BMI metrics and their association with adiposity, cardiometabolic risk factors, and biomarkers in children and adolescents

Fat-free mass index is a feasible predictor of insulin resistance in women with polycystic ovary syndrome: Evidence from a cross-sectional study

BACKGROUND

Insulin resistance (IR) and adipose tissue amplify the metabolic and reproductive outcomes in women with polycystic ovary syndrome (PCOS). It has been widely discussed that body composition influences metabolic health. Still, limited studies were focused on the role of the fat-free mass index (FFMI) in assessing IR in PCOS women.

AIMS

We aimed to explore the associations between FFMI/fat mass index (FMI) and IR in women with PCOS and assess the role of FFMI in predicting IR in women with PCOS.

METHODS

In the current cross-sectional study, women with PCOS aged between 18 and 40 years were enrolled from October 2018 to July 2022. Baseline demographic information was obtained using standardized self-administered questionnaires. Anthropometric, biochemical, and hormonal information was measured and recorded by investigators. Pearson's correlation and multivariable logistical regression were used to analyze the associations of FFMI/FMI and IR. In addition, receiver operating characteristic (ROC) curves were implied to measure the predictive role of FFMI/FMI for IR in women with PCOS.

RESULTS

A total of 371 women with PCOS, reproductive age (27.58 ± 4.89) were enrolled. PCOS women with IR have higher levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), homeostatic model assessment of insulin resistance (HOMA-IR), FMI, and FFMI than that without IR. FMI (r = 0.492, p < 0.001) and FFMI (r = 0.527, p < 0.001) were positively associated with IR. After adjusting for potential confounders, FMI and FFMI were significantly associated with IR in PCOS women, and the OR was 1.385 (95%CI: 1.212-1.583) and 2.306 (95%CI: 1.675-3.174), respectively. Additionally, the FFMI (0.847, 95%CI: 0.784-0.888) has a larger area of ROC (AUC) than the FMI (0.836, 95%CI: 0.799-0.896), while there is no difference in predicting IR (95%CI: -0.18-0.41, p = 0.456).

CONCLUSION

These results indicated that FFMI and FMI could significantly increase the risk of IR, both of which could be feasible predictors of IR in PCOS women.

Lower adiponectin is associated with higher anthropometry and insulin resistance but not with low cardiorespiratory fitness in adolescents

PURPOSE

The aim of this study was to analyze the relationship between adiposity, cardiometabolic risk and cardiorespiratory fitness (CRF) according to different groups of adiponectin concentration.

METHODS

255 adolescents of both sexes, aged 11-17 years old, participated. Anthropometric and biochemical parameters such as body mass, height, abdominal circumference (AC), waist circumference (WC), fat mass, fat-free mass, total cholesterol (TC), high-density lipoprotein (HDL-c), low-density lipoprotein (LDL-c), triglycerides (TG), glucose, insulin, adiponectin, blood pressure, peak oxygen consumption (VO2peak) were measured. Body mass index (BMI), z-score BMI (BMI-z), triponderal mass index (TMI), waist-to-height ratio (WHtR), homeostasis model to assessment insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) were calculated. Adiponectin was categorized: low adiponectin concentration (LAC  ≤ 5.18 µg/mL-1), intermediate (IAC = 5.18 and 7.63 µg/mL-1) and high (HAC ≥ 7.63 µg/ml-1).

RESULTS

LAC showed higher BMI, BMI-z and TMI than the other groups (p < 0.05) and higher AC, WC and WHtR that the HAC (p < 0.05). IAC showed lower values of TC, LDL-c and TG, and the LAC presented the highest values of insulin, HOMA-IR and QUICKI (p < 0.05) to the IAC and HAC. HAC presented the lower VO2peak than the other groups (p < 0.01). BMI, TMI, glucose, insulin, HOMA-IR showed inverse, and QUICKI a direct and weak correlation with adiponectin (p < 0.05). No significant association was found between adiponectin and VO2peak (p > 0.05).

CONCLUSION

The LAC group had higher means in the anthropometric variables and the worst results related to insulin resistance and sensitivity. Thus, adiponectin may play an important role in obesity and reduced concentration may be a factor in the development of obesity-associated morbidities.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

The effect of parental diabetes prevention program participation on weight loss in dependent children: a prospective cohort study

INTRODUCTION

Obesity has reached epidemic proportions in children and adolescents in the United States. Children's behaviors are strongly influenced by parental behaviors, and weight loss in parents is positively associated with weight changes in their overweight/obese children. Research is limited on how parents' National Diabetes Prevention Program (DPP) participation affects the health outcomes of their dependent children. Analyzing the impact of parental DPP participation on weight loss in their dependent children may provide valuable insight into an important secondary benefit of DPP participation.

METHODS

In this study, we identified 128 adults with prediabetes who were offered the opportunity to participate in a DPP (n = 54 DPP participants and n = 74 DPP non-participants) and who had at least one child 3 to 17 years of age living with them. Age and BMI percentile for dependent children were collected from insurance claims data for 203 children (n = 90 children of DPP participants and n = 113 children of DPP non-participants). Parental practices related to diet and physical activity were assessed by surveys.

RESULTS

There were no significant changes in BMI percentiles of overweight or obese children (i.e. BMI percentile ≥ 50%) of DPP participants vs DPP non-participants with prediabetes over one-year. Parents who enrolled and did not enroll in the DPP did not report differences in their parenting practices related to diet and physical activity.

DISCUSSION

These results are not consistent with the literature that suggests parent-based interventions may influence their children's weight trajectories. Limitations include small sample size, short time span of intervention, and limited availability of additional health/biographic data on dependent children. Future studies should collect primary outcome data on children, investigate whether there is a minimum duration of parental involvement and level of parental adherence, and assess the effect of parent-child dynamics on child weight trajectories.

The Challenge of Weight Stigma for Women in the Preconception Period: Workshop Recommendations for Action from the 5th European Conference on Preconception Health and Care

Weight stigma is a well-recognised public health issue affecting many members of society including women during the preconception period. The impacts of preconception weight stigma on women are significant and may result in decreased access to and uptake of healthcare, and mental health concerns. The consequences of this weight stigma may translate to negative maternal outcomes and even intergenerational effects on the child. Eliminating weight stigma is therefore imperative. The aim of this paper is to report recommendations to reduce weight stigma for preconception women produced at a workshop with clinical and academic experts on preconception health and weight stigma at the 5th European Conference on Preconception Health and Care. The recommendations are related to two key areas: general societal recommendations prompting all people to acknowledge and adjust our attitudes towards larger-bodied people; and healthcare-specific recommendations imploring clinicians to upskill themselves to reduce weight stigma in practice. We therefore call for urgent approaches to address societal weight-stigmatising attitudes and norms related to both the general population and preconception women, while providing professional development opportunities for healthcare professionals relating to weight stigma. Eliminating weight stigma for preconception women may have positive impacts on the outcomes for mothers and children during pregnancy and beyond.

Performance of triponderal mass index, body mass index z scores, and body mass index performance in the diagnosis of obesity in children and adolescents

OBJECTIVES

Childhood obesity is a global health problem that affects millions of children and causes obesity-related adverse health outcomes in both childhood and adulthood. Although body mass index (BMI) z scores and percentiles are used in the diagnosis of obesity in children, it has been emphasized in recent years that the triponderal mass index (TMI) may be more accurate for this purpose. We aimed to compare TMI with BMI in the diagnosis of obesity in Turkish children and adolescents.

METHODS

The records of 3540 children who applied to Gülhane Training and Research Hospital were retrospectively scanned and the data of 1161 children were included in the study. The body fat percentage (BF%) was calculated by a formula, and children with body fat in the ≥95th percentile were classified as obese. Receiver study characteristics analysis was performed to compare the effectiveness of TMI and BMI in the diagnosis of obesity.

RESULTS

TMI correlated more with BF% (r = 0.863) than fat mass (r = 0.664); BMI correlated more with fat mass (r = 0.957) than BF% (r = 0.714) (P < 0.001). TMI had the highest area under the curve (AUC) in boys at diagnosis of obesity (6-11 y = 0.981; 12-15 y = 0.994). Girls ages 6 to 11 y had the same AUC for all 3 indexes (AUC = 0.977), whereas girls ages 12 to 15 y had the highest AUC for TMI (AUC = 0.967). However, the AUC values between all indices were very close.

CONCLUSIONS

TMI can be used to diagnose obesity in Turkish children and adolescents in both boys and girls similarly and with good performance. The correlation with BF% and stability of TMI makes this index more advantageous. However, it should be noted that the performance of each of the 3 indices is very close to that of the others, and adjustments should be made according to age and sex.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

The implications of defining obesity as a disease: a report from the Association for the Study of Obesity 2021 annual conference

Unlike various countries and organisations, including the World Health Organisation and the European Parliament, the United Kingdom does not formally recognise obesity as a disease. This report presents the discussion on the potential impact of defining obesity as a disease on the patient, the healthcare system, the economy, and the wider society. A group of speakers from a wide range of disciplines came together to debate the topic bringing their knowledge and expertise from backgrounds in medicine, psychology, economics, and politics as well as the experience of people living with obesity. The aim of their debate was not to decide whether obesity should be classified as a disease but rather to explore what the implications of doing so would be, what the gaps in the available data are, as well as to provide up-to-date information on the topic from experts in the field. There were four topics where speakers presented their viewpoints, each one including a question-and-answer section for debate. The first one focused on the impact that the recognition of obesity could have on people living with obesity regarding the change in their behaviour, either positive and empowering or more stigmatising. During the second one, the impact of defining obesity as a disease on the National Health Service and the wider economy was discussed. The primary outcome was the need for more robust data as the one available does not represent the actual cost of obesity. The third topic was related to the policy implications regarding treatment provision, focusing on the public's power to influence policy. Finally, the last issue discussed, included the implications of public health actions, highlighting the importance of the government's actions and private stakeholders. The speakers agreed that no matter where they stand on this debate, the goal is common: to provide a healthcare system that supports and protects the patients, strategies that protect the economy and broader society, and policies that reduce stigma and promote health equity. Many questions are left to be answered regarding how these goals can be achieved. However, this discussion has set a good foundation providing evidence that can be used by the public, clinicians, and policymakers to make that happen.