Parental obesity is associated with the severity of childhood obesity and its comorbidities

Martínez‐Villanueva, Julián; González-Leal, Rocío; Argente, Jesús; Martos‐Moreno, Gabriel Ángel

doi:10.1016/j.anpede.2018.06.010

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…Previous studies highlight the role of both parents’ weight status in the development of childhood obesity; however, they could not come to an agreement regarding the importance of parent’s weight status in the development of obesity in children. Some studies indicate a stronger association between maternal overweight/obesity and childhood obesity, especially at birth and infancy compared to paternal overweight/obesity [ 33 – 35 ]. Nevertheless, there are studies indicating paternal overweight/obesity tends to show their effects later [ 36 ], and hence, its effect should not be neglected.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of different maternal obesity phenotypes in predicting offspring obesity in a family-based cohort study

et al. 2021

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Background Parental weight is studied as an important determinant of childhood obesity; however, obesity-related metabolic abnormalities have been less considered as determinants of childhood obesity. This study aimed to investigate the association between maternal obesity phenotypes and incidence of obesity in their offspring. Methods This longitudinal study was conducted within the framework of the Tehran Lipid and Glucose Study. A total of 2151 non-obese children who had complete parental information were followed for incidence of obesity over a mean of 148.7 ± 34.7 months. Obesity in children was defined using the World Health Organization criteria. Maternal body mass index (BMI) was classified into three categories: normal weight, overweight and obese. Dysmetabolic status was considered as having metabolic syndrome or diabetes. Metabolic syndrome and diabetes were defined according to the Joint Interim Statement and American diabetes association criteria, respectively. Considering maternal BMI categories and metabolic status, six obesity phenotypes were defined as followed: 1) normal weight and normal metabolic status, 2) overweight and normal metabolic status, 3) obese and normal metabolic status, 4) normal weight and dysmetabolic status, 5) overweight and dysmetabolic status, and 6) obese and dysmetabolic status. The association between maternal obesity phenotypes and incidence of obesity in children was studied using Cox proportional regression hazard model. Results In male offspring, the risk of incidence of obesity significantly increased in those with maternal obesity phenotypes including overweight/normal metabolic: 1.75(95% CI: 1.10–2.79), obese/normal metabolic: 2.60(95%CI: 1.51–4.48), overweight/dysmetabolic: 2.34(95%CI: 1.35–4.03) and obese/dysmetabolic: 3.21(95%CI: 1.94–5.03) compared to the normal weight/normal metabolic phenotype. Similarly, in girls, the risk of incidence of obesity significantly increased in offspring with maternal obesity phenotypes including overweight/normal metabolic: 2.39(95%CI: 1.46–3.90), obese/normal metabolic: 3.55(95%CI: 1.94–6.46), overweight/dysmetabolic: 1.92(95%CI: 1.04–3.52) and obese/dysmetabolic: 3.89(95%CI: 2.28–6.64) compared to normal weight/normal metabolic phenotype. However, maternal normal weight/dysmetabolic phenotype did not significantly change the risk of obesity in both male and female offspring. Conclusion Except for normal weight/dysmetabolic phenotype, all maternal obesity phenotypes had significant prognostic values for incidence of offspring obesity with the highest risk for obese/dysmetabolic phenotype. This study provides valuable findings for identifying the first line target groups for planning interventions to prevent childhood obesity.

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Section: Discussionmentioning

confidence: 99%

Prognostic value of different maternal obesity phenotypes in predicting offspring obesity in a family-based cohort study

et al. 2021

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“…This degree of acceptance of obesity is further enhanced if a positive background of familial obesity exists [31], with more severe obesity and higher prevalence of comorbidities observed in the offspring of parents with obesity at the time of consultation [33], with this being closely related to ethnic, socio-economic and cultural factors [31]. Although the parental academic level background distribution in our cohort was similar to that of the general population in our country [34], no family economic data were registered.…”

Section: Discussionmentioning

confidence: 99%

Conservative Treatment for Childhood and Adolescent Obesity: Real World Follow-Up Profiling and Clinical Evolution in 1300 Patients

et al. 2021

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Background: Limited therapeutic tools and an overwhelming clinical demand are the major limiting factors in pediatric obesity management. The optimal protocol, environment, body mass index (BMI) change targets and duration of obesity-oriented interventions remain to be elucidated. Aims: We aimed to characterize the singularities of follow-up, anthropometric and metabolic evolution of a large cohort of pediatric patients with obesity in a specialized university hospital outpatient obesity unit. Patients and methods: Follow-up duration (up to seven years), attrition rate and anthropometric and metabolic evolution of 1300 children and adolescents with obesity were studied. An individualized analysis was conducted in patients attaining a high level of weight loss (over 1.5 BMI-SDS (standard deviation score) and/or 10% of initial weight; n = 252; 19.4%) as well as in “metabolically healthy” patients (n = 505; 38.8%). Results: Attrition rate was high during the early stages (11.2% prior to and 32.5% right after their initial metabolic evaluation). Mean follow-up time was 1.59 ± 1.60 years (7% of patients fulfilled 7 years). The highest BMI reduction occurred in the first year (−1.11 ± 0.89 SDS, p < 0.001 in 72.5% of patients). At the end of the follow-up, improvements in glucose and lipid metabolism parameters were observed (both p < 0.05), that were highest in patients with the greatest weight reduction (all p < 0.01), independent of the time spent to achieve weight loss. The pubertal growth spurt negatively correlated with obesity severity (r = −0.38; p < 0.01) but patients attaining adult height exceeded their predicted adult height (n = 308, +1.6 ± 5.4 cm; p < 0.001). “Metabolically healthy” patients, but with insulin resistance, had higher blood pressure, glucose, uric acid and triglyceride levels than those without insulin resistance (all p < 0.05). Preservation of the “metabolically healthy” status was associated with BMI improvement. Conclusions: Behavioral management of children with obesity can be effective and does not impair growth but is highly conditioned by high attrition. The best results regarding BMI reduction and metabolic improvement are achieved in the first year of intervention and can be preserved if follow-up is retained.

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“…Нутритивный статус родителей имеет важное значение. Известно, что у детей с избыточной массой тела часто матери страдают ожирением [13,14]. Ожирение у одного из родителей увеличивает риск ожирения у ребенка в 2-3 раза.…”

Section: Introductionunclassified

Artificial intelligence predicting the risk of obesity in children

Чубаров

Zhdanova

Sharshova

et al. 2022

Aspirantskiy Vestnik Povolzhiya

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Aim to find effective methods for detecting and preventing obesity at early age. Material and methods. A dataset including the risk factors for child obesity was processed with artificial neural networks (ANN) and Statistica Neural Networks software. Clinical observations of 30 patients were used. The neural network was trained to predict the risk of obesity in children depending on the values of the selected parameters: standard deviation of body mass index from the norm, sex, age, obesity in parents, birth weight, duration of breastfeeding, deviation of body fat tissue content from the norm, and deviation of nutrition calories from the recommended values. Results. After training, the neural network MLP-8-7-1 was selected due to its high coefficients of determination 0.999999; 0.999407; 0.984930 for the training, test and control samples, respectively. This indicates the high performance of the trained ANN, the adequacy of which was checked graphically by constructing a histogram of residuals the difference between the entered and received by the network values of the risk of obesity development in children. Conclusion. The trained neural network can be used to predict the degree of risk of obesity in children and develop the necessary preventive measures in patients from risk groups.

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Parental obesity is associated with the severity of childhood obesity and its comorbidities

Cited by 9 publications

References 32 publications

Prognostic value of different maternal obesity phenotypes in predicting offspring obesity in a family-based cohort study

Prognostic value of different maternal obesity phenotypes in predicting offspring obesity in a family-based cohort study

Conservative Treatment for Childhood and Adolescent Obesity: Real World Follow-Up Profiling and Clinical Evolution in 1300 Patients

Artificial intelligence predicting the risk of obesity in children

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