Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

IMPORTANCE

The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection.

METHODS

A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling.

RESULTS

Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight.

CONCLUSIONS

United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Genetic risk score for common obesity and anthropometry in Spanish schoolchildren

IntroductionCommon or non-syndromic obesity is a complex polygenic trait conditioned by biallelic or single-base polymorphisms called SNPs (Single-Nucleotide Polymorphisms) that present an additive effect and act synergistically. Most genotype-obese phenotype association studies include body mass index (BMI) or waist-to-height ratio (WtHR), and very few introduce a broad anthropometric profile. ObjectiveTo verify whether a genetic risk score (GRS) developed from 10 SNPs is associated with the obesity phenotype assessed from anthropometric measures indicative of excess weight, adiposity and fat distribution. Material and methodsA series of 438 Spanish schoolchildren (6-16 years old) were evaluated anthropometrically (weight, height, waist circumference, skinfold thickness, BMI, WtHR, body fat percentage [%BF]). Ten SNPs were genotyped from saliva samples, generating a GRS for obesity, establishing genotype-phenotype association. ResultsSchoolchildren categorised as obese by BMI, ICT and %BF had higher GRS than their non-obese peers. The prevalence of overweight and adiposity was higher in subjects with a GRS above the median. Similarly, between 11 and 16 years of age, all anthropometric variables presented higher averages. ConclusionsGRS estimated from the 10 SNPs can be a diagnostic tool for the potential risk of obesity in Spanish schoolchildren and could be useful from the preventive perspective.

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

BACKGROUND

Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.

OBJECTIVE

This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.

METHODS

We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques.

RESULTS

We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review.

CONCLUSIONS

This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

The Risk of Atrial Fibrillation Increases with Earlier Onset of Obesity: A Mendelian Randomization Study

Background: Obesity is a well-established risk factor for atrial fibrillation (AF). Previous epidemiological research on obesity and AF often focused on adult populations and now broadened to earlier in life. Therefore, this study aimed to determine the relationships between obesity at different periods of life and the risk of AF. Methods: A two-sample Mendelian randomization (MR) study design using summarised data from 6 genome-wide association studies (GWASs) was employed in this study. Single nucleotide polymorphisms (SNPs) associated with adult obesity, childhood obesity, childhood body mass index (BMI), waist-to-hip ratio adjusted for BMI (WHRadjBMI), birth weight and AF were independently retrieved from large-scale GWASs. For SNP identification, the genome-wide significance threshold was set at p <5.00×10^-8. To obtain causal estimates, MR analysis was conducted using the inverse variance-weighted (IVW) method. The weighted median, MR-Egger methods and MR-robust adjusted profile score (MR-RAPS) were used to evaluate the robustness of MR analysis. Results: A total of 204 SNPs were identified as the genetic instrumental variables (5 SNPs for childhood obesity, 13 SNPs for childhood BMI, 137 SNPs for birth weight, 35 SNPs for adult WHRadjBMI, and 14 SNPs for adult obesity). The results of MR analysis demonstrated that the genetically predicted adult obesity, childhood BMI, and birth weight were associated with AF risk. Notably, a 1 unit standard deviation (1-SD) increase in adult obesity was related to a 13% increased risk of AF [p=6.51×10^-7, OR, 1.13 (95% CI, 1.08-1.19)], a 1-SD increase in childhood BMI was related to a 18% increased risk of AF [p=1.77×10^-4, OR, 1.18 (95% CI, 1.08-1.29)], and a 1-SD increase in birth weight was related to a 26% increased risk of AF [p=1.27×10^-7, OR, 1.26 (95% CI, 1.16-1.37)]. There was no evidence of pleiotropy or heterogeneity between the MR estimates obtained from multiple SNPs. Conclusion: Our study reveals the association of genetic susceptibility to obesity with a higher risk of AF. Moreover, an earlier age at obesity was associated with an increased risk of AF. Therefore, public awareness of the dangers of obesity and active early weight control may prevent the development of AF.

Weight regain after bariatric surgery: Promoters and potential predictors

Obesity is globally viewed as chronic relapsing disease. Bariatric surgery offers the most efficient and durable weight loss approach. However, weight regain after surgery is a distressing issue as obesity can revert. Surgical procedures were originally designed to reduce food intake and catalyze weight loss, provided that its role is marginalized in long-term weight maintenance. Consequently, it is essential to establish a scientifically standardized applicable definitions for weight regain, which necessitates enhanced comprehension of the clinical situation, as well as have realistic expectations concerning weight loss. Moreover, several factors are proposed to influence weight regain as psychological, behavioral factors, hormonal, metabolic, anatomical lapses, as well as genetic predisposition. Recently, there is a growing evidence of utilization of scoring system to anticipate excess body weight loss, along with characterizing certain biomarkers that identify subjects at risk of suboptimal weight loss after surgery. Furthermore, personalized counseling is warranted to help select bariatric procedure, reinforce self-monitoring skills, motivate patient, encourage mindful eating practices, to avoid recidivism.

Healthful plant-based dietary patterns, genetic risk of obesity, and cardiovascular risk in the UK biobank study

BACKGROUND & AIMS

People with a higher genetic risk for obesity are more likely to develop cardiovascular disease (CVD), and healthy plant-based dietary patterns may be associated with decreased risks of obesity and cardiovascular events. We investigated whether adherence to healthy plant-foods-rich dietary patterns might attenuate risks of obesity and related cardiovascular abnormalities for people at genetically higher risk of obesity.

METHODS

This study included 121,799 middle-aged adults in UK Biobank who were initially free of metabolic diseases and cancer. We calculated a healthful plant-based diet index (hPDI) based on 17 major food groups as well as a genetic risk score (GRS) for obesity consisting of body mass index (BMI)-associated variants. The incidence of cardiovascular events (myocardial infarction, MI, or stroke) was prospectively followed during a mean (SD) 5.1 (0.9) years.

RESULTS

We found significant interactions between GRS and hPDI on adiposity (P_interaction <0.0001); adherence to hPDI was more strongly associated with lower levels of adiposity among participants with higher GRS than those with lower GRS. Further, we found a similar pattern of GRS-hPDI interactions on untreated hypertension (P_interaction = 0.0036). When we tested GRS-hPDI interactions on cardiovascular events, adherence to hPDI was more strongly associated with a decreased risk of MI among people with high GRS (above median) than those with low GRS (P_interaction = 0.006). Among participants with high GRS, high adherence to hPDI (the top tertile of hPDI) was associated with an HR 0.54 (95% CI: 0.39, 0.74) for MI, as compared to low adherence.

CONCLUSIONS

Adherence to healthy plant-based dietary patterns significantly attenuated risks of cardiovascular abnormalities for people at genetically higher risk of obesity. Our results support the precision medicine strategies considering genetics and dietary habits to modify cardiovascular health for people at higher risk of genetically determined obesity.

Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study

Obesity is the result of interactions between genes and environmental factors. Since monogenic etiology is only known in some obesity-related genes, a genetic risk score (GRS) could be useful to determine the genetic predisposition to obesity. Therefore, the aim of our study was to build a GRS able to predict genetic predisposition to overweight and obesity in European adolescents. A total of 1069 adolescents (51.3% female), aged 11-19 years participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study were genotyped. The sample was divided in non-overweight (non-OW) and overweight/obesity (OW/OB). From 611 single nucleotide polymorphisms (SNP) available, a first screening of 104 SNPs univariately associated with obesity (p < 0.20) was established selecting 21 significant SNPs (p < 0.05) in the multivariate model. Unweighted GRS (uGRS) was calculated by summing the number of risk alleles and weighted GRS (wGRS) by multiplying the risk alleles to each estimated coefficient. The area under curve (AUC) was calculated in uGRS (0.723) and wGRS (0.734) using tenfold internal cross-validation. Both uGRS and wGRS were significantly associated with body mass index (BMI) (p < .001). Both GRSs could potentially be considered as useful genetic tools to evaluate individual's predisposition to overweight/obesity in European adolescents.

Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models

Objective: Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. Results: Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC. The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. Our result shows that all proposed models achieved satisfactory performance, with the AUC values ranging from 0.826 to 0.850. Among the seven predictive models, the gradient boosting tree model outperformed other models, achieving an AUC of 0.850. Conclusions: Our risk prediction model shows great promise in automating real-time diagnosis, supporting healthcare practitioners to target high-risk individuals efficiently, and helping healthcare practitioners tailor proactive strategies to prevent the onset or delay the progression of the chronic diseases.

Interaction Effect of the Mediterranean Diet and an Obesity Genetic Risk Score on Adiposity and Metabolic Syndrome in Adolescents: The HELENA Study

Obesity and metabolic syndrome (MetS) are worldwide major health challenges. The Mediterranean diet (MD) is associated with a better cardiometabolic profile, but these beneficial effects may be influenced by genetic variations, modulating the predisposition to obesity or MetS. The aim was to assess whether interaction effects occur between an obesity genetic risk score (obesity-GRS) and the MD on adiposity and MetS in European adolescents. Multiple linear regression models were used to assess the interaction effects of an obesity-GRS and the MD on adiposity and MetS and its components. Interaction effects between the MD on adiposity and MetS were observed in both sex groups (p < 0.05). However, those interaction effects were only expressed in a certain number of adolescents, when a limited number of risk alleles were present. Regarding adiposity, a total of 51.1% males and 98.7% females had lower body mass index (BMI) as a result of higher MD adherence. Concerning MetS, only 9.9% of males with higher MD adherence had lower MetS scores. However, the same effect was observed in 95.2% of females. In conclusion, obesity-related genotypes could modulate the relationship between MD adherence and adiposity and MetS in European adolescents; the interaction effect was higher in females than in males.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Longitudinal association of a body mass index (BMI) genetic risk score with growth and BMI changes across the life course: The Cardiovascular Risk in Young Finns Study

BACKGROUND

The role of genetic risk scores associated with adult body mass index (BMI) on BMI levels across the life course is unclear. We examined if a 97 single nucleotide polymorphism weighted genetic risk score (wGRS97) associated with age-related progression in BMI at different life stages and distinct developmental trajectories of BMI across the early life course.

METHODS

2188 Cardiovascular Risk in Young Finns Study participants born pre-1980 who had genotype data and objective measurements of height and weight collected up to 8 times from age 6 to 49 years. Associations were examined using Individual Growth Curve analysis, Latent Class Growth Mixture Modelling, and Poisson modified regression.

RESULTS

The wGRS97 associated with BMI from age 6 years with peak effect sizes observed at age 30 years (females: 1.14 kg/m²; males: 1.09 kg/m² higher BMI per standard deviation increase in wGRS97). The association between wGRS97 and BMI became stronger with age in childhood but slowed in adolescence, especially in females, and weakened at age 35-40 years. A higher wGRS97 associated with an increased BMI velocity in childhood and adulthood, but not with BMI change in adulthood. Compared with belonging to a 'normal stable' life-course trajectory group (normal BMI from childhood to adulthood), a one standard deviation higher wGRS97 associated with a 13-127% increased risk of belonging to a less favourable life-course BMI trajectory group.

CONCLUSIONS

Individuals with genetic susceptibility to higher adult BMI have higher levels and accelerated rates of increase in BMI in childhood/adolescence, and are at increased risk of having a less favourable life-course BMI trajectory.

Age-Specific Estimates and Comparisons of Youth Tri-Ponderal Mass Index and Body Mass Index in Predicting Adult Obesity-Related Outcomes

OBJECTIVES

To estimate and compare tri-ponderal mass index (TMI) and body mass index (BMI) at each age from childhood to young adulthood in the prediction of adulthood obesity-related outcomes.

STUDY DESIGN

Participants of this observational study (n = 432) were from a 20-year infancy-onset randomized atherosclerosis prevention trial. BMI and TMI were calculated using weight and height measured annually from participants between ages 2 and 20 years. Outcomes were aortic intima-media thickness (at the age of 15, 17, or 19 years), impaired fasting glucose and elevated insulin levels, homeostasis model assessment of insulin resistance index, serum lipids, and hypertension at the age of 20 years. Poisson regressions, Pearson correlation, logistic regression, and area under the curve (AUC) were used to estimate and/or compare associations and predictive utilities between BMI and TMI with all outcomes.

RESULTS

The associations and predictive utilities of BMI and TMI with all outcomes were stronger at older ages. BMI had significantly stronger correlations than TMI with insulin (at age 16 years), systolic blood pressure (age 5-20 years), and triglycerides (age 18 years). BMI had significantly greater predictive utilities than TMI for insulin resistance (at age 14-16 years; difference in AUC = 0.018-0.024), elevated insulin levels (age 14-16 years; difference in AUC = 0.018 and 0.025), and hypertension (age 16 to 20 years; difference in AUC = 0.017-0.022) but they were similar for other outcomes.

CONCLUSIONS

TMI is not superior to BMI at any ages from childhood to young adulthood in the prediction of obesity-related outcomes in young adulthood.

The Association of Parental Genetic, Lifestyle, and Social Determinants of Health with Offspring Overweight

INTRODUCTION

In the UK, the number of comorbidities seen in children has increased along with the worsening obesity rate. These comorbidities worsen into adulthood. Genome-wide association studies have highlighted single nucleotide polymorphisms associated with the weight status of adults and offspring individually. To date, in the UK, parental genetic, lifestyle, and social determinants of health have not been investigated alongside one another as influencers of offspring weight status. A comprehensive obesity prevention scheme would commence prior to conception and involve parental intervention including all known risk factors. This current study aims to identify the proportion of overweight that can be explained by known parental risk factors, including genetic, lifestyle, and social determinants of health with offspring weight status in the UK.

METHODS

A cross-sectional study was carried out on 123 parents. Parental and offspring anthropometric data and parental lifestyle and social determinants of health data were self-reported. Parental genetic data were collected by use of GeneFiX saliva collection vials and genotype were assessed for brain-derived neurotrophic factor (BDNF) gene rs6265, melanocortin 4 receptor (MC4R) gene rs17782313, transmembrane protein 18 (TMEM18) gene rs2867125, and serine/threonine-protein kinase (TNN13K) gene rs1514175. Associations were assessed between parental data and the weight status of offspring.

RESULTS

Maternal body mass index modestly predicted child weight status (p < 0.015; R2 = 0.15). More mothers of overweight children carried the MC4R rs17782313 risk allele (77.8%; p = 0.007) compared to mothers of normal-weight children. Additionally, fathers who were not Caucasian and parents who slept for <7 h/night had a larger percentage of overweight children when compared to their counterparts (p = 0.039; p = 0.014, respectively).

CONCLUSION

Associations exist between the weight status of offspring based solely on parental genetic, lifestyle, and social determinants of health data. Further research is required to appropriately address future interventions based on genetic and lifestyle risk groups on a pre-parent cohort.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Study protocol: The Dutch 20|30 Postmeningitis study: a cross-sectional follow-up of two historical childhood bacterial meningitis cohorts on long-term outcomes

Background

Bacterial meningitis (BM) is a serious, life-threatening infectious disease of the central nervous system that often occurs in young children. The most common severe to moderate sequelae following BM are sensorineural hearing loss, neuromotor disabilities and mental retardation, while subtle sequelae include academic and behavioral disabilities. It is largely unknown whether these more subtle sequelae persist into adolescence and adulthood. Therefore, this study will investigate the very long-term effects of childhood BM in later life. Better understanding of long-term effects and early identification of adverse outcomes after BM are essential for more timely interventions. Additionally, certain single nucleotide polymorphisms (SNPs) are associated with disease severity and might predict adverse sequelae. These include SNPs in genes encoding for pathogen recognition and immune response upon infection. Accordingly, a secondary objective of this study is to investigate the role of genetic variation in BM and use any insights to predict short- and long-term outcomes.

Methods

In the Dutch 20|30 Postmeningitis study, adolescents and young adults (n = 947) from two historical cohorts with a prior episode of BM during childhood will be enrolled into a cross-sectional follow-up investigation using mainly questionnaires that examine executive and behavioral functioning, health-related quality of life, subjective hearing, mood and sleeping disorders, academic performance, and economic self-sufficiency. The results will be compared to normative data by one-sample t-tests. Multivariable regression analysis will be used to assess for any associations with causative pathogens and severity of BM. Participants that complete the questionnaires will be approached to provide a swab for buccal DNA and subsequent sequencing analyses. Logistic regression models will be used to predict sequelae.

Discussion

The unique follow-up duration of this cohort will enable us to gain insights into the possible very long-term adverse effects of childhood BM and how these might impact on quality of life. The investigation of host genetic factors will contribute to the development of prediction models which will serve as prognostic tools to identify children who are at high risk of adverse outcome after BM.

Trial Registration

Dutch Trial Register NTR-6891. Retrospectively registered 28 December 2017.

Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents

Common genetic variants predispose to obesity with varying contribution by age. We incorporated known genetic variants into genetic risk scores (GRSs) and investigated their associations with overweight/obesity and central obesity in preadolescents. Furthermore, we compared GRSs with lifestyle factors, and tested if they predict the change in body size and shape in a 4-year follow-up. We utilized 1142 subjects from the Finnish Health in Teens (Fin-HIT) cohort. Overweight and obesity were defined with age- and gender-specific body mass index (BMI) z-score (BMIz), while central obesity by the waist-to-height ratio (WHtR). Background data on parental language, eating habits, leisure-time physical activity (LTPA) and sleep duration were included. Genotyping was performed with the Metabochip platform. Weighted, standardized GRSs were derived. Of the11-year-old children, 25.5% were at least overweight and 90.8% had Finnish speaking background. BMI-GRS was associated with higher risk for overweight with odds ratio (95% confidence interval) of 1.39 (1.20; 1.60) and obesity 1.41 (1.08; 1.83), but not with central obesity. BMI-GRS was weakly and inversely associated with the changes in BMIz and WHtR in the 4-year follow-up. Waist-to-hip ratio-GRS was not related to any obesity measures at baseline nor in the follow-up. The effect of BMI-GRS is similar to that of low LTPA on overweight. An interaction between parental language and BMI-GRS was noted (P = .019): BMI-GRS associated more strongly with overweight in Swedish than in Finnish speakers. We further identified two suggestive genetic variants near LOC101926977 and LOC105369677 associated with BMIz in preadolescents which were replicated in the adult population. In preadolescents, known genetic predisposing factors induce a risk for overweight comparable to low LTPA. However, the GRS was poor in predicting short-term changes in BMI or WHtR.

Identifying a risk score for childhood obesity based on predictors identified in pregnant women and 1-year-old infants: An analysis of the data of the Hokkaido Study on Environment and Children's Health

This study aimed to construct a childhood obesity risk index based on predictors identified in pregnant women and 1-yr-old infants. The primary outcome was an identified obesity index of > 20% at 6–8 yr of age. Of a total sample size of 6,846 mother-child pairs, 80% and 20% were randomly allocated to the derivation and validation cohorts, respectively. For the derivation cohort, univariate and multivariate logistic regression analyses of data were conducted to identify the final predictors to determine the childhood obesity risk score algorithm. These included pre-pregnancy body mass index (BMI), child’s gender, smoking during pregnancy, education, and obesity index at one yr of age. The β coefficients for categories of predictor variables were each divided by the smallest value among them. The quotient was rounded off to the integer and assigned to the risk score, and a value of zero was assigned to reference categories. A total risk score was calculated for each individual. A cutoff point ≥ 16 had 22.2% and 21.8% positive predictive values in the derivation and validation cohorts, respectively. In conclusion, the childhood obesity risk score algorithm was constructed based on generic predictors that can be easily obtained from maternal and child health handbooks.

Polymorphisms in Adipokines in Mexican Children with Obesity

The high prevalence of childhood obesity in Mexico is alarming in the health-science field. We propose to investigate the contribution of adipokines and cytokines polymorphisms and common BMI/obesity-associated loci, revealed in genome-wide association studies in Caucasian adult cohorts, with childhood obesity. This study included 773 Mexican-Mestizo children (5-15 years old) in a case-control study. The polymorphisms included were ADIPOQ (rs6444174), TNF-α (rs1800750), IL-1β (rs1143643), IL-6 (rs1524107; rs2069845), NEGR1 (rs34305371), SEC16B-RASAL2 (rs10913469), TMEM18 (rs6548238; rs7561317), GNPDA2 (rs16857402), LEP (rs2167270), MTCH2 (rs10838738), LGR4-LIN7C-BDNF (rs925946), BCDIN3D-FAIM2 (rs7138803), FTO (rs62033400), MC4R (rs11872992), MC4R (rs17782313), and KCTD15 (rs29942). No significant contribution was found with adipokines and cytokines polymorphisms in this study. Only both TMEM18 (rs6548238; rs7561317) polymorphisms were found associated with obesity (OR=0.5, P=0.008) and were in linkage disequilibrium (r²=0.87). The linear regression showed that the rs7561317 polymorphism of TMEM18 is negatively associated with obesity. This report highlights the influence of TMEM18 in Mexican-Mestizo children obesity, while adipokine and cytokine polymorphisms were not associated with it.

Clinical relevance and validity of tools to predict infant, childhood and adulthood obesity: a systematic review

OBJECTIVE

To determine the global availability of a multicomponent tool predicting overweight/obesity in infancy, childhood, adolescence or adulthood; and to compare their predictive validity and clinical relevance.Design/SettingThe PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. The databases PubMed, EMBASE, CINAHL, Web of Science and PsycINFO were searched. Additional articles were identified via reference lists of included articles. Risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. The National Health and Medical Research Council's Levels of Evidence hierarchy was used to assess quality of evidence. Predictive performance was evaluated using the ABCD framework.

SUBJECTS

Eligible studies: tool could be administered at any life stage; quantified the risk of overweight/obesity onset; used more than one predictor variable; and reported appropriate prediction statistical outcomes.

RESULTS

Of the initial 4490 articles identified, twelve articles (describing twelve tools) were included. Most tools aimed to predict overweight and/or obesity within childhood (age 2-12 years). Predictive accuracy of tools was consistently adequate; however, the predictive validity of most tools was questioned secondary to poor methodology and statistical reporting. Globally, five tools were developed for dissemination into clinical practice, but no tools were tested within a clinical setting.

CONCLUSIONS

To our knowledge, a clinically relevant and highly predictive overweight/obesity prediction tool is yet to be developed. Clinicians can, however, act now to identify the strongest predictors of future overweight/obesity. Further research is necessary to optimise the predictive strength and clinical applicability of such a tool.

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

BACKGROUND

National surveys in public health nutrition commonly record the weight of every food consumed by an individual. However, if the goal is to identify whether individuals are in compliance with the 5 main national nutritional guidelines (sodium, saturated fats, sugars, fruit and vegetables, and fats), much less information may be needed. A previous study showed that tracking only 2.89% of all foods (113/3911) was sufficient to accurately identify compliance. Further reducing the data needs could lower participation burden, thus decreasing the costs for monitoring national compliance with key guidelines.

OBJECTIVE

This study aimed to assess whether national public health nutrition surveys can be further simplified by only recording whether a food was consumed, rather than having to weigh it.

METHODS

Our dataset came from a generalized sample of inhabitants in the United Kingdom, more specifically from the National Diet and Nutrition Survey 2008-2012. After simplifying food consumptions to a binary value (1 if an individual consumed a food and 0 otherwise), we built and optimized decision trees to find whether the foods could accurately predict compliance with the major 5 nutritional guidelines.

RESULTS

When using decision trees of a similar size to previous studies (ie, involving as many foods), we were able to correctly infer compliance for the 5 guidelines with an average accuracy of 80.1%. This is an average increase of 2.5 percentage points over a previous study, showing that further simplifying the surveys can actually yield more robust estimates. When we allowed the new decision trees to use slightly more foods than in previous studies, we were able to optimize the performance with an average increase of 3.1 percentage points.

CONCLUSIONS

Although one may expect a further simplification of surveys to decrease accuracy, our study found that public health dietary surveys can be simplified (from accurately weighing items to simply checking whether they were consumed) while improving accuracy. One possibility is that the simplification reduced noise and made it easier for patterns to emerge. Using simplified surveys will allow to monitor public health nutrition in a more cost-effective manner and possibly decrease the number of errors as participation burden is reduced.