1 and between FU1 and FU2, respectively, demonstrated negative indirect effects through life-style factorsFU2 on WAIST z scoreFU2. Both cross-sectionally and longitudinally, higher levels of well-being showed lowering indirect effects on homeostasis model assessment, BP, and triglyceride z scores and an increasing indirect effect on high-density lipoprotein cholesterol z score through both life-style factors and WAIST z score. Conclusions These results supported our hypothesis that a healthier life-style may be one mechanism through which higher well-being is linked with lower abdominal obesity and fewer other cardiometabolic disorders in young populations. Trial Registration: Pan-European IDEFICS/I.Family children cohort, ISRCTN registry number: ISRCTN62310987 (http://www.isrctn.com/ISRCTN62310987).

[1]  David Mort.  The Statistics , 2020, Sources of Non-Official UK Statistics.

[2]  Andrew Steptoe,et al.  Happiness and Health. , 2019, Annual review of public health.

[3]  E. Fitzsimons,et al.  Development and predictors of mental ill-health and wellbeing from childhood to adolescence , 2018, Social Psychiatry and Psychiatric Epidemiology.

[4]  Mònica González-Carrasco,et al.  Subjective Well-Being Decreasing With Age: New Research on Children Over 8. , 2018, Child development.

[5]  M. Wake,et al.  Associations of mental health with cardiovascular risk phenotypes and adiposity in adolescence: A cross‐sectional community‐based study , 2018, Journal of paediatrics and child health.

[6]  Ilse de Bourdeaudhuij,et al.  Bidirectional associations between psychosocial well-being and adherence to healthy dietary guidelines in European children: prospective findings from the IDEFICS study , 2017, BMC Public Health.

[7]  H. M. Schenk,et al.  Associations of Positive Affect and Negative Affect With Allostatic Load: A Lifelines Cohort Study , 2017, Psychosomatic medicine.

[8]  P. De Boeck,et al.  On the Interpretation and Use of Mediation: Multiple Perspectives on Mediation Analysis , 2017, Front. Psychol..

[9]  C. Oldmeadow,et al.  Systematic Review of Universal Resilience-Focused Interventions Targeting Child and Adolescent Mental Health in the School Setting. , 2017, Journal of the American Academy of Child and Adolescent Psychiatry.

[10]  P. Kendall,et al.  Self-reported quality of life and self-esteem in sad and anxious school children , 2016, BMC psychology.

[11]  N. Sin.  The Protective Role of Positive Well-Being in Cardiovascular Disease: Review of Current Evidence, Mechanisms, and Clinical Implications , 2016, Current Cardiology Reports.

[12]  S. De Henauw,et al.  The role of emotion regulation in childhood obesity: implications for prevention and treatment , 2016, Nutrition Research Reviews.

[13]  W. Ahrens,et al.  Impact of a community based health‐promotion programme in 2‐ to 9‐year‐old children in Europe on markers of the metabolic syndrome, the IDEFICS study , 2015, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[14]  I. Pigeot,et al.  Behavioural effects of a community‐oriented setting‐based intervention for prevention of childhood obesity in eight European countries. Main results from the IDEFICS study , 2015, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[15]  I. Pigeot,et al.  The IDEFICS intervention trial to prevent childhood obesity: design and study methods , 2015, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[16]  C. Ryff,et al.  Psychological Well-Being and Metabolic Syndrome: Findings From the Midlife in the United States National Sample , 2015, Psychosomatic medicine.

[17]  H. Neuhauser,et al.  Association Between Chronic Stress and Blood Pressure: Findings From the German Health Interview and Examination Survey for Adults 2008–2011 , 2015, Psychosomatic medicine.

[18]  T. Lehtimäki,et al.  Insulin and BMI as Predictors of Adult Type 2 Diabetes Mellitus , 2015, Pediatrics.

[19]  I. Huybrechts,et al.  Cross-Lagged Associations Between Children’s Stress and Adiposity: The Children’s Body Composition and Stress Study , 2015, Psychosomatic Medicine.

[20]  A. Midei,et al.  Positive attributes protect adolescents from risk for the metabolic syndrome. , 2014, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[21]  W. Ahrens,et al.  Percentiles of fasting serum insulin, glucose, HbA1c and HOMA-IR in pre-pubertal normal weight European children from the IDEFICS cohort , 2014, International Journal of Obesity.

[22]  W. Ahrens,et al.  Blood lipids among young children in Europe: results from the European IDEFICS study , 2014, International Journal of Obesity.

[23]  I Pigeot,et al.  Percentile reference values for anthropometric body composition indices in European children from the IDEFICS study , 2014, International Journal of Obesity.

[24]  W. Ahrens,et al.  Blood pressure reference values for European non-overweight school children: The IDEFICS study , 2014, International Journal of Obesity.

[25]  L. Moreno,et al.  Filling the gap: international reference values for health care in children , 2014, International Journal of Obesity.

[26]  Ilse de Bourdeaudhuij,et al.  Are context-specific measures of parental-reported physical activity and sedentary behaviour associated with accelerometer data in 2–9-year-old European children? , 2014, Public Health Nutrition.

[27]  T. Lehtimäki,et al.  Youth Overweight and Metabolic Disturbances in Predicting Carotid Intima-Media Thickness, Type 2 Diabetes, and Metabolic Syndrome in Adulthood: The Cardiovascular Risk in Young Finns Study , 2014, Diabetes Care.

[28]  W. Ahrens,et al.  Determinants of Attrition to Follow-Up in a Multicentre Cohort Study in Children-Results from the IDEFICS Study , 2013 .

[29]  E. Goodman,et al.  Association of optimism with cardiometabolic risk in adolescents. , 2013, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[30]  A. Hebestreit,et al.  Relative validity of the Children's Eating Habits Questionnaire–food frequency section among young European children: the IDEFICS Study , 2013, Public Health Nutrition.

[31]  W. Ahrens,et al.  Prevalence and determinants of misreporting among European children in proxy-reported 24 h dietary recalls , 2012, British Journal of Nutrition.

[32]  S. Zubrick,et al.  Associations between anxious-depressed symptoms and cardiovascular risk factors in a longitudinal childhood study. , 2012, Preventive medicine.

[33]  F. Hu,et al.  A systematic review and meta-analysis of epidemiological studies , 2012 .

[34]  Sara E. Field,et al.  Longitudinal Study of Depressive Symptoms and Progression of Insulin Resistance in Youth at Risk for Adult Obesity , 2011, Diabetes Care.

[35]  I. Pigeot,et al.  The IDEFICS cohort: design, characteristics and participation in the baseline survey , 2011, International Journal of Obesity.

[36]  W. Ahrens,et al.  Reproducibility of food consumption frequencies derived from the Children's Eating Habits Questionnaire used in the IDEFICS study , 2011, International Journal of Obesity.

[37]  W. Ahrens,et al.  Evaluation of the Children's Eating Habits Questionnaire used in the IDEFICS study by relating urinary calcium and potassium to milk consumption frequencies among European children , 2011, International Journal of Obesity.

[38]  S. Yanovski,et al.  Psychological symptoms and insulin sensitivity in adolescents , 2010, Pediatric diabetes.

[39]  J. Wardle,et al.  Perceived Stress and Weight Gain in Adolescence: A Longitudinal Analysis , 2009, Obesity.

[40]  F. Zitman,et al.  Depression Is Associated With Decreased Blood Pressure, but Antidepressant Use Increases the Risk for Hypertension , 2009, Hypertension.

[41]  M. Bullinger,et al.  Psychometric properties of the KINDL-R questionnaire: results of the BELLA study , 2008, European Child & Adolescent Psychiatry.

[42]  S. Stevens.  On happiness and health. , 2006, The Health service journal.

[43]  Amalia Waxman,et al.  Who Global Strategy on Diet, Physical Activity and Health * , 2004, Food and nutrition bulletin.

[44]  C. Keyes,et al.  The mental health continuum: from languishing to flourishing in life. , 2002, Journal of health and social behavior.

[45]  M. Bullinger,et al.  Assessing health-related quality of life in chronically ill children with the German KINDL: first psychometric and content analytical results , 1998, Quality of Life Research.

[46]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[47]  I. Pigeot,et al.  Instruments for Health Surveys in Children and Adolescents , 2019, Springer Series on Epidemiology and Public Health.

[48]  N. Menold,et al.  Design of Rating Scales in Questionnaires , 2019 .

[49]  I. Pigeot,et al.  The Transition from Childhood to Adolescence in European Children – How I.Family Extends the IDEFICS Cohort , 2017 .

[50]  Iris Pigeot,et al.  Estimating Age- and Height-Specific Percentile Curves for Children Using GAMLSS in the IDEFICS Study , 2016 .

[51]  Andreas Ritter,et al.  Structural Equations With Latent Variables , 2016 .

[52]  I. Huybrechts,et al.  Longitudinal association between child stress and lifestyle. , 2015, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[53]  Беляева Татьяна Александровна.  Система образования российской Федерации в рамках Международной стандартной классификации образования (International Standard Classification of education - ISCED) , 2011 .

[54]  Maurizio Vichi.  Studies in Classification Data Analysis and knowledge Organization , 2011 .

[55]  L. Wichstrøm,et al.  Health and Quality of Life Outcomes , 2008 .

[56]  U. Ellert,et al.  [Health-related quality of life of children and adolescents in Germany. Norm data from the German Health Interview and Examination Survey (KiGGS)]. , 2007, Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz.

[57]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[58]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[59]  K. Bollen,et al.  DIRECT AND INDIRECT EFFECTS: CLASSICAL AND BOOTSTRAP ESTIMATES OF VARIABILITY , 1990 .